We estimate whether a land reform program led to higher incomes for ethnic minority households. In 2002, in the Central Highlands of Vietnam, Program 132 directed the transfer of farm land to ethnic minority households that had less than one hectare of land. Using the 2002 Vietnam Household Living Standards Survey as a baseline, in 2008 we resurveyed over one-thousand households to provide a retrospective evaluation of the impact of their participation in Program 132. Contrary to official reports, our findings show that there was considerable deviation from the planned program parameters: many eligible households did not receive land, while ineligible households often did. We estimate that beneficiaries of the program in the province of Kon Tum experienced increases of household income largely in line with what one would expect from a small plot of poor farm land. Outside Kon Tum, where participation rates were substantially lower, the story is more mixed, and household incomes did not improve with program participation. Overall, our results underscore the limitations of simple transfers of land as a mechanism for improving the living standards of ethnic minorities. Our results also show the significant gap that can exist between program design and decentralized implementation. We discuss the potential implications for program evaluation. In 2002, the government of Vietnam announced a plan to redistribute land to land-poor ethnic minority households in the Central Highlands (CH) region. This was prompted by long simmering ethnic conflict that boiled over between indigenous minorities, and the more recently settled Kinh, Vietnam’s largest ethnic group. Policymakers hoped that by granting secure access to agricultural land, ethnic minority households could better participate in the rapidly expanding commercial agriculture sector, and thereby improve their poor economic status. Program 132 as drafted in Hanoi covered three single-spaced pages and defined precise eligibility criteria: Ethnic minority households with less than one hectare of farmland would be topped up to one hectare, subject to local land availability.According to official reports and our own interviews with local officials, the policy was executed in line with the original pronouncement. One-sixth of ethnic minority households, and over half of those households deemed eligible, were reported to receive land, with an average transfer of about half a hectare. Program participation rates were especially high in the more sparsely populated province of Kon Tum, where almost forty percent of ethnic minority households received land.We investigate whether participation in Program 132 led to higher incomes for ethnic minority households, and, if so, how effective the land transfers were for improving low living standards. We used the 2002 Vietnam Household Living Standards Survey (VHLSS) as a baseline and then, in 2008, resurveyed 1126 households within fifty communes in the Central Highlands. This provides us with detailed household-level data on agricultural land holdings and economic outcomes before and after the implementation of Program 132.We paint a rich picture of the “before and after” economic outcomes of minority households in the Central Highlands, and draw plausible conclusions about the causal impact of Program 132 on the living standards of these households. First, “treatment” through the program may not have been as high as officially reported and implementation details varied significantly across communes. In particular, the type of land  and area used to define eligibility differed across communes, with many communes using a lower threshold than stipulated in the program. Second, land was frequently given to apparently ineligible households. While land was transferred to minority households—there appears to have been no leakage to Kinh households—it was not targeted to those with the least amount of land or lowest incomes. Third, in Kon Tum province, we find that households granted access to land classified as annual  saw their crop income increase in line with the returns to this type of land. Outside Kon Tum, the effects on income were negligible, reflecting lower treatment rates and lags in the maturity of perennial crops like coffee and cashews. Overall, the program did little to improve the relative position of minority households: There is only so much income that a half-hectare of land can generate.Our results indicating the weak link between program eligibility and actual participation add to the growing literature that underscores the challenges of targeting the poor for transfers, even on as simple a proxy as land holdings. While it is true that any transfer to minority households in the Central Highlands (even a random one) is probably welfare improving, targeting on the basis of land alone may lead to less efficient transfers. First, land is only loosely related to household per capita income, and far from sufficient to generate agricultural income. Second, even well-executed targeted programs have errors  could transfer less land than those where land was plentiful . The challenges to impact evaluation in this context arise at least as much from endogenous variation of treatment across communes as deviations from intended treatment within communes. We believe our analysis is informative for discussions about program evaluation. In theory, the program had a clearly defined rule for defining an eligible household (less than a hectare of land) and the corresponding amount of land the household should receive. However, in practice, local implementation varied significantly from these national guidelines.What does this teach us about program evaluation? Consider a well-designed randomized control trial (RCT). In the current context, it randomly assigns some communes to be treated by the programs and others to not be : Who received land from Program 132, and how did this relate to eligibility as predicted in 2002? We compare results from different data sources, and show the poor targeting performance of the program. We then explore the potential impact of treatment, first on household land holdings, and second on household income, including a detailed discussion of the evolution of minority household incomes between 2002 and 2008. To do this, we estimate the value of a hectare of land to a minority household, and compare this to the estimated effect of program participation. In our final section, we draw together our conclusions, and potential lessons from this exercise.Despite rising absolute living standards, minorities lag significantly behind the Kinh. For example, between 1998 and 2010 per capita consumption rose 7.4% for minorities, but was a full 2.0 percentage points slower than for Kinh households .Implementation of Program 132 was delegated to lower levels of government, with responsibility spread across several ministries. The provincial Ministries of Agriculture and Rural Development (MARD) had primary responsibility, shared with Provincial Peoples Committees, Provincial Ministries of Finance (to oversee budgetary issues), Provincial Ministries of Natural Resources and the Environment , and local Committees for Ethnic Minorities. From the provincial level, implementation was further delegated to the district, and ultimately, the commune level. Land redistribution was subject to local land availability, and local needs . Whatever elements of common program design existed would be subject to local constraints in implementation. Commune governments were typically responsible for assessing eligibility, and the actual distribution of land. In some communes, the new land was assigned by commune officials, while in others, households drew lots to choose new plots.The sources of available land also varied. In some communes, land was available from adjacent agro-forest plantations, typically operated by state-owned forestry companies. Some communes also had publicly managed land that could be made available to households. Land could also be purchased from other households by the government for redistribution. If in compliance with environmental regulations, forestland could also be transferred to households. Finally, land reclaimed from free land, treeless hills, and non-used land, in other words, land with nebulous status, could also be transferred to households. The transferred land need not be plough-ready, and households were given up to 4 million VND (about US$235) to cover the costs of land reclamation.Shortly after Program 132 was announced, in 2004 Program 134 was implemented. Program 134 essentially extended Program 132 to ethnic minority households outside the Central Highlands. One key difference was that the land thresholds and redistribution targets were not as high as in Program 132 . In addition, Program 134 added housing and drinking water to the existing Program 132 infrastructure. While we do not evaluate the housing and water dimensions of Program 134, because of the overlap in program administration, we treat Programs 132 and 134 as a package, though referring primarily to Program 132, as its parameters were most relevant for farmland in the Central Highlands.There have been a number of official assessments of Programs 132 and 134. These draw on a combination of commune, district and provincial-level reports. The main objective of these assessments was to account for the extent of land redistribution, and tally how many households benefited from the program. Of these, MARD  is probaThe other rows of Table In summary, Table Central Highlands Vietnam Living Standards Survey (CHVLSS) included 1126 panel households  with complete information.The 2002 VHLSS provides an excellent baseline survey of households just prior to the implementation of Program 132. For our purposes, the VHLSS has detailed information on household land holdings and ethnicity, the key determinants of program eligibility, as well as a rich array of pre-program outcomes like household income. For the post-survey, we sought to resurvey 1250 households: All of the original sample of 25 households per commune, drawn from 50 out of 120 Central Highlands communes in the 2002 VHLSS. We