rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 2011-13 Number Regional share<br>4} 1990-92(millions)2011-13 1990-92(%)2011-13<br>@ Developed regions 20 16 2 2<br>© Southern Asia 314 295 371 ES)<br>@ Sub-Saharan Africa 173 223 7 26<br>© Eastern Asia 279 167 27 20<br>South-Eastern Asia = 140 65 4 8<br>@ Latin America and<br>the Caribbean 6 47 6 6<br>Western Asia and<br>Northern Africa 13 24 1 3<br>@ Caucasus and<br>Central Asia 10 6 1 1<br>@ OceaniaTotal a1 1 a 0 0<br><!-- End of picture text -->
Table 4. Impacts of Extremism on Educational Attainment

||Education<br>Index<br>(1)|Literate [=1]<br>(2)|Some<br>Education [=1]<br>(3)|Primary [=1]<br>(4)|Secondary [=1]<br>(5)|
|---|---|---|---|---|---|
|_Panel A. Taliban Exposure_||||||
|_ETi_|-0.136***|-0.073*|-0.185***|-0.019|-0.267***|
||(0.037)|(0.045)|(0.056)|(0.059)|(0.084)|
|FDR q-values|[0.001]|[0.056]|[0.002]|[0.177]|[0.002]|
|R-squared|0.192|0.178|0.182|0.053|0.073|
|_Panel B. Contested Territories Exposure_||||||
|_ECi_|-0.058***|-0.034*|-0.070***|-0.014|-0.115***|
||(0.014)|(0.017)|(0.022)|(0.023)|(0.032)|
|FDR q-values|[0.001]|[0.027]|[0.002]|[0.121]|[0.001]|
|R-squared|0.193|0.178|0.182|0.053|0.073|
|_Panel C. U.S. - Allies Exposure_||||||
|_EAi_|0.047***|0.027**|0.058***|0.010|0.092***|
||(0.011)|(0.013)|(0.017)|(0.018)|(0.025)|
|FDR q-values|[0.001]|[0.023]|[0.002]|[0131]|[0.001]|
|R-squared|0.193|0.178|0.182|0.053|0.074|
|Mean Dependent Variable|0.599|0.911|0.848|0.141|0.495|
|Observations|5,592|5,592|5,592|5,592|5,592|
|_Controls in All Panels_||||||
|Individual Covariates|Yes|Yes|Yes|Yes|Yes|
|Household Size in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|Enterprise Property in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|Dwelling Contract in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|Dwelling Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|Income Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|Number of Assets in Afghanistan|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Dependent variables: Education Index is constructed using the average of the variables in columns (ii) to (v). See Appendix B for details of variable’s constructions. Individual controls include: age, gender, employed, student status in Afghanistan, and number of friends before moving to Tajikistan. Household controls include the variables listed in Table 2. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

43
of income support through public investment programs (as in Kazakhstan, Latvia, Russia and Turkey). LiTS II respondents were asked if, in the previous 12 months, a household member had applied for any of four types of state benefits, the status of the application and whether the first payment had been received. Such information provides only a partial picture of the coverage of public safety nets (it does not include pensions,<sup>7</sup> for example) and does not distinguish between safety net assistance received regardless of the crisis and that provided as part of a specific policy response to the crisis.

**Figure 6. Coverage of four categories of government benefits**

<!-- Start of picture text --> Slovenia<br>Czech Republic<br>Latvia<br>Slovak Rep.<br>Hungary<br>Estonia<br>Lithuania<br>Bulgaria<br>Russia<br>Romania<br>Kazakhstan<br>Uzbekistan<br>Croatia<br>Ukraine<br>Mongolia<br>Armenia<br>Moldova<br>Tajikistan<br>Serbia<br>Albania<br>Belarus<br>Macedonia<br>Poland<br>Azerbaijan<br>Turkey<br>Georgia<br>Montenegro<br>Bosnia-Herzegovina<br>Kyrgyz Rep.<br>0 10 20 30 40<br>Proportion of poor households affected by the crisis that received benefits (percent)<br><!-- End of picture text --> Source: LiTS II (2010).

Note: Government benefits received in the past 12 months, including unemployment, child support and/or targeted social assistance/guaranteed minimum income. “Poor” households refer to the poorest third of households in every country as measured by consumption. Households affected by the crisis are those that reported being affected “a great deal” or “a fair amount”.

