rafmacalaba/data-use-mentions-extended huggingface dataset:
# 1. Introduction

Poverty in Uganda has fallen substantially over the past 20 years. From 1992 to 2005, the proportion of the population living in poverty declined from 56.4 percent to 31.1 percent. This can be partially traced to a “peace dividend,” or the impact of the restoration of political and economic stability following the conclusion of the Ugandan civil war, which raged from 1980 to 1986. Peace in the Northern region was accompanied by a package of liberalization reforms introduced by the new administration and engendered a positive response from firms and households to stability. Poverty continued to decline at a slower rate during the period 2005-2012, reaching 19.7 percent in 2012(see Figure 1a). The average rate went down from 2 percent a year (for the period 1992-2005) to 1.5 percent a year (for the period 20052012) as pointed out by the latest Uganda Poverty Assessment Report (World Bank 2016), mainly as a result of slower overall economic growth.

The poverty headcount index increased modestly from 19.7 percent of the population in 2012 to 21.4 in 2016. The increase in the poverty headcount index measured at the international poverty line - $PPP (2011) 1.90 per capita per day) - was more noticeable, going from 34.6 to 41.6, indicating a considerable proportion of households clustered on both sides of this line. This represents a reversal of the downward trend observed since the early 2000s. Two factors can help explain this reversal. First, the overall sluggish economic growth observed since 2012; GDP per capita grew only 1.1 percent per year on average between 2012 and 2016 (while between 2005 and 2009 it grew at 4.5 percent per year on average). Secondly, the drought that affected the country for the better part of 2016 and 2017 negatively affected the consumption of rural households, and rural poverty went up.<sup>2</sup> Poverty in urban areas has remained unchanged
., Kilic, T., and Lobell, D. B. (2019). A new spin on an old debate: Errors in farmerreported production and their implications for inverse scale - productivity relationship in uganda. _Journal of Development Economics_ , 141:102376.

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- Michler, J. D., Josephson, A., Kilic, T., and Murray, S. (2022). Privacy protection, measurement error, and the integration of remote sensing and socioeconomic survey data. _Journal of Development Economics_ , 158:102927.

- Ngufor, C., Van Houten, H., Caffo, B. S., Shah, N. D., and McCoy, R. G. (2019). Mixed effect machine learning: A framework for predicting longitudinal change in hemoglobin a1c. _Journal of Biomedical Informatics_ , 89:56–67.

- Quartagno, M., Carpenter, J. R., and Goldstein, H. (2019). Multiple Imputation with Survey Weights: A Multilevel Approach. _Journal of Survey Statistics and Methodology_ , 8(5):965– 989.

- Rubin, D. B. (1987). _Multiple Imputation for Nonresponse in Surveys_ . Wiley.

- Storm, H., Baylis, K., and Heckelei, T. (2019). Machine learning in agricultural and applied economics. _European Review of Agricultural Economics_ , 47(3):849–892.

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studies also have to deal with potential simultaneity between infrastructure and income levels. For example, richer or faster-growing countries are likely to devote increased resources to infrastructure development. Failing to control for these and similar forms of reverse causality implies that estimates of the output elasticity of infrastructure may be confounded with the income elasticity of the demand for infrastructure services, and hence may suffer from upward biases.<sup>4</sup> Finally, studies using cross-section or panel macroeconomic data typically fail to account for the potential heterogeneity in the output elasticity of infrastructure across countries or states, which could arise from technological features such as network effects, scale economies and other factors that may affect the output elasticity of public capital.<sup>5</sup> This paper estimates the contribution of infrastructure to aggregate output using a production function framework including as inputs infrastructure assets, human capital, and noninfrastructure physical capital. We use a large cross-panel dataset comprising 88 countries and over 3,500 country-year observations, drawn from countries with very different levels of income and infrastructure endowments.

One distinguishing feature of our approach is that, in contrast with much of the earlier literature, we use physical measures of infrastructure rather than monetary ones – such as a public investment flow or its accumulation into a public capital stock. We do this for two reasons. First, as an abundant literature has argued, public expenditure can offer a very misleading proxy for the trends in the public capital stock, as the link between spending and capital is mediated by the extent of inefficiency and corruption surrounding project selection and government procurement practices, which can vary greatly across countries and over time (e.g., Pritchett 2000; Keefer and Knack 2007). Second, our interest here is infrastructure capital, rather than broader public capital, and in many countries the two can be very different owing to the involvement of the public sector in non-infrastructure industrial and commercial activities (a common occurrence in virtually all countries over a good part of our sample period),
governance if only the village head was trained but there was a large improvement in governance if both the village head and the community leader were trained (Lieberman, 2005).<sup>26</sup> Once we had identified all of the on-the-line cases, we selected all cases where a positive on-the-line case was paired with a negative on-the-line case in the same geographic strata (defined by wards and land classification). This resulted in the selection of four “village head only” villages and six “village head plus community leader” villages for qualitative research, with these villages falling into four distinct geographic strata.