skewed our selection of communes towards maximizing the number of potentially treated ethnic minority households, based on observed land holdings in 2002. For administrative reasons, we also excluded all communes that in 2002 were in Dak Lak, but became part of Dak Nak province after 2003. Relative to their share of the rural population in the Central Highlands, we over-sampled in Kon Tum and Gia Lai, and under-sampled in Dak Lak and Lam Dong, as our objective was to maximize the number of households that would have been eligible for treatment. For households, we adapted the full VHLSS questionnaire, with additional modules on Program 132 and 134 participation. We also conducted surveys at the commune and district level, with modules added relating to 132 and 134 implementation. The resulting household survey, the The objective of Program 132, and to a slightly less extent 134, was to redress differences in land endowments between ethnic minority and Kinh households in the Central Highlands through allocation of land to the former. Thus, it is useful to examine differences in landholdings between the two types of households before the policies were implemented, which we report in Table We report summary measures relating to both the mean and distribution of landholdings for annual, perennial, and annual plus perennial land for minority and non-minority households. The land indicators in the surveys do not perfectly line up with the categories in the policy documents . The policy was directed primarily at annual land, though as land is clearly fungible, and perennials are important in the Central Highlands, it makes sense to explore the sensitivity of conclusions to various definitions of land holdings. It is also unlikely that households with significant holdings of perennial, but not annual land, were the intended beneficiaries of the program.In the case of Kon Tum, ethnic minority landholdings were significantly smaller than those of their Kinh counterparts. Average total ethnic minority landholdings were 1.12 hectares per household compared to 1.97 for Kinh, or a difference of nearly 75 percent.A slightly different picture emerges outside Kon Tum. Ethnic minority households on average have more land than the Kinh, a product of larger holdings of annual land. Holdings of perennial land are nearly identical. There remains a significant percentage of ethnic minorities with annual or total agricultural land holdings less than either 0.5 or 1.0 hectare, but the percentage is typically no higher, and usually lower than we observe for the Kinh. Overall, 69% of Kinh households report landholdings less than a hectare, compared to 44% for ethnic minority households. As in Kon Tum, about half of the ethnic minority households appear eligible for program participation. Unlike Kon Tum, forestland is relatively unimportant in the rest of the Central Highlands.In the next part of Table Comparing other key variables, ethnic minority households are significantly larger than their Kinh neighbors are, with almost six members per household, vs. five for the Kinh. There are striking differences in levels of education across households. First, households in Kon Tum have about 1.5 years less education per person (measured by the most educated adult in the household), irrespective of ethnicity. The gap between ethnic groups is staggering: Almost 3 to 4 full years of education, with Kinh having almost double the years of schooling. To the extent that human capital is an important determinant of income, on and off the farm, it seems at the outset that improving education for ethnic minorities might yield a bigger bang than changing the land distribution. This is consistent with previous researchers ? Were there deviations from program design that undermine our use of predicted eligibility as the foundation for an identification strategy? Is there evidence that the program was implemented differently than intended?We begin with a summary in Table The first three columns of Table These thresholds varied for several reasons, the most important of which was local land availability. In land abundant areas like Kon Tum, it was easier to find land to top up a household to 1.0 hectare, while in land scarce areas, this was too expensive. Moreover, the implied value of a land transfer would have been higher in these communes. In short, the amount of land transferred and the targeting of land to households is correlated with the value of land itself. As a result, the returns to program participation may be correlated with household eligibility, at least across communes. In land abundant areas, a hectare of land may not have amounted to much benefit, especially if it needed significant reclamation.In Appendix C Table In the final three columns of Table In summary, the province and commune level data reveal significant heterogeneity in treatment rates. This heterogeneity likely comes from a number of sources including: (1) variation in the cut-offs used for determining eligibility; (2) variation in the number of eligible households; and (3) most importantly, variation in available land, and budget for project implementation.We do not know whether a particular household was deemed eligible for the program. However, as discussed in the context of Table In our survey, we directly asked households whether they received land from Programs 132 or 134. While program eligibility status is fuzzy for reasons described above, even treatment status is potentially ambiguous, as households may not be fully aware of their own treatment status. We, therefore, provide information on treatment by initial landholdings using several alternative definitions of treatment: (1) whether the household reports receiving land from Program 132; (2) whether the household reports receiving land from Programs 132 or 134; and (3) whether the household reports receiving land from Programs 132 or 134, or report they reclaimed land since 2002. Our third measure of treatment is the most liberal estimate of treatment, and allows for the possibility that households may not know the channel by which they received the additional land. A significant amount of program land required reclaimation, and land reclaimed by households may have been program land, even if they did not remember the legal source.Turning to the cross tabulations in Table The implied participation rates are still lower than the official sources, especially if we use the measures of treatment that most directly refer to program land. Why might this be the case? There are two possible explanations aside from over-reporting by local governments. First, imperfect recall by households over the legal sources of their land over the previous 6 years may be a factor. While households might be familiar with the programs, attributing an individual plot of land to the program may be difficult. This will be especially true for land that they may have been informally working prior to Program 132, and where the program effectively secured property rights to existing land. It is less likely that program land has already been disposed of, given the restrictions placed on selling this land. Of course, less formal transfers  could lead to leakage at the household level.Ex post household treatment rates  would be exaggerated. Furthermore, new households make reconciling results from the panel and the aggregate sample more difficult. By construction, the panel nature of our data limits our ability to measure treatment of new, younger households. Especially if the new households are more likely eligible than the panel households, our sample will understate the extent of treatment, while giving an accurate estimate of treatment in the base households from 2002. This highlights an inevitable limitation of retrospective, panel-based surveys.Second, new household formation and our sample design may also be a contributing factor to the gap. An examination of the 2001 and 2006 Agricultural Censuses reveals a significant increase in the number of ethnic minority households, and a decline in average household size over a period that spanned the implementation of Program 132. We do not believe that Program 132 alone caused a significant increase in household formation – the value of the relevant land is not high enough for that – but other factors could coincide with ethnic minority household splitting and formation. The housing component of Program 134, for example, may have encouraged younger generations to form their own households. Formation of new households would have implications for the estimation of official, aggregate participation rates: If land was given to new households, then the appropriate denominator would include the new households, not just the original 2002 households. As for linkages with eligibility, the easiest comparison is between those households with less than or greater than one hectare of land . In Kon Tum, as well as in the remaining Central Highland provinces, there appears to be significant leakage in the treatment, with the ineligible almost equally likely to be treated. In Kon Tum, 20% of households with land in 2002 under one hectare received land from Program 132 or 134, while 14.7% of ineligible households also received program land. While it is true that eligible households had a higher probability of treatment, the relationship between eligibility and treatment seems very weak. In the final two columns of Table An underlying premise of Programs 132 and 134, and the allocation of land to ethnic minorities is the view that landholdings are positively correlated with household incomes. Thus, targeting land for land-poor households will improve the welfare of the neediest rural ethnic minority households. Land transferred through 132/134 may not have been directed to the land poor, but it may still have been directed to poor households. In Table In Table Of most interest from an equity perspective, household-reported treatment rates are fairly similar across quartiles, though higher in the middle. In Kon Tum, treatment rates peak in the third quartile. The same pattern holds outside Kon Tum, though the treatment rates are much lower in magnitude. In