rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4.** SDM Data: Distribution of posterior model probability and model size. (MCMC sampler with 2 million recorded draws after a burn-in of 1 million draws.)

|Number|Posterior|Number of Regressors|
|---|---|---|
|of Models|Prob.|Mean<br>St.Dev.|
|1|0.07|6<br>-|
|5|0.10|6.3<br>0.5|
|25|0.18|6.4<br>0.7|
|50|0.23|6.3<br>0.9|
|100|0.29|6.4<br>1.0|
|752|0.50|6.6<br>1.2|
|4,627|0.75|6.8<br>1.3|
|13,527|0.90|6.9<br>1.4|
|21,948|0.90|6.9<br>1.4|
|42,363|0.99|7.0<br>1.4|
|126,844|1.00|7.0<br>1.4|

Table 5 presents the marginal inclusion probabilities for those regressors that are included more than 1% of the time<sup>8</sup> as well as the moments of the corresponding _β_ ’s in the same format as Table 2. Again, note the high correlation between the absolute values of the unconditional standardized posterior means of the _β_ ’s and the posterior inclusion probabilities. There are, however, some interesting differences with the results based on the FLS data set. In spite of the fact that the posterior model probability is more concentrated with the SDM data, there are no regressors that are virtually always included. Nevertheless, fewer regressors have marginal inclusion probabilities exceeding 10% and there is a longer tail of marginally less important variables (53 out of the possible 67). In addition, the important determinants of growth are often not the same ones: only Past GDP, Primary school enrollment, Fraction Confucian, Life expectancy, Fraction GDP in mining and the Sub-Saharan and Latin American dummies receive inclusion probabilities above 10% for both data sets, and appear in a very different ordering in both. By comparing the moments of _β_ conditionally upon inclusion, we can assess whether the effects of these regressors are similar in both datasets.<sup>9</sup> Comparing Tables 2 and 5 reveals that they all have the same signs, but some have rather different magnitudes: the main difference is in the effect of Past GDP, which is almost twice as large with the FLS data set (the difference is more than 2.5 times the posterior standard deviation).

The results on posterior inclusion probabilities and posterior moments of _β_ are,<sup>10</sup> however,

> 8 This is merely for ease of presentation, but the sampler was run over the entire model space with 67 potential regressors.

> 9 These particular regressors were all measured in the same way in both data sets, and the raw moments were very similar, so that the standardisation does not affect this comparison.

> 10 For the moments of _β_ this can not be assessed directly from Table 5, but is clear from running the sampler without standardizing the data.

11
Table <u>5. The effect of COVID-19 exposure on educational outcomes by level of instructio</u>

||Initial|Primary|Lower<br>secondary|Upper<br>secondary|
|---|---|---|---|---|
|_A. Dropout_|||||
|Warning index|0.006|0.005|0.004|0.001|
||(0.001)***|(0.001)***|(0.002)*|(0.002)|
||[0.001]|[0.001]|[0.113]|[0.783]|
|Mean outcome|0.033|0.073|0.176|0.070|
|Adjusted R<sup>2</sup>|0.040|0.146|0.167|0.086|
|Observations|2,412,052|9,416,097|2,981,696|1,585,079|
|_B. Effective promotion_|||||
|Warning index|-|-0.018|0.005|-0.000|
|||(0.003)***|(0.003)*|(0.005)|
|||[0.001]|[0.112]|[0.930]|
|Mean outcome||0.815|0.754|0.837|
|Adjusted R<sup>2</sup>||0.107|0.156|0.133|
|Observations||9,413,743|2,969,515|917,887|
|_C. Switched to public school_|||||
|Warning index|0.004|0.001|0.000|-0.000|
||(0.001)***|(0.000)***|(0.000)**|(0.000)|
||[0.001]|[0.001]|[0.065]|[0.153]|
|Mean outcome|0.009|0.008|0.018|0.004|
|Adjusted R<sup>2</sup>|0.142|0.114|0.067|0.015|
|Observations|2,412,052|9,416,097|2,981,696|1,585,079|
|_D. Switched to private school_|||||
|Warning index|-0.004|-0.002|-0.001|-0.001|
||(0.000)***|(0.000)***|(0.001)**|(0.000)**|
||[0.001]|[0.001]|[0.058]|[0.065]|
|Mean outcome|0.025|0.022|0.082|0.009|
|Adjusted R<sup>2</sup>|0.069|0.092|0.165|0.030|
|Observations|2,412,052|9,416,097|2,981,696|1,585,079|
|Controls|Yes|Yes|Yes|Yes|
|Year effects|Yes|Yes|Yes|Yes|
|Fixed effects|School|School|School|School|

_Source_ : Authors’ calculations from Guatemalan educational administrative records.

