rafmacalaba/data-use-mentions-extended huggingface dataset:
firms exporting to high-income countries—a dummy that takes a value of one when a firm exports to a country at the top 25% of the per capita GDP spectrum. As it can be seen, the results are qualitatively similar as before. Exporting is associated with higher skilled employment and in particular higher utilization of engineers, and there is an additional effect on the demand of skills and of skilled tasks for firms that export to high-income, rich countries.

These results suggests that both the quality and the required services mechanisms are present in the Chilean case. The presence of the quality mechanism is consistent with the widespread support for it listed above. The presence of the required services mechanism is also expected. Pellandra (2017), for instance, finds support for the Matsuyama interpretation using similar Chilean data. Building on Brambilla, Lederman and Porto (2012), Pellandra includes fixed costs of reaching different export destinations and those fixed costs are essentially covered with skilled workers. This suggests that the required services mechanism is also present. Incidentally, this is consistent with the results in Brambilla, Lederman, and Porto (2012), who also find support for both mechanisms using Argentine data.

The most novel result of this paper is, however, the finding that exporting requires the skills of specific tasks within skilled labor, namely those performed by engineers rather than by directors or administrative workers. The quality provision and quality valuation mechanisms just discussed can rationalize these findings by assuming for example a technology to produce quality that is more intensive in those specific skilled tasks. In the working paper version of this paper (Brambilla, Lederman, and Porto 2016), we develop a model with some of these features. We combine elements from the literature on quality and skilled utilization with element from theories of trade and tasks. In particular, we build on recent models of international trade where goods are produced with tasks using assignment rules (Acemoglu and Autor 2011; Costinot and Vogel 2010). Production involves many different tasks, such as managing
**<mark>Table 7. Regression: Vignettes</mark>**

|**Variables**|(1)<br>**Latrine use**<br>**(baseline**<br>**survey)**|(2)<br>**Latrine use**<br>**(endline**<br>**survey)**|
|---|---|---|
|Most use latrine|0.138***|0.143***|
||(0.0211)|(0.0173)|
|Most think OD is wrong|0.158***|0.160***|
||(0.0211)|(0.0173)|
|Constant|0.729***|0.776***|
||(0.0122)|(0.00998)|
|Observations|812|1,010|
|Standard erro|rs in parenthes|es|
|*** p<0.01, *|* p<0.05, * p<0|.1|

<mark>We also explored mental models by asking narrative questions, for example, associations that respondents made with the word “ganda” (dirty). Analysis at baseline suggest a strong association of the word “ganda” with latrines. For example, a word frequency count showed that latrine was the third most mentioned word, after feces and drainage, when people were asked to talk about things that they considered “ganda”. Topic modeling using latent dirichlet allocation showed that the word latrine appeared frequently in three of the top five topics, along with words such as feces, garbage, drains, etc.</mark> This <mark>demonstrates the importance of changing social norms but also mental models regarding latrine use.
service sectors (Asian Development Bank 2018).

However, service industries that rank in the top third of the labor-productivity growth distribution between 2000 and 2010 accounted, on average, for about 30 percent of total service employment, and close to 20 percent of overall employment (IMF 2018). What is more, most service sectors that exhibit “productivity-enhancing” characteristics are less likely to be associated with employment creation for unskilled labor. This is reflected in Nayyar, Cruz and Zhu (2018), which classifies manufacturing and service subsectors based on firm-level data from six LMICs along a range of trade, innovation, learning-by-doing and factor-use characteristics. IT services, for example, are classified as “high” or “medium” with regard to their potential for scale economies; exports; and innovation as measured by new products, new processes, and R&D spending, but also belong to the group that is “high” in skill intensity. Therefore, without sufficient human capital, there are limits to how much labor can be absorbed in highly skill-intensive service sectors – it is comparatively easy to turn a rice farmer into a garment factory worker than a software engineer.

Much of the employment expansion across developing countries over the past few decades was accounted for by wholesale and retail trade, hotels and restaurants, and construction (IMF 2018; Nayyar 2012). Yet, these unskilled labor-intensive services are less likely to provide much by way of productivity gains. Again, this dichotomy is reflected in Nayyar, Cruz and Zhu (2018) where 11
Niger, and Chad than in Senegal. Work in agriculture and livestock is also about five times more prevalent in rural areas compared to urban areas, pooling the data from all five EHCVM countries.

