rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 8: Producer prices in the C-4 and the A index, real CFAF (1976-2004)**

<!-- Start of picture text --> 800 2500<br>700<br>2000<br>600<br>500<br>1500<br>400<br>1000<br>300<br>200<br>500<br>100<br>0 0<br>Year<br>Benin Burkina Faso Chad Mali A Index<br>Nominla producer price (US$/tonne) Nominla A index (US $/tonne<br>1976 1979 1982 1985 1988 1991 1994 1997 2000 2003<br><!-- End of picture text --> Source: Baffes (2007) 26
indicate a significant impact of the wage-subsidy voucher on employment. But when cross-checks were made against central administrative data, supplemented by informal interviews with the hiring firms, it was found that there was very low take-up of the wage subsidy by firms (Galasso et al., 2004). The scheme was highly cost effective; the government saved 5% of its workfare wage bill for an outlay on subsidies that represented only 10% of that saving.

However, the supplementary cross-checks against other data revealed that _Proempleo_ did not work the way its design had intended. The bulk of the gain in employment for participants was not through higher demand for their labor induced by the wage subsidy. Rather the impact 54
8|4.85|66.67|3|1.42|25.00|12|
|Slovak Republic|1|0.13|12.50|0|0.00|0.00|1|0.61|12.50|6|2.83|75.00|8|
|United Kingdom|140|18.67|78.21|32|16.49|17.88|7|4.24|3.91|0|0.00|0.00|179|
|Total|750|100.00|56.78|194|100.00|14.69|165|100.00|12.49|212|100.00|16.05|1,321|

Notes: Authors’ elaboration on Eurostat data. By-category percentages are defined on column totals. By-country percentages are defined on row totals. High-income regions are those with an average yearly GDP per capita over the period 2003-2017 equal to or greater than the 90% of the sample average. Transition regions are those with an average yearly GDP per capita over the period 2003-2017 equal to or greater than the 75% of the sample average, but lower than the 90% of the sample average. Less developed regions are those with an average yearly GDP per capita over the period 2003-2017 equal to or greater than the 50% of the sample average, but lower than the 75% of the sample average. Low-income regions are those with an average yearly GDP per capita over the period 2003-2017 lower than the 50% of the sample average.

8
1960s. Penner et. al. (2022) showed that although worker characteristics and selection effects play an important role in the pay gap, data that identify women and men who do the same work for the same employer often reveal significant within-job pay differences in the 11 countries they study.

There is also strong indirect evidence that the gender wage gap is due to views and attitudes rather than ability. Maloney (2022) shows that misogyny, as proxied by derogatory internet search terms, is an economically meaningful and statistically significant predictor of the wage gap at the local level. Such drivers are often referred to as taste-based discrimination, as opposed to motivations based on differential outcomes such as average group productivity levels, which are often referred to as forms of statistical discrimination (Bertrand and Duflo, 2017). Taken collectively, these and similar studies suggest that discrimination against women remains a consequential factor in wage differences around the world.

One commonly cited factor potentially driving lower pay for women than men is often referred to as the “paradox of the contented female worker.” It is supposed that an apparent tendency for women more often to report satisfaction with their work compared to men in the same occupations (and at times the same employer) could contribute to persistent gender pay differences. But as Adriaans and Targa (2023) show, in many countries, overall satisfaction with work does not always translate to satisfaction with compensation. Women are in fact more likely than men to be dissatisfied with their own level of compensation in 15 of the 28 countries studied by the authors. Thus, potential satisfaction differentials appear context specific and are unlikely to explain systematically lower pay for women at the global level.

These concerns are especially relevant in the Central Asian countries of Uzbekistan, the Kyrgyz Republic, and Kazakhstan—where gender inequality in labor market outcomes poses significant challenges. According to government statistics, on average, pay among working women is about 39 percent less than men's pay in Uzbekistan, 25 percent less in the Kyrgyz Republic,
--|---|---|---|---|
|Mongla|22.7|71.5|31.0|41.9|77.0|57.2|
|Dacope|24.9|77.6|37.9|44.5|82.2|67.8|
|Sarankhola|28.2|83.1|33.6|48.0|88.1|57.2|
|Koyra|29.1|86.0|56.4|49.1|89.2|95.2|
|Shyamnagar|33.8|92.3|107.6|50.2|91.0|159.8|

> 15 See Report of the Household Income and Expenditure Survey/HIES 2010.  Bangladesh Bureau of Statistics, Government of Bangladesh.

