rafmacalaba/data-use-mentions-extended huggingface dataset:
simulation to capture the potential economic gains that can be achieved by operating within a hypothetical, high input use scenario, as depicted in the geospatial data on potential agricultural yields that are disseminated by the Global Agro-Ecological Zone (GAEZ) initiative. The geospatial, modeled GAEZ data on potential crop yields factor in crop growth cycles, climate factors such as rainfall and temperature, soil moisture levels, geospatially-derived soil properties, among other factors. For each MAPS plot, the GAEZ potential maize yield under the high-input scenario was extracted based on the GPS coordinates of the plot centroid.<sup>21</sup> If the GAEZ highinput potential (as estimated for S1 plots) yields were to be attained on the sampled plots, the economic gains would increase dramatically, with households producing up to USD 880 per season on average. In this scenario, those in the richest tercile would enjoy greater gains over their current production levels than those in the poorest tercile. Female-headed households, on average, if operating at GAEZ high-input potential and irrespective of factors other than land quality and climatic covariates, still suffer from lower gains than male-headed households, with potential gains capped at USD 729 per agricultural season compared to USD 918.

# **6. Conclusions**

In this paper we estimate a multi-dimensional measure of maize-specific soil suitability based on existing standards, across a sample of approximately 900 households, spanning 4 districts in Eastern Uganda, the leading maize-producing region in the country. This is made possible by collecting and laboratory-testing plot-level soil samples following international best practices in the context of a methodological household survey experiment. In addition to the plot-level soil data, analysis of maize-specific soil suitability is replicated using publicly available geospatial soil data. This research provides a greater understanding of both the heterogeneous productivity constraints and the potential maize-based production and income gains, across crop-specific soil suitability profiles.

Classifying the sampled plots into three suitability classes, namely highly-suitable, moderatelysuitable, and marginally-suitable, and leveraging plot-level crop-cutting-based maize yields allows for comparison of the distributions of observed maize yields
|Bottom 50% income<br>share|26.44%|26.45%|26.83%|
|---|---|---|---|
|Middle<br>40%<br>income<br>share|44.33%|44.32%|44.12%|
|Top10%income share|29.23%|29.23%|29.06%|

_Source: Own simulations based on administrative tax data._ _*Sample includes wage earners and those who move out of formal employment (assumed to move to other employment opportunities in the formal or informal sector with close to zero earnings). This represents the lower bound of inequality reduction._ _**Sample restricted to wage earners, assuming all dismissed workers move to unemployment or inactivity._ _Note: The Gini coefficient and Theil Index are measured over labor income and do not include non-labor income sources._ _Scenario 2: Convergence to Living Wage Estimate_

**Aligning the statutory minimum wage with a living wage estimate is expected to result in a substantial short-term increase in average wages. However, it may also lead to notable job losses, particularly affecting low-wage workers and the younger demographic.** Table 7 illustrates the results. The increase directly influences a significant portion of full-time employees, accounting for 16.41%. Among those directly affected, formal employment reduction is projected to be significant, around 7.72% in the short run and 11.22% in the long run, with the most significant job losses occurring among the youngest workers. Among all formal sector employees, the minimum wage hike is also expected to result in moderate job losses, affecting 1.44% of full-time employees in the short run and 2.09% in the long run.

**While aligning the statutory minimum wage with a living wage estimate offers sizable short-term benefits for a specific group, it poses potential long-term
respect to larger FDI firms. The _FDI-sizebias hypothesis_ is thus the following: on female entrepreneurship, FDI should have a larger general spillover effects among small firms than among medium-and-large firms, but an ambiguous intra-industry effect when comparing the effects among the two types of firms.

# **3. Data set**

The main data source is the World Bank’s Enterprise Surveys (WBES) in 709 cities of 128 countries. The WBES data are collected by the World Bank to benchmark the business 7
# 1 Introduction

Academic and policy discussions regarding health care increasingly focus on the quality of medical care as a crucial determinant of health outcomes (Cutler 2003). Policy levers to address quality disparities in health care must cover a variety of underlying causes (Baicker and others 2004; Kerr and others 2004; Fisher and others 2004). If geographic variation in practice-quality re fl ects differences in provider competence, the solution lies in geographical incentives for doctors and/or greater movement of patients.<sup>1</sup> If variation in practice quality arises from differing provider effort, stronger performance incentives are called for.

New data on provider competence and effort show that competence and effort both play a role in the quality of medical care received by patients. These data show that what doctors do in practice is very different from what they know they should do.<sup>2</sup> This gap re fl ects the cost of effort relative to reward. And it responds to incentives: Those on a fee-for-service payment scheme are closer to their "knowledge frontier" than those on a fi xed salary. Furthermore, effort and competence are complements, not substitutes. More competent doctors spend more time with patients, ask more questions, and do more examinations. Less competent practitioners deliver poorer health care services because of the direct effects of lower competence and the indirect effect of lower effort-in-practice.