summary, land transfers were not disproportionately directed to land poor households. Nor were transfers directed toward poorer households. If anything, transfers went to the middle and upper income households, contrary to the stated objectives from Hanoi.As we ultimately wish to relate treatment to outcomes, was land targeted to certain types of households? To explore the issue further, we estimate richer versions of the treatment regressions reported in Table In Kon Tum, we find that households with less perennial land were slightly more likely to receive land. Outside Kon Tum, we find no evidence that households with less farm land were more likely to be treated. On the other hand, while forestland holdings are rare at the household-level outside Kon Tum, there is some correlation between having forestland, and treatment status. None of the household characteristics, notably income and education, were correlated with treatment. The most significant variables, by far, are the commune dummies.There are several implications of this exercise. First, there is very little predictable variation of treatment within communes. The program was not implemented the way that it was designed, at least based on the three-page sketch from Hanoi. The variation of treatment across communes means that it is not likely to be valid to exclude commune dummies from any analysis. The most reliable analysis will have to be conducted within communes. The nature of the implementation of the program also underscores the value of separating Kon Tum from the rest of the analysis. The absence of a link between observable household characteristics in 2002, especially land holdings, and subsequent treatment, undermines any identification strategy that relies on these serving as instruments, either for conventional Instrumental Variables, or for Fuzzy Regression Discontinuity. However, there is no evidence of departures from random assignment, at least to the extent that program participation excluded those with the highest income or education. We still need to be concerned that households were granted land based on the perceived value of land to them: Which households could put the land to its best use? At least for Kon Tum, our evidence is also consistent with broad participation of ethnic minority households in the program, with land assigned essentially randomly to households . Furthermore, as emphasized by Alatas et al. , and KarWe are interested in assessing the impact that the distribution of land through Programs 132 and 134 had on rural household incomes in the Central Highlands between 2002 and 2008. Given that our identification strategy will be driven by before-and-after comparisons between treated and untreated households, it is useful to look at the broader changes that occurred in both land and incomes over this 6-year period.We wish to know whether minority households, as a group, experienced improvements in their land holdings. We have detailed information in the 2002 and 2008 surveys to compare household land holdings irrespective of treatment status, and we also asked households in 2008 whether they received land from the programs. We can compare changes in the land distribution between minorities and Kinh—who would not have been treated—and also between ethnic minority households that report being treated or not.In Table The shares of households by land-size category  reveal that the increase in mean landholdings was accompanied by similar reductions in the percentage of households with less than a hectare of agricultural land. In Kon Tum, the percentage of households with land under one hectare declines from 50.7% to 43.5%. Outside Kon Tum, there is a similar sized drop in the percentage of households with less than a hectare of farmland, from 43.7% to 37.4%. Note that for the Kinh the percentage of households with zero land doubles, from 9.0% to 18.6%. Even while some Kinh households were expanding their plantings of perennials, other Kinh households were exiting agriculture. This also corroborates the point raised in Ravallion and van de Walle  that lanThese simple summaries of the aggregate land distribution hide important subtleties. First, in moving from annual to perennial land, farm sizes generally shrink . Second, there is a great deal of shuffling between annual and perennial land that may have an even greater impact on income changes than the simple total acreage. Finally, the averages hide the significant amount of churning in land among households. In Fig. Starting with Kon Tum, the vast majority of households experienced changes in annual land holdings of at least 0.25 hectares. For ethnic minority households, annual land increased upwards from 0.25 hectares, consistent with having participated in Program 132. Some households also experienced declines in their land holdings. Kinh households divested themselves of annual land. For perennial land, changes in both directions largely applied to the Kinh, with increases outweighing decreases. Outside Kon Tum, we also witness a majority of ethnic minority households experiencing changes in their annual land holdings, with increases of at least 0.25 hectares. Compared to Kon Tum, ethnic minority households elsewhere in the Central Highlands shifted into perennial land. Except for perennial land in Kon Tum, the panels of Fig. What does this imply about potential linkages between Program 132 and the land distribution? One possibility is that these changes are largely a consequence of the policy. In our survey, we asked whether households had acquired new land since 2002. Overall, more than a quarter of all ethnic minority households reported new land that they had acquired since 2002, with the percentage in Kon Tum 44.2%. In Kon Tum, the three primary sources of this land are identified as coming through Programs 132 or 134, newly reclaimed land, and land that they were allocated long-term use rights to (presumably by the Commune). Outside Kon Tum, 24.0% of all households report acquiring new land, but land that might be linked to the state is significantly less than in Kon Tum. More important is land that is either inherited, or bought. Clearly, program land is not the only way for ethnic minorities to have increased their land holdings between 2002 and 2008. This complicates the estimation of treatment effects, as land of the untreated households changed through other means than the program. The existence of other channels to acquire land makes it more difficult to distinguish differential outcomes for treated and untreated households, especially outside Kon Tum.In Table Turning to ethnic minority households, in 2002 real household incomes in Kon Tum were 17.1 million VND. Per capita incomes were 3.1 million, while per capita expenditures were slightly lower at 2.5, implying that these households were saving in upwards of twenty percent of their incomes. Nearly three-quarters of income came from cropping and agriculture-related sidelines, e.g., forestry and animal husbandry, with income from wages making up most of the rest. Incomes outside Kon Tum were modestly lower, with income from wages (sidelines) playing a more (less) important role. We observe a sharp divergence in the trends in incomes between provinces. In Kon Tum, total household as well as household per capita real incomes fell between 2002 and 2008. Similar behavior is seen for per capita expenditure, confirming that this is not an artifact of problems in measuring income. Cropping income rose by nearly 30%, and wages by 43%, but these increases are more than offset by the collapse in sideline income, primarily forestry and livestock, of 79%. There are a number of alternative explanations for this decline. The fact that the reduction in sideline incomes included significant losses in earnings from forestry, livestock, and hunting and trapping suggests changing rules on access and use in Kon Tum. In Appendix D we explore the drop in sideline income in more depth. We confirm that the decline is strongly associated with the loss of access to forest land, though the timing of the decline suggests that it was specific to 2008, and had little to do with Program 132 or 134.Contrast this behavior with the growth in the Central Highlands outside Kon Tum, where per capita incomes almost doubled from 2.8 million to 5.6 million VND. Largely driving this growth is the sky-rocketing growth of income from the cropping sector, which can be linked to land in perennials in 2002. This increase is complemented by growth in wage income, which is slightly larger in percentage terms than we observe in Kon Tum. The Central Highland provinces outside Kon Tum also experience a reduction in incomes from sidelines, but of much smaller magnitude.The aggregate evidence suggests that the changes in the distribution of land are consistent with the implementation of Programs 132 and 134. However, it is not clear whether this is due to the program or part of the dynamic changes in landholdings taking place. Even less obvious is whether program participation did anything to improve ethnic minority living standards. Ethnic minorities outside Kon Tum experienced increases of income far beyond anything a half-hectare of land could generate, while households in Kon Tum saw their incomes decline. In order to better identify the potentially causal links between the Program 132 and household outcomes, we now turn to household-level treatment status and outcomes.ex ante preferred indicator: Self-reported household participation in Programs 132 or 134 (“Treat 2”). Results are generally not sensitive to this choice.We estimate a program evaluation regression of the form:The standard problem in program evaluation is that the effect of treatment is confounded with unobservable determinants of the outcomes, in this case, changes in land holdings or growth of incomes. The most feasible way to deal with this is to include a rich set of covariates, and assume that treatment status is conditionally independent of the potential outcomes. We include a vector of land holding indicators for the amount of annual, perennial, and forest land controlled in 2002. These variables control for