rafmacalaba/datause-encoder-data huggingface dataset:
<mark>Finally, actual 2020 micro data for Brazil shows that the simulations underestimated the magnitude of the shock throughout the distribution, especially for the wealthy, because they failed to account for the extent of the earnings decline. Consequently, it underestimated the increase in poverty. The evidence shows that during shocks employers or the government may protect employment, but use reduced hours and reduced earnings as the margin of adjustment to the shock, and this needs to be better taken into account in these models. The underestimation of the shock was even larger when</mark> using employment projections based on elasticities instead of actual employment data.

<mark>This work provides valuable lessons about conducting microsimulations to analyze the distributional impacts of shocks. First, the use of employment projections based on past data could underestimate the magnitude of job losses and, consequently, poverty increases. The use of actual employment data when available is advised to reflect changes in employment levels more</mark> precisely.

<mark>Second, even when using actual employment data, projections of income changes can be biased if the adjustment mechanisms used during crises are not captured by the model. Adding more complex features to the model, such as the possibility of labor adjustments in the intensive margin, may allow to obtain a better simulation (closer to the real values) of income changes. A similar caveat applies to other sources of income that our model did not capture, such as non-labor incomes different from remittances. In the same line, using a sector-specific scale factor to adjust labor incomes up or down (instead of a single scale factor) could be an improvement. Such data could be obtained from ILO sectoral-specific wage data, although it would only capture earnings of wage</mark> employees.

<mark>Third, in the context of the COVID-19 crisis, only the agriculture sector was growing in some countries. This allowed us to implement a simplified model where there was no sectoral reallocation. However, in other contexts, where there are substantial job gains in multiple sectors, the model will need to be adjusted to incorporate the possibility that a worker who loses a job in a</mark> certain sector gains a job in some other.

28
-2-

- (a) How has the labor market responded to changes in macroeconomic conditions and to related government policies? In particular, does the labor market present a significant obstacle to "successful" adjustment policies?

- (b) To what extent has government intervention affected the microeconomic functioning of the labor market?

The plan of the paper is as follows. In section 2 we briefly review the main macroeconomic trends experienced by the set of countries over the 19709 and first half of the 19809. Section 3 explores some theoretical relationships betwee&z. changes in domestic policies and the external environment upon the laboi market. In section 4, we look at the labor market trends that have actually occurred and relate these as far as possible to the material provided by the country studies. In Section 5, we turn to some institutional aspects of LDCs' labor markets. Finally, a summary and conclusions appear as section 6.

# 2. The Maccroeconomic Scenario

The growth performance of nearly all the countries in our sample has deteriorated significantly since 1980. The annual average growth rate of GDP, as shown in Table 1, was generally lower between 1980 and 1986 than during earlier periods in all countries except India and Pakistan. Most LDCs in fact started to run into difficulties of one kind or another following the slowdown in the world economy after 1973. In sub-Saharan Africa, for example, exports grew on average at only 1.1Z p.a. during the 1970s as compared with an average rate of over 6Z during the previous decade. This largely reflected a rather poor growth performance of the agricultural sector arising from a combination of terms of trade changes, an overvalued exchange rate, low agricultural pricing policies and often inadequate levels of public investment in rural
Appendix Table 12 Probability of Firms Using Public Programs: Including COVID Risk Variables

|||Credit Program|
|---|---|---|
||(1)<br>Original|(2)<br>COVID Risk Variables|
|Risk|-0.264<br>(0.022)||
|Low COVID Risk _×_ Risk||-1.418<br>(0.123)|
|High COVID Risk _×_ Risk||-1.746<br>(0.360)|
|Increase in Sales Dummy|0.014|0.096|
||(0.006)|(0.041)|
|Decrease in Sales Dummy|0.014|0.091|
||(0.006)|(0.041)|
|Use Employment Program|-0.009<br>(0.004)|-0.065<br>(0.023)|
|Use Credit Program|||
|Dependent Variable Mean|0.913|0.915|
|Dependent Variable Std. Dev.|0.281|0.279|
|Number of Firms|36,609|37,313|
|R<sup>2</sup>|0.030|0.007|
|Industry FE|Yes|No|
|Municipality FE|Yes|No|
|_Predicted Default Probability_|||
|Firms with Previous Loans|0.090|0.090|

