rafmacalaba/data-use-mentions-extended huggingface dataset:
||**Share Engaged**|**I**<br>**Mean**|**ncome (USD)**<br>**Standard Deviation**|
|---|---|---|---|
|Livestock Product Income|0.187|93|178|
|Wages|0.260|1,598|1,419|
|Non-Farm Enterprises|0.624|2,926|4,109|
|Domestic Remittances|0.266|110|115|
|Foreign Remittances|0.034|267|295|
|In-Kind Remittances|0.125|47|61|
|Cash, Food, or In-kind Assistance|0.042|54|54|
|Pension|0.030|689|900|
|Rental Income (Non-Ag)|0.049|347|437|
|Rental Income (Ag)|0.039|45|88|
|Savings, Interest, Investment|0.021|277|804|
|Other|0.011|610|485|
|Observations||1,950||

_Note_ : The table displays the share of households engaged in each category of livelihood activity and the mean and standard deviation of income earned from that category. In the LSMS-ISA data, income is reported in the local currency. To allow for cross-country comparisons, we convert income values to US dollars using 2019 exchange rates.

29
Classical measurement error in imports then generates non-classical (multiplicative type) measurement error in the ratios. To deal with this problem, we replace the levels of each ratio with their logs.<sup>32</sup> Because an important component of the effect of FVA operates at the multilateral level, we also relax the set of fixed effects to use time-series variation, in addition to cross-sectional

> 31A subtle point is that import quantities are evaluated at exporter prices in the first two ratios and at importer prices in the third. We suppress this distinction in our empirical work, as we are not able to measure imports at different prices in the same data set that we use to construct the numerators.

> 32Intuitively, classical measurement error in imports is particularly influential over the value of the ratio when imports are small (equivalently, the ratio is large). Taking logs of the ratios down-weights variation among these large, poorly-measured observations.

19
The most direct measure of the quality of country PFM systems, and the one with the most comprehensive data coverage, is question 13, titled “Quality of Budgetary and Financial Management,” from the World Bank’s Country Policy and Institutional Assessments (CPIA). For brevity we re-name this variable “PFM Quality.” Higher ratings reflect a comprehensive and credible budget linked to policy priorities, effective financial management systems to ensure that the budget is implemented as intended, and timely and accurate accounting and fiscal reporting. In a robustness test, we use a second CPIA indicator (question 16), titled “Transparency, Accountability and Corruption in the Public Sector.” For brevity, we name it “Transparency.” It assesses the extent to which the executive can be held accountable for its use of funds and the results of its actions by the electorate and by the legislature and judiciary, and the extent to which public employees within the executive are required to account for the use of resources, administrative decisions, and results obtained. Weaker country systems as measured by lower scores on PFM Quality and Transparency reflect increased risks to donors of corruption scandals, diversion of funds to lower priority uses, or inability to account for how funds were spent. The Paris Declaration explicitly asserts that corruption “inhibits donors from relying on partner country systems.” We therefore expect coefficients to be positive for both PFM Quality and Transparency, in our use-of-country-systems regressions.

The CPIA indicators are produced annually by World Bank staff for aid allocation purposes, for approximately 135 developing countries. Assessments are on a 1 to 6 scale, including half-point increments.<sup>5</sup> We use a one-year lag of the CPIA ratings in our tests, to reduce the potential for capturing reverse causality. In our main sample PFM Quality ranges from 2 to 5.5, with a mean of about 3.4. The mean value of Transparency is about 3.0, with a

> 5 For example, if a country meets some
in urban areas but not rural ones, reflecting the likely informal nature of food purchases in rural areas. For these three reasons, the greater rural poverty increase is driven by the underlying welfare distribution and not modeling characteristics.

Figure 12: Fiscal policy’s impact on poverty headcount ratio by area of residence <!-- Start of picture text --> 3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Full system Direct Indirect<br>interventions interventions<br>Cambodia Phnom Penh Other Urban Rural<br>Percentage point change<br><!-- End of picture text --> Source: Authors’ calculations based on CSES 2019/20 and fiscal data. Note: Pre-fiscal poverty headcount; Cambodia=17.7%, Phnom Penh=4.1%, Other urban = 12.4%, Rural = 22.6%.

