rafmacalaba/datause-encoder-data huggingface dataset:
Seecaline program in the economically disadvantaged environment suggests that if a large-scale nutrition intervention is designed to phase in over time, then it pays to target the needier communities first.

# **7. Conclusion and Discussion**

This paper quantifies the impact of a large scale community-based nutritional intervention targeted at the children below the age of three, lactating women, and pregnant women implemented in Madagascar since 1999. The contribution of the study is to show how to community-level administrative data that is collected as part of the regular process of monitoring program activities can be used for the purpose of documenting the impact of the program. Such exercise can have high potential payoffs in terms of applications to other contexts, since most social programs in developing countries are carefully monitored by a monitoring system. The data produced during this process has two key attractive features that are not shared by household level data: ‘scale’ and ‘time’. Scale implies that monitoring data does represent the entire universe of participants and therefore can be potentially used to characterize the heterogeneity of impact. The time dimension enriches the analysis to be implemented at different point in times, documenting the time evolution of impact. This type of data however comes with its own limitations. First, monitoring data (and administrative data in general) do not generally have a large set of covariates. We argue that this disadvantage can be overcome by complementing these data with census-based data with socio-economic information available at low levels of geographical disaggregation. Second, by its nature monitoring data is observed only for participating communities. This does not allow the researcher to estimate the counterfactual of no participation. However, we argue that monitoring data can be used to define a new parameter of interest that measures the _differential returns to exposure to the program_ .

This parameter is estimated by comparing the malnutrition rates of communities that were exposed to the program for a longer duration with (comparable) communities that were exposed to the program for a shorter period of time. With the data at hand the comparison can be done at different points in time. We exploit the phase-in features of the program to model the selection of communities into different program durations and use the method of matching to estimate the differential effects of the length of exposure on aggregate community malnutrition rates. Our results show that on average there are positive returns to exposure: communities that were exposed to 23
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Hollingsworth, Bruce (2003). "Non-Parametric and Parametric Applications Measuring Efficiency in Health Care.” _Health Care Management Science,_ 6:203–218.

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Lindelow, Magnus (2006). “Tracking Public Money in the Health Sector in Mozambique: Conceptual and Practical Challenges,” East Asia Human Development Unit. Washington, DC: The World Bank. Draft.

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Newton, Paul N, Sue J Lee, Catherine Goodman, Facundo M. Fernández, Shunmay Yeung, et al. (2009) “Guidelines for Field Surveys of the Quality of Medicines: A Proposal.” _PLoS Medicine_ 6(3): e1000052. doi:10.1371/journal.pmed.1000052 ______________________________________________________________________________________

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# **Figure 1: Gold and Oil Production in Ghana**

# **a. Gold production, 1990–2018**

<!-- Start of picture text --> 150<br>125<br>100<br>75<br>50<br>25<br>0<br>Year<br>Natural oil and gas production, 2010–2021<br>80 500<br>400<br>60<br>300<br>40<br>200<br>20<br>100<br>0 0<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Year<br>Annual oil production Cumulative oil production<br>Gold production (unit: 1000 Kg)<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Annual oil production (billion barrels)<br>Cumulative oil production (billion barrels)<br><!-- End of picture text -->

# **b. Natural oil and gas production, 2010–2021**

_Source_ : Authors’ estimates based on information obtained from PIAC (2021) and the United States Geological Survey (USGS) Database. https://www.usgs.gov/.

_Note_ : Gold production figures include reported artisanal and small-scale output.

29
price/ethanol price in the univariate model, and the reference fuel price and relative prices of other fuels in the multivariate model. The residual has three components: μ is the time-invariant entity effect, λ the entity-constant time effects, and v the remainder disturbance that varies with entity and time. Equation (1) is the static postulation, and Equation (2) the dynamic postulation. The double-log models would use the natural logarithm of both y and P, where β represents short-run elasticity and β/(1-γ) represents longrun elasticity.