**Figure 1. Share of the population living in households where the head is primarily engaged in different employment sectors over the last 12 months by country and rural-urban**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Burkina Faso Mali Niger Senegal Chad All countries<br>Agriculture Livestock/fishing Industry/construction Services/other Not working<br>months (percent)<br>working primarily in each sector over the last 12<br>Share of population living in a household with a head<br>Rural Urban Total Rural Urban Total Rural Urban Total Rural Urban Total Rural Urban Total Rural Urban Total<br><!-- End of picture text --> Note: Individual-level sampling weights applied. Primary occupation is the one in which the household head spent the most time working in the previous 12 months. Source: EHCVM and World Bank estimates.

Cultivating fields and owning animals were also widespread across the EHCVM sample, especially in rural areas. Pooling all five countries, some 66.4 percent of households own animals and 68.1 percent of households had cultivated fields in the previous agricultural season (Figure 2). Animal ownership is 2.3 times more common in rural areas than urban areas, while cultivating fields is about 4.5 times more common. There is also variation between the countries, with Senegalese households being far less likely than households in the other four countries to cultivate fields, in line with the statistics presented on household head occupation.

8
In Mexico, starting in 2010, pension fund management companies (AFORES for their Spanish acronym) were authorized to invest up to 20% of their assets under management in foreign securities. Interestingly, while these institutional investors quickly raised their foreign exposure following the relaxation of the foreign limit –international holdings grew from 7% to 13% between 2010 and 2013 – further increases in foreign exposure stopped after 2013. Overall, AFORES do not seem to have taken fully advantage of the benefits from additional exposure to foreign assets. In fact, the constraint model suggests that had AFORES continued to increase their share in foreign assets following 2013, to a level of up to 19% (the benchmark level), pension portfolios would have received an additional 1.00% of yearly forward-looking excess returns. Note that such returns are not realized returns, but instead, these are expected returns calculated at the end of each year.

One potential explanation for the slowdown in the rate that funds added foreign assets, might be related to regulatory constraints. Before 2018, AFORES were not allowed to invest in international mutual funds. Notably, as we documented in section 4, this is the preferred investment instrument to access foreign securities among pension fund management companies in the region. Lack of access to international mutual funds might limit an AFORES’ ability to enhance, due to the added costs of direct holdings in international markets. A key remaining question is whether the most recent investment regulation, which among other conditions, authorized multiple international holdings in mutual funds starting in 2019 would yield better outcomes in the following years. We should continue our country surveillance to assess the gains from new asset classes, and to examine whether such exposure is at a pace that is closing the gap from the current level of underexposure.

In addition to Mexico, we use our model to estimate the time profile of foreign holdings and gains/losses from diversification in Romania. As the mandatory funded pillar has grown in
_γs_ reflects indicator variables for whether _i_ belongs in strata _s_ . Randomization was stratified by whether the respondent markets on Facebook at baseline (regardless of whether she marketed on her personal profile or a Facebook page). _α_ 1 is the main coefficient of interest and reflects the effect of being assigned to the bundled intervention on outcome of interest _Yis_ . Note that I winsorize all continuous outcomes such as number of clients, revenues, and profits at the 95th percentile to reduce the effects of extreme values. I also impute any missing values in the Lasso-selected baseline control variables with the variable median and hold fixed effects for missing values. Most results are robust to randomization inference adjustment of p-values, also reported in the tables. Finally, table 1 confirms that the treatment assignment was successfully randomized and is balanced across baseline variables, with an F-stat from the F-test of joint significance equal to 0.57. The follow-up survey had limited attrition at 12%, also balanced across treatment and control arms. Table A.2 in the appendix confirms that treatment assignment is also balanced for the sample of participants found in the follow-up survey.

