rafmacalaba/datause-encoder-data huggingface dataset:
The data on SP spending in response to the pandemic form a single cross-sectional data set, as distinct from the longitudinal (panel) data we assembled for looking at SP spending prior to the pandemic. So, we cannot include country fixed effects. However, as a check for bias associated with latent country characteristics, we include the estimated country effect from the pre-pandemic panel data regressions as an extra control in the encompassing model.<sup>34</sup> Notice that, on _a priori_ grounds, the sign on this latent effect could go either way. On the one hand, countries with greater prior experience with SP interventions may have seen lower costs of implementing an extra SP effort during the pandemic. But, on the other hand, they would presumably have less need for extra SP effort during the pandemic.

Similarly to our analysis of the pre-pandemic data, we build up our tests using regression controls to augment Figure 6. <u>Table 5</u> provides the results in testing the hypotheses one-at-atime. Relative prices for human development services do not shift the SPEC much. We find mixed support for the Accountability Hypothesis. In the linear regression, the VA and Polyarchy indices have positive coefficients, significant at the 5% level, but these effects are not robust to allowing for nonlinearity using the PLREG estimator. The GDP effect remains strong, however, so we cannot conclude that the Accountability Hypothesis explains that effect.

There is stronger support for the Governance 2 Hypothesis in the SP response to the pandemic. Strikingly, we find that GE knocks out GDP per capita, leaving it with a small effect not significantly different from zero. The GE indicator has a positive and significant effect in both the linear and non-linear SPECs.

We also find support for the Technology Hypothesis, with mobile phone usage having a significant effect at the 5% level; the GDP effect is attenuated somewhat when we control for mobile phones but remains strong. In contrast to the pre-pandemic SP spending data, we find no significant effect of internet usage in the pandemic responses.

There is support for the Ageing Hypothesis in the linear specification, with the population share over 65 having a significant effect at the 5% level, but this is not robust to allowing for nonlinearity in the SPEC.

> 34 Since this is a generated regressor, we bootstrap the standard errors. We use the country effects estimated in the OLS specification of pre-pandemic data (third column in Table 4).

24
to human capital, location, and size. These advantages are the outcome of long term investments in human capital, infrastructure and institutions. Our findings highlight three implications for policy makers interested in supporting formalization, business creation and productivity. These policy instruments include : (i) increase infrastructure provision by investing in local infrastructure; (ii) increase access to finance, (iii) local tax reforms, and (iv) increase the public-private dialogue at local level to better understand the other heterogenous constraints that firms face.

# **References**

- [1] Alby, P., Auriol, E., and Nguimkeu, P., (2014). “Does Social Pressure Hinder Entrepreneurship in Africa? The Forced Mutual Help Hypothesis”, _Working Paper_ .

- [2] Amaral, P. and E. Quentin, (2006). “A Competitive Model Of The Informal Sector”, _Journal of Monetary Economics_ , 53: 1541-1553.

- [3] Ana Maria Oviedo., Mark R. Thomas., Kamer Karakurum-Ozdemir, (2009). “Economic Informality : Causes, Costs, and Policies - A Literature Survey", _World Bank Publications_ , The World Bank, 5917.

- [4] Armington, C. and Acs, Z., (2002). “The determinants of regional variation in new firm formation". _Regional Studies_ , 36: 33-45.

- [5] Baland RM, Yseboodt M, Rivoldini A, Van Hoolst T., (2017). “Obliquity of Mercury: Influence of the precession of the pericenter and of tides", _Icarus_ . 291, 136159. doi: 10.1016/j.icarus.2017.03.020.

- [6] Behrens, K., Duranton, G., Robert-Nicoud, F., (2014). “Productive Cities: Sorting, Selection, and Agglomeration", _Journal of Political Economy_ , 122(3): 507-553.

- [7] Benhassine, N, D McKenzie, M Santini and V. Pouliquen, (2018). "Does Inducing Informal Firms to Formalize Make Sense? Experimental Evidence from Benin?, _Journal of Public Economics_ , 157: 1-14.

- [8] Briant, A., P.-P. Combes, and M. Lafourcade, (2010). “Dots to Boxes: Do the Size and Shape of Spatial Units Jeopardize Economic Geography Estimations?", _Journal of Urban Economics_ , 67(3): 287-302.

- [9] Bruhn, M., (2011). “License to Sell: The Effect of Business Registration Reform on Entrepreneurial Activity in Mexico”, _Review of Economics and Statistics_ , 93(1): 382-386.

- [10] Ciccone, A., and R. E. Hall, (1996). Productivity and the density of economic activity. _American Economic Review_ , 86(1): 54-70.