The availability and efficiency of public safety nets in protecting vulnerable households varies substantially across countries. Coverage was lower in south-Eastern Europe but higher in EU member states, especially Germany, Sweden and Slovenia where more than one-third of

> 7 Pensions serve as a safety net and have broad coverage in some countries. In Armenia, Romania, Russia and Turkey, minimum pensions were significantly increased in 2009 to protect the poor (see World Bank, 2011).

13
either individually or in small groups. Coaching took place from April to June 2010. In parallel, students also received supervision from one of their university professors.

In June 2010, the business plans were completed and defended by students at their university as part of the graduation requirements. After the defense, program participants were eligible to submit their business plan to a national business plan thesis competition ( _concours des meilleurs plans d’affaires_ ). The jury selected 50 winners who were eligible to receive seed capital for establishing the business outlined in their business plans. The first five winners were eligible for seed capital of 15,000 dinars each (approximately US$10,000), the next 20 winners, 7,000 dinars; and the last 25 winners, 3,000 dinars. Prizes were only paid if students had been able to secure all the complementary funding needed to set up their project. Fewer than 15 winners fulfilled that requirement and actually cashed the prize.

# **3. Experimental Design, Timeline and Data**

A randomized controlled trial was embedded into the first year of implementation of the entrepreneurship track.<sup>5</sup> In 2009/10, 18,682 students (68 percent female and 32 percent male) were enrolled in the third year of _licence appliquée_ in Tunisian universities. All these students were invited to fill in an application form for the entrepreneurship track in November or December 2009. In total, 1,702 students (or 9.1 percent of all eligible students nationwide) applied to participate in the newly established entrepreneurship track. Of those, 1,310 students applied individually and 392 applied in pairs, so that in total, 1,506 projects were registered. In line with the majority of university students being female, this track had a relatively higher intake of women (67 percent female and 33 percent male).

The baseline data for the study were obtained from two sources. An application form was collected in November and December 2009. The application form contained information on students’ socio-economic background and employment experience, as well as their parents’. Additional information was collected through a phone survey in January and February 2010.

> 5 The entrepreneurship track has kept running every academic year since then. Some adjustments were made along the way in terms of the overall scope of the entrepreneurship track, for instance in targeting a broader set of Master’s and engineering students. However, the changes were relatively minor, and the impact evaluation results have direct relevance for the entrepreneurship track as it is currently implemented.

8
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Policy Research Working Paper 7986

# **Abstract**

Simultaneity of borrowing, withdrawal of savings, and loan defaults due to the pronounced seasonality of ag-riculture often leads to investment failure of rural financial institutions. Lack of borrowing leads to lack of in-come- and consumption-smoothing, and in turn, causes inefficient resource allocation by rural households. Financial institutions that are active in rural areas take different measures to address the covariate risks in in-termediation. For example, microfinance institutions have sought various measures such as supporting non-farm activities to diversify income, introducing seasonal loans, and bringing flexibility in loan repayments to reduce non-payments in lean seasons. This paper examines whether the financial inclusion policies of micro-finance institutions have successfully helped reduce the adverse effects of covariate risks. Analysis of household and program level data from Bangladesh suggests that despite the innovative measures taken by the MFIs to cope with the covariate risks, seasonality of income still affects seasonality of borrowing and invest-ment decisions of both the households and MFIs beyond and above what is caused normally by agricultural seasonality. Innovation is needed to promote, among other things, sectoral diversification of financial inter-mediation and to avert the extreme seasonality of rural income. Rural labor markets should be diversified enough to address the seasonality of income and consumption. Public policies guiding rural financial interme-diation must reflect such realities of rural economies.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hsamad@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **4 Regression Analysis and Results**

In this section, by using event study and differences in difference designs, we empirically investigate how Covid-19 affected export outcomes for a) all importers and exporters, b) firms that participate in GVCs compared to firms that do not participate in GVCs and c) firms that face NTMs in the pre-period compared to firms that did not face NTMs in the pre-period.

## **4.1 All Importers and Exporters**

We do the following event study to analyze the impact of the Covid-19 shock on firm-level imports and exports. We also investigate whether the effects differ depending on whether firms participate in GVCs or not, that is, run equation 1 separately for GVC versus non-GVC firms in section 4.2.

