rafmacalaba/data-use-mentions-extended huggingface dataset:
)|
|**Beni**|-0.3124***|-0.3003***|-0.3125***|-0.3053***|-0.3038***|
||(0.067)|(0.063)|(0.067)|(0.070)|(0.071)|
|**Pando**|-0.3320***|-0.3197***|-0.3313***|-0.3237***|-0.3298***|
||(0.064)|(0.062)|(0.065)|(0.072)|(0.070)|
|**Observations**|322|322|322|322|322|
|**R2**|0.236|0.239|0.236|0.239|0.245|
|***** p<0.01, ** p<0.05, * p<0.1**||||||

Note: Marginal effects reported; standard errors reported in parenthesis. Source: Authors from World Bank (2010) APER database.

Annex 5 conducts robustness checks by including additional political and managerial controls of the municipality and further investigating the association of agricultural spending with specific improvements of vulnerability (as well as specific cases of vulnerability deterioration). Key messages do not change.

# **6. Conclusions**

This paper explores the reduction of food insecurity in Bolivia, adopting a supply side approach that analyzes the role of agricultural spending on vulnerability. Previous findings on the role of 21
significant, indicating data manipulation. However, lights are evidently not robust to different samples, which can obscure this result. For instance, Column 2 extends the time sample through 2013, which is the end of the DMSP-OLS sample. In this case, the interaction has a gradient of zero. And, when we change the sample to 2005-2020 in Column 3 using VIIRS data, the sample we use in our baseline results, the gradient becomes negative. One possibility is that this spurious result is in some way due to the inclusion of the 2020 downturn. For example, it has been speculated is that manipulation is more likely to occur during recessions in China (Wallace, 2016). In Column 4, we investigate this possibility by including an interaction dummy for whether the year is 2009 or 2020, which we designate a crisis, and find no difference.

The results change, however, when we use NO2 as the signal of economic activity instead of lights. In Column 5, we re-estimate the same specification used in Column 3 through 2020, simply replacing lights with NO2. Again, a positive interaction appears, indicative of data manipulation previously seen in lights over 1992-2008. Moreover, in Column 6, we see that there is statistically significant evidence of misreporting during crisis years specifically, consistent with theory. Thus, using NO2 has measurable benefits in global applications.

**Trend Economic Growth.** Despite these findings supporting NO2, night lights are highly complementary, and applicable in different contexts. In Online Appendix B.2, we repeat the analysis described in Tables 2, 3, and 4, but removing country-specific time trend controls. Thus, predictive power for trend economic growth is now under consideration. One difference is that the overall explanatory power of night lights remains, regardless of what additional signals are available. Moreover, examining the differences by-grade, we find that lights now explain trend growth not only in grade D and E nations, but C as well. Finally, it is unclear how to
sample of children who switched teachers were not randomly assigned, selective matching might bias our estimate. A second interesting possibility was the role of uncertainty in teaching inputs on household investments. The model shows that greater uncertainty in teaching inputs leads to an ex ante response among households through greater precautionary spending. Faced with greater uncertainty in teaching inputs, the movers would have higher precautionary spending and thus be less susceptible to ex post shocks.

We would have liked to directly test which of these mechanisms is responsible for the difference in estimates. Do households really undertake precautionary schooling investments? That is, do parents of movers spend more time or money with their children than those of non-movers? Unfortunately, our data on household inputs does not allow us to investigate this directly. Although we surveyed households matched to these schools (see Das and others 2004a), these were all rural households and our sample of non-movers is too small to draw any meaningful inferences.

If we believe that households play an important role in determining educational outcomes of their children, this paper suggests a direction for future work. Using a model allowing for household responses to teacher and school inputs allows for richer insights than standard production function approaches. The impact of current year shocks on learning achievement depends on other sources of uncertainty (non time-varying attributes of the teacher in this paper) and this has important implications for future evaluation work.<sup>17</sup> Currently there is little research on the link between household and school inputs, and none on the precautionary motive discussed in this paper. Evidence either way would be helpful.