21
, than with additional school-level factors. The finding from NALO 2018 that more boys than girls have repeated a grade at school because of poor academic performance is worth noting in this regard. Similar data on the extent of grade repetition are available from PISA 2018 (OECD 2019), where 13.0 percent of 15-year-old boys in Saudi Arabia reported repeating at least one grade, compared to 9.8 percent of girls. These findings hint at the likelihood that early disadvantages and difficulties with learning in the early grades may compound over time, and that there is a need for stronger learning supports for students with special educational needs to enable progression through the education system. Although this issue affects both boys and girls in Saudi Arabia, the figures from NALO and PISA indicate that such compounding educational disadvantage is more clearly apparent among boys.

30
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_Note:_ Data on “regulatory quality” and “voice and accountability” are from the world governance indicators. The regulatory quality score reflects perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. This indicator has a correlation of 0.45 with the safeguard score and 0.60 with the enabler score. The voice and accountability score reflects perceptions of the extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. This indicator has a correlation coefficient of 0.45 with the safeguard score and 0.50 with the enabler score.

The first point to note is that the safeguard and enablers scores show quite a high correlation with the wider regulatory quality index from the World Governance Indicators (figure 12a). This makes sense as countries with a relatively strong regulatory culture tend to do well on regulation across a wide range of sectors, including data. The regulatory quality indicator reflects the public’s trust in the government’s ability to formulate and implement sound policies and regulations that permit and promote private sector development. Moreover, more regulatory good practices in enablers are associated with greater confidence in various government functions and entities, including public services, the civil service and its independence from political pressures, policy formulation and implementation and the government’s commitment to policies. This could be related in particular to the dimension of public intent data under the enabler pillar, which aims to facilitate the use/reuse of public data to improve government efficiency.

Furthermore, the author finds that more robust regulatory frameworks for data governance are associated with countries where citizens feeling more empowered in terms of both voice and accountability, as reflected in their perceived ability to participate in selecting their government, along with their freedom of expression, freedom of association, and a free media (figure 12b).

Though no causal relations can be drawn from these correlations,
economics of internal organization."</mark> _<mark>University of Illinois at Urbana-Champaign's Academy for Entrepreneurial Leadership Historical Research Reference in Entrepreneurship</mark>_ <mark>(1975).</mark>

- Williamson, Oliver E. 1979. Transaction-Cost Economics: The Governance of Contractual Relations _The Journal of Law & Economics_ , 22(2), Oct.: 233-261.

- Williamson, Oliver E. 1985. _The Economic Institutions of Capitalism_ . New York: Free Press.

- <mark>Williamson, O</mark> liver E. <mark>1991. Comparative economic organization: The analysis of discrete structural alternatives.</mark> _<mark>Administrative science quarterly</mark>_ <mark>, pp.269-296.</mark>

- World <mark>Bank.</mark> 2002. _World Development Report 2002: Building Institutions for Markets_ . Washington, DC: World Bank.

- World <mark>Bank.</mark> 2017. _World Development Report 2017: Governance and the Law_ . Washington, DC: World Bank.

- World Bank. 2020. _World Development Report 2020: Trading for Development in the Age of Global Value Chains._ Washington, DC: World Bank.

Worldwide Governance Indicators (www.govindicators.org)

- Yan, Ting and Roger Tourangeau. 2008. "Fast Times and Easy Questions: The Effects of Age, Experience and Question Complexity on Web Survey Response Times." _Applied Cognitive Psychology_ . 22: 51–68.

59
number of contexts where mobile phone data are being made available for humanitarian purposes (Milusheva et al., 2021). For example, in just the past few years, mobile money was used to make cash transfer payments in countries including Bangladesh (Ali & May, 2021), Ghana (Karlan et al., 2021), Liberia (USAID, 2021), and Malawi (Paul et al., 2021). Mobile phone _data_ has been used to guide cash transfers in Colombia (Gentilini et al., 2020), the Democratic Republic of Congo (Gentilini et al., 2021), Pakistan (Gentilini et al., 2020), and Togo (Aiken et al., 2021).<sup>1</sup> – The context of our empirical analysis identifying ultra-poor households in Afghanistan – is a particularly challenging environment for data collection and program targeting, as 62% of the households classified as not _ultra_ -poor still fall below the national poverty line. In such environments, when traditional options for targeting are not feasible, these methods may provide a viable alternative for identifying households with the greatest need. Given the policy relevance of these results, we conclude our analysis by discussing important ethical and logistical considerations that may influence how CDR methods are used to support targeting efforts in practice.

# **2 Data and Methods**

## **2.1 Targeting the ‘Ultra-Poor’**

Our empirical analysis relies on survey data collected as part of the Targeting the UltraPoor (TUP) program implemented by the government of Afghanistan with support from the World Bank. The TUP program was a “big push,” providing multi-faceted benefits to 7,500 ultra-poor households in six provinces of Afghanistan between 2015 and 2018 (Bedoya et al., 2019). Our analysis uses data from the baseline and targeting surveys from an impact evaluation of the TUP program conducted in Balkh province.