In each of the ten villages, the qualitative research involved conducting interviews and focus groups. First, we interviewed the village head alongside advisers of his or her choosing. Second, we conducted two focus groups, one with a group of about 8 villagers organized by the village head, and one with the random sample of 8 villagers included in the follow-up survey. In addition, in each village, we conducted a one-on-one interview with the civil society leader included in the training sessions (or, in cases where no civil society leader was trained, the civil society leader the village head said he would have liked to have included in the training sessions if he had been given the opportunity).

We use the qualitative evidence to consider three aspects of the dynamics of change relevant to adjudicating the mechanism: (1) Did the civil society leader become a competitor to or integrated into the traditional village governance structure? (2) Did the civil society leaders simply change the capacity of the village head to accomplish reforms they already favored or did they also change their incentives to implement reforms? (3) Did the civil society leader directly pressure the village head to accomplish change, or did he or she levy pressure indirectly by informing higher leaders or mobilizing villagers?

The qualitative evidence suggests that the civil society leader who was trained alongside the village head often took on new responsibilities and powers after the training session
better rewarded in the public sector. It may simply be a relic of the past policy _of allocating_ educated labor to public sector positions. Most of the workers in the private sector have primary education or less, while public sector workers usually have at least a primary education. However, the fact that in the public sector workers with no or very little education earned more, on average, than primary, secondary or vocational school graduates (Table 2) suggests that there are significant distortions in public sector pay. It is likely that factors other than education (perhaps, as in China, membership in the Communist Party) had an impact on public sector pay in Vietnam, as in other command economies.

Table 5: Vietnam: Earnings Functions by Sector of Employment (both sexes)

||Sector of E<br>Public|mployment<br>Private|
|---|---|---|
|Constant|0.265|1.168|
|Years of schooling|0.062|0.039|
||(6.3)|(4.0)|
|Years of experience|0.046|0.072|
||(4.4)|(8.4)|
|Experience-squared|-0.001|-0.001|
||(3.3)|(7.3)|
|Log hours-worked|0.878|0.617|
||_(10.5)_|(6.7)|
|R-squared|0.145|0.085|
|N|950|1,309|
|Source: VLSS 1992-93|||
|Note: t-statistics in parentheses|||

The earnings function is fitted using dummy variables for those who have completed a particular level of education (Table 6). The results are reasonable, except that for males the coefficient on primary education is not significant. Also it would appear that those with less
17,958|
|Number of Banks|1,280|1,280|1,280|1,280|
|Mean Dep. Var.|0.156|0.794|0.120|0.033|
|SD Dep. Var.|0.116|0.174|0.111|0.038|

_Note:_ This table reports OLS estimates. The unit of observation is bank level, and bank, month, state, year, and state _×_ year fixed effects are included. Standard errors are clustered at bank level. Deposits is a variable reporting the overall deposits at bank level; it is continuous and measured in USD. Safe assets is the amount of liquid assets held by banks in cash, bank-to-bank notes, real estate assets, and other private assets; it is continuous and measured in USD. Loans and Discounts embodies all lending operations at bank level; it is continuous and measured in USD. Profits and Losses is the difference between the asset-generated income and liability-induced costs after taxes; it is continuous and measured in USD. The means and standard deviations of these variables are reported in the final two rows. The following states are included in the sample because they are treated in some year by the policy: CT, GA, MA, MD, ME, NC, NH, NJ, NY, PA, RI, and VT. However, because of data limitations we observe a bank balance sheet before and after the policy only for the following six states: MA and ME in 1812; NH and RI in 1822; NY in 1839; CT in 1850. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

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118-136.

Bayraktar, N., and H. Fofack. 2011. “Capital Accumulation in Sub-Saharan Africa: Income-Group and Sector Differences.” _Journal of African Economies_ 20 (4): 531-561.

Buigut, S. 2016. “Trade Effects of the East African Community Customs Union: Hype versus Reality.” _South African Journal of Economics_ 84 (3): 422-439.

Calderón, C., and L. Servén. 2010. “Infrastructure and Economic Development in Sub-Saharan Africa.” _Journal of African Economies_ 19, AERC Supplement 1: i13−i87.

Chaney, N. W., J. Sheffield, G. Villarini, and E.F. Wood. 2014. “Development of a HighResolution Gridded Daily Meteorological Dataset over Sub-Saharan Africa: Spatial Analysis of Trends in Climate Extremes.” _Journal of Climate_ 27, no. 15: 5815-5835.

