rafmacalaba/data-use-mentions-extended huggingface dataset:
these theoretical predictions, we estimate the effect of the two experiments on trust. Trust is widely measured by asking survey respondents whether they trust others.<sup>9</sup> Following the survey literature on measuring trust in economics (Fehr, 2009; Glaeser, Laibson, Scheinkman, & Soutter, 2000), we directly asked parent and teachers about their trust in each other. Specifically, we asked parents, "do you think that most teachers can be trusted?" and asked teachers, "do you

> 9The most frequently used measure of trust is based on the American General Social Survey (GSS) and the World Values Survey (WVS), which asks, “Generally speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people?” Miller and Mitamura (2003) demonstrates that this wording measures both beliefs about the trustworthiness of others as well as preferences towards taking social risks. We follow the recommendation by Miller and Mitamura (2003) and remove the risk preference aspect of the question.

19
divided into three distinct subsets: (1) phase-3 failers who did not enter the program any time later; (2) phase-3 failers who entered the program later in phase 4 (crossover schools); and (3) phase-3 passers who entered the program at that time (as noted before, the treatment take-up rate by passers was virtually 100%, and there have been no dropouts to date). While the pass rate cutoff rule strictly determines program eligibility status for phase-3 SLQAT takers, the processes and factors behind the phase-4 application decision of phase-3 failers are unobservable to us and likely to be nonrandom. Given this, the eventual participation status of this group of schools is also considered to be driven partly by selection on unobservables.

In our data, we find that the probability of program participation as a function of the pass rate in the phase-3 SLQAT, _E_  _di_ | _zi_   Pr  _di_  |1 _zi_  , consists of a positive but smaller jump than one at the cutoff. The probability of program participation will still be discontinuous in the assignment variable _z_ at the cutoff. This feature implies that the phase-3 SLQAT taker data are more appropriately fitted with a fuzzy RD design. Under this design, apart from the identifying assumptions for the sharp design, if we assume that treatment<sup>_d_</sup> _i_<sup>is nondecreasing in the</sup> assignment variable _z_ at the cutoff (local monotonicity) and that potential outcomes are independent of treatment conditional on the assignment variable _z_ at the cutoff (local conditional independence), then the average causal effect of the treatment is identified by

where the denominator of (6) is nonzero given the discontinuity in the probability of treatment at the cutoff (Hahn et al. 2001; Todd 2007).

This treatment parameter represents the RD analog to the local average treatment effect (LATE) (Imbens and Angrist 1994; Angrist et al. 1996). It can be interpreted as the average 19
2007.

At the same time, bank capital buffers have continued to strengthen in the last decade (Figure 3, E and F). Non‐performing loans increased since the crisis, remained stable, and only improved in 2017. However, poor performers seem to face debt overhang issues (Figure 4, A and B). Median non‐performing loans (NPLs) as a fraction of total gross loans increased to 5.5% during the crisis and only came down in 2017 to 4.2%. Countries with larger NPL problems (75<sup>th</sup> percentile) have seen a slight improvement since the crisis, but remain relatively high at 10%, suggesting debt overhang problems. Notwithstanding important caveats regarding NPL data integrity and cross‐ country comparability, median regulatory capital to risk‐weighted assets has steadily increased 19
# **For Online Publication: Appendix**

## **A Comparisons with Contemporaneous Work**

There are at least two possible reasons why the conclusion of our paper differs from that of Jaravel (2018), a contemporaneous work which concludes that higher SNAP take-up rates among eligible households reduce inflation rates. At a high level, we differ in (1) data series and time period; (2) explanatory variation; and (3) methodology used to address endogeneity. Whereas we examine both Nielsen Retail Scanner and Nielsen Consumer Panel prices over 2006-2015, Jaravel (2018) examine Nielsen Consumer Panel prices over 2004-2008. Whereas we focus on the intensive-margin variation in SNAP benefits using an IV approach, Jaravel (2018) focuses on the SNAP take-up rates by regressing 2004-2008 prices on 2001-2007 SNAP take-up rates among eligible households using a long-difference approach.

One possible reason for the discrepancy is that both conclusions are valid and limited only by external validity: that is, mechanisms specific to each time period and/or each explanatory variation (intensive versus extensive) explain the difference.