This table reports probit estimations of the probability of a firm with a previous loan getting approved for the credit program on a set of firm-level and COVID Risk characteristics. Firms with previous loans are those that either have bank credit outstanding in December 2019 or receive a bank loan over the period 2012–2019. Column 1 and 2 correspond to the original estimation of Table 2, Column 3, and the interactions of COVID Risk Exposure and Municipality Exposure, respectively. Risk corresponds to the fitted values of the regression specification reported in Table 1, Column 8. Low COVID Risk is a dummy variable equal to one if COVID Risk Exposure and Municipality Exposure dummy variables are equal to one and is zero otherwise. High COVID Risk is a dummy variable equal to one if COVID Risk Exposure and Municipality Exposure dummy variables are equal to zero and is zero otherwise. Increase (decrease) in sales dummy is equal to one if sales growth is greater (lower) than or equal to 2% (-2%), and zero if sales growth is between -2% and 2%. Use credit program and employment program are dummy variables equal to one for program participation and is zero otherwise. Use both programs is a dummy variable equal to one for credit and employment program participation, and is zero otherwise. The table uses the firms with previous loans of the credit program eligible firms sample. Some observations are dropped when estimating the model with fixed effects. Standard errors, shown in parentheses, are clustered at the industry and municipality level.

30
_Social Capital, Household Welfare and Poverty in Indonesia_ 34 capital can help escaping from poverty. In this context, it is important to note that the ownership of social capital (as measured by the interactive social capital index) is fairly equally distributed: the social capital index for the richest quintile is only about 30% higher than for the poorest quintile-about the same degree of inequality as for years of education. Physical assets are distributed much more unequally, especially land and household durables (animal ownership is only weakly related to income, and ownership of farm equipment, which is very low overall, declines with income level).

**Table 6: Ownership of Assets, by Quintile of Household Expenditure per Capita**

||||**Quintiles|**||||
|---|---|---|---|---|---|---|
|I<br>|~~~~~~~~~~~|**(Poorest)**|**2**<br>|<br>**2IQ(R**|<br> <br>**3**<br>|<br>**ichest)**|<br> <br>**4**<br>||<br>**_5_**<br>**l**|**All**<br>Al|
|Social Capital Index|14.99|16.65|16.65|18.00|19.89|17.23|
|Years of Education|4.32|4.64|4.59|5.04|5.65|4.85|
|Land Ownership (hectares)|1.45|1.28|1.96|3.90|2.52|2.17|
|Animal Ownership (number)|4.64|3.22|3.42|3.14|4.88|3.86|
|Fann Equipment Ownership<br>(number)|0.71|0.69|0.67|0.64|_0.55-_|0.65<br>I|
|<br>Household Durables (number)|1.25|1.76|2.13|2.69|3.07|2.18|

The question remains however whether this relative accumulation of social capital assets by the poor is rational, in the sense that indeed it helps them escape from poverty or at least provides them with relatively higher returns than other assets. We address this question in several ways. First, we estimate a probit model of the likelihood to be poor.21 The results indicate that social capital can significantly reduce the probability to be poor (Table 7). The average household with 5.5 memberships has a 7.26 percentage points lower probability to be poor than a household with no memberships. In contrast, a household with an average education level (4.8 years per adult) reduces its probability to be poor by 6.0% relative to a household with no education. This suggests that investing 21 The poverty line was set at two-thirds of mean household expenditure per capita.
**Figure 1.5. Trends of Income per capita Net of Government Pensions and Gini Coefficients, RLMS 1994-2015**

<!-- Start of picture text --> 9.5 .55<br>.5<br>9<br>.45<br>8.5<br>.4<br>8<br>.35<br>Net income per capita<br>7.5<br>Ginni for net income<br>.3<br>Gini coefficient<br>Log of net income<br>1994 1995 1996 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

**Note:** Estimation results are obtained based on total household income per capita net of government pensions, using the repeated cross sections for each year. All numbers are deflated with December to December regional CPIs and weighted with population weights.

