rafmacalaba/datause-encoder-data huggingface dataset:
which is a weighted average of health levels, where the weights are as graphed in Fig 2 and average to one. It turns out<sup>7</sup> that this index is simply equal to:

## _(7) I(v) = f(-_ ' _C(v)) ._

Consider the case where the health indicator is a measure of ill health (so high values of _I(v)_ are considered bad) and _C(v)<0_ (ill health is higher amongst the poor). Inequality serves to raise the value of I(v) above the mean (making achievement seem worse than it seems when looking just at the mean). So, for example, two countries might have the same value of I(v), but one might have a high mean but an equal distribution across income groups while the other might have a lower mean but an unequal distribution across income groups to the disadvantage of the poor. Or suppose that the mean stays unchanged over time but the distribution of health becomes more prorich. In this case, even though ,u has not changed, _I(v)_ rises, assuming that v>l. If ill-health declines monotonically with income, the greater the degree of inequality aversion, the greater the wedge between the mean and the value of the index I(v).

# **_3._ Empirical illustrations**

In this section, these methods are illustrated for three health indicators-underfive mortality, child malnutrition, and fertility. The computations are based on grouped data from 44 developing countries, taken from tabulations by Gawtkin and others (2000) on data from the Demographic and Health Survey (DHS). The tabulations show average values for each of five "wealth" quintiles.

## _3.1. Data and methods_

Three indicators have been selected. The first is under-five mortality (U5MR), which is simply the proportion of children dying before they reach their fifth birthday. The second is child malnutrition, as measured by the proportion of under-five children who are classified as underweight, based on anthropometric measures (Alderman 2000).

> 7 ThS is most simply seen by substituting eqn (5) into eqn (7) and rearranging to get eqn (6).

_9_
# **1 Introduction**

The impact of anti-poverty and pro-growth policies and spending programs depends upon how well they are implemented or delivered by government bureaucracies. The quintessential “delivery unit” in developing countries consists of local governments helmed by appointed bureaucrats and locally elected politicians. Institutional arrangements in the local state, or the last mile at which delivery and implementation happens across countries such as India, Indonesia, Nigeria, Brazil, Tanzania and Uganda, provide space for locally elected politicians to facilitate and monitor the administration of nationally funded programs (Ahmad and Brosio, 2006 and 2009). These local institutions correspond to the setting examined in principal-agent theory of the public sector: multiple principals and agents; and, complexity of tasks and objectives (Tirole, 1994; Dewatripont, Jewett and Tirole, 1999; Dixit, 2002). The theory suggests that productivity in such organizations can be enhanced through organizational design that increases motivation and improves selection of agents, going beyond incentives (Besley and Ghatak, 2005; Acemoglu, Kremer and Mian, 2007; Akerlof, 2017). However, there is little to no empirical evidence on whether selection of agents matters, and furthermore, which traits matter for performance.<sup>1</sup> We provide the first evidence on how political and bureaucratic selection traits correlate with the productivity of public sector organizations. We measure traits of local politicians and bureaucrats in an institutional context which is typical of the local state in poor countries–district governments in Uganda–and examine whether these measures are robustly and substantively correlated with variation in service delivery by these districts.

Surveys were undertaken during September-December 2015 in 75 of Uganda’s 112 districts at that time,<sup>2</sup> the relevant local jurisdiction where both directly elected district politicians and nationally appointed bureaucrats share responsibility for implementing public

> 1Finan, Olken and Pande (2015) review a growing literature which measures selection traits of agents, focusing on frontline service providers, such as health workers, and their individual productivity. For example, Donato et al (2017) and Callen et al (2015) find that public health workers with better personality traits are less likely to be absent from their jobs. There is no evidence yet available in the literature on whether selection traits of bureaucrats and politicians who manage public sector organizations matter for the productivity of those organizations.

> 2The number of districts in Uganda has been changing, due to the splitting of districts to create more new ones (Green, 2010; Grossman and Lewis, 2014). The current number is closer to 121.