14
estimated quantities of gasoline and diesel in the PECS 2016/2017. The quantity of household consumption of gasoline and diesel was estimated by dividing households’ self-reported expenditure in the survey by the 2017 final consumer price. The indirect effects of gasoline and diesel subsidies (used as inputs in the production process) were calculated based on the West Bank IO table and the “Cost-Push Model" (Inchauste and Jellema, 2018). We applied the indirect effects from gasoline and diesel subsidies to all applicable household residents’ expenditures in the West Bank<sup>59</sup> .

- **4.6 In-kind benefits**

**The assessment includes in-kind benefits for public education and public health.** Both allocation methods impute “average benefits” to users and individuals eligible for these public services, respectively, based on information from the PECS 2016/17 and government administrative data.

# **Education**

**This study follows the “average-cost approach” for users of public education services to allocate in-kind public education benefits.** First, the in-kind benefits for public education services (per student) were calculated at the national level based on administrative data of (executed) public expenditure on education divided by the number of total students enrolled in public schools from administrative accounts; different benefits were calculated for the basic and secondary levels of education. Second, the study infers potential beneficiaries from public in-kind education benefits in the PECS survey based on the following reported variables: (i) individuals currently enrolled in public schools; and (ii) self-reported information on “highest grade of education achieved” plus one year.<sup>60</sup> The admin numbers indicated that for 2017 there were 585,821 students enrolled in (public) basic education and 214,663 students enrolled in (public) secondary education. In the case of (public) basic education, the inference approach from the PECS resulted in a larger number of students than the administrative target<sup>61<
researchers have stepped in to provide data hand-coded from official announcements, press reports and other sources.

This paper creates a data set on credit events on Chinese loans based on multiple sources created by researchers. The starting point was our own preliminary work in Horn et al. (2021), which we revise, update, and expand in a much more comprehensive data compilation here. Most importantly, we draw on AidData’s newly released Chinese Official Finance Database 2.0 (Custer et al. 2021), as well as on new data sets and academic work. On the latter, the contributions by Acker et al. (2020), Bon and Cheng (2020, 2021), as well as Kratz et al. (2019, 2020) were particularly helpful and substantive.

> 2 China does regularly cancel debts of aid-like Zero-Interest Rate Loans given out by the Ministry of Commerce to poor countries, but the amounts lent and forgiven tend to be very small. See Appendix I for details.

4
Y|Y|Y|Y|Y|Y|Y|||
|Demographics & employment|Y|Y|Y|Y|Y|Y|Y|Y|Y||

**Note:** Normal linear regression with bootstrapped standard errors is used. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. All estimates are obtained with population weights. The normal linear regression model with the theoretical distribution of the error terms employs cluster random effects. Imputed consumption per capita for the second round uses the estimated parameters based on the data from the first round. 100 simulations are implemented. True consumption per capita is the estimate directly obtained from the survey data. Estimates shown in boldface or with a “*” respectively fall within the 95% confidence interval or one standard error of the true rate.

38
3 the whole, in land-scarce, labor rich countries - like Pakistan and much of Latin America - inadequate access to land may tend to "push" poorer rural households out of agriculture and into the nonfarm sector. Thus, in these countries, nonfarm income may have a positive impact on inequality and poverty. The obverse, then, could hold in land-rich, labor-scarce countries - such as Africa - where ample land access may tend to keep most people in agriculture and to "pull" only richer households into the nonfarm sector.

This paper proposes to clarify the impact of nonfarm income and unequal land distribution on rural income inequality by analyzing the results of two new, nationallyrepresentative household surveys in Egypt and Jordan. The choice of these two countries for analysis is conscious: both countries lie in the MENA (Middle East and North African) region and thus share many economic and social similarities. However, for the purposes of this paper, they also share one key difference: while Egypt is a land-scarce country, where the poor lack access to productive land and are thus "pushed" to work in the nonfarm sector, Jordan represents a different type of land-scarce country, in which the irrigated land mass is so small that the rural rich are "pulled" (by more attractive rates of return) into the nonfarm sector.