program eligibility, and given the importance of land holdings for income growth, also control for linkages between household land investments prior to 2002 and subsequent income growth. The treatment variable will then identify the effect of new land that potentially came through the program. We exploit the panel structure of the data and also include controls for household predictors of income growth, specifically household education and size in 2002, as well as a vector of household outcomes in 2002, most notably total income (and log per capita income), and income from various sources . To the extent that these variables are correlated with both program participation and potential outcomes, their inclusion helps control for some types of endogenous assignment to the program. Finally, in all specifications, we include commune fixed effects. This allows for the fact that programs were implemented differently across communes, and that these differences are possibly correlated with the potential changes in outcomes across communes. This implies that our specification is identifying the effect of program participation by comparing treated and untreated households within the same commune, holding constant (in a linear regression sense) a household’s land endowments and income in 2002.The identification strategy rests on the assumption that within communes, otherwise identical households did not receive program land for reasons correlated with unobserved determinants of future income growth, or their ability to access land. What could undermine this? On one side, commune officials might want to give land to those households that could put it to the most productive use. In this case, we expect an upward bias in the returns to program participation. We cannot rule this out, but the fact that we find no correlation between treatment status and other predictors of potential income growth, such as education or income in 2002, leads us to believe that this source of bias is likely small. In the other direction, officials may favor households that most need the land, in which case there is a downward (negative) bias in the estimated coefficients. Since we find no evidence that program participation was higher for low-income households or those with less land, we do not view this as being a large bias.Our identification strategy uses ethnic minority households in the same commune that did not receive land as the counterfactual. Are there alternative estimation strategies? Based on the wording of Program 132, initially the most promising was to use landholdings from 2002 to construct instruments. As we saw previously, however, land-holdings in 2002 are uncorrelated with reported treatment status, eliminating this as a useful source of instruments. It also turns out that land holdings in 2002 fail to satisfy exclusion restrictions as incomes rose fastest for those households with greater land holdings of perennial land in 2002, and changes in land between 2008 and 2002 were also correlated with land holdings in 2002 for reasons that have nothing to do with program participation. Hence, an instrumental variable strategy based on initial land holdings will not work.Are there other ways to estimate the counterfactual? The Kinh are not a plausible control group. While not eligible or treated, their income growth is a poor counterfactual for what would happen to minorities in the absence of treatment. In the same vein, minorities outside the Central Highlands are a poor control group, given the heterogeneity of income growth across locations. Moreover, as we cannot reliably disentangle the impact of land received from Program 132 or 134, these minority households cannot serve as a control group as they were treated in Program 134. In summary, and acknowledging its limitations, the best counterfactual for treated minority households in the Central Highlands are untreated minority households in the same commune with similar covariates.Before estimating the impact of program participation, we estimate the potential impact that an additional plot of land can have on household income. In Table In 2002, for the pooled sample, the returns to perennial land are about double that for annual land. The average return is 5.968 million VND compared to 3.026 for annual land. To help put these numbers in perspective, average per capita incomes in 2002 were approximately 3 million VND. For ethnic minorities in Kon Tum, returns to annual land are higher than for perennial land (4.249 million VND vs. 1.738 million VND), while the reverse is true for ethnic minorities outside Kon Tum, where perennials had much higher returns.For 2008, the returns to both annual and perennial land are significantly larger, but the increase is much more pronounced in the case of perennials. Amongst the ethnic minority, the returns to both types of land are also higher in 2008, especially for perennials outside Kon Tum. The returns to perennial land could increase for a variety of reasons. The trees  may be more mature and yielding more output, or the prices of these crops may have increased. In fact, it was likely both factors.These regressions are complemented by two specifications based on changes in crop income between 2002 and 2008. The first (in column 3) is slightly unconventional as a production function. We estimate the change in income as a function of the levels of variables in 2002. This, however, mimics our main program evaluation regressions, where we estimate the effect of program participation conditional on a similar vector of 2002 characteristics. In column 3, we see that those households that had perennial holdings in 2002 had the greatest increases in crop income. Indeed, holding one hectare of annual land  did not yield any increase in crop income (though it of course yielded the same level of income per hectare). In this specification, it is clear that access to perennial land in 2002 was critical to increased crop income over this period as being in the annual business yielded no income growth. It also illustrates that perennial holdings in 2002 would be a poor instrument for program participation, as they had a direct effect on crop income growth.In the last column (4), we estimate a more conventional first difference specification: Were changes in land associated with changes in crop income? This gives us the best possible estimate for the potential impact of program land. For minorities in Kon Tum, we see that an extra hectare of annual land for an ethnic minority household yielded an average of 4.480 million VND, while an extra hectare of perennial land yielded 3.311 million VND. The return to annual land was significantly higher in Kon Tum than outside. As we move to our more formal estimates of the effect of treatment, it is clear, however, that land is a valuable asset for households in the Central Highlands, especially in Kon Tum. Receiving free annual land should increase crop income. An important caveat, however, is that a hectare of program land may not be the same as land acquired in other ways: Households with no experience in cultivating fixed plots of annual land may not be able to achieve the returns suggested by Table Regression results for the program evaluation equations are presented in Table For Kon Tum, we estimate that program households received 0.28 hectare of annual land, which is not far from the official reports, while their holdings of perennial land remain unchanged, again consistent with the official reports. Treated households saw their crop income rise by 2.69 million VND, which is in line (given sampling error) with what 0.28 hectares of additional annual land would generate (from Table less annual land, have correspondingly less crop income, and work fewer days in farming. These results are internally consistent, and point to treated households moving disproportionately out of agriculture. It is not obvious why this should be the case. However, as in Lam Dong, having forest land in 2002 is a strong predictor of treatment status in Dak Lak. If treated households lost access to their forest-based farm land, replacement with annual land closer to the village may have left them worse off, with less land and less ability to earn a living from it.In the second row of the table, we report the results for the Central Highlands outside Kon Tum. If anything, we see a negative effect of participation on land accumulation and income. Treated households experienced reductions of annual land, modest increases of perennial land, and reductions of total income, especially sideline income. Very few of the coefficients are statistically significant. To gain a clearer perspective on what may underly the results outside Kon Tum, in Appendix B Table At the outset of our project, we expected a relatively clean evaluation of Program 132. We surveyed over 800 minority households yielding a panel of households observed just before, and shortly after the implementation of the program. Official reports suggested that the program was implemented in line with its objectives, transferring land to eligible ethnic minority households with small landholdings. Given the reported levels of participation—60% of eligible households—and compliance with the targeting criteria, we anticipated our main problem would be fine tuning estimates to address potentially endogenous assignment of land to households. As our household survey revealed, however, there is little evidence that the program was implemented in line with official reports. Participation rates were lower than expected, especially outside Kon Tum. Furthermore, there was almost no link between measures of eligibility, initial land holdings, and subsequent treatment.Our results highlight the value of retrospective surveys in evaluating and auditing the implementation of government programs, and by implication, the value of continuously conducting household surveys that can serve as baselines. With decentralized programs like 132 and 134, it should come as no surprise that local implementation may deviate from the original plan, especially in the case of a land program that hinged on the availability of local land. To predict the impact of this program, a pilot of the program in a sample of randomly selected communes was needed in order to study how local officials interpret and comply with program parameters. As in Alatas et al. , randomiOf course, a more intensive household-level RCT can help evaluate how valuable a plot of land is to a household. On that front, we believe that our more conventional panel-data based estimates are informative. We estimate that households receiving program land experienced increases of income in line with the returns from land distributed through the program. Those returns, while non-negligible, are not large enough to make much difference to the lives of ethnic minority households, and underscore the limitations of land redistribution as a means of raising farm incomes in a dynamic agricultural economy. Households may be able to grow basic staples for household consumption, but serious income growth requires households being able to grow perennials and other crops that require human capital and credit, as well as integration with outside markets. Programs 132 and 134 succeeded in distributing land to a significant number of households, and provided the government with an opportunity to demonstrate its concern over land and ethnic minority issues. Nevertheless, the benefits of the program were diffuse, and far greater investments will be required for ethnic minorities to close appreciably the gap in living standards with their Kinh neighbors.Supplementary Information