Another possible reason may be biases due to endogeneity or measurement error in using take-up rates to proxy for benefits. Jaravel (2018) writes that take-up rate changes were mainly affected by state-level policy changes and that “these policies had a large impact on the take-up rate for food stamps and their implementation across states was not correlated with economic fundamentals such as unemployment,” mentioning Ganong and Liebman (2018). However, the results of Ganong and Liebman (2018) say that not all of the take-up rate changes during this period stemmed from state policy changes, and that much of the variation could have been endogenous. Indeed, Ganong and Liebman’s (2018) estimates that in some years between 2001 and 2007, 20% to 30% of the take-up variation could be attributed to changes in unemployment, suggesting that simply controlling for a long difference in unemployment rates between years 2001 and 2007 may not be sufficient to partial out unobservable economic
Policy Research Working Paper 10786

# **Abstract**

The Covid-19 pandemic, followed by financial tightening due inflationary pressure, has raised public debt in developing economies as governments grapple with public health investments to curb the pandemic and collapse in revenues due to slower economic activity. The rise in debt may further disrupt the formal private sector in developing economies. Using two to three waves of panel firm-level data across developing economies, this study finds that higher public debt is correlated with low investment by formal private sector firms. The finding is largely driven by small and medium-size enterprises, domestic firms, and non-exporters—raising concerns about the distributional impacts. Potential channels are uncovered. High levels of debt reduced the accessibility of finance for private sector firms, limiting investment. Furthermore, a regulatory channel is observed. As public debt rises, firms spend more time with regulatory and tax officials, which is possibly indicative of higher efforts of governments to raise revenues. This channel is stronger for small and medium-size enterprises.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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 aislam@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
- Task ratings in the WH database were consolidated into three categories. High (= 1) corresponds to “extremely effective”, “highly effective” and “very effective” ratings. Medium (= 2/3) to “satisfactory”, “moderately satisfactory”, “effective” and “moderately effective”. Low (= 1/3) is for the rest.

- Google Scholar, Research Papers in Economics, and the Social Science Research Network were used to assess the technical skill of TTLs. Those who showed up in at least two of these platforms were deemed Top (= 1). Those in one only were considered Good (= 2/3), and the rest Unknown (= 1/3).

- The numerous units in charge of delivering knowledge products were consolidated into four groupings: Global Practices – which also includes central Cross-Cutting Solutions teams dealing with topics such as gender, or climate change – DEC, regional Chief Economist offices, and the rest.

The consolidated database of knowledge products assembled through this process is available on request.

# 4. Composition and summary statistics

While the consolidated database is structured around P-codes and Z-codes, this may not be the most informative breakdown to assess the quality of the analytical products in it. Admittedly, knowledgerelated activities are monitored by World Bank based on ASA budget codes, which are a subset of P-codes. Quite often their oversight involves counting how much was spent on them, and how many activities were completed over a specific period. This approach allows disciplining the use of budget resources and ensuring the delivery of committed products.

As for the quality of the analytical work, the conventional wisdom is that it is inversely correlated to the number of tasks management needs to oversee. A new standard monitoring report for ASA products introduced at the beginning of the period covered by this study was used to drive a substantial “cleanup” of analytical products (World Bank 2019). Since then, considerable managerial effort has gone into ensuring that the number
Though a percentage reduction will have relevance to all respondents, people will have different meanings of 1-percent reductions.<sup>11</sup> We ideally would like to customize these meanings to each household. Our data indeed include information on average monthly fuelwood consumption and across our sample we find that the average fuelwood consumption is reported to be 298 kilograms per month. Only three-quarters of our sample respondents reported their fuelwood consumption, however, and we are concerned that those who did not provide information differ systematically from those who provided information. We also worry that consumption may differ from fuelwood collections, which is our interest in this paper. For example, some households may buy fuelwood, which would cause collections and consumption to differ.

To create a standard meaning of a 1-percent reduction in fuelwood collections, we utilize a nationally representative estimate from the literature. We argue that this approach is appropriate, because our communities are randomly chosen from a nationally representative random sample, with half CFs and half non-CFs, half of which are in the hills and half in the _Terai_ , which are the two most important and relevant stratification dimensions. Households within communities are also chosen randomly, so the moments of the sample should be representative of the population. Using these features of our sampling frame, we assume that a 1-percent fuelwood collection reduction in our sample on average has the same meaning as a 1-percent reduction in average, national, rural household fuelwood collections.