1
6 TABLE 1 : Sumary statistics on the level and percentage change li new planting and replanting , 1987-1985.

|Variable|RPDAR|MPDAR|DLRPBRA|DLNPBAB|
|---|---|---|---|---|
|**Maximum**|8788|31568|2.93|2.63|
|**Minimum**|449|773|-1.28|-.77|
|Mean|2572|10651|<br>0.14|0.18|
|Std. dev.|2465|9098|0.97|0.74|

Notes : Fgures for RPBAH and NPBAH are In hectares. Those for DLRPBAH and DLNPBAH are annual percentage rates of change for RPBAH and NPBAN. respectively.

Table 2 : Estimated Age Distribution of Productive Cocoa Trees, Bahia, 1938-66 <!-- Start of picture text --> Age group, in yeare<br>>10 >20 >30 >40<br>Year <20 <30 <40<br>1936 88 60 44 48<br>(36.8) (25.1) (18.4) (20.1)<br>1946 116 88 60 92<br>(32.7) (24.8) (16.9) (25.8)<br>1956 11 116 88 152<br>(3.0) (31.6) (24.0) (41.2)<br>1966 33 11 116 86<br>(8.3) (2.8) (29.0) (22.0)<br><!-- End of picture text --> Notes: Figures are thousands of hectares and (in parenthesis) percentage of total. Source Knight (1976).
<!-- Start of picture text --> N<br>sig<br>4<br>4 Sai : ihyhaianghai<br>h<br>Unit: oC /month<br>{| no data<br>mmi-4 }<br>G5- 10<br>11 - 14 Taiwan<br>Hi 15-18<br>Hi 19 - 25 PRONG<br>jacau<br>a0 a200 =a 400 | a a oa800| Miles Y<br><!-- End of picture text -->
Figure 10. Alternative Estimates for Age-Stratified Fatality Ratios <!-- Start of picture text --> China Italy<br>30<br>20<br>10<br>0<br>Spain South Korea<br>30<br>20<br>10<br>0<br>0 10 20 30 40 50 60 70 80+ 0 10 20 30 40 50 60 70 80+<br>Age cohorts<br>Fatality ratio (percent)<br><!-- End of picture text --> Source: Istituto Superiore di Sanità (2020; March 30), Korean Ministry of Health and Welfare (2020; May 6) and Spanish Ministry of Health (2020; May 4).

As noted, the results are sensitive to the age gradient present in the base epidemiological parameters. While according to Verity and others (2020) the assumption of age-neutral infection prevalence is robust, the J-shaped gradient implicit in the age-stratified infection fatality data by the same authors will matter. For these reasons it useful to conduct a robustness check to ensure that the variation seen across other estimates does not fundamentally change the results.

To verify robustness, we recalculated the results based on age-stratified case fatality ratios from Italy, Korea and Spain (Figure 10). These case fatality ratios do not correct for ascertainment and censoring biases and hence show a steeper age gradient since ascertainment bias is more significant for older cohorts (Verity and others, 2020). Relative to the baseline of Figure 9, the case fatality rates of Italy would boost the HIC share by 0.1 ppts; the Korean data would raise it by 3.1 ppts; and the Spanish data would raise it by 0.2 ppts. The fact that these deviations are small suggests that the first-order effect of demographic structure is robust and outweighs reasonable variations one might see in underlying epidemiological assumptions providing that we apply the same assumptions across all countries.

# **Comorbidities and Environmental Factors May Present Further Upside Risk**

The second step in the thought experiment is to consider how relative to the baseline shown in Figure 11 the results would change if we relax our assumptions. Specifically, the IPRs and IFRs may differ significantly across countries as well as between country income groupings. For this purpose, we review a number of host-specific factors (other than age, which was already discussed) as well as environmental factors. We note however that collectively these factors do not offer a convincing explanation why the share of developing countries in the global total death toll should be systematically lower. In fact, there may be various reasons to suggest why these alternate factors would have the opposite effect of raising the developing country share further.

19
17 are listed in Appendix Table A1. The median change among points with coding corrections was 20 percent.<sup>18</sup>

# 3.3. <u>New data points</u>

The 2019 version of the HEFPI database includes 535 newly added data points. Most new points come from surveys which were not included in the 2018 HEFPI database, but there are also a number of new points from previously included surveys. For instance, we added mammography data points from the previously included Jordan 2002 and 2007 DHS, and the child stunting and underweight data points from the previously included Myanmar 2000 and Albania 2005 MICS.