The selection of foods made by athletes during competition can impact performance, yet to date, the quality of their food choices has not been investigated. The aim of this study was to describe the food selection of athletes in a buffet-style dining hall setting in terms of diet quality, food variety, and volume of food and compare to their self-rating of their meal, reasons for the choosing the food items, access to previous nutrition advice, and use of nutrition labelling. A total of 81 athletes  from 24 sports across 58 countries at the 2018 Commonwealth Games  participated in this study. A digital photograph was taken of the athletes’ meal after selection from the buffet and prior to consumption. Each participant was asked a series of questions in relation to their food selection. The photographs were coded into recommended serves of food groups based on the Australian Guide to Healthy Eating. The nutritional analysis and photograph of a standard serve size were used to quantify the energy and nutrients for the meal. Most athletes chose adequate quantities of macronutrients, which agreed with their reasons for the food choice, but the majority did not include fruit (80.2%) or dairy (65.4%) in their food selection, while 54% of males included discretionary foods (0.25–7.0 serves). The median self-rating for food choice was 8/10. Most reasons for food choices were nutritional attributes, sensory factors, performance, usual eating practices and physiological factors . This suggests that athletes may need more education on the quality of food selected from buffet settings. There is currently a move towards food-based research away from a focus on nutrients in the general population , but thiAthletes are unique in that they are often exposed to many and varied food environments that are likely to influence food selection and subsequently the quality of their diet. Athletes travel to competition and training venues and commonly live in an unfamiliar environment with a different food supply. Live-in buffet-style dining halls present many options for the athlete, resulting in difficult choices about what to eat. The sheer volume of offerings can be confusing and potentially lead to over- or underconsumption or inappropriate food choices, impacting dietary intake, as has been previously demonstrated in this environment . EstabliDespite environmental influences being a key determinant in food choice , little Use of photography provides a novel way to visually assess the food selection of athletes during and post-competition. Previous studies have used digital images to assess real-world environments, such as self-serve dining settings, and have been shown to provide objective and reliable measures of food selection and servings ,18. To dThe aim of this study was to describe the food selection of athletes in a buffet-style dining hall setting in terms of diet quality, food variety and volume of food and compare to their self-rating of their meal, reasons for the choosing the food items, access to previous nutrition advice and use of nutrition labelling.www.caternut.com); Version 1.0, Maroochydore, Australia). During the event, each item was plated on a standard dining plate as a single serve on three separate occasions, and was weighed, photographed and labelled. The mean weight was determined to be the standard serve.This research took place during the 2018 Commonwealth Games  within the athletes’ village dining hall in April 2018. The dining hall consisted of a range of service areas that provided both hot and cold menu items. Each item on the menu was coded to Australian Food Composition database  and analysed by the research team in advance of the event using custom-made nutritional analysis software .A photograph was taken of the meal using a tablet at an approximate 45-degree angle with a fiducial marker. All food was served into standard sized disposable plates and bowls, and serving trays that were provided in this environment. With the exception of juice, plain milk, tea and coffee, all drinks were bottled and labelled. The participants were asked a series of short questions that were entered into SurveyMonkey by the researcher. The questions included demographic characteristics, source of nutrition information, self-reported influences on their food selection, any dietary regimen followed, competition phase and self-rating of their food selection on a scale of 1 (very poor) to 10 (excellent) framed in the context of performance nutrition. Open-ended responses to individual reasons for food choice were preferred over existing survey tools as the purpose of the study was to investigate specific reasons for the meal selection, not broader factors influencing food choice. Field notes were taken by the researcher in the situation where the image did not accurately show all items on the tray. This study primarily aimed to investigate food choice, not actual dietary intake; therefore, waste was not recorded.Each image was assigned a unique identifier that was linked to the participant survey and was reviewed by two researchers to identify the menu items on the plate. This was complemented by the photographs of standard serves and field notes that recorded additional information about the food selection. Both researchers cross-referenced their food selection to ensure consistency.The coding system used for food groups was adapted from Christoph et al. . Some moEnergy, protein, carbohydrate, fat, sodium and fibre content of each menu item in the photograph was based on the estimated serve size. This was also determined by visually comparing the meal image of the item to the image of the standard serve of the same item. In addition, each researcher had access to a reference plate and bowl, the fiducial marker and the field notes. The prior nutritional analysis of each menu item was used to quantify the energy and nutrients for the meal serve. The estimated quantity of the nutrient for the meal as a whole was determined by adding all values together, plus any additional items that were listed in the field notes but not visible in the image.The results from both the qualitative and quantitative dietary assessment and survey were combined and entered into Statistic Packages for Social Science (SPSS) statistical analysis software . Athletes were grouped according to sport  and region  for analysis. As no variables were normally distributed, nonparametric tests (Mann–Whitney U test (MW-U), Kruskal–Wallis (KW) ANOVA with Bonferroni correction, Chi-square test and Spearman’s correlation) were used to compare differences between groups and relationships between variables. As the nature of the study was not designed to accurately determine the exact quantity of each nutrient in the meal, energy and nutrients were described as a median (Md) score with an interquartile range (IQR) and relevant food groups represented as a range. In addition, quantitative analysis of energy (kJ), protein (g), carbohydrate (g), fat (g), fibre (g) and sodium (mg) were grouped into categories of negligible, low, medium and high. Categories were determined by the researchers based on consideration of Australian food labelling standards , nutrienp = 0.026) and were on average significantly older (p = 0.027).A total of 81 athletes  from 24 sports across 58 countries participated in this study. The demographic characteristics are shown in More meals were tested prior to and during the individual’s competition (69.1%) than after completion (30.9%). There was no significant difference between the proportions of meals tested before/during and after competition across the different meal periods breakfast, lunch and dinner period , nor werThe estimated quantitative and qualitative analysis of the meals is presented in p < 0.01 have been reported. There was a positive relationship between energy (kJ) and serves of grains , meats , core food groups  and discretionary items ; protein (g) and serves of meat , discretionary items  and core food groups ; carbohydrate (g) and grains , serves of fruit , fruit juice  and discretionary items ; and fat (g) with serves of meats , grains  and discretionary items . Sodium (mg) was correlated with serves of discretionary items  and meats , while fibre (g) was correlated to serves of grains .As expected, there were a number of correlations between nutrients and food groups in the meals. Only those with a significance of p = 0.046) was significantly more in meals selected prior and during (Md 8.0 g) competition than post (Md 5.0 g). There were no apparent differences between different regions and nutrient composition of the meals.The median grams of fibre , with younger athletes rating