> 17 Suppose that an experiment were designed to study the effect of absenteeism on learning achievement. The ”treatment on the treated” estimator will represent the average effect, averaged across children who changed and remained with the same teacher. This paper suggests that the external validity of the experiment may be compromised due to this important source of heterogeneity–an empirical implication is to try and capture information on this and other changes that have
# **1 Introduction**

A growing body of literature suggests that export participation leads to an increase in skill utilization within firms (Verhoogen 2008; Brambilla, Lederman and Porto, 2012). This relationship is consistent with several intuitive theoretical mechanisms. First, exporting may require expertise in international business and foreign languages (Matsuyama, 2007; Brambilla, Lederman and Porto, 2012). Second, it may induce firms to upgrade product quality, which is a skill intensive activity (Verhoogen, 2008; Bastos and Silva, 2010; Kugler and Verhoogen, 2012; Brambilla, Lederman and Porto, 2012; Bastos, Silva and Verhoogen, 2014). Third, by leading to an expansion in the scale of operations, entry in export markets may make it profitable for firms to pay the fixed costs associated with the adoption of more skill-intensive technologies (Yeaple, 2005; Bustos 2011).

While previous empirical research has documented a robust causal effect of export participation on skill intensity—as measured by workers’ schooling or occupation—, little is known about whether it leads to skill upgrading of the existing workforce through job training. Since skilled labor is a scarce factor and skill-acquisition is costly, training may be a relevant channel of adjustment to globalization, with potentially important implications for productivity and labor earnings. In this paper, we examine these hypotheses in a rich combination of administrative data for the Brazilian manufacturing sector, linking a worker-firm labor census with customs data on export status and detailed training records from the main provider.

The worker-firm data we use cover the universe of manufacturing firms formally registered in Brazil, and include longitudinal records on earnings and worker attributes, as well as firm characteristics. Using unique firm identifiers, we have linked these data with information from customs declarations indicating whether the firm had positive export revenues in each year. Using unique worker identifiers, we have matched these data with detailed administrative records on training activity at the trainee-level from the main provider in the manufacturing sector. The individual training records contain detailed information
our sample. In both cases, however, we show that the coverage remains satisfactory.

There is a risk of underestimating the share of overall industry output covered in FactSet since about only one-fifth of U.S. entities in our FactSet sample have information on sales data available. Aggregating over their sales, thus, may lead to under-representing the actual share of U.S. output observed. In spite of this bias, the coverage is very high. In 2021, the revenue from U.S. agribusinesses with available sales data totaled approximately $4,000 billion—representing about 75%-85% of the total U.S. industry output as per BEA data (see Figure A.14). This share decreases slightly to about 70%-75% when considering only those agribusinesses that are actively reporting in the relationship data each year.

There is also a possibility of overestimating the sales data in our sample compared to the BEA benchmark, as some companies in our sample may report revenues from diverse business segments not directly related to agribusiness.<sup>12</sup> To address this, we adjust for sales from activities two a broad that ex- non-agribusiness using approaches: approach cludes sectors like mining, construction, and services, and a narrow approach that also excludes non-agribusiness manufacturing and trade sales. The broad approach shows a slight reduction in the share of U.S. output represented in our sample, while the narrow approach suggests a coverage ratio of just under 70% for all U.S. agribusinesses with sales data, dropping to around 65% for the subset of actively covered firms.<sup>13</sup> In conclusion, the above considerations suggest that the FactSet coverage of U.S. agribusinesses is satisfactory, with monitored firms constituting a significant portion of the overall U.S. agribusiness output. This detailed approximation helps frame the scope and scale of our investigation into the agribusiness sector and suggests that our dataset captures a

> 12As entities
needed to trace its relationship back to the government. This module also provides a novel indicator denoted as multiple links to denote whether more than one public entity (e.g., different ministries) act as shareholders in the company on behalf of the government and therefore it can provide more than one ownership path that connects the firm to the government.<sup>47</sup> For indirectly owned firms, the number of layers can go from two (e.g., Bangladesh, Cabo Verde) to as deep as thirteen levels (e.g., the Russian Federation). Private firms with no ultimate links to public authorities or governments are not included in the data set.

