rafmacalaba/data-use-mentions-extended huggingface dataset:
small-area estimation, which provides the methodological foundations for poverty mapping, was pioneered by Elbers et al. (2003) and extended by e.g. Tarozzi and Deaton (2009), Tarozzi (2011) and Elbers and van der Weide (2014). Fujii (2010) has modified the approach to obtain small areas estimates of the prevalence of

> 1For example, across the 26 low-income countries in Sub-Saharan Africa over the period between 1993 and 2012, the national poverty rate and prevalence of stunting for children under five are on average reported only once every five years and once every ten years in the World Development Indicators.

> 2This approach could of course be expanded to other outcome variables of interest (such as health outcomes), while surveys other than labor force surveys could be considered to further extend the frequency of estimates. The same approach could also be adopted to construct a measure of consumption poverty that alleviate concerns of comparability over time when the original consumption data is deemed incomparable due to changes in the questionnaire. See for example the debate on the comparability of poverty estimates in India documented in e.g. Deaton (2003), Deaton and Dr`eze (2002), Kijima and Lanjouw (2005), Tarozzi (2007), and Deaton (2005).

2
by exploiting the constant orbital shift of satellites, which brought back older DMSP satellites (F15 and F16) into a usable day/night cycle to continue data collection with DMSP satellites. To have harmonized series for CAR, we select the period 2005-2017 as 2005 saw major structural reforms for Enerca (the main energy utility company of the CAR) and a policy shift towards liberalization of the electricity sector. We extract the sum of the NTL within a buffer zone of a 10 km radius around the capital location of each country. The sum of the NTL gives an aggregate that is a good proxy for electric power output (37) as well as economic activity (38) both at the national and local level. We use the capital as a reference city for a country rather than the all country to improve the signal-to-noise ratio of the NTL time series, as LICs tend to have poor electricity infrastructure, and the NTL data would measure large areas of noisy data. Capitals are usually the most infrastructure rich and economically dynamic locations (especially in LICs) and are therefore indicative proxies in the case of economic shocks.

Data on the share of industrial (including construction) and manufacturing value added over GDP are derived from the World Development Indicators from 2009 (the first year available for CAR) up to 2017. Due to lack of data the pre-treatment period for these two outcomes is limited, but the use of these indicators enables us to check consistency in our results. Due to large differences in sectoral composition among our donor pool of LICs and CAR, we convert the indicators in index base 100 in 2012 (i.e., just before the first clashes) which enables us to capture sectoral dynamics after the start of the civil war.

7
oppose government policies, they are also aimed at enhancing the state’s role in service delivery in the eyes of its citizens.

# **4 Can the provision of services by donors and non-state actors the con- strengthen fiscal tract?**

Many scholars argue that the effect of aid is contingent on the institutional environment in place (Burnside and Dollar, 2000, 2004; Kosack, 2003; Morrison, 2009, January 2010). Where accountable government institutions are in place survey was funded by an Opportunity Fund generously provided by the World Justice Project.

7
**<u>Table 3</u>** indicate that NSP improves attitudes to government figures at almost all levels, including district and provincial governors, central government officials, the President of Afghanistan, Members of Parliament, and government judges. Magnitudes of these effects vary from between 8 percentage points for Members of Parliament to 4 percentage points for the national police. There is also a positive effect of NSP on the attitudes of villagers toward NGOs and ISAF soldiers. The results for the summary measure indicate that NSP improves villagers’ attitudes by 13 percent of a standard deviation. Results for the two eastern districts, however, are completely different. There is no positive effect of NSP on attitudes toward any government bodies, ISAF soldiers, or NGOs, and the effect on attitudes towards the president and the police is significantly negative.

Overall, NSP improves attitudes to government and allied entities. This provides strong support for Hypothesis 2. However, the positive effects on attitudes are not observed in areas with high levels of initial violence, with villages receiving NSP in the eastern districts reporting significantly lower approval of the President and of police.

# **VI.3. Hypothesis 3**

Hypothesis 3 is tested using data from the follow-up survey on male and female villagers’ security perceptions and data on security incidents from villager surveys and from ISAF data. A summary index is also used here to estimate the aggregate effect of NSP on perceptions of security.

As reported in **<u>Table 4</u>** , NSP improves villagers’ perceptions of security. The proportion of male respondents in NSP villages who report an improvement in the security situation in the past two years is 6 percentage points higher, whereas the proportion of respondents who think that security has deteriorated is 3 percentage points lower.<sup>19</sup> Among females, the proportion of female respondents who think that women and girls feel safer compared to two years ago is higher in NSP villages by 5 and
to do the reverse as the EUSILC gathers more extensive information on individual characteristics, household wellbeing, and social protection programs. EU-SILC data contains a richer set of information and is therefore more attractive as a recipient dataset. Nevertheless, the European Commission (2014) recommends using the larger dataset as the donor. We do not follow this approach here, as we are interested in conducting microsimulations based on EU-SILC related variables in future projects.