their meal selection less than older athletes. There were no differences in self-rating across sex, regions and sports, nor stage of competition. There was also no significant relationship between self-rating and previous nutrition advice or label use. There was no correlation between self-rating and any component of the meal. Athletes’ self-rating of their meal selection was a median (range) of 8 (2–10) out of 10. There was a positive correlation between age and self-rating of food selection  and was correlated to the choice of discretionary food and meats. While this is not an issue for the majority of athletes , for those attempting to make weight, it may be more challenging where recommendations for sodium intake are around 300 mg/MJ , and thuThere is currently less evidence around quality of the food athletes are eating. We chose to consider whether meals contained a particular food group as a reflection on whether the athlete intended to eat a variety of foods across different food groups. Both fruit and dairy products were not represented in the majority  of meals. We do not know if individuals chose to not eat these foods or they intended on returning to get them after they had finished. Fruit was readily available as a snack to take outside the dining hall and thus may not have been chosen from the buffet. This may be the case for dairy items such as chocolate milk that may have been used as a recovery drink by some teams. Regardless, the lack of dairy choices in the majority of meals may be of greater concern given that this is a significant source of protein, calcium and vitamin D ,28 and wA number of athletes commented that they had made food choices that were related to their training and competition. There was a trend to mention macronutrients (particularly carbohydrate and protein) having an influence on their food selection. This is supported by literature that suggests performance factors are important in decisions around food choice in this environment ,24 but mThere was no relationship to use of food labels, and most did not use the label to make their selection. However, we found a higher proportion of females reported using the labelling than males, which is similar to previous studies that found that females place more value on nutrition labelling  and use Interestingly, the cohort of participants rated their choices highly despite around one third choosing discretionary foods, the majority not choosing fruit and around one third not choosing vegetables. This may suggest that athletes are not considering the quality of the meals they are selecting but are more focused on the macronutrients that fuel their sport. This is supported by the comments on the reasons for selecting the food, with a large number of participants mentioning carbohydrate and protein. There was a relationship to age, which suggests that younger athletes may feel less confident with the food they chose to eat. Most athletes had previously had nutrition advice, and of these, most had been from a dietitian. Individual rating may be reflective of confidence in their choice, not just a knowledge (or lack) of what would be appropriate for their sport. Alternatively, other factors such as sensory appeal and usual eating practices may take priority over performance, as was suggested in some of the reasons for food selection . InteresThe strengths of this study were the lack of interference in the athletes’ food selection, use of photography to capture food selection, the ability to record additional information by the researcher to ensure accurate data collection and concurrent capture of the reasons for food selection, thus not relying on subject recall. However, it is feasible that some foods were missed during data collection and analysis. Sauces, oils, dressings and other condiments may have been overlooked and not recorded in field notes. Furthermore, estimating the serve sizes of these foods is difficult, and this could impact the quantitative analysis in particular. It is also feasible that the self-reported reasons for food selection were influenced by the presence of the researcher. Despite these limitations, this was a relative non-invasive and inexpensive method to collect information about food choice of athletes during competition. Use of computational tools to analyse digital images would be a useful addition as the time taken to code and analyse foods was lengthy.These results do not indicate actual intake of athletes, as they may not consume all the food that is selected and could return for additional plates of food, nor was this necessarily representative of their intake across the entire day, but this study does give an indication of the types and quality of food that athletes are choosing at major competition events. We did not find differences across geographical regions, but this may be because of the large number of countries that were represented in this study and the limited sample size. As this is the first study to examine meal selection at a large-scale competition event, we aimed to collect data from a wide range of sports and countries to be able to examine the extent of variation in food choice.Future research could monitor second helpings by athletes as well as photographing waste to get a better understanding of actual dietary intake and to understand the reasons for not consuming certain foods. This would also help caterers to plan the menu and avoid excessive waste, which is increasingly considered a cost and sustainability issue at these major events . It woulThis study used a novel method of digital images to determine the food selection of athletes during competition in a large-scale dining facility. The results suggest that athletes may be more focused on quantity of macronutrients rather than quality of their food selection and influenced by a range of factors other than performance, despite having previous nutrition advice. Better education of athletes on diet quality ensuring selection from a range of food groups and improvements to the food environment to assist with food selection could help individuals and teams to plan for the impact of buffet-style eating, where the extensive range of items can lead to inappropriate choices or excessive intake.


The term spondyloarthritis (SpA) encompasses a broad clinical spectrum characterized by chronic inflammatory conditions affecting the sacroiliac joints, the spine but also peripheral joints and tendons and being additionally associated with the involvement of organs, such as bowel, eye and skin . Musculo The term FM was initially proposed in 1975 . The eviSeveral sets of classification criteria have been proposed over the last decades not only to ensure the correct identification of patients displaying different clinical features, but also to acknowledge and incorporate the evolution of imaging techniques and the compelling need to reduce the diagnostic delay . The humThe complexity of SpA and FM clinical pictures, along with the different classification criteria available, account for the difficulty to compare studies exploring FM prevalence in SpA and vice-versa. A recent systematic literature review and meta-analysis discussed 10 studies assessing FM prevalence in SpA using the ACR 1990 and/or 2010 classification as inclusion criteria  but, as The study considering nr-axSpA as a separate entity reported a prevalence of FM of 23.9% and 36.4% using the ACR 1990 criteria and the FiRST tool, respectively . In periWhen looking at how many patients with FM would fulfill SpA criteria, the number of related publications is much lower and, as a consequence, the results even less clear Table . In one The observation that FM patients may fulfill SpA criteria and the need to differentiate active SpA features from FM for appropriate therapeutic management, point out the importance to complement clinical assessment with imaging techniques. With regard to IBP, radiographic sacroiliitis has long been a hallmark of the classification of AS, within the axSpA spectrum . HoweverMusculoskeletal US with power Doppler (PDUS) is a valid and reliable means of evaluating SpA enthesitis . On thisGiven the considerable coexistence of SpA and FM, another crucial point is the impact of the latter on SpA outcome measures and therefore the implications of therapeutic tailoring . PatientWhen focusing on the involvement of enthesis, the enthesitis score used in SpA has been criticized owing to its lack of correlation with objective measures of enthesitis and overlaps with the FM tender point score , 62. In Having concurrent FM is associated with a higher likelihood of having received biologic therapy in a study from the British Society for Rheumatology Biologics Register in Ankylosing Spondylitis (BSRBR-AS) . Bello eFM diagnosis could predict a lower probability to achieve minimal disease activity or remission in patients with PsA , 45 and FM occurs in a significant proportion of SpA patients increasing the chronic symptom burden, reducing overall function, and worsening QoL. This has a dramatic impact not only on the individual but more in general on society in terms of decreased work productivity. While FM may coexist with SpA, disease activity measures that include subjective elements, such as pain and patient global reports may be overstated, even when more objective measures, such as swollen joint count or CRP suggest the achievement of remission or low disease activity. Therefore, PROs may be less valid and reliable as a true measure of inflammatory disease activity and to be used as a basis for therapeutic decision-making in clinical practice in some patients with long-standing disease, since they may lead to intensification of, or switching immunotherapy when it is not necessarily warranted.On this basis, the coexistence of FM should be assessed in patients with SpA based on the clinician's judgment and the use of validated positive criteria/questionnaires, instead of by elimination criteria, with input from both patient and clinician. In addition, sensitive and advanced imaging techniques should be necessarily implemented to differentiate SpA inflammatory pain from FM pain, therefore establishing