64
information on all private schools in the country at that time. The Educational Management Information Systems (EMIS) is collected by the provincial Education departments and provides information on public schools; due to data limitations we use the EMIS for Punjab province only (more on this below). Finally, the Pakistan Integrated Household Survey (PIHS) conducted also by the Federal Bureau of Statistics is a nationally representative household survey carried out in 1991, 1998 and 2001. We use the 1991 and 2001 rounds to examine the growth of private schooling across provinces, the rural/urban divide and across income groups, and the 1998 round to look at enrollment differences between the rich and the poor in villages with and without private schools.<sup>11</sup> These data were linked through an extensive matching process so that we could examine schoollevel attributes in conjunction with village-level data such as population and village infrastructure. There was a 100 percent match between the population census, the census of private schools and the PIHS since these were collected by the same agency. The EMIS data was collected by the provincial government and had a different coding scheme. To match these data at the village-level we used text-matching algorithms followed by a manual match. This process allowed us to match 85 percent of the schools in the EMIS database with the census; for these villages, we then have complete data on both village-characteristics and the existence of public and private schools.<sup>12</sup> In addition to these government data sources, data on teachers’ profiles and wages were collected in 2004 as part of an ongoing project—Learning and Educational Achievement in Punjab Schools (LEAPS). A unique characteristic of this data is the sampling frame, which was constructed in two stages. In the first stage we stratified the province of Punjab in three regions—North, Central and South—and chose one district from each region.<sup>13</sup> In the second stage, villages were chosen randomly in each of these districts from a list frame of villages _with at least one private school_ . The sampling-frame allows us to examine variation in teacher’s wages across public and private schools _in the same village_ , thus abstracting from differences due to geography, labor market segmentation or other village specific features. Across the three districts, our sample consists of 4,880 teachers interviewed in 800 public and private schools in 112 villages.

> 11 In order to do so, we need the names of villages where the surveys were conducted. We have access to the list of villages for 1998, but not 2001.

> 12 The problem is somewhat complicated since there is no standardization of transliteration of village names into English. The same village names can be spelled quite differently in the two data sets.

> 13 The choice of one district each from North, Central and South Punjab follows an accepted stratification in the Province, where the Southern districts are typically thought to be the least developed followed by the center.

7
Policy Research Working Paper 6669

# **Abstract**

Entering and successfully surviving in export markets is a costly process for firms. The process involves learning about the existence of foreign demand, “discovering” production costs of exportable goods, building up reputation, succeeding in product branding to reduce competitive pressures and to be constantly upgrading quality standards to better serve demanding international clients, and remaining competitive vis-à-vis other players in the global marketplace. This paper argues that tourism can help alleviate some of these costs by providing a relatively inexpensive platform for cost-discovery and by acting as a low-cost “in-house” trade fair, accessible to all domestic producers. The analysis combines productlevel data on world and Nepal’s exports (both for goods that are related and unrelated to tourism) with Nepalese data on tourist inflows and expenditures and macro indicators on relative prices. For tourism-related goods, the analysis reveals a positive association between tourist inflows from given destinations and their expenditures, with future merchandise exports to those destinations. Instead, for goods a priori unrelated to tourism, the data reveal no connection between tourism flows and future exports. The results suggest spillovers from tourism into merchandise export performance and diversification and would imply that there are gains from cooperation between tourism and export promotion agencies.

This paper is a product of the International Trade Department, Poverty Reduction and Economic Management Network. 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 gvarela@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
Table 6: Impact on Behaviors - Chores and Care

||Comple<br>domestic<br>past wee|ted any<br>tasks in<br>k (Yes=1)|Hours s<br>domestic<br>average<br>week|pent on<br>tasks in<br>day last<br>(0-24)|Index: Ca<br>sibling of<br>gende|ring for<br>opposite<br>r (0-1)|
|---|---|---|---|---|---|---|
||Boys<br>(1)|Girls<br>(2)|Boys<br>(3)|Girls<br>(4)|Boys<br>(5)|Girls<br>(6)|
|Panel A: Endline|||||||
|_CHOICES_|0.070*|0.012|-0.027|-0.150|0.117**|0.114|
||(0.04)|(0.02)|(0.13)|(0.20)|(0.06)|(0.08)|
||[0.07]|[0.83]|[1.00]|[0.88]|[0.04]|[0.17]|
|_EMB (marginal)_|-0.013|0.015|-0.102|-0.019|0.007|-0.044|
||(0.04)|(0.01)|(0.17)|(0.14)|(0.05)|(0.06)|
||[1.00]|[0.22]|[1.00]|[1.00]|[1.00]|[0.97]|
|Control group mean|0.50|0.94|3.61|5.08|0.01|-0.01|
|Adjusted R<sup>2</sup>|0.04|0.02|0.02|0.01|0.11|0.09|
|Observations|<br>1,285|1,358|1,285|1,358|1,287|1,271|
|ANCOVA|Yes|Yes|Yes|Yes|No|No|
|Panel B: Midline|||||||
|_CHOICES_|0.008|-0.015|-0.149|0.046|0.214***|0.202*|
||(0.03)|(0.01)|(0.15)|(0.16)|(0.07)|(0.12)|
||[1.00]|[0.31]|[0.47]|[1.00]|[0.00]|[0.11]|
|_EMB (marginal)_|0.087**|-0.007|0.305*|-0.075|-0.037|-0.078|
||(0.04)|(0.02)|(0.16)|(0.14)|(0.09)|(0.09)|
||[0.05]|[1.00]|[0.08]|[1.00]|[1.00]|[0.71]|
|Control group mean|0.53|0.96|3.24|5.03|0.00|-0.02|
|<br>Adjusted R<sup>2</sup>|0.03|0.07|0.03|0.07|0.10|0.14|
|<br>Observations|<br>939|1,048|939|1,048|967|1,026|
|ANCOVA<br>Notes: * significant at 10% le|Yes<br>vel ** significan|Yes<br>t at 5% level *|Yes<br>** significant|Yes<br>at 1% level|No|No|