The paper seeks to make three contributions. First, it uses standard decomposition techniques based on the Gini coefficient to pinpoint the contribution of different sources of rural income - including nonfarm income - to rural inequality in Egypt and Jordan. This analysis finds that nonfarm income has very different impacts on inequality in the two study countries: in Egypt nonfarm income improves inequality, while in Jordan nonfarm income has a negative impact on inequality. Second, in an effort to understand the reasons for this difference, the study then decomposes the sources of nonfarm income inequality in order to understand how the various types of nonfarm
# **I. Introduction**

The Eastern Europe and Central Asia region had one of the least efficient manufacturing sectors in the world because of the region’s historical emphasis on heavy industries and artificially low energy prices during the Soviet era.<sup>3</sup> Since the transition period started two decades ago, the sector’s energy productivity (value added per unit of energy input) has consistently improved – by almost 35 percent between 1998 and 2008. As a result, although manufacturing value added increased by more than 50 percent over the period, energy consumption had been stabilized at around 250 million tons of oil equivalent (mtoe). How did the region achieve this impressive decoupling of economic growth from energy use? This paper seeks to answer the question by empirically examining the trend and determinants of manufacturing energy use for 28 countries in ECA.<sup>4</sup> Using detailed sector and country-level data, we conduct three types of analysis. First, we compare energy productivity performance over time and across countries. We ask the following questions: Is the trend of declining energy intensity consistent across the region? Do lagging countries catch up with technological leaders? Second, we decompose the manufacturing sector into 11 subsectors and investigate whether the reduction in energy intensity is more attributable to changes in the mix of production activities (the structural effect), or to the fundamental improvement in the use of energy (the efficiency effect). Finally, we identify the economic factors that have driven changes in energy intensity, energy efficiency and the structural composition of industrial activities.

The analysis reveals strong evidence of cross-country convergence: not only the inter-country variation in energy intensity has reduced; but there is also a tendency of more rapid improvement in countries that had high energy intensity initially. Beyond this general trend, we find some Central Asian countries are notable exceptions where energy intensity had remained largely unchanged. Interestingly, intrinsic efficiency improvement turns out to have contributed to most of the reduction in manufacturing energy intensity
scores (worse mental health) decreases the probability of being employed by 6.4 percentage points (or 14.1%, which equals 6.4/45.4); it also decreases weekly labor income by $192 Australian dollars (or 26.8%).<sup>11</sup> These effects are highly statistically significant at the 1% level, and the IV-Logit-FE and IV-FE estimates are between 1.4 and 3.8 times larger than the FE estimates.<sup>12</sup> While we offer the first estimates of the impacts of mental illness on refugees’ labor outcomes, it can be useful to compare our estimated effects with those on the general population in previous studies. For example, analyzing data from the HILDA, Frijters et al. (2014) show that a one standard deviation decrease in mental health leads to a 30 percentage point decrease in the probability of being employed.<sup>13</sup> Using data from the National Comorbidity Survey-Replication (NCS-R) in the United States, Chatterji et al. (2011) find that psychiatric disorder is associated with reductions of 13-14 percentage points in the likelihood of employment. Findings from our study add new and useful evidence for policies to support refugees, who are especially vulnerable to mental illness as discussed earlier. We return to further comparison in Section 4.3.

Table 2 shows that mental illness also has a negative impact on other labor market outcomes.<sup>14</sup> Several findings stand out from this table. First, refugees with higher mental health scores are 3.5 percentage points (or 8.4%) less likely to participate in the labor force (Column (1)). Second, refugees with worse mental health are less likely to have a permanent job, although the effect is statistically insignificant (Column 2). They are also 3.2 and 5.6

> 11 Our findings remain consistent when using the inverse hyperbolic
Panel c. Income-risk jobs (non-essential and non-teleworkable) <!-- Start of picture text --> 50%<br>48%<br>46%<br>44%<br>42%<br>40%<br>38%<br>36%<br>34%<br>32%<br>30%<br>Natives Foreign EU15 NMS13 Non-EU28<br>born migrant migrant migrant<br><!-- End of picture text --> Panel d. Health-risk jobs <!-- Start of picture text --> 30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Natives Foreign EU15 NMS13 Non-EU28<br>born migrant migrant migrant<br>% of health-risk jobs<br><!-- End of picture text --> Source: Own calculation based on EU-LFS 2018 data, following EC directive (2020) and Fasani and Mazza (2020a).