the most appropriate treatment approach in partnership with the patient.AA and FC conceived the idea of this review article. AA, FC, and SS prepared a first draft of the manuscript that was critically revised by XB, RGe, RGi. All authors approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Informal settlements are becoming more entrenched within African cities as the urban population continues to grow. Characterised by poor housing conditions and inadequate services, informal settlements are associated with an increased risk of disease and ill-health. However, little is known about how informal settlement upgrading impacts health over time. A systematised literature review was conducted to explore existing evidence and knowledge gaps on the association between informal settlement characteristics and health and the impact of informal settlement upgrading on health, within South Africa, an upper-middle income African country. Using two databases, Web of Science and PubMed, we identified 46 relevant peer-reviewed articles published since 1998. Findings highlight a growing body of research investigating the ways in which complete physical, mental and social health are influenced by the physical housing structure, the psychosocial home environment and the features of the neighbourhood and community in the context of informal settlements. However, there is a paucity of longitudinal research investigating the temporal impact of informal settlement upgrading or housing improvements on health outcomes of these urban residents. Informal settlements pose health risks particularly to vulnerable populations such as children, the elderly, and people with suppressed immune systems, and are likely to aggravate gender-related inequalities. Due to the complex interaction between health and factors of the built environment, there is a need for further research utilising a systems approach to generate evidence that investigates the interlinked factors that longitudinally influence health in the context of informal settlement upgrading in rapidly growing cities worldwide. In Africa, and across the global South, urbanisation and urban growth are dramatically restructuring the nature of cities. The majority (55%) of Sub-Saharan African urban dwellers now live in slums and informal settlements—a proportion that is notably larger than the global average (30%) and other developing regions including South Asia 31%) [% [1]. InThese environments pose an unequal threat to the health of the urban poor and contribute to the spread of infections due to poor sanitation; a higher incidence of respiratory infections and conditions, including asthma; injuries; and the prevalence of mental disorders, including depression and stress ,4,5,6, tInternational development agendas and reports such as the New Urban Agenda , the ComThe urban system is comprised of a complex network or assemblage of interconnecting factors, both human and non-human, that act on and react with each other in response to changes and feedback loops ,14. One Housing, and its complexities, is one of the many underlying factors within an urban system that are able to shape and determine human health, defined as complete physical, mental and social wellbeing . This isIt has been argued that urban studies theory has largely been based on urbanisation trends of the Global North, thereby highlighting the need for continued research into the prominent features of Global South cities, such as proliferating of informal settlements, and how these may, in turn, be contributing to the complex urban challenges in developing countries ,22. CorbSA is one of the most urbanised countries in sub-Saharan Africa with 66% of the population residing in urban areas and is continuing to experience an increase in the number of urban inhabitants . Like maIt has been argued that the eradication of slums should happen indirectly, as a result of prioritising the improvement of informal settlement environments and conditions through in situ upgrading, which aims to reduce the amount of disruption to people’s lives ,30. HoweGiven the geographical and conceptual knowledge gap on the health impact of informal settlement upgrading, this review, using the case study of South Africa, focuses on exploring available literature and research to better understand the state of health in South African informal settlements using the WHO’s four dimensions of housing  and to iBetween March and June 2017, a systematised review of peer-reviewed studies was conducted. A systematised review includes some components of a systematic review, as it seeks to systematically review available peer-reviewed literature on the topic of interest, however it is not exhaustive, is useful when there is no intervention being evaluated and if there is an absence of multiple reviewers . Peer-reUsing two databases, Web of Science and PubMed, an initial search was conducted using the terms “UPGRADING” AND “INFORMAL” AND “HEALTH” AND “SOUTH AFRICA”. The abstracts of the articles were reviewed, and studies were included if they met all of the following criteria: the study was specific to the context of SA; the study had an explicit link to informal settlements and could be linked to one or the four dimensions of housing ; and if the study had an explicit link to health within the context of informal settlements. Studies were excluded if they were discussion papers, ongoing studies or if their methodology was not well described; if the study was only vaguely linked to general poverty or homelessness ; and if the full text was not available. Studies were excluded if they were published earlier than 1998, as the South African National Housing Code, which incorporates an informal settlement upgrading plan, was implemented in 1997. A search strategy has been provided detailing the search terms used, the inclusion and exclusion criteria and the included references (available as a supplement).The Web of Science database produced three results but only two studies were included in this study, as the third study was a discussion paper on international housing legislation. One of these was a mixed-methods review of research for the case-study site of Freedom Square in Bloemfontein, SA  and the Due to the paucity of studies measuring the health impact of informal settlement upgrading in SA, the scope was broadened to review any peer-reviewed studies in SA that investigated the state of health or health risk factors in informal settlements (able to be linked to the WHO’s four dimensions of housing ), or acrBetween June and October 2018, an additional search was conducted to identify any recent studies or studies that were previously missed due to the selected search terms and databases. The search was expanded to all databases linked with Web of Science, including MEDLINE and SciELO Citation Index. Additional combinations of search terms were used, as detailed in the search strategy in the Although there is a growing interest in research investigating health in the context of informal settlements, our search results identified a paucity of literature investigating health impacts of informal settlement upgrading. Only one longitudinal study was identified that specifically monitored changes in health in response to improvements made to the informal settlement or township, through the investigation of the level of satisfaction with quality of life with informal settlement improvements over time . As partOne qualitative research study investigated residents’ perceptions of safety within their community after upgrading had occurred, however this was not a longitudinal study and health variables were not explored . Most stAn overview of the characteristics and possible associated health conditions for the informal settlement living environment is displayed in The house structure has been linked with certain health outcomes. Mathee et al.  investigp < 0.05) association between mental health difficulties and crowded housing conditions [Crowding within houses is a common feature in informal settlements and is suggested to increase the risk of exposure to, and transmission of infectious diseases, such as tuberculosis (TB), and increase the risk of mental illness  45,48,5,548,51. nditions . The assnditions  who invenditions .In a study investigating the cause of burns in a cohort of children admitted to a Cape Town hospital, findings show that most burns to children occur in informal settlements . The cosOne study, conducted in the poorest areas of Johannesburg, found the counterintuitive finding that respondents in informal dwellings were healthier and had significantly fewer chronic health conditions  than respondents living in formal dwellings . The stuFindings from a review of two decades of literature and research investigating the association between health and urban development in SA, suggested that the upgrading of housing typology from shacks to subsidised housing may lead to improvements in noise, violent crime, safety, as well as reductions in alcohol and substance abuse . It has p < 0.05) between housing status, particularly security of tenure, and personal quality of life over time [Despite possible health risks associated with improved housing, the only identified longitudinal study of residents in an informal settlement upgrading site in Soweto, Johannesburg showed a significant association (ver time . The stuver time . Compariver time . In addiver time . The levp = 0.059). The significance of the association increased if the containers were not thoroughly cleaned (p < 0.0001) [The findings from a literature review investigating health and urbanisation over 20 years in SA, highlight the importance of water services and sanitation for addressing infectious diseases and improving wellbeing . One stu 0.0001) . However 0.0001) . Not onl 0.0001) . Unclean 0.0001) . In one  0.0001) .Escherichia coli (E. coli) [E. coli, other pathogens, and chemicals due to the surface runoff from the settlements, leading to serious health consequences, including bacillary dysentery and cholera in some instances, and even cancer in areas with high levels of Uranium and Arsenic in rivers [Another concern