9 Both Rubin (1986) and Renssen (1998) recommend concatenations in settings of complex survey designs.

10
One objective for the design of mNAIS insurance products is that the value of insurance products for the same crop in the same district should not vary significantly between insurance units.<sup>4</sup> If the value of products does vary considerably between insurance units in the same district the insurer would be exposed to the threat of adverse selection, and insurance purchase in the district may become dominated by farmers in the insurance units for which the products were particularly good value.

One technically feasible solution would be for farmer and government premium rates to be fixed by government at a crop or crop/state level, and threshold yields to be calculated on an actuarial basis according to the principles of risk classification. Such an approach would have no actuarial disadvantages compared to risk classification through premium rates, but could yield significant political economy advantages by allowing fixed premium rates, and therefore fixed premium subsidies, across a state in addition to a low variation in threshold yields for a given crop across the state.

However, if rates under mNAIS are to be set at the crop/district level and the threshold yield in each insurance unit must be based on a seven year moving average yield, at the very least these moving average yields should be smoothed within the district to improve predictive stability. This second best solution is described and motivated in the suggested calculation of probable yields.

A rule for calculating indemnity limits is also suggested. The calculation rule for threshold yields affects the equity of mNAIS; if the variation in commercial premium rates is high within a state then state and central government subsidies may be unevenly spread between farmers, with farmers from insurance units with high commercial premium rates capturing a larger share of subsidies than other farmers. The indemnity limit rule is intended to reduce the variation in commercial premium rates within a state.

# _Probable Yields_

# <u>Description of step</u>

First, the yield data to be used in calculating the probable yield must be determined according to the rules of mNAIS. For the
This might suggest that as women increasingly join the labor market worldwide, girls who have attained schooling might realize large returns if they can obtain skilled work and might partially explain why in cross-country Mincerian returns estimates women have higher returns to schooling (Psacharopoulos and Patrinos 2018). Of note, the flip in the gender gap might be due to selection. In regions where enrollment is low, only high-achievers might be taking assessments. This explanation is consistent with trends observed for the Middle East and North Africa. However, in Sub-Saharan Africa, where enrollment is second lowest, the learning gender gap is negative, as is the enrollment gap, indicating selection is unlikely the only driver. We present the contrast in gender gaps in schooling versus learning not as definitive, but rather to motivate further in-depth exploration, which we hope this database can enable.

We next examine the relationship between human capital and economic development on a global scale. A development accounting literature studies the relative contribution of human capital in cross-country income differences. However, this question remains unsettled, in part due to difficulties in measuring human capital. While direct measures of years of schooling exist, the quality of schooling has often been inferred or measurement of quality has covered only a limited sample of countries.

Several approaches have emerged to estimate the quality of schooling, including cross-country differences in Mincerian wage returns (Hall and Jones 1999; Caselli 2005), immigrant returns (Schoellman 2011), and cross-country skill premia (Caselli and Coleman 2006). However, these approaches have encountered challenges such as the substitutability between skilled and unskilled workers (Jones 2019). The challenges in measuring quality have contributed to substantial variation in estimates of the role of human capital in accounting for cross-country income differences, ranging from nearly all to potentially none (Jones 2014; Caselli and Ciccone 2018).

In this paper, we provide a more direct and reliable measure of the quality of schooling on a global scale. We construct human capital stocks using our learning outcome data and produce development accounting results
. Finally, any such increase (decrease) in the policy rate must be meaningful and lasting, defined as |∆it| ≥0.50 percentage point over one month, or at least two rate changes (of any size) occurring over a year, such that the policy rate is at least 30 basis points higher/lower one year after the first rate change.

Appendix 1 provides more information on the details of the criteria used to identify the cycle dates, the application of each of the additional criteria, and the specific countries and dates affected by each of these criteria.

# II.2. Database: Policy Rates and Balance Sheet Programs

To define rate cycles, we focus on the policy interest rate for several reasons. First, policy interest rates are currently the primary tool used by central banks to affect the monetary policy stance. Since our goal is to inform central bank decisions today, this is an obvious measure on which to focus. Employing market-determined measures of interest rates would incorporate fluctuations in rates that are not directly under the control of central banks. Second, data for policy interest rates are widely available over a long period, allowing us to analyze their evolution over the past 55 years (1970-2024) for 24 advanced economies. A long panel is particularly important for our empirical decomposition of the factors driving rate cycles and for the identification of precedents comparable to the ongoing rate cycle.