The third module collects economic and financial performance variables focused on employment, operating revenues (turnover), and net profit/losses (after tax). Researchers use these variables to analyze SOE performance, profitability, and contribution to the economy. However, in many countries, data on SOE income statements and balance sheets are hard to obtain. The global BOS database provides information on total employment (permanent and temporal workers), operating revenues and net profit/loss after tax. The financial information is provided in unconsolidated terms (i.e., parent company reporting as single entity and subsidiaries reporting their respective operations) to avoid potential double counting issues.

The fourth module contains variables related to corporate governance, including the reporting line ministry, level of government, audit status, among others. This final module identifies which firms are owned by the central or subnational (e.g., municipal governments), the ministry line and oversight entity of the SOE, the sectoral regulator, and the audit status.

# 6. Application: New Facts on SOEs Based on the BOS Database

In this section, we present descriptive statistics of SOEs for a subsample of countries in the database. In particular, we characterize SOE patterns for employment, revenues, and state participation by region, type of contestability, type
impact on probability of revictimization is unknown.

# **_Batterer programs_**

Most batterer programs have been carried out in high-income countries. In Canada, Europe, and the United States, there are many treatment programs for abusers that include a variety of theoretical and programmatic approaches. Most programs have a duration of 8–12 weeks and are court-mandated. Few evaluations have been conducted to measure the effectiveness of this strategy. Attendance data, however, indicate that approximately half of the men drop out before completing the program. Of those that do finish, approximately half of them stop using physical violence, at least for some period of time (Edelson 1995). The only randomized controlled trial to-date was carried out on a batterers program run by the United States Navy, which found no reduction in abuse compared with controls (Dunford, 2000).

Increasingly, NGOs and governments in Latin America and the Caribbean have attempted to reduce violence against women by organizing treatment programs for male batterers. Most are run by NGOs, but they often depend on court-mandated attendance as an alternative to criminal sanctions. Prominent examples include the Instituto Noos in Brazil (White, Greene, and Murphy, 2003) and CORIAC in Mexico (Morrison and Biehl, 1999). Battered women often identify treatment or counseling for their husbands as a high priority (e.g. Ellsberg,
thus increasing the opportunity cost of schooling and perhaps reducing rates of return to schooling.

Education is an important determinant of earnings in market economies. The higher is one's level of education, the higher is that person's starting salary and the steeper the rise in earnings during the early working life. The evidence from centrally planned economies is relatively limited, including from economies in transition. Nevertheless, it has been shown that in centrally planned economies returns to schooling are usually low, for example, in China (see Jamison and van der Gaag 1987), but they tend to increase as market reforms take place, for example, in Slovenia (Orazem and Vodopivec 1995) and Hungary (Varga 1995). This paper examines the returns to education in Vietnam in the early stages of transition (based on earnings data fro 1992-93). It is hoped to update these estimates at a later date, to examine the effects of market liberalization and education reforms.

# KEY LABOR MARKET REFORMS

The Vietnamese government's strategy for development places high priority on investment in human capital. But there are problems. There is a high population growth rate and a large rural labor force that is mostly poor. In addition, there are large numbers of migrants, refugees, disabled veterans and demobilized soldiers that must be integrated into the labor market. The largely successful economic reforms have resulted in the retrenchment of up to _1.5_ million workers from government service and state owned enterprises (World Bank 1995b). Unemployment has increased and real wages have declined by 30 to 50 percent (ILO 1994).