46
**<u>Figure 3: Distance from Kebele centroid to nearest commercial farm, by crop</u>**

<!-- Start of picture text --> 0 50 100 150 200 250 0 50 100 150 200 250<br>Distance to nearest maize farm Distance to nearest teff farm<br>2004 2008 2004 2008<br>2012 2014 2012 2014<br>0 50 100 150 200 250 0 50 100 150 200 250<br>Distance to nearest sorghum farm Distance to nearest wheat farm<br>2004 2008<br>2004 2008<br>2012 2014<br>2012 2014<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>Prob <= distance Prob <= distance<br>.2 .2<br>0 0<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>Prob <= distance Prob <= distance<br>.2 .2<br>0 0<br><!-- End of picture text --> 28
# TABLE OF CONTENTS

|I.|INT|RODUCTION .....................<br>......|**1**........<br>|
|---|---|---|---|
|II.|THE|PUBLIC SECTOR DEBT STRATEGY.|4|
||A.|Evolution of Public Debt ......<br>...............|.......<br>5|
||B.|Factors Explaining the Change in the Debt .... ....<br>to GDP Ratio<br>External Debt ...................................<br>Domestic Debt ...................................|... 16<br>.... 16<br>.... 28|
|III.|THE|ROLE OF PUBLIC SECTOR DEFICITS AND FINANCING.|5|
||A.|The Real Public Sector Deficits .35||
||B.|Sources of Finance and Inflation<br>.44||
|IV.|EFF|ECTS OF PUBLIC INDEBTEDNESS<br>ON THE FINANCIAL SYSTEM|51|
||A.|The Transfer Problem .51||
||B.|Effects on the Domestic Financial System .60||
|V.|CON|CLUSIONS<br>..|65|
||Refe|rences .67||

1/ This paper was done as part of the EMENA Technical Department Regional Study on External Debt and Inflation. The authors would like to thank Roberto Rocha for allowing them to use his information on Yugoslavia.
# **_What Are the Components of the Analysis?_**

**The analysis uses cross-country data from 78 developing and developed countries between 2000 and 2010.** All variables, except initial FDI/GDP level and natural resources per capita, are average annual growth rates for 2000-05 and 2000-10 and are calculated as Ẋ = (XT/X1)<sup>(1/T)</sup> −1, where XT is the observation of the last year, i.e. T= 11 for time period 2000-10 and T= 6 for time period 2000-05, X1 is the observation for the first year (2000). The dependent variable is the growth of inward FDI/GDP from 2000-10, and the independent variables are all growth rates over the first half of the period—with two exceptions. The initial level of inward FDI/GDP is the arithmetic average of the variable over the first half of the period and natural resources per capita is measured in 2000 because it does not change much over the period under study and is a stock variable. A brief description of each of the variables and their expected impact on the growth of inward FDI follows:<sup>18</sup>

**_Inward FDI Stock as a Share of GDP:_** As is discussed above, _growth_ of inward FDI stock as a share of GDP is the dependent variable—ultimately, the variable of interest. However, its level at the beginning of the growth period is an explanatory variable and plays an important role in our analysis. It proxies for all the initial conditions the regressors might have on FDI growth, whether it is a low tariff rate at the start of the period, energy availability, or other factors. It may have a positive or negative sign depending on whether the agglomeration effect (greater FDI attracts more FDI) outweighs the convergence effect (diminishing returns to additional FDI).<sup>19</sup> Data on the _inward FDI stock_ are from UNCTAD Statistics, Catalogue of Foreign Direct Investment.

**_Corporate Tax Rates:_** Higher corporate tax rates are expected to act as a deterrent to FDI by decreasing investment returns.  In this study, we use _total tax rate (as a percentage of commercial profits)_ available from the World Bank’s World Development Indicators (WDI).

**_Macroeconomic Stability:_** Researchers (e.g., Bloom, Bond and Van Reenen 2007; Bloom 2009) have found that unstable economic conditions hinder the FDI inflows by increasing uncertainty in forecasts of investment returns, regardless of whether the investment is foreign or domestic. To proxy macroeconomic stability, we use the coefficient of variation for the real exchange rate, controlling for the domestic and US CPIs.  We use this simple measure of the bilateral real exchange rate (RER) with respect to the US dollar, rather than an effective RER due to greater data availability. CPI inflation and exchange rates are available from the IMF’s International Financial Statistics (IFS).

> 18 The summary statistics table is provided in Annex.

> 19 For example, see (Wheeler and Mody 1992), (Barrell and Pain 1999), (Campos and Kinoshita 2003), and (Walsh and Yu 2010).

21
> and capital expenditures, we now subject them to a series of tests, to ensure that they are not due to some statistical fluke. The tests are not formal ones. Rather, they are based on our

> views on possible factors which could be driving these results but were not connected with the productivity of public spending.