_Yit,pc_ : outcome variable for firm _i_ selling HS-8 product _p_ to country _c_ at year-month _t_ . We use the inverse hyperbolic sign transformation of the dependent variable so that zero observations are not dropped out. We control for firm, HS-8 product, and partner country fixed effects. Standard errors are clustered at the 8-digit product level. The period of our analysis is February 2019-June 2021 and the frequency is monthly. The time period in our analysis is limited by the availability of high-frequency firm-level data from Panjiva. The omitted month is March 2020, when the WHO characterized Covid-19 as a pandemic. We plot the _βt_ coefficients associated with the outcome variables for each month.

### **Fact 1: Exports were more resilient to the Covid-19 shock than imports (Figures 19, 20,**

### **21).**

This is both on the intensive margin (export volumes Figure 19) and extensive margin (export transactions Figure 20 as well as the number of products exported Figure 21). Even though we observe a decline in imports and exports in our results, the drop in imports for all three measures (Figures 19b, 20b, 21b) is more significant - both in terms of magnitude and duration - than for exports (Figures 19a, 20a, 21a).

Our findings are corroborated by Arenas et al. (2022), who compare the effects of domestic and external lockdowns on trade in the Philippines and also come to the conclusion that overall imports were more affected by the crisis than exports. When differentiating between the different lockdowns, the authors find that domestic lockdowns did not affect international trade, while external lockdowns affected imports and exports negatively. Arenas et al. (2022) also conclude that this decline was largely due to a drop in the number of products. This, in turn, aligns with the results we obtained, as illustrated by Figure 21, which shows a large drop in quantity around May 16
age population. The low-income group is domninated by China and India which account for 70% of the group's working age population. Reliable and recent data were sought out for these countries, and the group aggregates (especially when wage and nonwage work are aggregated) are robust to the inclusion and non-inclusion of many of the other countries in each group.

Regional statistics are also dominated by a few large countries. Again, reliable data were sought for the largest in each group and when wage and nonwage work are aggregated, the source data are fairly representative. Data on the status of workers is hard to obtain, and was predicted for many countries<sup>6</sup> (see Table 4b). In addition, the reliability of some of these data is questionable. These problems are particularly important for the Sub-Saharan African group.

In interpreting the results, care must also be taken as to which countries are included and excluded from each group or region. Two issues are of note here. First, from the _1994_ to the _1995 World Development Indicators_ [World Bank 1994, 1995] classifications, Indonesia changed from a low-income economy to a middle-income economy. The size of Indonesia's population makes the results (particularly those for middle-income economies) sensitive to this change in classification. Second, the high-income OECD regional group does not consist of all the .OECD economies. Greece, Mexico, Portugal, and Turkey are excluded. So, for example, the number of unemployed persons in the high-income OECD group is estimated at 30.9 million. If Greece, Portugal, and Turkey are included in the group, the estimated number of unemployed pepsons would be 35.3 million. This compares to a projection for 6None of the results are qualitatively effected when only actual data are used in caculating group aggregates.

8
# **Appendix Table 1: Donor Countries with Banking Crises**

|Source<br>|Laeven and Valencia<br>[LV], (2008)|Caprio et al. [CKLN], (2005)|
|---|---|---|
|Donor|start dates|start-end dates|
|Australia|‐|1989‐92*|
|Austria|‐|‐|
|Belgium|‐|‐|
|Canada|‐|1983‐85*|
|Denmark|‐|1987‐92*|
|Finland|1991|1991‐1994|
|France|‐|1994‐95*|
|Germany|‐|1976‐79*|
|Iceland||1985‐86*, 1993*|
|Ireland|‐|‐|
|Italy|‐|1990‐95*|
|Japan|1997|1992‐2006|
|Korea, Rep. of|1997|1997‐2002|
|Luxembourg|‐|‐|
|Netherlands|‐|‐|
|New Zealand|‐|1987‐90*|
|Norway|1991|1990‐93|
|Portugal|‐|‐|
|Spain|1977|1977‐85|
|Sweden|1991|1991‐94|
|Switzerland|‐|‐|
|Turkey|1982, 2000|1982‐85, 1994*, 2000‐2002|
|United Kingdom|2007|1974‐76*, 1980‐99*|
|United States|1988, 2007|1988‐91*|
|*non‐systemic crises|||