We utilize the average fuelwood collection estimate of 2.40 tons per household per year from Somanathan and Bluffstone (2015), which is derived from the nationally-representative 2010/2011 Nepal Living Standards Survey (NLSS). This estimate is slightly lower than the 2.54 tons per household per year from Nepal et al. (2010), which used the 2003/2004 NLSS and is about 8% below

> 11 This is not unique to our analysis, but is inherent in all carbon forestry PES programs implemented in primarily subsistence agriculture settings.

18
crisis of 2009-10 (China pursued a counter-cyclical fiscal policy, and the fiscal expansion further stimulated its demand for Africa’s commodities). In fact, per capita income and GDP-trade shares increased during this period. Similarly, GDP shares of mineral and oil rents increased over and above the 2002–2007 shares. The patterns of income, trade, and resource rent indicators during 2008–2013 are certainly interesting in their own way. But, as we previously noted, to avoid the contaminating effects of the financial crisis our analysis is confined over 1997–2007 periods.

_B. China–Africa trade relations in perspective_ In order to understand the association between China’s accession and its impact on African economies, we now turn to the trade relations between China and Africa. We present our discussions in two ways. First, we use data on metals and mineral production to show the average patterns of metals and minerals production across African countries during the 1996–2013 period. Similarly, we present the patterns of metals and minerals imports by China during the same period. We have two aims: (i) to show the general patterns of metals and minerals production from Africa and imports by China, and (ii) to infer the behavior of associations from these patterns. Second, we bring to bear the analysis of average trade flows on several commodities between China and Africa. For a meaningful comparison, we break up this analysis into four successive periods: 1990– 1995, 1996–2001, 2002–2007, and 2008–2014. This analysis shows the magnitude of average trade flows between the two trading partners. Our end goal is to empirically document the 6
# **Estimating Quarterly Poverty Rates Using Labor Force Surveys: A Primer**<sup>**1**</sup>

_Mohamed Douidich, Abdeljaouad Ezzrari_<sup>_2_</sup> _, Roy Van der Weide and Paolo Verme_<sup>_3_</sup>

**JEL:** D6, H53, I3, R13

**Keywords:** Poverty, cross-survey imputations, Morocco, forecasts, simulations

**Sector board:** Poverty (POV)

> 1 The work was undertaken in the framework of the cooperation agreement between the World Bank and the High Commission for the Plan of Morocco. The authors are grateful to Peter Lanjouw, Tomoki Fuji , Nobuo Yoshida, Roy Katayama, Gladys Lopez and participants to several workshops in Washington and Morocco where the paper was presented for useful comments.

> 2 National Observatory of Living Standards, High Commission for the Plan, Morocco

> 3 The World Bank; The study has received financial support from the World Bank’s Knowledge for Change Program for which the authors are grateful.
segments within a mauza had the same probability of being selected. The field team listed all households within the boundaries of the selected segment, subject to field protocols where necessary.

With the listing data available, the third stage was to select 13 households in each of the listed segments, with every household having the same probability of being selected. The selected households were to be interviewed.

# _Selection probability for hosts_

> 34 Since the selection was done using probability proportional to the population size of the mauza with replacement, bigger mauzas had higher probability of being selected more than once.

16
Policy Research Working Paper 5731

# **Abstract**

Household surveys in Guatemala, India, Indonesia, Kenya, Pakistan, and Sri Lanka were analyzed using a two-stage Heckman model to examine the factors influencing the decision to use liquefied petroleum gas (stage 1) and, among users, the quantity consumed per person (stage 2). In the first stage, liquefied petroleum gas selection in all six countries increased with household expenditure and the highest level of education attained by female and male household members. Electricity connection increased, and engagement in agriculture and increasing household size decreased, liquefied petroleum gas selection in five countries; urban residence increased selection in four countries; and rising firewood and kerosene prices increased selection in three countries each. In the second stage, the quantity of liquefied petroleum gas consumed increased with rising household expenditure and decreasing price of liquefied petroleum gas in every country. Urban residence increased and engagement in agriculture decreased liquefied petroleum gas consumption. Surveys in Albania, Brazil, Mexico, and Peru, which did not report quantities, were also examined by calculating quantities using national average prices. Although fuel prices faced by individual households could not be tested, the findings largely supported those from the first six countries. Once the education levels of men and women were separately accounted for, the gender of the head of household was not statistically significant in most cases across the ten countries. Where it was significant (five equations), the sign of the coefficient was positive for men, possibly suggesting that female-headed households are burdened with unmeasured economic disadvantages, making less cash available for purchasing liquefied petroleum gas.