Dauvin, M., and D. Guerreiro. 2017 “The Paradox of Plenty: A Meta-Analysis. ” _World Development_ 94: 212-231.

EBRD (European Bank for Reconstruction and Development). 2019. _Eight Things You Should Know About Middle-Income Transitions_ . April. London: European Bank for Reconstruction and Development.

European Commission. 2018. “Association Implementation Report on Moldova.” Joint Staff Working Document. European Commission: Brussels.

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Policy Research Working Paper 9312

# **Abstract**

Frequent measurement of poverty is challenging, as measurement often relies on complex and expensive expenditure surveys that try to measure expenditures on a comprehensive consumption aggregate. This paper investigates the use of consumption “sub-aggregates” instead. The use of consumption sub-aggregates is theoretically justified if and only if all the Engel curves are linear for any realization of prices. This is very stringent. However, it may be possible to empirically identify certain goods that happen to have linear Engel curves given prevailing prices, and when the effect of price changes is small, such a sub-aggregate might work in practice. The paper constructs such linear sub-aggregates using data from Rwanda, Tanzania, and Uganda. The findings show that using sub-aggregates is ill-advised in practice as well as in theory. This raises questions about the consistency of the poverty-tracking efforts currently applied across countries, since obtaining exhaustive consumption measures remains an unmet challenge.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at lchristiaensen@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
households with earnings under

> 9 This list of programmes is not exhaustive but covers the largest components.

> 10 In 2019, 25,000 households were added, 30,000 more were added in 2020, and further 30,000 more were planned for 2021, first enrolling the ‘poorest’ (according to the Takaful targeting methodology) who were not already benefiting from NAF monthly cash transfer programs. The selection of Takaful beneficiaries is made based on a combination of formal/informal earnings and asset ownership filters, and then on a ranking of households on a poverty score. The benefit level per household is also determined based on a formula score, being around JOD 100 on average.
student disciplinary measures and assessments. The results of these alternative specification are presented in Annex C. Consistent with the results from the disaggregated version of school autonomy (Table 4), the alternative specification shows a negative change, between 2000 and 2006, in the correlation between two measures of autonomy and learning outcomes: budget and personnel decisions and academic content. While in 2000 schools that had more autonomy in these measures showed significantly better learning outcomes, this was no longer true in subsequent years. These are particularly important results suggesting that there was a reduction in the effectiveness of Argentine schools to transform more legal authority into better results.<sup>12</sup>

# _5.2 Microsimulation_

We use the microsimulation techniques described in Section IV to decompose the changes in learning outcomes in Argentina measured by the PISA reading results of 2000, 2006, 2009, and 2012. The exercise identifies the relative importance of changes in the elements defining the production function to explain changes in mean learning outcomes and its distribution. To restrict the number of microsimulations presented in this section, we focus on the explanation behind two distinctive trends in reading learning outcomes as described in Section III, i.e. the decline between 2000 and 2006 and the subsequent partial recovery between 2006 and 2012. We create counterfactuals to decompose these two trends to the following three factors:

> 10 OECD (2013)

> 11 An alternative explanation of these results is that in 2000 and 2006, the perception of school directors was capturing legitimate, objective, shortages of learning material. By 2009, after a significant increase in resources spent on education inputs, the perception of directors was capturing relative shortages of materials, i.e. in comparison with what other schools were getting, which is not necessarily correlated with learning outcomes.

> 12 Notice that while overall autonomy in budget and personnel decisions did not change significantly between

> 2000 and 2006, the share of schools that decided on academic content had a substantial reduction (Table 3).

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.77|(874.02)|1,016|
|Diversity of non-food purchases|-0.03|(0.15)|[1.00]|0.01|(1.01)|1,054|
|Value of gifted non-food, last yr (USD PPP)|-18.87|(19.03)|[1.00]|11.68|(186.07)|1,100|
|Value of all durables acquired, last yr (USD PPP)|752.90|(862.95)|[1.00]|354.88|(1,977.68)|296|
|Value of all durables gifted, last yr (USD PPP)|146.24|(179.81)|[1.00]|16.05|(174.57)|296|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022 neighbor survey data. Each row is its own dependent variable.

- The outcomes that require definitions are: Diversity of food purchases is a standardized measure of the number of months per year the household consumes food from 9 different food groups. Food exp refers to the PPP adjusted amount of money spent in a typical week or last week. Value of gifted food is the value of food given to the household as formal or informal assistance in the past month. Food consumption, last year is an annualized measure of food consumption calculated from weekly food expenditure * 4.3 * months that food was consumed + annualized weekly food produced + annualized monthly food assistance. Non-food expenditure includes expenditures in the last 30 days from 9 different non-food expenditure categories. Diversity of non-food purchases is calculated in the same way as described above for food purchases. Value of gifted non-food includes all items from 9 non-food categories received as gifts in the past 12 months. Value of all durables acquired includes
active saving likely plays a significant part: household income growth starts to outpace household consumption growth around that time, and the saving rate increases by 20 percentage points between ages 40 and 65. This suggests that programs that aim to encourage formal saving may be most successful among individuals in that age range.