Figure 5. Negatively Correlated Emission Intensity and Real Fuel Price in Brazil <!-- Start of picture text --> Emission Intensity (lhs) Deflated Fuel Price (rhs)<br>48 2.8<br>47 2.6<br>46<br>2.4<br>45<br>2.2<br>44<br>2.0<br>43<br>1.8<br>42<br>1.6<br>41<br>1.4<br>40<br>39 1.2<br>38 1.0<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text --> Emission Intensity (CO2e ton/million R$) Price of Diesel oil B (R$/l) Price of Gasoline C (R$/l) 14
If a new data collection exercise is to be of lasting value and take its place alongside other important international data collection exercises it needs a clear **_conceptual framework_** . Ultimately, national authorities will become the main collectors of such data; but such efforts will have far greater impact if the collection is designed to approximate a common internationally accepted framework. Both demand and supply considerations are relevant in interpreting data on actual usage of financial services; if it is to yield useful results, the data collection exercise must contribute to understanding both sides.

- (i) The **_demand side_** evidently includes the **_contribution of financial access to household wellbeing and firm productivity_** .  Providers and policymakers both need to learn which dimensions are most important here, given their concern with willingness to pay (providers) and impact of policy success (policymakers).

- (ii) The **_supply side_** entails measuring cost conditions and other **_barriers to access_** , with a view to easing or eliminating them.  Here again the results are crucially important both in terms of improving design of product and service provision (providers) and benchmarking the design of financial sector policy reform (policymakers).

For each goal I suggest that there is already at least the skeleton of an appropriate conceptual framework.

We will argue that **_four different channels of data collection_** will still be needed into the future. Thus even with more in-depth inquiries from:

- (i) **_providers_** of financial services (whether directly or, probably better, through national regulators), these will still need to be supplemented by surveys of

- (ii) user **_households_** and

- (iii) **_enterprises_** .  In addition, (iv) **_expert_** surveys will also help to fill in some details at low cost.

The **_outputs_** should include

- (i) a limited number of national **_basic indicators_** collected on a broadly comparable basis across countries and updated every few years – I tentatively suggest about half a dozen indicative headings under which these could be classified;

- (ii) national **_microeconomic databases_** allowing for detailed research including market research.

The paper is arranged as follows. Section 1 discusses the goals of data collection and the overall conceptual framework that could guide the exercise. The remainder of the paper then proceeds to set out the different types of relevant data that have been collected and to provide an indication of the extent, depth and frequency of coverage. In Sections 2, 3, 4 and 5 I describe what is available respectively from household and enterprise surveys, 2
amounts to the assumption that the quality of education that children will receive in the future is the same as what is reflected in the most recently‐observed test scores.

It is also worth noting that for most countries, test scores are observed only at one grade level. This means that out of necessity quality measured at one grade level is assumed to be representative of the entire school system. This assumption however may however be questionable in countries where there is a lot of attrition as students move to higher grades. In this case, test scores observed in later grades reflect an element of selection, to the extent that lower‐ability students are more likely to drop out of school before reaching the level at which the test is implemented. This implies that observed average test scores in later grades likely overstate the quality of education in lower grades. Adjusting for this is difficult, as it requires some way to approximate the distribution of test scores among those students who did not take the test. However, a partial adjustment for this in the overall HCI comes through the fact that in such cases, the enrollment rates used to calculate expected years of school will also be lower, which in turn lowers the learning‐adjusted years of school measure.

A final caveat worth noting is that test scores are only an imperfect measure of learning. There are the usual concerns that test scores measure performance only on those items of the curriculum that are covered in the test. Beyond this, test scores also respond to other factors, including intrinsic motivation on the part of test takers. For example, extensive empirical evidence from schoolchildren in Chicago suggests that small immediate financial rewards for good performance have substantial effects on students’ standardized test scores (Levitt, List, Neckerman and Sadoff (2016)). To the extent that there are cross‐country differences in students’ intrinsic motivation when taking standardized tests, this will be conflated with the quality interpretation of the test scores. Student test scores also reflect the influence of the home environment. De Philippis and Rossi (2017) use US PISA data to study test scores of children attending the same school, but whose parents immigrated from different countries. Children of parents who immigrated from countries with high test scores also tend to do better on test scores themselves, holding constant the quality of education they received by focusing on children in the same schools.

