rafmacalaba/data-use-mentions-extended huggingface dataset:
18 households, households in the lowest decile group are more likely to be larger in size and more likely to come from rural areas. The finding about more household members might be expected, but the finding about rural residence might not be. However, in Guatemala households receiving internal remittances and living in rural areas are much poorer than their counterparts in urban areas. In the excluding remittances situation, rural households receiving internal remittances average 5168 quetzals/person/year, while their urban counterparts average 11,621 quetzals/person/year..

Table 7, which shows the logit results for households receiving international remittances, is also disappointing.<sup>19</sup> Again, only two of the variables – number of females in household over age 15 and number of household members over age 15 with secondary education – are statistically different between columns (1) and (2). The results for the education variable suggest that when compared to other international-remittance receiving households, households in the lowest decile group are less likely to have secondary education. However, this finding is not a very strong one because none of the other education variables in column (1) are negative and statistically significant.

# 5.  Conclusion

This paper has used a large, nationally representative household survey (N=7276 households) and predicted income functions to analyze the impact of internal remittances (from Guatemala) and international remittances (from the United States) on poverty in Guatemala. Four key findings emerge.

First, both internal and international remittances represent important components of household income in Guatemala. While less than 25 percent of all households in
0.172|58|
||[0.900]|(0.293)||
|_C. Provider Pre-Test Knowledge_||||
|Knows Malian Malaria Policy|0.519|-0.045|160|
||[0.503]|(0.095)||
|Correct Meds for Simple Malaria Treatment|0.568|-0.038|160|
||[0.498]|(0.111)||
|RDT Detection Rate w. Low Parasite Load 90% or Higher|0.111<br>[0.316]|0.082<br>(0.052)|160|
|RDT Detection Rate w. High Parasite Load 90% or Higher|0.333<br>[0.474]|0.195<br>(0.128)|160|
|Correct Symptoms Severe Malaria|0.506|-0.024|160|
||[0.503]|(0.108)||
|Correct Time Interval to Read RDT|0.136|0.026|160|
||[0.345]|(0.092)||
|Number of Correct Answers|2.173|0.197|160|
||[1.473]|(0.384)||

_Notes_ : Panel A uses clinics census information (one observation missing). Panels B and C use the health worker pre-training survey. Robust standard errors clustered at the clinic-pair level in parentheses, standard deviation in brackets. All regressions include strata fixed effects. *, **, and *** indicate significance at the 10, 5, and 1 percent levels respectively.

27
performing countries, the ratio of test scores relative to the leader is 300/625=0.48 in the worst‐ performing countries. This implies that a year of school in the worst‐performing countries is “worth” only about half as much as a year of school in the best‐performing countries.

- A fourth approach is suggested in Schoellman (2012), who relates estimated returns to education of immigrants in the United States to test scores in their countries of origin, and finds a strong positive relationship.  He then introduces this relationship into a model in which agents take education quality into account when choosing their investment in years of school.  His estimates suggest that the return to a year of school needs to be doubled relative to standard benchmarks to take into account the fact that in countries where school attainment is low, this reveals an endogenous response to low quality.  While this argument does not provide a direct mapping from individual country test scores, it suggests a factor‐of‐two quality adjustment which is similar to World Bank (2017) and Filmer, Rogers, Angrist, and Sabarwal (2018).

- A fifth approach can be anchored in the small literature that has jointly estimated returns to years of school and returns to cognitive ability, where cognitive ability is proxied using test scores.  For example, Hanushek and Yang (2009) estimate Mincerian regressions of log earnings on education and test scores and find a return per year of school of 10 percent, and a return per standard deviation of test scores of 10 to 20 percent (as cited in Hanushek, Ruhose and Woessmann (2015)) .  The ratio of these two estimated returns implies that one standard deviation of test scores is equivalent to 1 to 2 years of school in terms of its effects on earnings. Under the somewhat arbitrary assumption that a standard deviation of test scores in the Hanushek and Yang (2009) study is equivalent to a PISA standard deviation of 100, this would imply that the 300 PISA point gap in
oi:1( Y RLSE&ARCH- WORKING P aFR 21 66

# Summary fi'ndilngs

r:siPcemt _Albcrro_ Fujimlori created the Peruvian Social FLlind (FONCODES) in I 99 1 with the stated objectives of gencratilcnioptoviient helping to alleviate poverty, and i.mrprovmgo access to social services.