23
As mentioned earlier, the GDDS recommends every country to have poverty data every three to five years. The first indicator satisfies the minimum requirement of the GDDS’s recommended frequency of poverty data every five years. The second indicator approximately measures whether a country collects poverty data every three years. Empirical evidence shows that poverty rates can change even within relatively short time intervals. Experiences from the global financial crisis in 2009 suggest that the lack of frequent poverty data can pose challenges to policy makers. For example, in Bangladesh, where poverty data are collected every five years, the timing of the household consumption survey did not coincide with the economic shock in 2009, and as a result, policy makers had no information on the effect of the global financial crisis on poverty in the country and could not formulate policies to lessen the negative impact of the crisis based on strong empirical evidence. Thus, more than two poverty data points in ten years is desirable.

# **5. Setting Targets and a Goal**

To monitor progress regarding data availability _vis-a-vis_ the two indicators defined above, targets have to be set. To develop targets we go through the process of projecting the current trends of improvement/deterioration of the two indicators forward into 2030. As the discussion in section 3 shows, while both indicators show significant improvements, the pace of improvement differs across different time periods. Therefore, we calculated the annual average growth rate of these indicators for three different time periods: the last three years, the last five years, and the last ten years. Figure 5 summarizes the results.

**Figure 5: The pace of growth in Indicator 1 and 2**

<!-- Start of picture text --> Projected Growth of Indicator 1<br>160 163<br>146<br>140<br>131<br>120<br>98<br>100 85<br>82<br>76<br>80<br>Actual<br>60 Last 3 Years<br>40 Last 5 Years<br>Last 10 Years<br>20<br>0<br>1992-2001 1993-2002 1994-2003 1995-2004 1996-2005 1997-2006 1998-2007 1999-2008 2000-2009 2001-2010 2002-2011 2003-2012 2004-2013 2005-2014 2006-2015 2007-2016 2008-2017 2009-2018 2010-2019 2011-2020 2012-2021 2013-2022 2014-2023 2015-2024 2016-2025 2017-2026<br><!-- End of picture text --> 12
# Figures and Tables

Figures

**Figure 1: Migrant outflow for low-skilled work and remittance receipt**

<!-- Start of picture text --> 1975 1980 1985 1990 1995 2000 2005 2010 2015<br>year<br>S. Arabia U.A.E Oman<br>Malaysia Others Remittance (% of GDP)<br>1000<br>10<br>800 8<br>600 6<br>400 4<br>Migrant outflow ('000)<br>Remittance (% of GDP)<br>2<br>200<br>0 0<br><!-- End of picture text --> _Source:_ BMET (2019).

34
21 expenditures. Including rents -- even at their generally low levels -- brings the total up to 18.2%.'5

**Table** 7: **Poland - Transport, communication, and rent as percent of total expenditures**

|**quintile**|total energy|transport and<br>communication|rent|total utilities<br>transport and rent|
|---|---|---|---|---|
|I**(poorest)**|7.4|4.3|2.2|13.9|
|**2**|9.0|_5.1_|2.6|16.7|
|3|10.2|_5.9_|2.7|18.8|
|4|10.8|6.7|2.6|20.1|
|**_5_(richest)**|10.3|9.0|2.2|21.5|
|**All**|_9.5_|6.2|_2.5_|18.2|

Source: Authors' calculations from Poland Household Budget Survey.

# **III. Raising Energy Prices: Who Loses and by How Much?**

There are three types of effects on households associated with higher household energy prices: First, there are the _direct_ effects on the consumption and welfare of household consumers; these depend on the share of energy in total budgets and its substitutability with other fuels and with other goods. These costs will be higher in the short run than in the long run. Second, the _indirect_ effects resulting from higher revenues for utilities that permit needed investments, allowing producers to become more efficient and to provide cleaner energy, both of which benefit consumers.

15/ An important related issue is that of sequencing of the various price adjustments. One factor in determnining appropriate sequencing is the substitutability/cross price elasticity of various energy sources or services. This is particularly important in energy, as relative household energy prices should ideally be adjusted together to minimize allocative efficiency distortions (i.e, one should attempt to maintain the differences between prices which reflect differences between economic cost). This avoids serious mispricing of one fuel relative to another which would encourage the wrong type of interfuel substitution in the long-run. Other factors affecting sequencing are likely to be country specific, given the rate of growth of demand, country specific cost structure and overcapacity/undercapacity conditions.