# 3.4.1 **Fixed Effects Model**

The regression results reported in subsection **3.3** are based on panel data with the implicit assumption that there are no individual cross-sectional effects. It is likely, however, that there are country-specific characteristics that might influence per capita growth. While the countryspecific characteristics are generally difficult to measure (e.g. cultural factors), simply running pooled regression may bias the coefficient estimates. One simple way to account for country specific characteristics is to introduce country dummies. Given that we have **69** countries in the sample, this correction would result in a significant reduction in the degrees of freedom. Alternatively, we can apply the fixed-effects method which takes into account country-specific characteristics and models them as fixed effects within the country. In such a case we estimate the following individual-mean corrected regression model: where the variable X consists of all the independent variables of equation **(11).** The computational procedure (see Hsiao, **1992)** for estimating the parameters requires transforming the observed variables **by** subtracting out the appropriate time-series means, and then applying

**18**
Policy Research Working Paper 9964

# **Abstract**

- This paper investigates the causal impact of a Covid-19 lock down policy on the Comoros’s household welfare, poverty, and labor market outcomes. The identification strategy uses the national government lockdown policy implemented to curtail the unexpected outbreak of Covid-19. The lockdown policy coincided with the 2020 Harmonized Survey on Living Conditions of Households data collection, lending itself to a quasi-natural experiment in which households that were interviewed before the lockdown policy fall into the control group, while those that were interviewed after the lockdown fall into the treated group. The paper explores the impact of the Covid-19 using descriptive regression

analysis and estimates the causal impact using matching techniques. The analysis finds a reduction in household expenditure, increased poverty, and a reduction in the likelihood of employment. Investigation of differential impacts along the expenditure distribution finds larger impacts at the top of the distribution, suggesting that Covid-19 may have reduced inequality, although the poor were also negatively affected. The evidence also suggests that the ability to use assets as a coping mechanism was limited. In a context of limited safety nets and government interventions, stringent lockdown policies appear to increase the vulnerability of the poor.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at onsababera@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
While it is impossible to blind youth to their interventions, we attempted to minimize the likelihood of Hawthorne/John Henry/experimenter demand effects by not telling subjects that this was an experiment, or that alternative interventions were being offered in other settlements. The baseline survey was described as a research study aimed at understanding the desires and needs of Gambian youth. We asked both the Senegal and vocational training group participants why they thought the intervention was being offered. The two main reasons seen for the interventions were to provide them with more job opportunities, and secondly to reduce backway migration.

# **3.2 Ethical considerations**

Although deterring irregular migration is desired by European countries, revealed preference would suggest that the migrants themselves believe they are improving their lives by making this journey, raising the question of whether policy efforts to deter migration are desirable from a development perspective. There is indeed a vast literature showing that international migration in other settings has been more effective at increasing the incomes of poor people than almost any other development policy (e.g. McKenzie et al, 2010, Clemens et al, 2019). However, there are reasons to believe that revealed preference choices may still be suboptimal if potential migrants are misinformed about the severe risks involved in the migration journey. Indeed, a competing ethical consideration is the risk of accidentally encouraging more youth to attempt this journey and exposing them to these dangers. Finally, an additional ethical consideration arose due to the COVID-19 pandemic, which affected when it was safe to conduct surveys and offer in-person vocational training.

Our general approach to dealing with these ethical issues was to consider interventions that provided youth with more accurate information with which they retained their agency to make informed decisions, and to expand the set of potential opportunities they had available. We sought considerable local feedback on the design of the interventions to ensure they were accurate and appropriate for the context. Consultations with local organizations were done by the Gambian coauthor of this paper, and we worked with Gambian youth from the NGO YAIM to develop the content of the information intervention. Formal ethical approval was granted by the Nova School of Business and Economics, and presenting the design for Stage 1 Registered Report approval at the Journal of Development Economics also provided the chance for any ethical concerns to be raised by independent reviewers. We followed local guidelines with respect to the COVID-19 18
Policy Research Working Paper 6574

# **Abstract**

Microcredit programs in Bangladesh have experienced spectacular growth in recent years, with a growing number of borrowers availing credit from multiple microcredit agencies. There is a growing concern that if there are not sufficient returns to borrowing from microfinance institutions (MFIS), some borrowers might be taking loans that they will not be able to repay. A household may be considered over-indebted, for example, if its debt liability exceeds 40 percent of its income or assets. Using a long panel of household survey data from Bangladesh, the paper finds that some 26 percent of microcredit borrowers are over-indebted on this measure versus 22 percent of non-microcredit borrowers. Econometric analysis suggests that both MFI competition and multiple borrowing raise indebtedness. However, repeated borrowing, while it affects short-term liability adversely, does affect the long-term debt-asset ratio favorably. That is, repeated borrowing helps increase assets more than debt over time. Microcredit borrowers in Bangladesh are thus not necessarily over-indebted. But when borrowing is seen as protection against shocks such as floods even at the cost of being indebted, MFIs may offer micro-insurance schemes to safeguard borrowers against economic shocks.