_Notes_ : The table reports difference in difference estimates of the effects of differential exposure to COVID-19 (see Section 4 for details). Each column presents results from a separate regression for each educational level using specification (4) in Table 3. Clustered standard errors by municipality are shown in parentheses. We present q-values that adjust for multiple hypothesis testing in brackets, calculated using the method by Benjamini and Hochberg (1995) that controls for the false discovery rate described in Anderson (2008). Significance levels: ***p<0.01; **p<0.05; *p<0.10.

23
## **Figure 1: Overview of IPC distributions for 15 all countries**

The individual graphs show the entire distribution of IPC1 (green), IPC2, (yellow) and IPC3+ (red) for all 15 countries based on FEWS NET data. This distribution is based on the population-weighted IPC levels in subnational administrative regions. Prior to 2016, the reports were reported four times a year, afterwards three times a year (the vertical lines demarcate the reporting dates). The vertical axis runs between 0% and 100%.

<!-- Start of picture text --> Afghanistan Chad Ethiopia<br>Guatemala Haiti Kenya<br>Malawi Mali Mozambique<br>Niger Nigeria Somalia<br>South Sudan Uganda Zimbabwe<br>IPC1 IPC2 IPC3+<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br><!-- End of picture text -->

# **2 Data**

In our study we rely on the Integrated Phase Classification Acute Food Insecurity (IPC) framework for the definition of famine risk and food insecurity (see Table 1). We obtain the data from FEWS NET as shown in Figure 1. These data are reported on the first-level administrative country subdivisions (districts).

One of our main innovations is to model food insecurity distributions for a given country, rather than a single index metric.1 An example for this would be the established IPC framework, which classifies districts in a country based on the IPC stage the worst 20th percentile of the population finds itself in. For illustration, consider the two populations in Figure 2. The left population is distributed as 60%, 10%, 10%, 10% and 10% in increasing severity phases from IPC1 to IPC5. The overall IPC phase for the entire region would, applying the standard classification threshold of 20%, be IPC4, as the most severely affected 20% is in IPC4 or worse. However, the second population in the right panel receives the same classification, following the same rationale as before, even though its situation is undoubtedly more acute. Our distributional approach models dynamics of the distribution of district populations across all phases, rather than a single index. To calculate this food insecurity distribution for a country, we take the population share

> 1Such single metrics could be calculated as (weighted) averages, specific quantiles or other index construction methods that yield in a single metric.

4
-3 -

The OED report (World Bank, 1990) is a sustainability analysis of investment projects in Education. It examined a number of education projects to see whether the initial investment has cortinued to produce benefits well beyond project completion (about 10 years after completion), and investigated the factors affecting sustainability. Sustainability was defined as the continuation of desired benefits from a given investment, and was measured by several sustainability indicators. The Report argues that the following principal factors constituted the necessary conditions for sustainability: sound project design and relatively problem-free implementation; adequate funding for recurrent costs, and institutional support in the post-completion period; and clear policy support for the project from the borrower. However, it is stated that none of the above factors alone is sufficient for sustainability. Further, the report found: "the external economic and policy environment affects sustainability through the fiscal resources the borrower ma'.es (or does not make) available to support educational development, or through policy measures to back up specific sub-sectors. External factors also affect sustainability indirectly through macroeconomic effects on labor markets, which determine the capacity of the economy to absorb graduates." (p.viii) However, due to an insufficient number of observations (twelve observations),<sup>2</sup> the conclusions from the statistical analysis carried out are not quite robust (p.36). Moreover, there are measurement problems with the variables. The study evaluated each variable (both dependent and independent) for a given project in terms of a four-point scoring system. Each aspect of performance or policy<sup>3</sup> was judged to be good, fair, poor, or zero, with corresponding numerical values of 3, 2, 1, or 0. These scores are a mixture of quantitative and qualitative variable, and it is difficult to maintain consistency across projects such that a given score for a variable in one project represents the same thing in another. Furthermore, the 2 These include five case studies in Indonesia, Malawi and Columbia (3 projects), and eight projects from Jordan, Kenya, Liberia, Malaysia, the Philippines, Sierra Leone and Thailand. Columbia I and II are considered one observation. All of them were completed during 1969-79, and were re-evaluated during 1988-89.