Most newly added surveys come from the MICS family, and there mainly from the addition of all currently available fifth and sixth round surveys. We also added a number MICS surveys from earlier rounds that were missing from the 2018 HEFPI version, namely the round two surveys from Bosnia and Herzegovina (2000), Equatorial Guinea (2000), Indonesia (2000), Iraq (2000), Madagascar (2000), South Sudan (2000), Trinidad and Tobago (2000), Uzbekistan (2000) and Zambia (1999); the round three survey from Jamaica (2005), and the round four surveys from Bangladesh (2012), Saint Lucia (2012), South Sudan (2010), and Trinidad and Tobago (2011). With these additions, we have included all MICS surveys from the second to the fifth round which are nationally representative and where the micro-data are publicly available.<sup>19</sup> We also added points from newly available surveys of the seventh round of the DHS, namely Albania (2017), Benin (2017), Burundi (2016), Haiti (2016), Jordan (2017), Malawi (2016), Maldives

> 18 Appendix Table A1 and our calculation of the median change exclude the pap smear indicator, as all values of the indicator changed between the 2018 and 2019 database versions due the change in the indicator’s reference period from three to two years.

> 19 Due to problems with the structure and labeling of the raw micro-data, we could not include the second-round Botswana (2000) and the fourth-round Guinea-Bissau (2014) surveys.
<!-- Start of picture text --> 7C: Investment rate has increased over the years 7D: Financial development in India has<br>increased over the years 20<br>Investment Rate Domestic Credit Provided by<br>40 Financial Sector (as % of GDP)<br>35 100<br>30<br>80<br>25<br>20 60<br>15<br>40<br>10<br>20<br>5<br>0 0<br>India Median in EM7 countries India Median of EM7<br>7E: India has strong and reliable institutions<br>Rule of Law Index* Index of Bureaucracy<br>Effectiveness**<br>0.2<br>4<br>0.1<br>3<br>0<br>2<br>-0.1<br>-0.2 1<br>-0.3 0<br>India Mean of EM7 India Mean of EM7<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016<br><!-- End of picture text --> Note: Data is from World Development Indicators and IMF International Financial Statistics. The latter’s date range is 1960 to 2011. *: World Bank Worldwide Governance Indicators, Kaufmann et. al. 2002. Rule of Law Index (Value between -2.5 and 2.5). Date Range: 1996 to 2016 (with gaps); **: Political Risk Service (values between 0 and 4, 4 being the best). Date Range: 1984 to 2014. Bars show average over time and, in the case of EM7, across countries Below we look at India’s growth record since the early 1990s more granularly, in order to reconcile its positive long-term growth trend with the perceived growth deceleration in recent years.

# **3. Three Phases in India’s Growth Trajectory Since the Early 1990s**

> 20 Domestic credit provided by the financial sector includes all credit to various sectors on a gross basis, with the exception of credit to the central government, which is net. The financial sector includes monetary authorities and deposit money banks, as well as other financial corporations like finance and leasing companies, money lenders, insurance corporations, pension funds, and foreign exchange companies.

16
common language or border) reduce exports through both the extensive and intensive margins and lead to lower exporter concentration. These results are similar to results obtained using data on U.S. exports (Lawless 2010).

One potential concern about the average exporter size results is that they may pick up the activity of intermediaries, individual firms that intermediate trade for many producers. An intermediary will be observed in our data as a single firm, but it actually consolidates exports for many firms, and there is no way to distinguish these firms from direct exporters. Ahn, Khandelwal, and Wei (2011) offer evidence on the importance of these firms in trade. They find that a significant share of exports in China went through intermediaries in the early 2000s. However, they also find that as trade expands, the importance of intermediaries declines—their share fell from 35 to 22 percent between 2000 and 2005 as China’s exports surged. In part, this is because as firms expand their exports they switch from exporting through intermediaries to direct exporting. This suggests that the average exporter size variable is more likely to overstate actual exporter size and the number of exporters is more likely to understate exporter count in the less developed or small countries as compared with richer or larger countries. To examine explicitly the importance of intermediaries we estimate specifications where GDP per capita and GDP enter by themselves and interacted with a dummy variable identifying sectors with a larger presence of export intermediaries.<sup>25</sup> The corresponding estimates shown in Table 5 reveal significantly larger numbers of exporters, larger average exporter size and higher concentration in richer countries and in larger countries as in Table 4.<sup>26</sup> In sectors with more intermediaries, the overall effect of income

> 25 We follow Chan (2014)’s choice of sectors with a larger proportion of firms exporting indirectly according to World Bank Enterprise Surveys data for 118 countries as those with a higher prevalence of export intermediaries.