ScLhadY USes province-level data on monthly cxpenleturcs, socioeconomic indicators, and electoral outcomes to analvze political influences on the timing an **g' ographic** distribut;on of FONCODES expenditures between 1991 and 1995.

***** FONCODES projects were directed at poor provinces, as well as provinces in which the mar-ginal political impact of expenditures was likely to be greatest.

The results are robust to many specifications and controls. The Peruvian data thus support predictions made in the literature on political business cycles as well as the literature on political influences on the allocation of discretionary funds.

He finds that: CONCODDES expenditures mcreased significantly .-efoTe electiotns.

- This -pper - a product of the Poverty Division, Poverty Reduction and Economic Management Network is part of a laracr I **_rfo;r_** im the netwvork to understand the functioning and impact of social funds. Copies of the paper are available free **fion'** the World Bank, 1818 H Street N-W, Washington, DC 20433. Please contact Norbert Schady, room MC4-629, t eph oru e 202-45 8-8247, fax 202-222-3283, Internet address nschady(@worldbank.org. Policy Research Working Papers **ale** a,)0 potsed on the Web at http:/","ww.worldbank.org/html/dec/Publications/Workpapers/home.html. August 1999. (30 p i"'s?

> _ihc Se ollc Resea,ch_ **_kVorkXkng_** _Paper Series disserninates the findings of work in progress to encourage the exchange of ideas_ **_aboot_** _oIeuesopmZent issiues._ **_Ani_** _objective of the series is to get the findings out quickly, even
Chudik et al., 2017). But th</mark> e literature does not rule out the possibility of country-specific debt thresholds—including ones in debt intolerant countries that result in “extreme duress” if crossed (Reinhart et al., 2003 and Reinhart and Rogoff, 2009).

More directly, our paper is related to the evidence of the impact of government debt on firms’ investment, which is much more scant. <mark>Huang et al. (2020) is closest to our approach. They use Chinese firm-level data to estimate the crowding-out effect of rising public debt in China. They find that local public debt in China crowded out private firms' investment by tightening their funding constraints while leaving state-owned firms’ investment unaffected. Moreover, the impact is stronger for small firms than larger firms, a finding</mark> 5
impacts of FPE (see the case study discussions in Munene 2016). It is against this backdrop that we turn to the focus of this new analysis, namely whether the effects of this rapid scale-up can still be felt in the classroom “today,” years after the initial reform period.

# 3. Data, empirical approach, and trends in teacher recruitment

## 3.1 Data

The data used here are from nationally representative surveys of public schools in six Sub-Saharan African countries collected as a part of the Service Delivery Indicators (SDI) project. This project (launched in 2010 as a collaboration between the World Bank and the African Economic Research Consortium, and later joined by the William and Flora Hewlett Foundation and the African Development Bank) collected detailed data on teachers, including recruitment date and subject content knowledge, and also administered an assessment of basic literacy and numeracy to grade 4 students. Details on this effort are discussed in Gatti and others (2021). In order to be able to systematically match teachers to student test scores, we focus here only on grade 4 teachers.

These data were collected between 2012 and 2016 in Kenya, Madagascar, Mozambique, Tanzania, Togo, and Uganda (Table 1). The number of students assessed in each country ranges from 1,744 (Mozambique) to 4,236 (Tanzania), and the number of teachers assessed ranges from 310 (Mozambique) to 1,327 (Tanzania). Across these countries, FPE was launched as early as 1997 (Uganda) and as late as 2008 (Togo). The gap in the number of years between the reform and data collection ranges from 5 (Togo) to 16 (Uganda), with a mean of 11.2 and a median of 11.5.<sup>6</sup> In each country, representative surveys of between 198 (Togo) and 472 (Madagascar) schools were implemented using a multistage cluster-sampling design. Primary schools with at least one fourth-grade class formed
with level of education and with occupation title; (4) formal in-house training is found to be associated with higher wages, while there does not seem to be any instant effect on productlvity; (5) an additional wage and productivity premium exists if enterprises are engaged in exporting activities; (6) ethnic origin of an employee is not important in the determination of wages. However, Europeans are more productive than other ethnic groups; (7) union members earn less than non-union members despite being found to be more producive; (8) larger firms are more productive and pay higher wages than smaller firms; (9) employees employed in the metal or textile sectors are paid higher wages than colleagues in the food sector.