Table 4: Fiscal policy’s impact on inequality and poverty by area of residence

||Market income|Disposable<br>Income<br>Con<br>inco|sumable<br>me|Final<br>income|
|---|---|---|---|---|
|||Gini coefficient (%)|||
|Cambodia|32.4|32.2|32.2|31.4|
|Phnom Penh|34.8|34.5|34.6|34.0|
|Other Urban|30.9|30.8|30.9|30.2|
|Rural|28.0|28.0|28.0|27.5|
|||Poverty headcount ratio (%)|||
|Cambodia|17.7|17.8|19.8|
Policy Research Working Paper 7560

# **Abstract**

Economic theory predicts that a rise in police presence will reduce criminal activity. However several studies in the literature have found mixed results. This study adds to the literature by exploring the relationship between the size of the police force and crime experienced by firms. Using survey data for about 12,000 firms in a cross-section of 27 developing countries, the study finds that increasing the size of the police force is negatively associated with crime experienced by firms. The results are confirmed using a panel of firms for a subset of countries for which data are available. The study also finds that this negative relationship is stronger under certain macro-economic circumstances.

This paper is a product of the Global Indicators Group, 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://econ.worldbank.org. The author 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 Bank or the governments they represent._ Produced by the Research Support Team
sample versus out-of-sample municipalities, expanding sampling to all target areas may yield marked improvements in predictive power. The best survey design for small area estimation with big data thus remains a fruitful area for future research. Extending small area estimation models also remains an important area of research. For example, in the future, sub-area models could better incorporate sampling variability, while unit-context models might benefit from the incorporation of sub-area random effects.

Overall, these results demonstrate, in the Mexican context, that augmenting household survey data with geospatial data substantially improves the accuracy and precision of survey-based estimates of monetary poverty. In countries where census-based estimates are heavily outdated or nonexistent, we believe the results of this evaluation exercise are sufficiently encouraging to justify combining survey and satellite data to produce small area estimates of monetary poverty.

34
important than those in the quantity of schooling. In the absence of any variable capturing the quality of schooling in the earnings function one was left with a nonsignificant coefficient for the education variable.

# 5.2 Quality of Education

It has been known for sometime from detailed analysis done in Latin America that when school quality varies widely across time and space, years of schooling may be a very imperfect indicator of human capital attained (Behrman and Birdsall; Harbison and Hamushek). The general model which emerges from these discussions is that the impact of education on earnings is best captured in a two-equation system. The first equation traces the deterninants of earnings to human capital attainment, not years of schooling. The second equation attempts to estimate the level of human capital attainment as a function of both quantity and quality of schooling. Algebraically the two equations are as follows:

- _(1) In W = g(Hf E, A)_ where W is wage, H a measure of human capital attained, E is experience, and A individual ability unrelated to H;

(2) _H = h (S,_ Q, _A, B)_ where S is years of schooling (quantity), Q is quality of schooling, A is individual ability, and B is family background.

In this model the impact of schooling on earnings is spelled out as working indirectly through an enhancement of human capital attained, along with other factors. The total effect of schooling years on earnings can be determined by combining the partial derivatives from the two equations: _(3) 8 In W/8dS = a In W/aH x aff/aS_ To implement this model empirically, we need a measure of H, human capital attained. Glewwe (1996) in the work on the 1988-89 survey uses scores of 'cognitive skills' available in the data set. The survey data contained the results of three tests of 'cognitive skills' applied to a sub-sample of individuals in the _9-55_ age category. These
Samuel Hall / NRC / IDMC (2018) Escaping war: where to next? A research study on the challenges of IDP protection - in Afghanistan. Available at <u>https://www.nrc.no/globalassets/pdf/reports/escaping war---where-to-next/nrc_idp_escaping-war_where-to-next.pdf (accessed 10 December 2021).</u>

- United Nations Institute for Training and Research (UNITAR) (2011) UNOSAT Brief 2011—satellite applications for human security. Geneva, Switzerland: United Nations Institute for Training and Research. Available at <u>https://reliefweb.int/sites/reliefweb.int/files/resources/UNOSAT_Brief_Sat_App_for_Human_Sec_2011_0. pdf</u> (accessed 10 December 2021).