This paper is a product of the Agriculture and Rural Development 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 skhandker@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
# **The World Bank Primer on Reinsurance**

_Dontald A. Mclsat .,and David F. Babbel '_ The authors are indebted to Kiim 13 Staki n ot the Ilnter-Aineiic;mii DuL)\cIopnw. i-hm **_K._** DLxi\ **_ti_** [u h ol the Internaitional Finance Corporationi. and Robelt Potvi n and Johill HolmIes ol the ( )tarto **Illsurllcctt** COmmission tOI their insightful comments on an earlier- draft of this manuscript.

'Senior I nsutance Specialist, Financial Sector- Development Departn.ent, WorldL BHanlk

-Financial Economist-Consultant. Financial Sector Development Department. Worl-d Batik, Associate Professor of Insurance and Risk Management, and Professor o Finarnce, Wharton School. lUniversity of Pennsylvania
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Total income over the past 12 months (which is our key variable) is obtained by aggregating incomes from 8 different sources of income (which are collected via individual questions): (a) wages, salary, commissions, bonuses or tips, (b) self-employment income, (c) interest, dividends, net rental income, or income from estate/trusts, (d) social security or railroad retirement, (e) supplemental security income, (f) public assistance or welfare payments, (g) retirement, survivor or disability pensions, and (h) other regular sources of income, such as veterans payments, unemployment compensation, child support or alimony. Income data are collected for all individuals in the sample aged 15 or older (with the exception of 1960 and 1970, where 14 years olds were also included). The data are representative at the state level and income is made comparable over time by adjusting for inflation (all income data are expressed in 2010 prices). We aggregate income data over all members of a household and then divide it by the household size to obtain household per capita income.

Data on education is available for all individuals. Each respondent’s education attainment is assigned to one of the following 11 categories: no schooling; nursery to grade 4; grade 5 to 8; grade 9; grade 10; grade 11; grade 12; 1 year of college; 2 years of college; 4 years of college; and 5 or more years of college. These categories were chosen by IPUMS so as to maximize comparability over time. We construct the following education variables from these: “edushort1518” which measures the average “education shortfall” for individuals between the age of 15 and 18; and “edums_age2139” which measures the percentage of individuals between the age of 21 and 39 with 4 years of college or more. Education shortfall is defined as the difference between the expected grade given the respondents’ age and the actual realized grade of the individual. The “expected grades” are set to: grade 9 for age 15; grade 10 for age 16; grade 11 for age 17; and grade 12 for age 18. The variable is then calculated at the state level by calculating the average “shortfall” among all individuals between the ages of 15 and 18. As shown in the Annex, the average US value shows a steady decline: in 2010, it was 0.43 years, just slightly over 1⁄2 of what it was in 1960.

The IPUMS also provides data on the general employment status which indicates whether the respondent is: (a) employed, (b) unemployed (seeking work), or (c) not in the 8
# **Living on the Edge: Vulnerability to Poverty and Public Transfers in Mexico**<sup>*</sup>

Alejandro de la Fuente<sup>†</sup> Eduardo Ortiz-Juárez<sup>‡</sup> Carlos Rodríguez-Castelán<sup>§</sup>

**Keywords:** Vulnerability, Poverty, Public Transfers, Mexico **JEL codes:** H22, I30, O54 **Sector Board:** Poverty (POV)

* This study was originally prepared as a background paper for the World Bank’s annual World Development Report (WDR) 2014 “Risk and Opportunity”. The authors would like to thank Edgar Medina and his team at the Ministry of Social Development in Mexico (SEDESOL) for providing the Module of Social Programs data commissioned by SEDESOL as part of the National Household Income and Expenditure Survey (ENIGH) employed in the analysis as well as Maria Eugenia Bonilla-Chacin, Wendy Cunninghnam, Francesca Lamanna and Emmanuel Skoufias for their thoughtful comments.

† Senior Economist in the Poverty Global Practice at the World Bank (adelafuente@worldbank.org). Corresponding author.

‡ Economist in the UNDP’s Regional Bureau for Latin America and the Caribbean (eduardo.ortiz@undp.org).

§ Senior Economist in the Poverty Global Practice at the World Bank (crodriguezc@worldbank.org).