> 26 The coefficient on GDP per capita in the share of the top 5% of exporters regression in column (8) of Table 5 that is the most stringent specification including sector-destination-year fixed effects with only 37 degrees of freedom (of which only 3 correspond to developed countries) is significant at a slightly lower level than the 10 percent confidence level and the magnitude of the coefficient remains similar to previous specifications.

19
# **Cash Transfers and Child Schooling: Evidence from a Randomized Evaluation of the Role of Conditionality**<sup>∗</sup>

Richard Akresh University of Illinois at Urbana-Champaign Damien de Walque The World Bank Harounan Kazianga Oklahoma State University _Keywords_ : Cash transfers; Conditionality; Education; Africa _JEL classification_ : I21; I25; I38; J13; O15; C93

* These data were collected for a project evaluating social protection strategies in Burkina Faso, which greatly benefited from the support of Marie-Claire Damiba, Seydou Kabré and Victorine Yameogo from the Secrétariat Permanent du Comité National de Lutte contre le SIDA et les Infections Sexuellement Transmissibles in Burkina Faso and Nono Ayivi-Guedehoussou, Hans Binswanger, Ousmane Haidara, Timothy Johnston, Mead Over, and Tshiya Subayi-Cuppen at the World Bank. Data collection was supervised by Robert Ouedraogo, Jean-Pierre Sawadogo, Bambio Yiriyibin and Pam Zahonogo from the University of Ouagadougou. The project is funded by the NBER Africa Project and the following World Bank trust funds grants: Strategic Impact Evaluation Fund (SIEF), Bank-Netherlands Partnership Program (BNPP), Gender Action Plan (GAP), Knowledge for Change Program (KCP), WB-DFID Evaluation of the Community Response to HIV and AIDS, Trust Fund for Environmentally & Socially Sustainable Development (TFESSD), and Luxembourg Poverty Reduction Partnership (LPRP). The authors thank Emilie Bagby, German Caruso, Christine Jachetta, Marleen Marra, and Nga Thi Viet Nguyen for excellent research assistance. The authors also thank Adam Wagstaff and seminar participants at Dalhousie, Oklahoma State, and the World Bank for helpful comments on earlier drafts. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.

Contact Information: Richard Akresh, University of Illinois at Urbana-Champaign, Department of Economics, 1407 West Gregory Drive, David Kinley Hall, Room 101, Urbana, IL 61801.Email: akresh@illinois.edu; Damien de Walque, The World Bank, Development Research Group, 1818 H Street, N.W., Washington, D.C., 20433. Email: ddewalque@worldbank.org; Harounan Kazianga, Oklahoma State University, Department of Economics, 324 Business Building, Stillwater, OK 74708. Email: harounan.kazianga@okstate.edu
years since last transfer is associated with a 9% increase in baseline household consumption; this magnitude is relatively small and unlikely to bias our estimates.