34
# **Figure 1.1 Investment growth during credit booms and deleveraging episodes**

_In EMDEs, in the median credit boom, investment grew by about 1 percentage point of GDP above its long‐term trend until the credit boom peaked. It dropped below its long‐term trend by 1‐2 percentage points of GDP before deleveraging episodes reached their troughs._

## **A. Investment during credit booms**

## **B. Investment during deleveraging episodes**

<!-- Start of picture text --> Percent of GDP Median Percent of GDP<br>8 Upper and lower quartiles 8 Median Upper and lower quartiles<br>Asian financial crisis<br>6 2012-2015 6<br>4 4<br>2 2<br>0 0<br>-2 -2<br>-4 -4<br>-6 -6<br>-8 -8<br>-3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3<br><!-- End of picture text -->

## **C. Consumption during credit booms**

## **D. Consumption during deleveraging episodes**

<!-- Start of picture text --> Percent of GDP Median Percent of GDP<br>6 Upper and lower quartiles 6 Median Upper and lower quartiles<br>2012-2015<br>4 4<br>2 2<br>0 0<br>-2 -2<br>-4 -4<br>-6 -6<br>-3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3<br><!-- End of picture text --> Sources: Bank for International Settlements; Haver Analytics; International Financial Statistics, International Monetary Fund; World Development Indicators, World Bank.

Notes. The red lines show sample
21.4|25.0|8.3|
|**Total**<br> Sources: CFUWBES1, LMPS 20|15.8<br>16, and CMMHH|25.2<br>Jordan 2021.|25.5|23.3|

In contrast to _Table 5-25 Jordan: Percentage of PFPS Jobs Lost, by Wage Quintile_ Jordan, for **Wave 1 Wave 2** Georgia our **National nominal hourly** method **wage quintile** CFUWBES1 Modeled Modeled projects declining job **Q1 (bottom)** 19.0 <u>30.4</u> losses as wage **Q2** 16.6 26.5 levels rise. In **Q3** 14.0 22.6 the early **Q4** 14.1 21.0 stage of the **Q5 (top)** 13.5 21.4 crisis an **Total** 15.8 25.2 estimated 21.1 Sources: CFUWBES1, LMPS 2016, and CMMHH Jordan 2021.

## percent of

workers in the bottom wage range are projected to have lost their jobs and 16.4 percent of those in the top range (Table 5-26). By the winter, the top paying Georgian PFPS jobs are predicted to have completely recovered, whereas those jobs paying in the bottom range still are estimated to have experienced a cumulative job loss of approximately 11.2 percent. Because the GHFPS does not collect data on pre-COVID individual wages, we cannot provide any comparison to the larger population of private sector workers..

_Table 5-26 Georgia: Percentage of PFPS Jobs Lost, by Earnings Interval_

||**Wave 1**|**Wave 2**||
|---|---|---|---|
|**By monthly nominal net**<br>**earnings interval LCU**|
<!-- Start of picture text --> .13<br>.4 model (all firms) .09<br>.37<br>.34 .05<br>.31 .01<br>.28 −.03<br>.25<br>−.07<br>.22<br>.19 −.11<br>.16 −.15 data<br>.13 model (rest. sample) −.19<br>.1<br>−.23<br>.07<br>−.27<br>.04<br>data model (all firms)<br>.01 −.31<br>−.02 −.35<br>−1 0 1 2 3 4 −1 0 1 2 3 4<br>years since procurement shock years since procurement shock<br>(a) credit (b) sales to the private sector<br>cumulative growth rate cumulative growth rate<br><!-- End of picture text -->

**Notes:** This figure shows the cumulative impact of the estimate of _β_ 2<sup>_h_(anditsassociated10%confidencebands)</sup> from regression (1) for different time horizons _h_ = 0 _,_ 1 _,_ 2 _,_ 3 _,_ 4 _._ Panel (a) shows the results for the case of _x_ being firms’ total sales. Panel (b) shows the results for the case of _x_ being firms’ sales to the private sector. The line “data” shows the estimated coefficients using the real data, i.e., same results as in panel (a) of Figure 1. The line “model (all firms)” shows the results when using all firms in our simulated sample. The line “model (rest. sample)” shows the results