Finally, we focus on nominal policy rates, instead of attempting to estimate more complex measures of the overall stance of monetary policy.<sup>6</sup> Measuring the overall policy stance would require finding a comparable way to measure the impact of adjusting other policy tools (such as the money supply in certain periods) and modelling variables such as the neutral interest rate (which could be subject to substantial measurement error). Both of these tasks are extremely challenging for an individual country, and even more so for a cross-section of diverse economies

> 6 A series of papers have used alternative approaches
Schady, 2006). A most directly related to this analysis, the short-term impact evaluation of the FSSP in Pakistan –the program analyzed in this paper –shows an increase in the enrollment of girls of about 9 percentage points between 2003 and 2005 (Chaudhury and Parajuli, 2008). Although the impacts of these studies differ by magnitude, and the empirircal approaches vary, results appear to be consistent across programs and implementation contexts.

Nonetheless, the evidence of the connection between the provision of incentives for school participation and the well-being of adolescent girls in the longer-term remains very thin. Therefore, this paper aims to begin filling this evidence gap by assessing whether girls who benefited from a school stipend program show improved outcomes in the longer-term, or up to four years after their first exposure to the program.

# **3. Schooling Context and the Project**

## **_Schooling in Punjab_**

The literacy rate in Punjab in 2003 was approximately 54 percent (45 percent in Pakistan as a whole) compared to 56 and 63 percent for South Asia and all low income countries, respectively (Lloyd, 2004).<sup>3</sup> Divided into 36 districts (34 districts at the time the program began), Punjab is home to over 55 percent of the total Pakistani population.

School enrollment in Punjab, particularly in secondary education, is largely constrained by the scarcity of schools. Primary education is supplied by both public and private schools. On the contrary, the secondary level is dominated by the public sector, and not every village has access to middle schools (grades 6 to 8) and high schools (grades 9 and 10).<sup>4</sup> For instance, whereas 84 percent of the households in Punjab report that their children are within walking distance (15 minutes) of a primary school, only 55 percent of the households have access to a middle or high school within the same travel time (Pakistan Social & Living Standards Measurement Survey -
NL-2/2-1-88

- 48 -

The third stabilization was that of Martinez de Hoz, where the exchange rate became the main nominal anchor with the beginning of the tablita policy in early 1979.1/ It was abandoned in the beginning of 1981 in the midst of a severe crisis. The last stabilization is the Austral plan!/ which was a heterodox fiscal-exchange-rate package which started in June 1985 by the Alfonsin administration with a complete pegging of the exchange rate. The peg was shifted to a crawl in April 1986 and price controls were made more flexible from the middle of the year.

Figures 4 show that one common feature of all four stabilizations in the U-shaped pattern of the budget deficit.3/ There is always a drastic reduction in the deficits at the beginning of the program and the fiscal discipline remains in effect for some time; however, at a later stage, the budget deficit returns to, if not exceeds the, levels prevailing prior to the plan. The reduction in the rate of devaluation is the dominating force in pulling down inflation4/ but this policy is always abandoned with (or after) the rise in the fiscal deficit. It seems hard to dispute the conclusion that in Argentina the basic cause for the failure of stabilization programs is the

- 1/ See Calvo (1986) and Ramos (1986).

- 2/ For an analysis of this plan and a comparison with the Israeli 1985 stabilization program. see Blejer and Liviatan (1987).

- 3/ Deficit data are based on de Pablo and Martinez (1987). Other data World Bank and IFS, except for Figure 4d which is based on Machinea (1987). The deficit does not include the quasi-fiscal deficit of the central bank.

- 4/ This is seen clearly in figure 4C where the deficit started rising in 1978 while inflation continued to be pulled down by the declining rate of devaluation.
Surveys ask the main owners of informal businesses for how long they have lived in the current city. We consider as migrants all owners who lived in the city less than their entire life. Using this definition, 28.3% of businesses are owned by migrants. Curiously, there are no differences between migrant and non-migrant-owned informal businesses in terms of the average age of businesses, or of their owners, or the number of employees, or sectoral distribution.

# **3.2 Characteristics of Informal Businesses**

The uniqueness of the Informal Sector Enterprise Survey data used in this paper is the richness of information that they provide about informal businesses. The next sub-sections provide a descriptive overview of some of the key characteristics of informal businesses.