with the neighbourhood characteristics of informal settlements relates to exposure to contaminated water, either through an informal settlements’ proximity to rivers or through the accumulation of water in the area due to a lack of drainage. One study sampled water from surface runoff in four Cape Town urban poor communities comprising low-cost housing and informal backyard dwellings and found high levels of faecal bacteria and E. coli) . It was n rivers ,74,75,76It has been argued that ineffective services and a lack of sanitation in informal settlements is negating the possible health benefits that other informal settlement improvement strategies are supposed to have . Marais Box 1The warm Western Cape summer is accompanied by a seasonal rise in reported diarrhoea cases, with 25,000–30,000 cases for children under 5 years annually . HoweverOne such intervention is the use of geographic information systems to identify diarrhoea death ‘hot spots’ in informal settlements and the targeting of these hot spot areas for health and hygiene education ,81. In aPoverty and factors relating to low socioeconomic status are likely to increase levels of stress and feelings of depression . While tHowever, a cross-sectional study investigating the mental health of orphans and vulnerable children in a municipality in the Free State, unexpectedly found that the likelihood of having mental health issues decreased when residents had access to an outside tap and toilet and lived in an informal dwelling . Marais Very few studies were found that address the social environment of informal settlements. However, one study investigated health impacts for female-headed households (FHH) in contrast to other headed households (OHH) in informal settlements . The resInformal settlements are suggested to be lacking in social relationships and networks . These pIn a qualitative study exploring residents’ perceptions of safety in informal settlements that have recently undergone upgrading, most respondents who received a formal house reported they still felt unsafe in the community, where there was a high level of crime and gang violence . Of noteAnother social aspect emerging in the literature was intimate partner violence (IPV) ,84. A stWhile research in the context of SA broadly recognises the influence that urban and intra-urban environments have on health outcomes, there is a lack of evidence on the impact that improvements and upgrades to informal settlements have on residents’ health and wellbeing over time. This is concerning, as it likely reflects the administrative siloes within which the housing and health sectors operate ,87, despThe reported findings from this systematised literature review suggest direct and indirect associations between certain physical conditions in informal settlements and health outcomes. Direct associations include increased exposure to pathogens due to crowding and inadequate sanitation and polluted environments; examples of indirect associations include the influence that the physical environment has on mental wellbeing, community and social dynamics, and socioeconomic factors that mitigate or increase health risks. Health and housing have been shown to be intrinsically linked and the literature has further suggested a strong association between people’s satisfaction with life and conditions of health and housing.A question rising in the field of medical geography and urban health, is whether improvements to housing truly result in improvements to health and wellbeing ,70. WhilCrowding is shown to be associated with mental illness, particularly in children and an increased risk of exposure to infectious diseases such as HIV and TB. Moreover, fires spread easier in high density areas which increases the risk of burns and injury. Informal settlement upgrading should seek to minimise crowding both in housing units and in settlements.As mould and moisture within the home have been associated with an increased risk to asthma, allergies and respiratory illnesses, improvements to ventilation should be a priority for informal settlement upgrading. Improvements could include additional windows for dwellings, reorganising the informal settlement to improve ventilation between structures, or health awareness campaigns related to mould and damp within the home.The findings highlight the importance of basic services, such as toilets, access to water and electricity, for health outcomes. However, a lack of sanitation in informal settlements may negate any opportunities for health improvements, even if all other forms of basic services are provided. Moreover, unless suitable drainage for wastewater is provided and household refuse is collected regularly, the environment will continue to expose residents to sources of infection and disease, and particularly increase the pressure placed on the immune systems of ill residents. As many residents of informal settlements live in a state of poverty, the costs of accessing primary healthcare and adhering to medication places an added burden on their socioeconomic status. Moreover, improved access to primary healthcare, through an increase in the number of clinics and an increase in affordable transport are important for improving health.Of the studies available, there was very little focus on identifying vulnerable groups within the informal settlement environment. Overall three vulnerable groups were identified in the literature, namely females, HIV/TB-infected people, and children. Females are vulnerable to both socioeconomic and health impacts in the context of IPV and FHH. Gender-related inequalities in health, particularly in the context of IPV and FHH need to be considered when upgrading informal areas. The weaker immune systems of people with HIV and TB, as well as other chronic infectious and non-communicable diseases, increase vulnerability in unhygienic and unclean environments, and findings suggest that children are vulnerable to mental illnesses. Further investigations into identifying vulnerable groups in informal settlements are needed.Social networks tend to be unstable in informal settlements and are particularly volatile when a portion of households receives upgrading. This can increase feelings of frustration and dissatisfaction, and subsequently result in increased crime and vandalism. This highlights the importance of community participation during informal settlement interventions.Other indirect factors that can be addressed to improve health in informal areas is through providing health education regarding the factors that cause ill health in informal settlements. Providing education on how to report broken infrastructure and methods that can be used to clean the neighbourhood are other possible ways to indirectly address health in informal settlements.Available literature adopts a wide range of terminology to describe informal settlements and investigations into health aspects. Other terms, such as “slums”, “townships”, “settlements” and even “communities” were often used within the literature to refer to settings similar to informal settlements. Whilst different terms were employed in the search criteria of this study, including “slums” and the broader term of “informal”, it is possible that other terms colloquially used to describe informal settlements were missed, which may have resulted in omission of relevant studies.In addition, the absence of multiple reviewers was a limitation for this literature review. However, the search strategy was jointly developed, and co-authors scrutinised the search results carefully and systematically using the specified inclusion and exclusion criteria, including reviewing the full-text in addition to the abstract.Due to the complexity of informal settlement contexts and the intrinsic link between poverty and sociocultural factors, it is difficult to attribute causality to a single exposure. This highlights the need for further research utilising a systems approach ,90,91 toWhile there is a growing interest in research investigating health in the context of informal settlements, there is a paucity of longitudinal research investigating the impact of informal settlement upgrading or housing improvements in SA on beneficiaries’ health over time. This is particularly concerning for a country that has a proliferating number of informal settlements, especially within a global context that places an emphasis on the need to address poor living environments to improve urban health. In addition, there is a shortage of studies investigating NCDs within the context of informal settlement upgrading and intra-urban housing typologies.The available literature confirms that informal settlements are poor living environments in which there is a higher burden of disease and a high risk of illness. While the causality between health and factors relating to the informal settlement living environments is complex and not yet fully understood, available studies have suggested that efforts to address crowding, improvements to water and sanitation, and the upgrading of housing typology is likely to be associated with improvements in health and wellbeing. However, through upgrading, it is important for all interrelated dimensions of housing to be considered, namely the house, the home, the neighbourhood and community. For example, we highlight gender-related inequalities that may influence health patterns, and the importance of taking these into account in informal settlement improvement strategies. Lastly, there is a need for more disaggregated interoperable intersectoral data across health and other sectors that influence health, such as housing. These data will be important for informing the development and prioritisation of upgrading strategies for optimising health improvements, and for supporting further research that seeks to measure and evaluate the impact of informal settlement upgrades and interventions on local and wider health trends. Future research will need to include a systems approach to explore and understand the interaction between system components of the city and key health outcomes, particularly in rapidly urbanising low-middle to upper-middle income countries.