# **A2.4  Returns to Education**

Values for the return to education parameter φ can be anchored in the vast empirical literature that estimates Mincer (1958)‐style regression of log wages on years of school. Returns to education naturally vary across levels of education, by gender, and across countries. However, in the interests of 34
A number of findings stand out based on the host government data.

#  **_In all six host countries, the numbers of Chinese investment projects proved to be several times higher than those recorded by the MOC system, confirming the suspicions of many in China._**

Figure 4 below shows the number of Chinese investment projects that are “in operation” in each of the six host countries, contrasted to the number of projects reported by the MOC database. To be conservative, in all countries except Rwanda, the research uses numbers of projects “in operation,” rather than “committed.”<sup>14</sup> It is quite astonishing to see how closely the Chinese estimated, i.e., what is happening on the ground is significantly higher, by 2-7 times, than what is captured by MOC data in all six countries.

**Figure 4: Chinese Investment in Six African Countries, as of end-2011** (by project number)

<!-- Start of picture text --> 700<br>605<br>600<br>478<br>500<br>400<br>313<br>290<br>300<br>212<br>200<br>123 111 113<br>87<br>100 16 7 25<br>0<br>Nigeria Zambia Ethiopia Ghana Liberia Rwanda<br>Data from China Ministry of Commerce Data from Host Country IPA<br><!-- End of picture text -->

# _Source: Shen and Zhai._

More specifically, there are 2.9 times as much Chinese investment projects in Nigeria, 2.5 times in Zambia, 2.6 times in Ethiopia, 3.6 times in Rwanda, 5.5 times in Ghana, and 7 times in Liberia.

Host country data do not distinguish private and government ownership. However, the differences are ostensibly caused by private projects assuming SOEs generally comply with MOC certification requirements. Also, the size and sector distribution of the projects reported by host governments, to be discussed below, support such a supposition. Further, field investigations in two countries, Ethiopia and Nigeria, also to be discussed shortly, confirm this.

> 14 Rwanda only has “committed” figures.

12
<mark>WPS3574</mark>

# WAGE DIFFERENTIALS BETWEEN THE PUBLIC AND PRIVATE SECTORS IN INDIA

Elena Glinskaya and Michael Lokshin<sup>1</sup> _The World Bank_

## **ABSTRACT**

_This study uses 1993-94 and 1999-2000 India Employment and Unemployment surveys to investigate wage differentials between the public and private sectors as well as workers’ decisions to join a particular sector. To obtain robust estimates of the wage differential, we apply three econometric techniques each relying on a different set of assumptions about the process of job selection. All three methods show that differences in wages between public sector workers and workers in the formal-private and informal-casual sectors are positive and high. Estimates show that, on average, the public sector premium ranges between 62% and 102% over the private-formal sector, and between 164% and 259% over the informal-casual sector, depending on the choice of methodology. Our review of wage differentials (estimated using similar methodologies) across the world shows that India has one of the largest differentials between wages of public workers and workers in the formal private sector. The wage differentials in India tend to be higher in rural as compared to urban areas, and are higher among women than among men. The wage differential also tends to be higher for low-skilled workers. There is considerable evidence of an increase in the wage differential between 1993-1994 and 1999-2000._

## World Bank Policy Research Working Paper 3574, April 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1Address for correspondence: Elena Glinskaya, South Asia Poverty Reduction and Economic Management, <u>eglinkskaya@worldbank.org; Michael Lokshin, Development Research Group, mlokshin@worldbank.org,</u> both are at the World Bank, 1818 H Street NW, Washington, D.C., 20433, USA. We thank Rasika Ranasinghe for excellent assistance and data management and Arup Mitra and Madhu Raghunath for constructive and helpful contributions to this research. We also thank Martin Rama, Govinda Rao, Manuela Ferro, Stephen Howes, Marina Wes, Rasmus Helberg and Bob Beschel for their helpful comments and suggestions.
- 10 -

facilities, violating the validity of the instruments. This will ex post be examined by the conventional test of overidentifying restrictions.