**Ultra-Poor Designation** Eligibility for the TUP program was determined based on geographic
# **3.2.2 Profile of the bottom 40 percent of population**

In the previous section, we observed that if spatial deflators are measured consistently across two periods in time, the impact on shared prosperity indicators is rather minimal for most countries unless the spatial price adjustments were carried out inconsistently over time. Even if growth rates do not change, it is still important to adjust for spatial price differences because purchasing power of the same amount of expenditure or income differs significantly if the price level differs. Furthermore, the welfare ranking of people can change significantly based on spatial price adjustments. Spatially adjusted prices may change where certain people in the bottom 40 percent are located in the welfare distribution. We explore this concern empirically using a subsample of our database – data for Kazakhstan, Rwanda, Tajikistan and Iraq.

Table 3 shows the percentage of the population remaining in the same decile regardless of the choice of welfare aggregate: nominal or spatially deflated. Substantial reshuffling of people occurs starting from the second decile. In Rwanda for example, only 60 percent of the population in the fourth decile stays if the welfare aggregate is spatially deflated. A further 20 percent of those are found in the fifth decile if regional variation in prices is taken into account.

As a result of the change in rankings, the share of the population in the bottom 40 can shift across regions. This is illustrated in figure 8 using data for Kazakhstan. In ten of sixteen regions, the share of the population from the bottom 40 percent is significantly different depending on whether a nominal or spatially deflated welfare aggregate is used. For example, only 8 percent of the total population in Almaty was from the bottom 40 percent if nominal consumption per capita is used; however, the ratio increases to 14 percent if the welfare aggregate is spatially deflated (results for Iraq and Rwanda are provided in the annex).

**Table 3. Percentage of population staying in the same decile regardless of welfare aggregate: spatially deflated or nominal**
54 Moreover, the witlldrawal of food f ton, tl,e clild at home may have a net detrimental effec.: on his/her diet.

When the household finds it worthwhile to participate in the program, the question usually is "how does this compare with an income transfer of the same value?".26 Selowsky (1978) identifies two parameters that would determine the efficiency of the program which aims at increasing caloric consumption of children: "....(a) the food distributed (by the program) to each child as a fraction of the food previously consumed at home and (b) the marginal propensity to spend on children's food ....... " (p. 56). The product of these two (which are almost by definition less than unity) will yield the percentage increase in the caloric intake of the child. On the basis of this model, Knudsen (1981) shows with data from Tamil Nadu, India, that a food transfer of the value of 10% of family income, for example, will induce in a family with per capita caloiic intake of 2,110 calories (80% of FAO requirements) to increase the caloric intake of a child with a ration of 290 calories by only 40 calories.27 26 If the program is located in a school or day-care center, participation depends on the proportion who attend school and the regularity of their attendance. In many cases, the existence of the feeding program at the school may increase attendance by making school a more attractive option for the household. On-site programs also rely on regular attendance, which may be particularly difficult for children of working mothers.

27However, as the child is likely to be fed by the program a more expensive diet than it would receive at home, the family's value of this diet might be lower, and conseqtuently the child's net benefit from the ration may be even less than the benefit calculated above. As the
taxes, direct transfers, and indirect taxes drive the changes in poverty from market income plus pensions to consumable income. The impact of indirect taxes practically wipes out the poverty gains for moderate poverty and has a sizable effect on the extreme poverty reduction. The higher poverty reduction in extreme poverty is due to direct transfers being concentrated in the first quintile (see next section).

Fiscal interventions lead to an important reduction in both the poverty gap and the squared poverty gap. For the extreme poverty line, the poverty gap reduces to half and the squared poverty gap to a third, from the MIPP to consumable income. So, even though the fiscal system does not reduce the headcount, it does alleviate the situation of the poor.

**Table 3. Poverty headcount ratio by income concept**

||**Market income**<br>**pluspensions**|**Disposable**<br>**Income**|**Consumable**<br>**Income**|
|---|---|---|---|
|**Extreme poverty**||||
|Headcount Index|14.9%|7.1%|9.0%|
|Poverty Gap|10.5%|2.8%|3.6%|
|Squared Poverty Gap|8.9%|1.3%|1.8%|
|**Moderate poverty**||||
|Headcount Index|32.8%|26.0%|32.2%|
|Poverty Gap|19.1%|11.5%|14.4%|
|Squared Poverty Gap|14.5%|6.9%|8.6%|

Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data
# **Tables**

**Table 1:** Data situations

|Type|Extent of missin<br>crop-cut data|g<br>Typical survey situation|Imputation model|
|---|---|---|---|
|A|Partially<br>missing|(i) LSMS-ISA with SR on<br>all plots, CC on subsample<br>within the same survey|Within survey<br>imputations|
|||(ii) LSMS-ISA with SR on<br>all plots CC on subsample<br>CC with plot boundaries|Satelitte<br>crop yield maps|
|B|Completely<br>missing|LSMS-ISA with SR on<br>all plots|Survey to Survey<br>with SR yields|

**Table 2:** Number of observations by crop for each wave

|||2017|2018|
|---|---|---|---|
|Millet|1607|5199 (76%)|1615 (24%)|
|Sorghum|1155|3591 (63%)|2128 (37%)|
|Rice|4130|2990 (72%)|1140 (28%)|
|Maize|5777|3668 (63%)|2109 (37%)|
|Cowpea|1370|918 (67%)|452 (33%)|
|Groundnut|6401|4530 (71%)|1871 (29%)|

19
Figure 6: Poverty Eradication Capacity Indices <!-- Start of picture text --> EAS<br>ECS<br>LCN<br>MEA<br>SAS<br>SSF<br>0 .2 .4 .6 .8 1<br>Povery Eradication Capacity Index, I(P~p)<br>1<br>r)<br>.8<br>.6<br>.4<br>.2<br>Povery Eradication Capacity Index, I(P<br>0<br><!-- End of picture text -->

# **5.2 Politics**

Even if a country has the arithmetic capacity to eradicate poverty using domestic resources, it may not be possible to implement redistributive policies because political power is concentrated in the hands of a few, rich individuals whose interests contrasts with those of the poor. Countries with a higher concentration of political influence need to mobilize larger coalitions of individuals to counter-weight the political influence in the hands of the few.

Take the above example of Namibia and Senegal: the two countries have similar poverty rates and we showed that the optimal marginal tax rate on the non-poor is about 16 percent in Namibia and 50 percent in Senegal. From the point of view of the _arithmetics_ of domestic resource mobilization, therefore, Namibia seems better off than Senegal, since the tax effort on the non-poor is smaller. Nevertheless, Figure 7 shows that the _Political Influence Concentration Curve_ of Namibia always lies above that of Senegal: to counterweight each rich individual in their own countries, poor individuals in Namibia need to coordinate larger coalitions than poor individuals in Senegal. Therefore, although mobilizing resources in Namibia is easier than in Senegal considering a mere accounting exercise, it is exactly the opposite when taking into account the political equilibrium in the two countries.

Figure 8 shows the political influence concentration index and the poverty headcount 17
to 1995 is not enough to compensate for the change exerted by the other two channels. In conclusion, if there were no changes in barriers and preferences over the decades, the market output would be lower in Costa Rica (15-31 percent) and Panama (62-85 percent). In contrast, the output would be marginally higher (4-7 percent) in El Salvador.

Columns (9) and (10) provide perspective on whether these effects are notable compared to actual changes in GDP per capita. GDP per capita is not an exact benchmark for model output. The latter is derived using information from labor surveys. Moreover, the lack of data before 1995 does not allow me to solve the model for any period other than 2015. Even so, the change in real GDP per capita can serve as a first yardstick to assess the strength of the three channels. In general, the three channels –
's perspective, but efficient from the perspective of the entire economy. Surveyors need to think carefully about how to uncover the true costs of regulation, since estimates will be biased upwards by simply asking the firm whether a regulation is an obstacle.

# **State-Owned Enterprises (SOEs)**

Developing countries claim a higher share of SOEs in their economies than do industrialized countries (Haggarty and Shirley, 1997). While SOEs resemble private firms in many respects, surveys of SOEs must ask some different questions. These differences reflect the different objectives of SOE management and objectives. SOE principals are typically bureaucrats or 17
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Policy Research Working Paper 9410

# **Abstract**

Agricultural labor accounts for the largest share of child labor worldwide. Yet, measurement of farm labor statistics is challenging due to its inherent seasonality, variable and irregular work schedules, and the varying saliences of individuals’ work activities. The problem is further complicated by the presence of widespread gender stratification of work and social lives. This study reports the findings of three randomized survey design interventions over the agricultural coffee calendar in rural Ethiopia to address whether response by proxy rather than self-report has effects on the measurement of child labor statistics within and across seasons. While the estimates do not report differences for boys across all seasons, the analysis shows sizable self/proxy discrepancies in child labor statistics for girls. Overall, the results highlight concerns on the use of survey proxy respondents in agricultural labor, particularly for girls. The main findings have important implications for policymakers about data collection in rural areas in developing countries.

This paper is a product of the Knowledge and Strategy Team, Development Economics. 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 galdo@carleton.ca, ana_dammert@carleton.ca, and degnet06@yahoo.com.

_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