_3_ The policy variables include, overall level of government support, macroeconomic conditions, education sector conditions, and level of policy commitment, all measured by the four-point score system.
# Table of Contents

|Summary|1|
|---|---|
|Introduction|4|
|1. A Theoretical Approach<sup>: A Measure of the Willingness to Pay</sup>|5|
|2. An Empirical Test : Estimates<sup>and Simulations for 6 Highly Indeb</sup>|<sup>ted</sup>|
|Countries|11|
|3. The Determinants of the Willingness<sup>to pay</sup>|14|
|4. Conclusion|17|
|Bibliography|19|
|Tables|20|
|Annex|22|

1The authors are grateful to Stijn Claessens for his helpful comments and suggestions. They also<sup>thank Vikram Nehru. This paper was presented at the</sup> Meeting of the "Association<sup>Francaise de Finance"</sup> (Paris, December 1991)<sup>and</sup> at the Conference on Economic<sup>Developement (Champery (CH), March 1992).</sup>
Despite a continued significant policy emphasis on social assistance, little is known about the impact of these assistance programs on welfare measures and labor markets in Turkey.<sup>1</sup> The purpose of this study is to fill this gap in the literature, particularly with regard to the relationship between receipt of benefits and labor market activity. Although Alcan, Can, and Pektaş (2015) provide some empirical evidence that social assistance beneficiaries are more likely to transition into the labor force and are less likely to transition into the formal sector, this study explores these issues in more detail covering a longer time period, and using disaggregated social assistance variables which permits the analysis of different types of benefits. We used the _Survey of Income and Living Conditions_ , an individuallevel annual panel data set, in our empirical analysis of the period between 2006 and 2013. The panel nature of the data set allowed us to study the transitions in the labor market statuses of individuals. We used the multinomial logit model for this purpose and estimated the role of social assistance programs in labor market transitions.

Our benchmark estimates implied that in comparison to non-beneficiaries, social assistance beneficiaries are significantly more likely to be unemployed or working informally and less likely to be formally employed. Among the unemployed, the probability of moving to informal employment is found to be significantly larger for beneficiaries than for non-beneficiaries. Another interesting result is that social assistance beneficiaries are more likely to enter the labor force in search of work, suggesting that benefits are not associated with increased dependency.

This is not the first study to supply evidence on the interaction between social assistance and labor market outcomes. There has been a substantial effort in the empirical literature to identify the effects of social benefits on labor market outcomes in order to provide guidance to policy makers on optimally designing benefits programs. Given the relatively more generous nature of social safety nets in developed countries and the limited data availability in the developing world, most of the early literature has been focused on developed country experience, particularly in the United States. A number of studies reviewed in Moffitt (2002) found a reduction in work effort by 10–50 percent among beneficiaries of various programs in the United States (Aid to Families with Dependent Children and negative income tax programs). For instance, Burtless (1986) estimates a moderate earnings and employment reduction among negative income tax program participants of 7 and 17 percent for men and women, respectively. More recently, Hoynes and Schanzenbach (2012) and

> 1 There are a few exceptions, including Azevedo and Atamanov (2014) and Şeker and Dayıoglu (2014), who examine the role of the social protection system in poverty dynamics.

3
**Figure 10. Labor-Related Deprivations, 2015, Percent**

**Figure 11. Labor-Related Deprivations by Location, 2015, Percent**

<!-- Start of picture text --> 60% 52% 60%<br>46% 50%<br>50%<br>40%<br>40%<br>30%<br>30% 20%<br>22%<br>20% 15% 10%<br>0%<br>10%<br>0%<br>Yerevan city Other urban areas Rural areas<br>Decent jobs<br>Labor market participation Long-term unemployment<br>Decent jobs<br>Labor market participation Long-term unemployment Underemployment Underemployment<br><!-- End of picture text --> _Source:_ ILCS data.