# **References**

Attanasio, Orazio and Steven Davis, “Relative wage movements and the distribution of consumption,” Journal of Political Economy, 1996, 104 (6), 1227–1262.

Attanasio, Orazio and Guglielmo Weber, “Consumption and saving: models of intertemporal allocation and their implications for public policy,” Journal of Economic Literature, 2010, 48 (3): 693-751.

Banks, James, Richard Blundell, and Sarah Tanner, “Is there a retirement-savings puzzle?,” American Economic Review, 1998, 769–788.

<mark>Bonnet, Florence, Joann Vanek, and Martha Chen, “Women and men in the informal economy: A statistical brief,” International Labour Office, Geneva, 2019.</mark> <mark>Cagetti, Marco, “Wealth accumulation over the life cycle and precautionary savings,” Journal of Business & Economic Statistics 21.3, 2003, 339-353.</mark> Carroll, Christopher D, “Buffer-stock saving and the life cycle/permanent income hypothesis,” The Quarterly journal of economics, 1997, 112 (1), 1–55.

___ and Lawrence H Summers, “Consumption growth parallels income growth: Some new evidence,” in “National saving and economic performance,” University of Chicago Press, 1991, 305–348.

Cho,Yoonyoung and Zaineb Majoka, “Pakistan jobs diagnostic : promoting access to quality jobs for all,” Washington, D.C. : World Bank Group, 2020, Vol. 1 of 2.

Deaton, Angus, “The analysis of household surveys: a microeconometric approach to development policy,” The World Bank, 1997.

Deaton, Angus,
migrate or remain abroad are more sensitive to the business cycle than the employment rates of natives (Orrenius and Zavodny, 2009).

A key contribution of this paper is to show that the high vulnerability of migrant jobs to economic shocks is intimately tied to the large gains in wages that migration offers. The extent to which migration flows respond to shocks at destination depends on the output elasticity of demand for migrant labor and on the extent to which wage adjustment can occur through movements along the migrant labor supply curve. However, estimating this responsiveness in the context of bilateral migration flows is complicated by concerns that economic shocks also affect the migrant origin country, thereby also shifting the labor supply curve and preventing identification of the labor demand impact. In addition, reliable microeconomic data on migrant flows and the wages these migrants earn are extremely rare. We overcome both issues by using a 2
National Sample Survey of India (NSSO 2012). Monthly household expenditure was divided by household size to estimate consumption per capita. To account for the spatial variation in prices, the result was deflated by a year-specific price index that differs across states and between rural and urban areas. We ranked states on the basis of their average consumption per capita and divided them into three groups of equal size: richer, intermediate, and poorer. The sample for each administrative stratum was split evenly across these three groups of states. The sampling process is completed by conducting random draws without replacement for each of the 21 substrata.

# _The resulting sample of places_

The sample drawn through this process comprises 1,277 places, of which 405 are statutorily urban and 557 are urban in terms of the census; the remaining 315 places are villages (table 1). For the full sample we achieved a relative standard error of 8 percent, which is below the 10 percent threshold required for a survey design to be considered good (Knottnerus 2003; UN 2005).

Recognizing that the Central Limit Theorem may not apply for a finite sample, we used bootstrapping to generate an approximation of the distribution through a Monte Carlo simulation (Cameron and Trivedi 2005). The analysis confirmed that the sample was sufficiently large for each administrative stratum. Standard errors were below 3 percent for six administrative strata, and around 5 percent for industrial towns.

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for such migration as it took place over the last three years prior to the surveys of 1988-89 and 1991-92. These data are reproduced in Table 6.10 below.

# **Table 6.10: Migration between Urban and Rural areas in last three years, 1988-89 and 1990-91**

||_<br>1988-89|1990-91|
|---|---|---|
|Didnot migrate|78%|90.2%|
|Migrated from Rural areas|5.8%|3.9%|
|To Accra|0.4|0.2|
|To Other Urban areas|0.9|0.6|
|To Rural Areas|_4.5_|3.1|
|Migrated from Other Urban areas|14.5%|4.8%|
|To Accra|1.8|0.7|
|To Other Urban areas|3.6|1.6|
|To Rural Areas|9.2|2.5|
|Migrated from Accra|1.7%|1.1%|
|To Accra|_0.5_|_0.5_|
|To Other Urban areas|1.0|0.6|

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