## **Demography**

Figure 3 presents one of the most well-documented stylized facts about informal businesses: that they are very small (Kanbur, 2017; La Porta and Shleifer, 2014). The median informal business engages just two persons (including the owner/manager). Across the seven countries, 49% of the businesses are one-person operated, while another 31% engage two individuals.<sup>13</sup> Only 4.6% of informal businesses have five or more people working in the business. Importantly, these businesses generally have even fewer paid employees, presumably because the workers are often the owners and their family members, hence the residual beneficiary of the profits. The average number of paid employees (including the owner/manager) is one, with some variation on the right tail as about 67% of the businesses have at least one paid employee. Informal businesses in the manufacturing sector tend to engage slightly more individuals than resellers and businesses in other service sectors. This difference is not statistically significant and is of a lesser magnitude than might be anticipated given the economies of scale inherent in the manufacturing sector (see Panel A of Figure 3). Close to half of those in reselling businesses, which include hawkers, those in
br>including out-of-<br>pocket expenses and<br>some data on<br>household<br>consumption|Details of the survey and how to access the<br>microdata can be found at the MCSS<br>webpage.<sup>43</sup>|
|MNAPOV|World Bank Middle<br>East & North Africa<br>harmonized<br>household survey<br>collection|Ex post<br>harmonized|World Bank|World Bank’s<br>Middle East<br>& North<br>Africa region||Accessible only to World Bank staff via World<br>Bank Microdata Library.|
|SARLF|World Bank South<br>Asia Labor Flagship<br>harmonized survey<br>collection|Variety of<br>surveys in South<br>Asia countries,<br>often raw data|World Bank|World Bank’s<br>South Asia<br>region|Multiple types, many<br>not relevant to the<br>current database|Accessible only to World Bank staff via World<br>Bank Microdata Library.|
|SARMD|World Bank South<br>Asia harmonized<br>household survey<br>collection|Ex post<br>harmonized|World Bank|World Bank’s<br>South Asia<br>region|HBS, HIES,<br>multipurpose surveys|Accessible only to World Bank staff via World<br>Bank Microdata Library.|
|SEDLAC|Socio-Economic<br>Database for Latin<br>America and the<br>Caribbean|Ex post<br>harmonized|CEDLAS and the<br>World Bank|Latin America<br>& Caribbean|HBS, HIES,<br>multipurpose surveys|Details of the SEDLAC project are at the<br>SEDLAC website.<sup>44</sup>Website gives no details<br>of how to
effects of Syrian refugees on Turkey’s local labor markets. This research was pioneered by two related papers, Ceritoglu et al. (2017) and Tumen (2016). Using a differencein-difference estimation strategy applied to labor market outcomes for natives, these studies find moderately negative effects on the employment rates of natives employed in the informal sector, though no effects on wages. Their analysis employs data from the Turkish Household Labor Force Survey (2010-2013). Specifically, they estimate a 2 percentage-point decline in the employment-to-population ratio in the informal sector tied to the inflow of refugees. Furthermore, they show that these adverse effects disproportionately affect women and lower educated workers.

The study by DelCarpio and Wagner (2015) extends the previous analyses by adopting an instrumental-variables estimation approach that controls for distance to the Syrian border, which helps purge confounding geographic factors. Their estimates suggests that the arrival of 10 refugees leads to a 6-native worker reduction in the informal sector, with a greater incidence on Turkish workers without formal education. In addition, the study also documents employment creation in the formal sector, by about 3 additional workers for every 10 refugees, mostly benefitting males with medium educational attainment (but less than a college degree). This study also provides an explanation for the lack of wage effects in Ceritoglu et al. (2017) and Tumen (2016). Namely, the arrivals of Syrian refugees affected the composition of employment in Turkey’s labor market by inducing lower productivity workers to leave the labor force. The authors argue that, when these compositional changes are accounted for, wages indeed fell in the informal sector (for low-educated and female domestic workers).<sup>7</sup> Recent studies have also found evidence of effects on costs and prices: using price data, Balkan and Tumen (2016) find that consumer prices of goods typically associated with informal labor declined following the influx of Syrian workers in those regions. Likewise, Tumen (2018) documents that the wages for young workers in new low-skill
<br>1200 Xinjiang (province) 2007<br>1000<br>800<br>600<br>400<br>200<br>Urumqi<br>0 (capital)<br>2000 20000<br>Prefecture GDP per capita (RMB, log scale)<br>Public health spending per capita<br>Highest/ Standard Coefficient<br>M ean Highest Lowest lowest deviation of variance<br>234.98 466.45 124.63 3.74 97.16 0.41<br>Source: China Statistical Yearbooks and Provincial Statistical Yearbooks<br>per capita (RMB) per capita (RMB)<br>Prefecture health spending Prefecture health spending<br>per capita (RMB)<br>per capita (RMB)<br>Prefecture health spending<br>Prefecture health spending<br>per capita (RMB) per capita (RMB)<br>Prefecture health spending Prefecture health spending<br>per capita (RMB)<br>Prefecture health spending<br><!-- End of picture text --> 32