United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) (2020) Afghanistan: Snapshot of Population Movements - January to December 2019. Available at <u>https://reliefweb.int/sites/reliefweb.int/files/resources/afg_population_movement_snapshot_20200115.p df</u> (accessed 10 December 2021). United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) (2021) Afghanistan: Snapshot of Population Movements - January to December 2020. Available at <u>https://reliefweb.int/report/afghanistan/afghanistan-snapshot-population-movements-january-december2020-23-jan-2021 (accessed 10 December 2021).</u>

- Wang S, So E and Smith P (2015) Detecting tents to estimate the displaced populations for post-disaster relief using high resolution satellite imagery. _International Journal of Applied Earth Observation and Geoinformation_ , _36_ , 87-93.  https://doi.org/10.1016/j.jag.2014.11.013.

- Witmer FD (2015) Remote sensing of violent conflict: eyes from above. _International Journal of Remote Sensing_ , _36_ (9), 2326-2352. https://doi.org/10.1080/01431161.2015.1035412.

- Witmer FD and O'Loughlin J (
<!-- Start of picture text --> Model1, NonEnergy, Fg Model1, NonEnergy, Fad<br>1<br>.5<br>0<br>-.5<br>-1<br>0 5 10<br>Model1, NonEnergy, FemeIn<br>1<br>.5<br>0<br>-.5<br>-1<br>0 5 10<br>Years<br>95% CI Cumulative dynamic multipliers<br><!-- End of picture text --> Figure A.4: Cumulative effect of a permanent one-standard deviation increase in the log of the non-energy commodity price index, based on the regression model of columns 1-3 of table 8.

<!-- Start of picture text --> Model2, Agri, Fg Model2, Agri, Fad Model2, Metals, Fg Model2, Metals, Fad<br>2 1<br>1 .50<br>0 -.5<br>-1 -1<br>0 5 10 0 5 10<br>Model2, Agri, FemeIn Model2, Metals, FemeIn<br>2 1<br>1 .50<br>0 -.5<br>-1 -1<br>0 5 10 0 5 10<br>Years Years<br>95% CI Cumulative dynamic multipliers 95% CI Cumulative dynamic multipliers<br><!-- End of picture text --> Figure A.5: Cumulative effect of a permanent one-standard deviation increase in the log of the agricultural commodity price index (left panel) and the log of the metals and minerals commodity price index (right panel), over the estimated factors, based on the expanded regression model in columns 4-6 of table 8.

48
tests for (S,s) bands behavior under uncertainty using data at the level of the individual household. Based on Grossman and Laroque (1990), her (S,s) model assumes that households adjust their stock of durables to a desired share of wealth. Once this level of durables to wealth stock is achieved, households allow it to depreciate until it reaches a critical lower bound level. At this point they purchase a new durable. She distinguishes between liquidity constrained and non-liquidity constrained households and focuses only on those households facing an adjustment cost. She finds strong evidence for the (S,s) type adjustment which she also expects to translate to the aggregate level. However, according to Foote et al. (2000), the (S,s) type adjustment model has very little to tell at the aggregate level, because any discrete adjustments will be smoothed out due to the presence of agent heterogeneity.

Like previous studies, Bertola et al. (2005) also attempt to derive aggregate implications from effects observed at the micro-level and indeed find that the negative effects of uncertainty on individual households translates into a negative aggregate effect. Specifically, they assess the effect of uncertainty on the frequency and size of adjustments for three different types of durable goods: vehicles, furniture, and jewelry. They show that small adjustment costs can imply wide ranges of inaction under uncertainty. Higher uncertainty widens the range of inaction and a more uncertain future leads to a lower probability of adjustment, but the adjustment is larger if it does occur. They find evidence that lump-sum adjustment costs are the predominant source of inaction in their dataset. Most of their results only hold for the vehicles, most likely because the other two categories are less subject to adjustment costs.