2
4|27.8|
|**Lithuania**|33.6|32.2|30.4|
|**Luxembourg**|9.9|9.8|9.6|
|**Malta**|27.3|27.5|26.9|
|**Netherlands**|13.2|13.2|13.1|
|**Norway**|19.1|18.8|18.2|
|**Poland**|27.7|27.5|26.4|
|**Portugal**|22.8|22.9|23.2|
|**Romania**|34.1|32.8|30.9|
|**Slovak Republic**|18.9|18.3|17.2|
|**Slovenia**|27.0|26.4|25.3|
|**Spain**|22.7|22.4|22.3|
|**Sweden**|19.3|18.7|18.2|
|**United Kingdom**|12.7|12.5|12.3|
|**NMS average **|28.3|27.7|26.4|
|**Old Europe average **|18.2|18.0|17.6|

_Source: Schneider et al. (2010), own calculations_

**_Figure 1: Shadow economy in Europe: % of GDP, comparison of old EU members and NMS averages, 1999-2007_**

<!-- Start of picture text --> 31.0<br>29.0<br>27.0<br>25.0<br>23.0<br>21.0<br>19.0
br>_(0.15)_|-0.144<br>_(-0.46)_<br>|
|Urban population|-0.978<br>_(-1.21)_|-0.228<br>(-1.26)|-0.0937<br>_(-0.49)_|-0.525<br>_(-0.63)_<br>|
|GDP||0.91<br>(3.97)||1.415<br>_(4.28)_<br>|
|Natural resources<br>||0.92<br>(7.04)||1.214<br>_(3.89)_|
|AdjFR|0.08|0.433|0.04|0.56|
|Number of observations|236|236|29|29|

a Fixed-term effects were used for our panel data<sup>regressions.</sup> We estimated equation (2) for the panel data<sup>of 29 countries over the period 1990-</sup> 1997. Alternatively, we proceeded with cross-country<sup>regressions using the average</sup> values of the selected variable during the<sup>same period. The panel data regression</sup> includes fixed-term effects because the results<sup>from testing the homogeneity of such</sup> effects indicate that the changes in the FDI<sup>business climate include critical time-</sup> correlated elements common to all countries.

The estimated results of our panel regression<sup>indicate that GDP growth rate and</sup> trade openness have been positively and significantly<sup>correlated with the investment</sup> climate in Africa (table 4).13 The positive<sup>impact of trade
We include these targeting category dummy variables as instrumental variables for household receipt of aid, omitting the correct inclusion, poor district dummy under shocks and the correct exclusion, non-poor district under no shocks.

**Table 3:  Targeting Categories and Errors in 2016**

||**IH**<br>|**PS**|**FI**<br>|**AS**|
|---|---|---|---|---|
||# Obs|Percent|# Obs|Percent|
|**Shock Occurred**|1003||569||
|Moderate+ distribution, poor district|351|35%|293|51%|
|Limited distribution, non-poor district|232|23%|71|12%|
|Moderate+ distribution, non-poor district|229|23%|149|26%|
|Limited distribution, poor district|191|19%|56|10%|
|**Shock Did Not Occur**|1504||383||
|No distribution, non-poor district|552|37%|16|4%|
|No distribution, poor district|262|17%|52|14%|
|Positive distribution, poor district|456|30%|244|64%|
|Positive distribution, non-poor district|234|16%|71|19%|

# **4.2. Instrumental Variables, Zero Stage Results**

Table 4 presents select coefficient results from the zero stage BRGLMs for the IHPS sample and using the current period shock specification.<sup>20</sup> The second column gives results for the probability that a household receives aid (Aid), while the third column gives results for the probability of receiving aid when a shock occurred (Aid*Shock). We first note that experiencing a weather shock increases the probability of receiving aid and receiving
spring of 2020. Other potential reasons for discrepancies include the significant informality in the labor market in developing countries, and the difficulty that GDP projections face in incorporating the role of labor market institutions and labor market policy responses to the pandemic.

Interestingly, both household and firm survey measures of business revenues are more aligned with projected GDP growth. Using recent firm survey data and the HFPS question “Has the revenue from that business decreased since the pandemic started?”, the two panels of Figure 10 show that when available, business sales and revenue data are more correlated with the downward adjustment of the WEO GDP projections. This suggests that GDP projections are better at capturing the impact of the pandemic on business operations, which are more likely to be at least somewhat formalized or interact more closely with the formal economy.