This paper is a product of the Oil, Gas, and Mining Unit, Sustainable Energy Department. 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://econ.worldbank.org. The author may be contacted at mkojima@worldbank.
implications, since it highlights a policy lever through which policy makers can focus on increasing employment in developing countries.

The rest of the paper is organized as follows. Section II describes the data we use. Section III explains the empirical methodology. Section IV presents the empirical results. Section V concludes.

# **II. Data and summary statistics**

# **_A. Firm-level data_**

We use two firm-level data sets to analyze the link between access to finance and employment. First, we use World Bank Enterprise Survey (ES) data to analyze how firms’ access to finance affects firm level employment growth. The ES uses a common questionnaire and a uniform sampling methodology to produce survey data on manufacturing and service sector firms that is comparable across countries.<sup>9</sup> Stratification of the sample is on three criteria – sector, firm size (employees), and geographic location. The stratified random sampling methodology is used to generate a sample large enough to be representative of the nonagricultural formal private economy,<sup>10</sup> as well as key sectors and firm size classifications. The ES data set provides firm-level information on employment levels, employment growth rate, access to a loan by banks, as well as other firm characteristics. We restrict our analysis to countries with two or more surveys over the course of the period 2002-2014, so that we can control for country fixed effects. We further exclude firms with fewer than five permanent

> 9 Most firms in the Enterprise Surveys are single establishment firms (79%). All our results hold if we restrict our analysis to single establishment firms.

> 10 The Enterprise Surveys do not include firms with 100% state ownership. We control for government ownership in all our regressions.

7
, and will be discussed later. For the moment we can note that compared to the manufacturing sector, average earnings in the public sector were generally at a higher level than even those prevailing in the large firms. In 1992 mean earnings in the public sector were 58 per cent higher than the average in large firms and more than four times higher than the average earnings of workers in micro enterprises. It might be objected that the survey data for private manufacturing firms in 1992 had been collected too close to the period of the public sector wage hike, and the ripple effect of the latter might be more truly captured in the data for 'wave 2' of 1993. But even this comparison (which does not allow for price increase over the year) shows that mean public wages were 28 per cent higher than the average earnings in the large firms.

_15_
Policy Research Working Paper 10972

# **Abstract**

Against the backdrop of aging, Indonesia has started to address important policy challenges, including by gradually raising the retirement age. However, the increase in the retirement age and the prospect of longer employment of older persons raises concerns about its potential impact on the employment of younger persons. This paper is the first to analyze the relationship between the employment rate of older persons and the labor market outcomes of younger persons in Indonesia. Using data from the Indonesia Labour Force Survey (Sakernas) for 2016 to 2023, the analysis explores the relationship between the labor market outcomes – that is, the employment rates, unemployment rates, hours worked, and income – of younger persons and older persons. The findings show that overall, an increase in the employment rate of older persons is significantly associated with an increase in the employment rate of youth and prime-aged persons. The positive relationship is robust and is found across most of the specifications tested, that is, across genders, education levels, sectors, and within the formal sector. Encouragingly, there is no evidence of a significant negative relationship between the employment of older persons and younger persons. When there is a significant negative relationship between the employment of older persons and other employment outcomes of younger persons, the magnitude is small. These findings support the notion that raising the retirement age can address some of the challenges faced by an aging society. Finally, lifelong learning, upskilling, and reskilling are important to facilitate longer working lives by ensuring that older workers remain employable in a changing economy.

This paper is a product of the Social Protection and Labor 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 author may be contacted at aabdurrahman@worldbank.org.

_The Policy Research Working