This paper gives a broad picture of developments in the labor market and it is organized as fo'lows. Section two provides a brief presentation and discussion of economic developments in Zimbabwe. This is followed by a presentation of wage theories and empirical results. Sections three and four contain the estimated wage model and give emppircal evidence of whether the labor market in Zimbabwe is characterized by a macrowage curve. I'he final section provides conclusions.

# 2. TLrends

This section contains a brief overview of Zimbabwean labor markets over the past three decades. We apply time series data from 1964 to 1994 to show trends in output, wages, productivity, and employment.

After the declaration of Tndependence by Rhodesia in 1965 most nations imposed trade saactions against the regime. This caused a reduced flow of capital goods and industrial inmcuts foro, abroad. In 1980 a new government led by Robert Mugabe introduced iimport quotas and continued controlling prices introduced by the former regime. In the first years after physical capital investment boomed but without investment in long-term commitments to employees, meaning that the production was more capital intensive than labor intensive (Failon and Lucas; 1993).

2
2). Endogenous variables are instrumented with time-lagged or space-lagged exogenous determinants. In the wage equation, ∆lnL is instrumented with lagged demographic determinants of labor force growth; the exclusion of these variables from the wage equation identifies it. The labor supply equation is identified, more tenuously, via the exclusion of the market access variables and the government transfer variables<sup>6</sup> , and by the use of initial period wage rather than wage change. Table 6 lists the instruments for each endogenous variable.

Finally, a key issue in estimation is the likelihood of spatial autocorrelation of unobserved variables. Failure to account for this autocorrelation could lead to inaccurate estimates of standard errors. To address this issue, we use the GMM technique of Conley (1999) (as did Araujo, de Janvry and Sadoulet 2004 and Baicker 2005). In contrast to the more standard spatial econometric approach using a weight matrix, Conley’s nonparametric technique requires only that users locate data points in a real or virtual space. Users specify a cutoff distance beyond which spatial autocorrelation is thought to be unimportant. For our analysis we use the latitude and longitude of the MCA centroid to locate the data points. This technique is attractive because it is computationally feasible for large numbers of observations; allows for the use of

> 5 Readers are again referred to the color pdf version of this paper.  The figure shows that the northeastern and northern observations are clustered in the lower left of the diagram.

> 6 We assume that the transfers go mostly to old-age pensioners and thus do not affect labor supply
an expert group on guarantee schemes to identify and disseminate best practices (European Commission, 2006).  This literature converges on broad principles, including the need to build attractiveness while ensuring additionality through well designed eligibility criteria, proper coverage ratio and fees, sound risk management, and efficient operational procedures.

# **3 The MENA PCG Survey and the Review Methodology**

## _3.1 Basic Description of the MENA Survey_

**This paper is based on a survey of MENA PCGs conducted in the first quarter of 2010** . The questionnaire prepared for the survey covered the institutional set up, the main operational rules, and the main performance indicators. The questionnaire was partly based on Beck and al. (2008) to ensure comparability with other guarantee schemes around the world. The survey was initiated in February 2010 and completed in April 2010.  The authors met with several managers of surveyed schemes to present the objectives of the survey and discuss technical issues.  There was also a follow-up effort to ensure the timely completion of the survey and check the accuracy of the data.