_Source:_ ILCS data.

About 10 percent of households either cannot afford health services or lacks access to them (Figure 12). The regional breakdown demonstrates the persistence of greater deprivations in affordability in urban areas and in access in rural areas (Figure 13). Beyond affordability and access, there is also considerable need to improve health care quality. Finally, 16.5 percent of Armenians have had to terminate daily activities for health reasons.

**Figure 12. Health-Related Deprivations, 2015, Percent**

**Figure 13. Health-Related Deprivations by Locations, 2015, Percent.**

<!-- Start of picture text --> 30%<br>26%<br>25%<br>20%<br>16%<br>14%<br>15%<br>12%<br>10%<br>5%<br>0%<br>Affordability Termination Access to Quality of<br>of health of usual health health<br>services activity services services<br><!-- End of picture text --> _Source:_ ILCS data.

<!-- Start of picture text --> 35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Affordability Termination Access to Quality of<br>of health of usual health health<br>services activity services services<br>Yerevan city Other urban areas Rural areas<br><!-- End of picture text --> _Source:_ ILCS data.

16
Exporters are firms with an export share (direct or indirect) of at least 10% of total sales. (2) Importers are firms with an imported input share of at least 10% of total inputs. (3) GVC participants are firms that are classified as both exporters and importers. (4) FDI refers to firms with a foreign ownership share of at least 10%. The trade variables are dummies taking a value of 1 if the firm trades, and 0 if it does not. The model also controls for country‐sector, subnational region and year fixed effects.

# **2.2 Data**

Our dataset draws on two underlying datasets, published by the World Bank Enterprise Analysis Unit, namely the Enterprise Surveys Global Database and the Firm‐level TFP Estimates and Factor Ratios. The Enterprise Surveys Global Database covers 242 surveys in 140 countries over the period 2006 to 2017.

The Enterprise Surveys represent a comprehensive source of firm‐level data in emerging markets and developing economies. One major advantage of the Enterprise Surveys is that the survey questions are the same across all countries. Moreover, the Enterprise Surveys represent a stratified random sample of firms using three levels of stratification: sector, firm size, and region. Sectors are determined based on the ISIC Rev. 3.1 classification of the main product or service.

The Enterprise Surveys Global Database covers a wide range of indicators on firm characteristics, the business environment, innovation and technology, and workforce and skills among others. We merged this dataset with data on firm‐level output, value added, and capital stock obtained from the Firm‐level TFP Estimates and Factor Ratios dataset. All local currencies have been converted into US dollars and deflated using a GDP deflator in USD (base year 2009). Exchange rates and GDP deflators have been obtained from the World Development Indicators.

We apply the following rules to the dataset: (i) We include only the most recent Enterprise Surveys for each country. (ii) We only cover the years 2010 to 2017, to account for the shock of the global economic crisis of 2008. (iii) We exclude services firms from the sample, i.e. for which the ISIC Rev. 3 classification of the main product or service is higher than 36 and those that have been classified as services firm by the Enterprise Surveys, as information on female employment is hardly available. (iv) We drop countries for which female employment data were spotty<sup>9</sup> and firms with missing observations for

> 9 These include Lao PDR, Madagascar, Malaysia, the Philippines, Turkey, and Thailand.

6
Table 8: Key Performance Indicators (median)

|Region|Number of|Value of|Number of|Clients’|Other|
|---|---|---|---|---|---|
||Exporters|Exports|Clients|Satisfaction|KPIs|
|Asia|2|1|2|3|2|
|ECA|2|1|3|3|0|
|LAC|2.5|1|2|3|1.5|
|MENA|2|1|2|2|3|
|OECD|3|3|2|1|1|
|SSA|1.5|1.5|3|2.5|3|
|Total|2|1|2|2|1|

# **3 What do we know about the effects of export promotion agencies?**

Since the early 2000s there have been several empirical studies seeking to analyze the effects of EPAs on exports. This follows the trend of an increasing number of EPAs and a larger availability of data at the firm level.<sup>10</sup> The recent discussions about the effectiveness of export promotion policies differ from the debate in the 1980s, when most export promotion activities were marked by criticisms of their distance from the private sector.<sup>11</sup> As highlighted by Lederman et al. [2010], the number of EPAs has tripled over the past two decades and most of them have focused on assisting exporters in understanding and finding markets for their products.