This is the second of two papers on the quality of medical care in urban India that stem from a study of medical practitioners in seven neighborhoods of the capital, Delhi. The fi rst paper examined the “competence” or knowledge of practitioners using medical vignettes (Das and Hammer 2005). Vignettes–a set of standardized hypothetical cases–were presented to practitioners and the questions asked, examinations suggested, and treatments recommended were recorded. The performance of practitioners was then aggregated into an index of competence.

In a second phase of the study observers visited the same practitioners in their clinical practice for a whole day, recording
( ∆ log( _IFirmd_ )93 − 03 ), and between 1983 and 1993 ( ∆ log(IFirmd)83 − 93 ), and the growth of firms in services between 1993 and 2003 ( ∆ log( _SFirmd_ )93 − 03 ) and between 1983 and 1993 ( ∆ log(SFirmd)83 − 93 ). We estimate equation 1 using district-level employment figures that we construct from firm-level data.

Table 3 reports the regression results for equation 1, separately for light and heavy manufacturing, at the national level, and separately for the north and the south. Overall, the association between changes in employment in both heavy and light manufacturing and changes in employment in mining is positive. The elasticity of heavy manufacturing employment with respect to mining employment (.31) is almost 50 percent larger than the elasticity of light manufacturing employment with respect to mining employment (.21). The difference in the results between the north and the south supports a stronger production network between heavy manufacturing and mining in the south than in the north. The aggregate employment effect at the region level, however, is stronger for light manufacturing than heavy manufacturing in the south. Lastly, industrial development in the previous decades appears to play a less significant role compared to the growth in mining employment in explaining employment growth in manufacturing between 2003 and 2013.

Identification of intersectoral linkages in equation 1 relies only on geographic proximity to a mining firm. As such, it is not sensitive to the time lag between the establishment of a mining firm and a manufacturing firm. In our next model, production networks are identified jointly by the location of a mining firm and the year in which it was established, as follows: _NewFirmds_ , _t_ is an indicator variable, which takes the value of one, if at least one new firm enters in sector _s_ , in district _d_ , and in year _t_ , and zero otherwise. β 1 and β 2 denote the immediate and lagged (by one year) effect of the number of new
|-369|0|
|Proportion repaying on time (%)|||||
|Average|94.3|92.8|92.3|92.7|
|Excess over village average|1.5|0.1|-0.3|0.0|
|Expenditure per capita (baht per capita per month)|||||
|Average|4,115|4,696|3,507|3,647|
|Excess over village average|348|863|-129|-15|
|Source: Socio-economic Survey 2010.|||||

**<mark>Table 17.  Patterns of borrowing by expenditure per capita quintile</mark>**

||1(poorest)|2|3(middle)|4|5(richest)|
|---|---|---|---|---|---|
|Interest rate paid on VF borrowing (% p.a.)||||||
|Average|5.88|5.94|5.99|6.00|6.06|
|Excess over village average|0.014|-0.014|0.021|-0.019|-0.013|
|Loan from VF (baht per loan per year)||||||
|Average|15,954|16,000|17,011|18,292|18,634|
|Excess over village average|-503|-425|187|588|740|
|Proportion repaying on time (%)||||||
|Average|91.8|93.0|92.4|92.5|93.5|
|Excess over village average|-0.8|-0.1|0.1|0.9|0
average inflation rate then spikes again at 27 _._ 46% in the year of the pandemic, while the preliminary 2021 inflation estimate of 22 _._ 65% remains well above levels of the World Food Price Crisis of 2007-08. The results in table A7 highlight that price uncertainty has remained more constant. A striking result is that average price uncertainty was higher during the World Food Price Crisis of 2007-08, peaking at an annualized rate of 13 _._ 92%, while hitting 10 _._ 52% during the pandemic shock and even dropping to 8 _._ 1% in the preliminary 2021 estimate. This means that the recent high inflation event was not only stronger in terms of price increase, but also more significant when compared to natural price volatilities in the two periods. It is from this perspective again interesting to transform the estimates to Sharpe ratios. Both the World Food Price Crisis of 2007-08 and the post-pandemic inflation surge was associated with prices increasing relative to price uncertainty, and this price development is more pronounced in the post-pandemic inflation event. For example, from 2009 to 2018 the FCS-wide average annual Sharpe ratio for food prices was 0 _._ 55, but during 2007-08 it averaged 1 _._ 30. During 2020, this ratio hits 2 _._ 70, while the preliminary annualized 2021 estimate sits at 2 _._ 80, meaning that returns in food prices outweigh price volatility risks of holding food as an asset by nearly a factor 3. This means the ratio of inflation to price uncertainty is almost double that of the World Food Price Crisis of 2007-08.