Other studies have instead focused on the impact of uncertainty on consumer behavior specifically during downturn periods. In a study closely related to this one, Romer (1990) looks for evidence of the uncertainty hypothesis in the 1930’s recession, using data for the
30<br>IND EMDE (median)<br>EMDE (25-75%)<br>20<br>10<br>0<br>-10<br>-20<br>20 D: Public investment growth rates<br>Public investment growth rates<br>40 IND EMDE (median)<br>30 EMDE (25-75%)<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text --> 20 E: Private investment (% GDP) 20 F: Private investment growth rates

> 39 The level of investment in India has been large, despite recent correction.

36
performance evaluation, as well as her total outreach (number of clients); this raises concern over the content of their interactions with farmers. Introducing “softer” measures of performance in their assessment is therefore of interest both in reinforcing satisfaction with and demand for the service.

# 3   Experimental Design and Data

The experiment took place in 3 of Rwanda’s 30 districts: Karongi and Rutsiro districts in the Western province and Rwamagana district in the Eastern province. The sampling frame is all registered clients of OAF for Season 2013A, comprising 228 farmer groups.<sup>14</sup> We randomly sample 180 of the 228 groups, and 8 farmers within each of those groups.<sup>15</sup> With 1.8% attrition across two survey rounds, the final sample for analysis is 1,460 farming clients, in 20 cells.

We worked with OAF and the Ministry of Agriculture to introduce and test a variety of farmer feedback tools between September 2012 and June 2013, thus covering two agricultural seasons.<sup>16</sup> We devise a randomized-controlled trial (RCT) to test two types of feedback tools: scorecards and logbooks. A third intervention cross-cuts the scorecard treatment and explores the impact of announcing scorecards to FOs to capture the pure monitoring effect of adding a feedback tool. Table 1 summarizes the study design.

> 14 While the sampling frame is the population of OAF clients in 2013A, we use the OAF client registry in the subsequent two seasons (Season 2013B and Season 2014A) to track retention of members over seasons and the addition of new members.

> 15 The average of 8 individuals per farmer group gives us sufficient power to detect fairly small effects; at 80% power and assuming intracluster correlation 0f 0.1, the minimal detectable effect size is .43 standard deviations on the outcomes of interest for as few as 12 control groups (this is the most conservative estimate of power for the experiment, as comparisons between treatment arms will be higher
as the children. First, we create a data where we include individuals who are 15 to 43 years old. We then obtain years of education and birth district information for these individuals. After that, we take the average years of education for _±_ 3 ( _±_ 5 for robustness check) of the age group for each district. We then restrict that sample to 18 to 40 years old and merge the sample to the original

# **References**

**Frankenberg, Elizabeth, Lynn A Karoly, Paul Gertler, Sulistinah Achmad, IGN Agung, Sri Harijati Hatmadji, and Paramita Sudharto** , “The 1993 Indonesian family life survey: Overview and field report,” 1995.

- **Strauss, John, Firman Witoelar, and Bondan Sikoki** , _The fifth wave of the Indonesia family life survey: overview and field report_ , Vol. 1, RAND Santa Monica, CA, 2016.

6
at least consistent with the reductions in primary-school-age child employment (under 14) that we observe in Table 2. We find highly significant reductions in primary school enrollment rates with exposure to child labor provisions. Our estimates range from 0.3–1.2 percentage point drops which correspond reductions in primary school enrollment by 0.3%–1.3% of the mean. These findings are consistent with our weak, statistically insignificant findings from Table 2 which suggests that under 14 employment increases following exposure to child labor provisions in RTAs.