## **<u>Table 1. Summary statistics</u>**

|<br>Variable|Abb.|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Number of new patients who visited each CSB in 2016|_N_|892|<br>2378|<br>2510|<br>0|<br>22563|
|Population in fokotany served by each (CSB)|_POP_|892|11220|<br>15135|<br>787|<br>298597|
|Share of local people who visited CSB in 2016 (%)|_N_/_POP_|892|<br>28.5|<br>45.9|<br>0.0|<br>1039.8|
|Distance from fokontany to CSB (km)|_KM_FKT_|892|<br>6.3|<br>4.3|<br>0.0|<br>65.4|
|Rural Access index (%)|_RAI_|892|<br>14.0|<br>14.7|<br>0.0|<br>100.0|
|Poverty rate at the district level (%)|_POV_|892|<br>76.6|<br>16.4|<br>16.3|<br>97.6|
|Distance to the official road network (km)|_KM_RD_|892|<br>3.3|<br>5.6|<br>0.0|<br>45.5|
|Travel time from CSB to District capital (hours)|_TIME_DIST_|<br>892|<br>0.51|<br>0.52|<br>0.00|<br>4.41|
|Medicine availability (% of days a year):|||||||
|Aminophilline|_m1_|892|<br>93.9|<br>17.9|<br>0.0|<br>100|
|Amoxicilline|_m2_|892|<br>96.4|<br>11.3|<br>8.6|<br>100|
|Benzyl Pénicilline|_m3_|892|<br>94.3|<br>15.2|<br>0.0|<br>100|
|Captopril|_m4_|892|<br>77.9|<br>33.3|<br>0.0|<br>100|
|Chlorphéniramine|_m5_|892|<br>95.8|<br>11.6|<br>0.0|<br>100|
|Cotrimoxazole|_m6_|892|<br>96.9|<br>10.2|<br>0.0|<br>100|
|Fer acide folique|_m7_|892|<br>93.1|<br>15.6|<br>0.0|<br>100|
|Gentamicine|_m8_|892|<br>95.5|<br>13.4|<br>0.0|<br>100|
|Hydrochlorothiazide|_m9_|892|<br>64.0|<br>39.8|<br>0.0|<br>100|
|Ibuprofène|_m10_|892|<br>96.1|<br>11.7|<br>0.0|<br>100|
|Métoclopramide|_m11_|892|<br>86.1|<br>24.1|<br>0.0|<br>100|
|Métronidazole|_m12_|892|<br>97.2|<br>9.1|<br>0.0|<br>100|
|Paracétamol|_m13_|892|<br>97.1|<br>9.3|<br>0.0|<br>100|
|Phénobarbital|_m14_|892|<br>91.7|<br>18.1|<br>0.0|<br>100|
|SRO|_m15_|892|<br>87.9|<br>23.5|<br>0.0|<br>100|
|Average availability of all medicines|_M_|892|<br>90.9|<br>11.2|<br>13.3|<br>100|
|Instrumental variables:|||||||
|Straight-line distance to Antananarivo (km)|_KM_TANA_|892|164.0|<br>108.6|<br>0.2|<br>394.3|
|Slope at the location of each CSB (degree)|_SLOP_|892|<br>1.45|<br>1.41|<br>0.02|<br>11.22|
|Elevation at the location of each CSB (m)|_ELEV_|892|1034.7|<br>485.2|<br>0.0|<br>2398.0|