The monthly price estimates indicate that prices can rise and fall dramatically
as an endogenous variable.

As in previous studies (Gutierrez, 2012; Ribero and Nuñez, 2000; Schultz, 2002), to instrument for height we use variables measured at the level of the village: the _marginalization_ (poverty) index of the villages in 1995 and in 2000, the total population of the villages, and 1998 prices of several food items: nixtamal (corn prepared for making tortilla dough), wheat flour, pasta, cookies, beans, eggs, sardines, sugar and oil.<sup>15</sup> We use an F test to test the capacity of the proposed instruments to explain the variability in height, finding that while not perfect, these instruments are reasonably strong. Finally, we tested for over-identification of the model that could result in the correlation with the error term, finding a chi square test with a p-value of 0.134, indicating that we do not reject the null hypothesis.

Using the instrumental variables for height, the estimated return model is: Where ln�W�� is the natural logarithm of the income per hour reported by individual _i_ ; S<sup>�</sup> � is the predicted value of the health indicator from the first stage instrumental variables regression and is expressed here as the natural logarithm of height, hence the coefficient, δ, is an elasticity; H� is a vector of household characteristics (including the provider); X� is a vector of individual characteristics; E� is an indicator of individual _i_ ’s labor participation; and Z� is a vector of individual characteristics that determine labor market participation.

> 15The prices were collected in the village questionnaire of the ENCEL 1998-March.  In the cases where the prices were not reported in the village, the average of the municipality (~ county) or state was used for the same.

22
# 3.3. Application to Bhutan

## **_Modeling of Market Income_**

The most challenging part of the fiscal incidence analysis for Bhutan is the lack of information on labor and nonlabor income. Previous studies have dealt with this in several different ways. A popular approach is to use the survey-to-survey imputation to match the consumption survey with an income survey—this was done in Argentina (Lopez Del Valle et al. 2021), Ecuador (Llerena Pinto et al. 2015) and Paraguay (Canavire et al. 2021), for example. The feasibility and success of this approach depends on a number of factors, including the availability of income and consumption surveys that were conducted not too far apart in time and the existence of a sufficient number of common variables. While Labour Force Surveys are conducted annually in Bhutan, the questions are less suited to capture the earnings from the agriculture sector which is the most important source of livelihoods for the rural poor.

Another approach is to impute market income by assuming disposable income equals consumption aggregates, which is a reasonable assumption in a lower-middle-income country as it may be rather uncommon to have large savings, when information on direct transfers and taxes is available, . However, using this approach directly for Bhutan is challenging since there is no information in the BLSS on direct taxes and social security. Moreover, it is difficult to attribute aggregate household consumption into individual-level consumption unless some simplifying assumptions are made, such as dividing household consumption or income by the number of workers to get an estimate of average individual income (see Lara Ibarra et al. 2019 for the Arab Republic of Egypt and Hill et al. 2017 for Ethiopia). Since this method would not reflect income differences between household members, it could lead to an imprecise estimate of income tax liabilities.

We develop a more sophisticated approach for Bhutan by adopting a consumption-based approach to modeling market income. Specifically, we employ a Mincer earnings model to estimate individual net labor income and then recover gross income using information on
, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the field until 2011, divided into groups according to their mathematics achievement in 2007. This table reports coefficients from the ordinary least squares estimation of equation (1) in the paper, where the dependent variable in all columns is the mathematics test scores. In column (1), we show results for treated schools with high mathematics test scores in 2007, in column (2) schools with medium mathematics test scores, and in column (3) schools with low mathematics test scores. All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade sample.
drive the results is difficult to say. We would hope this might be resolved by future work examining labor market frictions in different contexts.

# **References**

> 15 In April 2009 surveys. 32 percent of wage workers and 27 percent of entrepreneurs said that “most people can be trusted.” Though this may seem low, it compares with rates in the general population in most Sub-Saharan African and Latin American countries, where less than 20 percent of the population respond similarly. [Calculations by authors using WVS data downloaded from <u>http://www.worldvaluessurvey.org .]</u>

28
mortality is sensitive to aggregate income shocks.</mark> <mark>The last set of findings presented here take a more detailed view of how poverty and the incomes of the poor are affected by defaults – albeit with a reduced sample that should be interpreted as a broad case study. Based on the 11 cases for which we have sufficient surveysourced data, we find that there is considerable heterogeneity in “excess poverty” outcomes. In four cases there are sizable increases in poverty over what would be implied by the growth shock. Another four cases remain close to zero excess poverty and two show negative excess poverty. As for incomes, we identify peak-to-trough windows in per capita GDP levels and look at the cumulative change in income going to different segments of the population. We find that the bottom 10 percent’s income level dropped on average by a cumulative 9 percent while the</mark>

> 9 <mark>It is worth noting that this literature is extensive and though there is agreement that poverty headcounts may be insufficient, there is ample debate about the right methodology to construct a multi-dimensional poverty index (Alkire 2015) or multiple indices (Ravallion 2011).</mark>

> 10 <mark>See OPHI GMPI.</mark>

6
municipalities (Table 2, Panel B and bottom panels in Figure 5). However, these findings do not hold when the models are estimated in a narrower sample around the threshold (Columns (3) and (4 )).