(1) CHOICES=1 if adolescent was assigned to CHOICES only or CHOICES+EMB treatment groups. EMB=1 if adolescent was assigned to CHOICES+EMB group, effectively giving the marginal impact of the EMB program in addition to the CHOICES program.

(2) ANCOVA estimation controls for the level of the outcome variable. All regressions include district fixed effects and a set of controls measured at baseline: dummy for whether household has multiple adolescents and age of respondent.

(3) Standard errors are clustered at the community level and are reported under the coefficient in parentheses. Sharpened q-values that correct p-values for the false discovery rate (FDR) are in square brackets. A sharpened q-value of 1.00 indicates the null hypothesis is not rejected at any level of FDR.

(4) Outcomes: "Completed any domestic tasks in the past week" =1 if adolescent completed any of the following household chores activities in the past 7 days: cooking, washing clothes, cleaning the house, or washing dishes. "Hours spent on domestic tasks in average day last week (0-24)" = number of hours spent on care for others and domestic tasks on an average day in the past week. "Index: Caring for sibling of opposite gender (0-1)" is based on whether the child mentions one thing they did in the past week to let their sibling know they care for them, and whether they have spoken to their parents about their sibling's wellbeing during the last month (Index standardized using method by Kling et al. 2007).

33
|**Table**|**Log Real Value of**<br>**A2. National results**|<br>**using the OLS estima**|<br>**tor**|
|---|---|---|---|
|||**Log Daily Calorie**|**Log Food**|
||**Monthly Food**<br>**Consumption**|<br>**Intake Per Capita**|<br>**Consumption Score**|
|LogWheat Flour Price|-0.425***|-0.184***|-0.189***|
||[0.0364]|[0.0244]|[0.0270]|
|Agricultural Household|0.0351***|0.0302***|0.0310***|
||[0.0100]|[0.00713]|[0.00727]|
|Log Real Value Monthly|0.0366***|-0.00489|0.0381***|
|HousingPer Capita|[0.00684]|[0.00444]|[0.00479]|
|Log Real Value Monthly|0.0996***|0.0375***|0.0571***|
|Durables Per Capita|[0.00444]|[0.00332]|[0.00303]|
|Log Real Value Livestock|0.0133***|0.00670***|0.0166***|
|Per Capita|[0.00128]|[0.000932]|[0.000999]|
|LogKerosene Price|0.233***|0.179***|-0.0286|
||[0.0644]|[0.0461]|[0.0477]|
|LogVegetable Oil Price|-0.140**|0.0136|-0.0329|
||[0.0598]|[0.0443]|[0.0442]|
|LogLocal Rice Price|-0.0059|-0.109***|0.0734***|
||[0.0398]|[0.0264]|[0.0263]|
|LogLamb Price|-0.128|-0.0488|-0.0199|
||[0.0903]|[0.0629]|[0.0644]|
|LogMilk Price|-0.140***|-0.143***|-0.103***|
||[0.0376]|[0.0280]|[0.0268]|
|Head Age|0.00173|0.0284|0.024|
||[0.0264]|[0.0213]|[0.0200]|
|Head Married|0.00754|-0.0323***|0.0621***|
||[0.0144]|[0.0106]|[0.0101]|
|Head Literate|-0.0371***|-0.000189|-0.0453***|
||[0.00775]|[0.00610]|[0.00572]|
|Number Men|0.00194|-0.004|0.0198***|
||[0.00382]|[0.00303]|[0.00294]|
|Number Women|-0.00192|-0.00502*|0.0167***|
||[0.00353]|[0.00275]|[0.00256]|
|Number Children|-0.0208***|-0.0212***|0.0128***|
||[0.00177]|[0.00144]|[0.00127]|
|Plateau|0.00367|5.49E-06|-0.00466|
||[0.0210]|[0.0141]|[0.0153]|
|Mountainous|-0.00373|0.00126|-0.0115|
||[0.0196]|[0.0141]|[0.0139]|
|Observations|20,483|20,483|20,483|
|R-squared|0.353|0.25|0.392|

Note: Each column represents a separate regression with population weighted estimates. Robust standard errors -in brackets- are clustered by stratum and adjusted for survey design. Real values reflect adjustments for spatial and temporal price differences. Plains is excluded category. *, **, and *** denote significance at 10%, 5%, and 1%, respectively.