# **III. Main estimation results**

Although the results may be biased and inefficient, the ordinary least squares (OLS) regression is first performed: The results are shown in **Table 2** . The estimated coefficients are generally consistent to our prior expectation. Clearly, transport connectivity is likely to
|Energy poverty incidence rate (absolute)|2,952|0.106|0.308|0|1|
|---|---|---|---|---|---|
|Energy poverty (> 10 percent)|2,952|0.530|0.499|0|1|
|Electricity (share)|2,952|0.0762|0.0792|0|3.063|
|Natural gas and town gas (share)|2,952|0.00132|0.00942|0|0.170|
|Liquefied hydrocarbons/butane/propane (share)|2,952|0.00111|0.00428|0|0.0630|
|Liquid fuels (share)|2,952|3.00e-05|0.00104|0|0.0519|
|Coal (share)|2,952|0.00329|0.0352|0|1.625|
|Other solid fuels (share)|2,952|0.0374|0.0790|0|1.075|
|Heat energy (share)|2,952|0.0118|0.0292|0|0.243|

_Notes: The indicators in this table are at the household level (N=2,952). We weigh each observation by household survey weights. The energy poverty incidence rate (share) is the 2M energy poverty indicator. The energy poverty incidence rate (absolute) is the M/2 energy poverty indicator. All variables are reported as shares._ _Source: Own estimates based on HBS (2019)._

# **Lower welfare quintiles report higher energy expenditure shares and higher energy poverty rates.**

Figure 1 demonstrates that those households that report the lowest average income spend a higher share of their overall household expenditure on energy (18.1 percent). The energy spending share falls by income quintile, with the highest income quintile reporting the lowest share (7.7 percent). Figure 1 reveals similar patterns for the case of energy poverty rates. Households in lower income quintiles report higher incidence rates than those in higher income quintiles.

17
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27
**Figure 2: Effective tax rates (percentage of turnover), by turnover quartile**

<!-- Start of picture text --> Panel 1: Self-reported Panel 2: Imputed<br>14.0 14.0<br>12.0 12.0<br>10.0<br>10.0<br>8.0<br>8.0<br>6.5<br>6.0<br>6.0<br>4.3<br>4.0<br>1.9 4.0<br>2.0 0.6 0.3 1.2 1.4 1.3 0.8 0.6 2.0 1.6 1.3 0.7 0.5 1.2 1.5<br>0.0<br>1 2 3 4 0.0<br>1 2 3 4<br>Turnover quartile<br>Turnover quartile<br>Period 1 Period 2<br>Period 1 Period 2<br><!-- End of picture text --> <!-- Start of picture text --> Panel 3: Self-reported, period 1<br>14.0<br>12.0<br>10.0<br>8.0<br>6.0<br>4.0<br>2.1<br>2.0 1.2 1.3 1.1 1.6 1.0<br>0.2 0.3<br>0.0<br>1 2 3 4**<br>Turnover quartile<br>Male-owned Female-owned<br>Panel 5: Imputed, period 1<br>14.0<br>12.0<br>10.0<br>7.8<br>8.0<br>5.5<br>6.0<br>4.0<br>1.9<br>2.0 1.2 0.5 1.0 1.3 1.0<br>0.0<br>1 2 3* 4<br>Turnover quartile<br>Male-owned Female-owned<br><!-- End of picture text --> <!-- Start of picture text --> Panel 4: Self-reported, period 2<br><!-- End of picture text --> <!-- Start of picture text --> 14.0<br>12.0<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>1.4 1.3<br>2.0 0.8 0.8 0.7<br>0.0 0.3<br>0.0<br>1 2 3 4***<br>Tunover quartile<br>Male-owned Female-owned<br>Panel 6: Imputed, period 2<br>14.0 12.2<br>12.0<br>10.0<br>8.0<br>6.0<br>4.0 2.2<br>1.6 1.3<br>2.0 0.4 0.5 0.7 0.2<br>0.0<br>1** 2** 3** 4<br>Turnover quartile<br>Male-owned Female-owned<br><!-- End of picture text --> Source: Author’s calculations using ESS 2018/2019 and 2021/2022.

Notes: Panels show the effective tax rates (tax as a percentage of turnover) by quartile of turnover. The test of means is significant at *** p<0.01, **p<0.05.

16