Another possibility is that FA could have differential effects among members of the family, especially on those that are the direct recipients of the transfer and more exposed to different sources of information due to the interventions of the program. More specifically, in the case of CCTs, mothers of the beneficiary child are usually designated to receive the transfer and are also required to participate in a number of activities at the community level (e.g. growth monitoring sessions and workshops on good nutrition, health and hygiene practices). This possibly encourages women more than men to interact and exchange information with community leaders and other program participants. We split the sample between women and men to test whether program effects vary with the gender of the parents. Tables 3 and A1 (in the appendix) show the same specifications of Table 2 for these two groups. They indicate that the results for the whole sample are mainly driven by a higher probability to register to vote among women (Table 3). Women are between 1.5 and 3 percentage points more likely to register to vote (panel A “FA”) and 2.6 to 3.5 percentage points more likely to register to vote after the onset of FA in their municipality (panel B “FA”).<sup>16</sup> In contrast, there is no evidence of systematic differences in the intent to vote between FA participant and non-participant men (Appendix Table A1).

Next we look at whether the relatively higher intention to vote attributed to FA translates into actual votes. Considering that voting is not compulsory, this point is of major importance in light of the large historical differences in registration and turnout rates in Colombia. For instance, 44.3% of the population registered in the electoral census
data is sizeable. A majority of those who think they have poor health do not rate themselves as poor. Amongst the 17% of adults who rated their health as “very bad” or “bad,” only 33% placed themselves on the lowest two rungs of the economic welfare ladder. And of the 27% of adults who rated their health as “good” or “very good,” 60% put themselves on the lower four rungs of the welfare ladder.

Table 2 exploits the panel structure of the RLMS to compare the changes over time in _SAH_ with changes in _SAW_ . This reduces the impact of any time-invariant individual effects (such as due to latent ill-health or personality traits) that jointly influence subjective health and welfare. (Although it can also reduce the signal-to-noise ratio in the presence of time-varying measurement errors or extraneous transient effects.) We find that 18% of adults in the panel thought that their health had improved, while 20% thought it had worsened. There is little difference by gender, though a slightly higher proportion of men reported that their health had worsened than did women. The panel data suggest an even lower statistical association between the two categorical variables, with an overall Cramer’s V of 0.05. Of the 32% of the panel sample who felt that they had moved down the welfare ladder, 21% felt that their health had declined (by one or more categories in the _SAH_ question). By contrast, this was true of 17% of the 38% who felt that they had risen on the _SAW_ ladder. Changes in perceived health are almost orthogonal to changes in perceived economic welfare. This holds for both men and women.

However, we find stronger signs of the gradient for a number of the more specific health indicators in the data, as can be seen in Figure 3. Most of the physical activity variables tend to show a deterioration at low incomes (though the causal interpretation is of course unclear). A number of the
typically shorter. Reasons for migration vary by distance of moves, especially for men, in particular when comparing work and family-related migration. Among men who moved in the last five years (regardless of marital status), moves within the district are less likely to be related to work than longer moves. About a quarter of those who moved across district boundaries reported moving for work-related reasons, compared to only 14% of men who moved within district. Work-related moves are more common among men than among women where rates peak at 7% for cross-district moves. Men are less likely to move across districts for a spouse than women (4% compared to 49%). But an additional 40% of male movers moving from a different district reported moving to live with a relative, compared to 23% of women moving from a different district. It is possible that these movers are also attending school or working in their new location. Among the subset of never-married young movers, slightly less than half of men and women moved to live with a relative, irrespective of whether the move occurred within or outside of the district. Across all groups, other reasons, such as seeking land, moving for school, returning from work elsewhere, divorce, family quarrel, illness recovery, and death of relative were very infrequently reported by young adults.

Because the IHS2 was not focused specifically on migration, and because the survey is cross-sectional, these data limit our understanding of how much migration is linked to childhood (moving with parents), schooling (since secondary school facilities are not in most villages), marrying (moving to spouse’s home village), or is economically motivated (such as job search). With the IHS2, we cannot explore how geographic mobility is correlated with changes in other circumstances, such as dating and sexual behaviors, or employment, since we do not observe these data before _and_ after the move.

8