35
Fiscal No. 53, Santiago MALONEY, William F. (1998a), "The Structure of the Labor Market in Developing Countries: Time Series Evidence and Competing Views" Policy Research Working Paper No. 1940, World Bank, Washington D.C.

- (1998b), "Are Labor Markets in Developing Countries Dualistic?" Policy Research Working Paper No. 1941, World Bank, Washington D.C.

(1999), "Does Informality Imply Segmentation in Urban Labor Markets? Evidence from Sectoral Transitions in Mexico" The World Bank Economic Review, Vol. 13, No. 2, pp. _275_ - 302, Washington D.C.

(2000), "Minimum Wages in Latin America: A Note" Poverty Reduction and Economic Management Department, World Bank, Washington, D.C.

(2001), "Informality Revisited" Poverty Reduction and Economic Management Department, World Bank, Washington, D.C.

MARSHALL, John, R., (1976), "Moral Hazard", The American Economic Review, Vol. 66, pp. 880-890 MASTRANGELO, Jorge, (1999), "Politicas Para la Reducci6n de Costos en los Sistemas de Pensiones: El Caso de Chile", CEPAL/ECLAC Serie, Financiamiento del Desarollo, No. 86, Santiago MESA-LAGO, Carmelo, (1991), "Social Security and Prospects for Equity in Latin America", World Bank Discussion Paper No. 140, Washington, D.C.

(2000), "Social Assistance, Pensions and Health Care for the Poor in Latin America and the Caribbean. In Nora Lustig ed. Shielding the Poor: Social Protection in the Developing World. Washington D.C.: The Brookings Institution and the IADB (2001), "La Cobertura de Pensiones de Seguridad Social en America Latina Antes y Despues de **la** Reforma Previsional", Socialis: Revista Latinoaniricana de Politica Social, No. 4, Abril, pp. 17 - 27 MINCER, Jacob, A., (1974), _Schooling, Experience and Earnings,_ New York, Colombia University Press.

MONTENEGRO, Claudio, (2001), "Wage Distribution in Chile: Does Gender Matter? A Quantile Regression Approach", World Bank Development Research Group, mimeo MORANDE, Felipe G, (1992), "Dynamics of real asset prices, the real exchange rate, trade reforms and foreign capital inflows: Chile, 1976- 1989" Journal of Development Economics, Vol. 39:[111]-39 July MORANDE, Felipe y Raimundo Soto, (1992) "Una nota sobre la construcci6n de series de precios de activos reales: tierra y cases en Chile (1976- 1989)" REVISTA DE ANALISIS ECONOMICO (CHILE); 7:[169]-177 ORSZAG, Peter R. & Joseph E. Stiglitz, (1999), "Rethinking Pension Reform: Ten Myths About Social Security Systems" in R. Holzmann & J. Stiglitz, Eds. New Ideas About Old Age Security. The World Bank PACKARD, Truman, (2001), "Is There a Positive Incentive Effect from Privatizing Social Security? Evidence from Pension Reforms in Latin America", World Bank Policy Research 37
not accounted for in disaster consequences since it corresponds to benefits that could have been captured in the absence of the disaster, through a classical stimulus policy.

The length of the reconstruction period will depend on many characteristics of the affected economy, including (1) the capacity of the sectors involved in the reconstruction process (especially the construction sector); (2) the flexibility of the economy and its ability to mobilize resources for reconstruction (e.g., the ability of workers to move to the construction sector, see Hallegatte, 2008); (3) the openness of the economy and its ability to access resources (e.g., skilled workers and materials for reconstruction); (4) the financial strength of private actors, households and firms, and their ability to access financial resources for reconstruction, through savings, insurance claims, or credit; and (5) the financial strength of the public sector and its ability to access financial resources to reconstruct (see the very thorough analysis of financing options in developing countries in Mechler, 2004).<sup>19</sup> With discounting at a rate r, the net present value of output losses is<sup>20</sup> :

> 19 Specific instruments such as contingent credit lines help with reconstruction financing. See for instance on the World Bank’s Cat‐DDO, http://treasury.worldbank.org/bdm/pdf/Handouts_Finance/CatDDO_Product_Note.pdf.

> 20 The variable noted with x� are the net present value of the future fluxes of x�t� .

38