**The survey covered the largest credit guarantee schemes in 10 MENA countries** . As shown in Table 1, the oldest guarantee fund in MENA was established in Morocco in 1949, while the youngest one in Syria starts operation in 2010.  The average equity is US$50 million, ranging from US$10 million in Syria, to US$75 million in Morocco. Half of these guarantee schemes are majority state-owned (Morocco, Tunisia, Jordan, Syria, Saudi Arabia, UAE), while the others are majority owned by banks (Lebanon, Egypt, Iraq) or donors (Palestine).

6
In this study, survey data are not only used to estimate the incidence of spending and revenue raising activities but are also the basis on which poverty and inequality amongst children are estimated. Table 5 shows the results of multidimensional child poverty, monetary poverty and inequality indices. On average, children are deprived in about 3.5 out of 10 measures of multidimensional deprivations included in this study. The indicator is similar for both boys and girls. On average, urban children are deprived in 1.5 measures, compared to about 4 for rural children. Over half of children are multidimensionally poor with no boy/girl differences. This incidence reaches as high as 66% and 10%, respectively, for rural and urban children. Over a third of children are monetarily poor with girls slightly poorer than boys. Though inequality is generally low, within-group inequalities outweigh between-group inequalities. Monetary child poverty and inequality profiles show substantial rural-urban differences.

Analyzing multidimensional and monetary child poverty separately hides the significant differences between these measures, while the intersection could provide refined information. Across the whole sample, 24% of children are poor, both multidimensionally and monetarily. A larger share of children (29%) is multidimensionally poor despite not living below the monetary poverty line. In fact, many of those children live in the richest quintiles (44% of children in the fourth and 31% of children in the fifth quintile are found to be multidimensionally poor). Likewise, 9% of children are found to be monetarily poor, but not considered to be multidimensionally deprived.

13
displacement is not considered to be protracted because disaggregated data on South Sudan is only available from the country’s independence in 2011.

Addressing some of these methodological issues produces an estimate of average duration that is lower than those published by UNHCR. A recent study by Devictor and Do, “How many years do refugees spend in exile” (2016), estimates the average duration of all refugee situations in 2014 (excluding Palestinian refugees) at 10.7 years, and the median duration at about 3 years, i.e. half of people who are currently refugees have spent 4 years or more in exile.<sup>57</sup> Since 1991, the average duration of all refugee situations has fluctuated between about 8 years in 1991 and a peak of 15 years in 2006. The study also finds that among current refugees, 5.3 million may have been displaced for five years or more (i.e. in protracted situations). The number of refugees in protracted displacement has been remarkably stable since 1991, at 5 million to 7 million throughout most of the period. At the end of 2014, the average duration of protracted refugee situations is estimated at 18 years.<sup>58</sup>

**Table 3: Alternative Estimate of Protracted Refugee Situations end-2014**

||**All**<br>**Refugee**<br>**Situations**|**Protracted**<br>**Situations**|**Refugee**|
|---|---|---|---|
|**Average duration**|10.7 years|18.3 years||
|**Number of refugees in protracted refugee**<br>**situations**||5.3 million||

_Source_ : Devictor and Do (2016).

Nevertheless, estimates of average duration, however flawed, do provide insights into the extent
Recent research that has used the D&B data includes Harrison, Love, and McMillian’s (2004) analysis of capital ‡ows, Black and Strahan’s (2002) study of entrepreneurial activity in the United States, Acemoglu, Johnson, and Mitton’s (2009) cross-country study of concentration and vertical integration, and Alfaro and Charlton’s (2009) analysis of vertical and horizontal activities of multinationals.

We impose a number of requirements in cleaning the data. First, we drop records that lack primary industry classi...cation and starting year information. Second, we restrict the data to establishments that report sales information for all years. Moreover, we include only establishments with positive sales and employment in 2005. Requiring positive employment helps to exclude establishments registered exclusively for tax purposes as well as self-employment businesses. These types of establishments constitute a signi...cant share of businesses in countries such as the United States. Finally, we drop agricultural and mining industries as well as countries with fewer than 100 observations. These criteria result in a ...nal sample of approximately 12 million establishments in 53 countries.