Two points on these primary school results warrant further discussion. First, primary school enrollment rates are unlikely to suffer the same misreporting issues that direct enumeration of under-14 workers in our survey microdata might suffer from. Consequently, these more precisely estimated drops in primary school enrollment rates might reflect stronger employment effects than we are able to directly identify with under-14 employment data. Second, primary school enrollment results might reflect under-14 labor market effects at both the intensive and extensive margin. Our child employment rates capture changes in child labor only at the _extensive_ margin—entry and exit from employment. But there may also be intensive margin effects of child labor provisions in RTAs, in which under-14 children who are employed end up working more hours on average. Such intensive margin effects would not be reflected in changes in country-level under-14 employment rates but may be reflected in lower primary school enrollment rates as work and school together become unfeasible.

We find similar patterns with secondary school enrollment rates in Panel B of Table 3, however the income effects from trade liberalization are now statistically and economically significant in some specifications. We estimate increases in secondary school enrollment rates of up to 3% of the mean following entry into force of RTAs without child labor provisions. As above, Columns (1)–(3) report models with country-RTA fixed effects and no time fixed effects, event time fixed effects, and calendar year fixed effects, respectively. Columns (4)–(6) report analogous models using our
relative to consumption (0.03 and 6.6 percent, respectively).

- **Transfers:** The total of CTP and in-kind transfers in the survey covered 65 percent of the administrative data, where income from pensions covered 107 percent (similar to the pension contributions). The value of indirect subsidies from domestic electricity and fuel (jointly) compared to the official administrative value cannot be estimated because the survey data includes both implicit and explicit subsidies, unlike the administrative data.  The value of in-kind benefits from primary and hospital public services both represented 85 percent of the administrative value. Lastly, for in-kind benefits from public education, the survey estimates covered 99 percent of the administrative value for basic education and 79 percent for secondary education. Originally basic education benefits were overestimated suggesting that the PECS 2016/17 overestimates total students enrolled in basic education at public schools. Recipients of the basic education subsidy are therefore scaled down as discussed in the methodology section. 78

> 77 It is not clear whether the national accounts amount includes East Jerusalem or not. If it does, then it is more appropriate to compare it with consumption including East Jerusalem also, which would result in a ratio of 95 percent.

> 78 As highlighted in the methodology, one limitation of the PECS is that the education model did not allow to identify ‘current grade where the student is enrolled’. Hence the current grade was estimated as ‘highest grade achieved’ + 1 year if the student was currently attending a public school.

55
# **1 Introduction**

Informality and under-employment are pervasive and associated with stagnant productivity in low and middle income countries (Busso et al. (2012)). Informality rates have not declined significantly in most countries despite a general trend towards reducing payroll tax wedges in formal employment, and even in the presence of high economic growth (La Porta and Shleifer (2014)). One reason for persistent informality is that the formal private sector has struggled to absorb bulging cohorts of youth exiting school, a problem that is likely to persist given that the median population age is lower than labor market entry in many cases.

This paper studies the extent to which the labor market opportunities faced by young men entering the labor market shape their access to formal employment and broader employment trajectories. It uses data from 14 rounds of the Arab Republic of Egypt’s labor force survey that record employment outcomes for individuals in successive graduation cohorts. The dataset contains information on multiple measures and proxies for formal vs. informal employment and under-employment, such as working without social security coverage, being employed full-time, working without a contract, working for a wage, working for the public sector, working in agriculture, and the size of the employing establishment (number of employees). We exploit variation across cohorts, regions, and schooling levels in the labor market conditions faced by an individual upon graduating to achieve identification, following Schwandt and von Wachter (2019).

Middle-Eastern and North African countries (“MENA region”), and Egypt in particular, exemplify the “youth bulge” phenomenon.<sup>1</sup> Public employment historically absorbed labor market slack, creating distortions in the supply of skills by encouraging credentialism and overeducation.<sup>2</sup> Youth labor markets are thus characterized by high levels of informality, unemployment, or underemployment while queuing for public jobs (Assaad and Krafft 2015). In this context, the opportunities of the successive cohorts entering the labor market can vary substantially depending on