# **4.5 Results**

Table 5: Transfer size and persistence

||||Efect on co<br>per unit o<br>|nsumption<br>f transfer<br>|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_β_1: Years since last transfer|-0.029|0.191|-0.024|0.235|-0.036|-0.003|
||(0.017)|(0.395)|(0.019)|(0.425)|(0.038)|(0.599)|
||{-0.062, 0.004}|{-0.502, 1.063}|{-0.061, 0.014}|{-0.518, 1.179}|{-0.114, 0.036}|{-1.106, 1.280}|
|_β_2: log transfer size|-0.151|-0.128|-0.136|-0.107|-0.246|-0.241|
||(0.051)|(0.070)|(0.058)|(0.080)|(0.093)|(0.142)|
||{-0.254, -0.052}|{-0.263, 0.015}|{-0.252, -0.022}|{-0.259, 0.061}|{-0.433, -0.065}|{-0.514, 0.057}|
|_β_3: Years since last transfer *||-0.032||-0.038||-0.005|
|log transfer size||(0.058)||(0.062)||(0.085)|
|||{-0.159, 0.069}||{-0.176, 0.072}||{-0.190, 0.151}|
|_στ_: Std. dev. of efects|0.053|0.069|0.061|0.080|0.076|0.098|
||(0.047)|(0.058)|(0.054)|(0.066)|(0.065)|(0.082)|
||{0.003, 0.175}|{0.003, 0.215}|{0.003, 0.201}|{0.006, 0.246}|{0.003, 0.242}|{0.005, 0.307}|
|Controls|||||||
|Transfer duration<br>|||X|X|||
|RCT fxed efects|||||X|X|
|# of RCTs|7|7|7|7|5|5|
|# of observations|18|18|18|18|16|16|

_Notes:_ Estimates of the model of impacts of UCT interventions on household consumption per unit of transfer in Equation 3 are presented in this table. Columns 1 through 6 report Bayesian posterior means of each parameter, with posterior standard deviations in parentheses and 95% credible intervals in curly brackets.

Bayesian estimates of Equation 3 are presented in Table 5. In Columns 1 and 2, we do not include any additional controls; Columns 3 and 4 include transfer duration as a control, while Columns 5 and 6 include RCT fixed effects.

First, in Column 1, we regress the effect of cash transfers on consumption per unit of transfer on years since last transfer and log transfer size. This corresponds closely with the descriptive analysis presented in Figure 2. The point estimate on years since last transfer implies strong persistence of the effects of cash transfers on consumption: a one year increase in years since last transfer corresponds with a 0.029 decrease in the effect of cash transfers on This consumption per unit of transfer, or a 8% annual decrease relative to the average effect. estimate is precise, as the 95% credible interval excludes fade out of effects any faster than 18% of the average effect per year. The point estimate on log transfer size implies a doubling 31
the plausibility of the female literacy data by showing that it is, as expected, positively associated with sanitation and negatively associated with IMR from the same district-year, with district and year fixed effects. The remaining columns replicate our main regression results and show that the coefficient is essentially unchanged with the addition of female literacy. Historical female literacy is not available for every district year; column 3 shows that restricting the sample to the observations with female literacy does not change our result. Column 4 shows that adding a control for female literacy does not change the coefficient on sanitation or IMR, and column 5 confirms that this remains true with the full set of regression controls.<sup>11</sup> Therefore, the stability of our estimates again support our interpretation of an effect of early-life disease exposure on adult economic outcomes.

# **3.6 Effects of IMR on Consumption**

Estimating the effect of the early-life disease environment on consumption is an important extension for two reasons. First, it is a robustness check of our main results: we would expect an increase in income to increase household consumption, especially if the adult male we study is an important source of household income. Second, consumption taxes – such – as value added tax are a larger fraction of government revenues in India than income tax, and thus if we are concerned about the fiscal impacts of the early-life disease environment, it is important to confirm that consumption is affected as well.

Table 6 presents effects of early-life IMR on the log of household monthly consumption per capita.<sup>12</sup> Column 1 repeats the estimate with controls of the effect of IMR on wages from Table 2; the effect on household consumption in column 2 is similar, although slightly smaller. Importantly, however, the adult men whom we are able to study are relatively young, and only some of them will be significant earners for their households. One common family structure in India is a joint household where adult men and their spouses live with the man’s parents; such households could have multiple brothers and a father earning income. Column

> 11We note that in both samples female literacy in the year of birth is positively and statistically significantly associated with adult wages in a simple regression, but its coefficient changes sign to negative, as seen in the table, when district and year fixed effects and our measures of the disease environment are added. This is surprising, but is beyond the scope of our paper.

> 12Because we only have sanitation data for very young workers, it is not feasible to estimate the effect of sanitation coverage on consumption, as young workers play a small role in shaping their household’s consumption. The results when we inappropriately attempt such a regression are inconsistent.

13