We use four categories of information for each establishment: (i) industry information including the 4-digit SIC code of the primary industry in which each establishment operates and, for most countries, the SIC codes of as many as ...ve secondary industries, listed in descending order of importance; (ii) ownership information including the establishments’ domestic and global parents, status (joint-venture, corporation, partnership), and position in the hierarchy (branch, division, headquarters); (iii) location information; (iv) operational information including sales, employment, and export status.

Establishment performance is measured in this paper by the annual percentage change of sales. An establishment is considered foreign owned if its parent ...rm is based in a di¤erent country. There are 43,513 foreign owned subsidiaries in the ...nal sample.
example), suggesting that these types of short-run poverty/food insecurity episodes suffer from the measurement errors and misspecification problems noted above. Although the presence of large marginal effects of economic growth and inflation rates on self-assessed food insecurity are encouraging, we must interpret trends in the latter quite cautiously.

# **<<A>>IV. MEASURING AND INTERPRETING KEY TRENDS IN THE GALLUP DATA**

In the introduction to this paper, we noted our basic result at the global level: 132 million fewer people were food insecure in 2008 relative to 2005–06. In this section, we examine Gallup trends in more detail by observing regional variations within this global trend, considering important exclusions from the sample, engaging in an important sensitivity analysis, and exploring the factors that might explain the surprisingly positive global trend.

In table 8, we report simple averages of the GWP food insecurity indicator by various regions of the developing world for 2005–06, 2008, and 2009. These years quite neatly correspond to a precrisis survey round, a food crisis round, and an early financial crisis round. Starting at the top of table 8, we observe what superficially explains the very positive global trend: in the eight most populous developing countries (excluding China), food insecurity decreased by 4.7 percentage points between 2005–06 and 2008. However, in many other regions of the world, food insecurity increased, including coastal West Africa (but not the Sahel), Eastern and Southern Africa, and Latin America. In other developing regions, there was either no change or some improvement. We also note that the deterioration of food insecurity in much of Africa and Latin America is consistent with a number of simulation studies (see Headey and Fan 2010 for a review).

27
)|-5 (0.005)||||
||Variance|of log earnings|100 (0.388)|100 (0.302)|100 (-0.086)||||

_Sources:_ Authors’ calculations based on data from Social Security Records: Rela ̧c ̃ao Anual de Informa ̧c ̃oes Sociais (RAIS) in Brazil, Caja Costarricense de Seguro Social (CCSS) in Costa Rica, and Instituto Ecuatoriano de Seguridad Social (IESS) in Ecuador. _Note_ : The table reports the results of the variance decomposition. Cells contain the share of the total variance explained by each decomposition element. The total variance explained by each decomposition element is shown in parentheses. AKM = Abowd, Kramarz, and Margolis (1999).

64
impact of the crisis on globally engaged firms. Although this finding is intuitive, given that most countries were impacted by the pandemic, it should, nonetheless, be taken with a grain of salt since our results are based on a small sample including only the trading firms that report their top destination and source countries. Yet, we do find significant differences in the recovery of firms exporting to high–income countries relative to those exporting to non-high–income countries (Figure 9, panel a). In contrast to the recent evidence for French exporters by (Lafrogne-Roussier et al., 2021), firms in the BPS data trading with China were not disproportionately affected. The initial impact and recovery path of such firms are comparable with those trading with other non-high income countries. The severity of the initial lockdown in response to the pandemic in destination and source countries also did not affect the magnitude of globally exposed firms’ sales decline in the initial and latest survey rounds (Figure 9, panels c and d).

Figure 9: Impact of trading partner’s characteristics on change in sales <!-- Start of picture text --> (a) By top export destination’s income (b) By top import source’s income<br>0 0<br>-20 -20<br>- 27 -29<br>-40 -34 -40 - 34 -36 -34<br>-44<br>-51 -51<br>-60 -51 -50<br>-60 -54<br>High-income Non-high income China High-income Non-high income China<br>Top Export Destination Top Import Source<br>First round Latest round First round Latest round<br>P-value of test that exporters with top export destination a high income country P-value of test that exporters with top export destination a high income country<br>are different that those to non-high income and China are different that those to non-high income and China<br>