Another important external source of information comes from the labor market indicators reported by the ILO. These indicators are based on national labor force surveys which ascertain labor market outcomes for all adults in the household and are based on larger samples than the phone surveys, which we employ for our analysis. We therefore compare the phone survey measure, the share of stopped working, with the ILO employment-topopulation ratio data available for the same time frame, quarter 1 and quarter 2 of 2020.<sup>9</sup> Comparing the phone survey measure, the share of stopped working, with the ILO employment-to-population ratio data available for the same time frame, unfortunately only 12 of the HFPS countries that have the share of stopped working variable for this period overlap with the quarterly ILO data, which also are limited in terms of current availability. Figure 11 shows that most of the countries seem to have higher values for the labor market change measured by the HFPS data than for the labor market changes measured by the ILO data, although the two measures are strongly correlated (0.74).<sup>10</sup>

> 9The data come from the ILO collection STLFS Short-term labor force indicators, updated December
Learning from Developing Country Power Market Experiences: The Case of Colombia

|**Special resource:**those in the process of|Between one and ten|
|---|---|
|construction or installation when the auction<br>is held|years|

Source: (CREG, 2007)

# 4.1.2 Call option and the Scarcity Price

As previously described, Gencos with Firm Energy Obligations receive a stable reliability payment. In exchange, Gencos commit to provide a given amount of energy (the “firm” energy allocated in the auction), at a fixed price during tight supply conditions (i.e. dry hydrology). Such tight conditions are defined as the hours when the spot price exceeds a pre-defined strike price, called _“scarcity price”_ .

The scarcity price is calculated and updated by the regulator (CREG), and its initial value was set so that 5% of the historic spot prices were above this scarcity price. The selected value was equal to the variable cost of the most inefficient operating power plant, which was therefore selected as a reference for the initial calculation. The scarcity price is therefore composed of three parts: fuel, variable O&M, and other variable costs of the National Transmission System. The fuel component of the scarcity price was originally set to 101.6 USD/MWh (in June 2006), and is updated monthly according to an international fuel price index (the New York Harbour Residual Fuel Oil 1% Sulphur LP Spot Price) (CREG, 2007). The variable O&M cost was set to 10.667 COP/kWh (in June 2006), and is updated monthly according to the Colombian Consumer Price Index. Other costs are calculated on a monthly basis depending on operating conditions, including the costs of Automatic Generation Control (AGC, see section 3.1) (CREG, 2006a).

The Firm Energy Obligation resembles a call option on electricity during tight supply conditions
current depressive symptoms with lagged outcomes, which we will attempt to do with prior waves of the GHS-Panel. A final method is to construct a control group via propensity score matching. However, there may be systematic differences in outcomes between those affected (by depression) and not, even after controlling for observables. In this case, this would violate the identification conditions for matching.

Unfortunately, we are unable to fully correct for the potential endogeneity. Thus, we frame our findings as a correlation between depression and the considered economic outcomes rather than a causal link.<sup>18</sup> Regression results in Table 4 show that being over the CES-D cutoff of 10 points reduces the likelihood that one is engaged in any work by 8 percentage points (column 1), in wage work by 2.5 percentage points (column 2), in agricultural work by 4.9 percentage points (column 3), and in non-farm enterprise by 3.5 percentage points (column 4).<sup>1920</sup> The effect of depressive symptoms on labor participation differs by gender (See Table A.1 in the Appendix). Being over the cutoff reduces the likelihood that men are engaged in agricultural work by 8.8 percentage points, while it is not correlated with the reduction in agricultural work among females. Instead, the indicator for depressive symptomology reduces work in non-farm enterprises by 9.6 percentage points; this correlation is not observed among males.

As expected, the experience of adverse events also has a relationship with labor force participation decisions (Table 4). Controlling for depression, the experience of covariate shocks increases the likelihood that one is engaged in any work by 4.4 percentage points, and in non-farm enterprise work by 9.5 percentage points (columns 1 and 4).<sup>21</sup> Conflict is not associated with changes in labor activities.

Next, we investigate the relationship between parents’ mental health with children's well-being