Two main approaches have been used to analyze EPAs’ effectiveness. One is based on aggregated and bilateral trade flows, and the other on impact evaluation analysis using firm-level data. Overall, most of these studies find positive impacts of export promotion efforts on different outcomes, across several countries. Moons and Bergeijk [2016] provide a comprehensive meta-analysis of the literature on the impact of economic diplomacy on trade and investment, including EPAs, covering 32 empirical papers published between 1986 and 2011.<sup>12</sup> As an example of research analyzing the effect of economic diplomacy on promoting exports, but not focusing on EPAs, Rose [2007] finds a positive effect of opening a country’s embassy on export performance.<sup>13</sup> Ferguson and Forslid [2014] also find a positive effect of opening Swedish embassies abroad on boosting the number of Swedish exporting firms, particularly medium-sized firms.<sup>14</sup>

> 10This section draws on the literature review in Cruz [2014].

> 11See Krueger [1980], Hogan et al. [1991] and Lederman et al. [2010].

> 12They analyze a large set of instruments of diplomacy aiming to boost exports and investments, including diplomatic relations, consulates, investment promotion agencies, embassies, state visits and trade missions, and EPAs.

> 13van Veenstra et al. [2011] suggest that the positive effects of EPAs on bilateral trade flows are usually driven by developing countries, while Moons [2012] suggest that economic diplomacy is more effective on promoting the extensive margin of trade.

> 14Those firms may be more sensitive to fixed entry costs related to informational barriers.

10
**Figure 1: Inflation (YOY) in Pakistan**

<!-- Start of picture text --> 16.0<br>14.0<br>12.0<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>Inflation<br>Jul_16 Oct_16 Jan_17 Apr_17 Jul_17 Oct_17 Jan_18 Apr_18 Jul_18 Oct_18 Jan_19 Apr_19 Jul_19 Oct_19 Jan_20 Apr_20 Jul_20 Oct_20 Jan_21 Apr_21 Jul_21 Oct_21 Jan_22 Apr_22<br><!-- End of picture text --> Source: Pakistan Bureau of Statistics (PBS) Although the core inflation has also been gradually rising, the most recent increase in headline inflation from August 2021 onward is primarily driven by food and energy prices. While the food prices (y-o-y) for urban areas increased slightly from 15.3 percent in May 2021 to 15.5 percent in May 2022, these increased from 12.8 percent to 19.0 percent for rural areas during the same period. Figure 2 confirms the elevated levels and volatile nature of food inflation in Pakistan.

**Figure 2: Food Inflation in Rural and Urban Areas in Pakistan**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>-5<br>Food Urban YoY Food Rural YoY<br>Inflation<br>Jul_16 Oct_16 Jan_17 Apr_17 Jul_17 Oct_17 Jan_18 Apr_18 Jul_18 Oct_18 Jan_19 Apr_19 Jul_19 Oct_19 Jan_20 Apr_20 Jul_20 Oct_20 Jan_21 Apr_21 Jul_21 Oct_21 Jan_22 Apr_22<br><!-- End of picture text --> Source: Pakistan Bureau of Statistics (PBS) 3
53

# (a) Developing countries

<!-- Start of picture text --> 10<br>0<br>1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-09<br>-10<br>-20<br>Africa<br>-30<br>LAC<br>Asia<br>-40<br>-50<br>-60<br>(b) High-income countries<br>Japan/ Korea<br>200 Non-EU WE<br>EU<br>Canada<br>150<br>US<br>Australia/ New Zealand<br>100<br>50<br>0<br>1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-10<br>-50<br>percent<br>percent<br><!-- End of picture text --> Figure 3. Developing and high-income countries’ RRAs to agriculture by region, 1955–2010 (%). Calculations use farm production–weighted averages across countries.

Source: Anderson 2009, Chap. 1, updated from estimates in Anderson and Nelgen 2012b.