rafmacalaba/data-use-mentions-extended huggingface dataset:
“unemployment rate” measure which is more commenly used in the literature (e.g., NguyenHoang and McPeak (2010) for Vietnam), if migrants move seeking _better_ employment opportunities. The land holding variable employed at the first stage is assumed to represent migrants’ expectation toward agricultural, forestry and fishery opportunities in potential destination areas. Finally, the distance in kilometers between the capital cities of the source province and those in potential destination provinces is included as a proxy for the cost of migration.

> <sup>Column 1 in Table 5 demonstrates the results of the conditional logit regressions by different</sup> control variables. The regressions are estimated on the sample of migrants surveyed in the VMS only. In the conditional logit model, the impacts of individual characteristics are not directly estimated since the latter variables do not vary across potential destinations. Thus, a strategy to run separate regressions by the subsets of different types of workers, namely by gender (columns 2-3), by educational attainment (columns 4-8), and for a subset of ethnic minority (column 9), is employed.

In Panel A, I begin with a specification that includes only the proportions of workers employed in foreign firms relative to total employment in potential destination areas. The coefficients for the foreign job turn out to be positively significant at the one percent level both for male and female suggesting that migrants on average tend to move to destinations with a greater presence of foreign job opportunities. The latter result contrasts with the first-stage model of outmigration, which did not reveal a clear insight on the role of foreign job opportunities on internal migration; and is consistent with Liang and White’s (1997) in-migration model which finds a positive impact of foreign capital in the destination province in pulling migrants in China.
from late 2010 to mid-2012 for the second wave, constituting an individual panel. The data provide, in particular, detailed information on marital trajectories, savings, and labor market participation at the individual level. The overall sample in the first wave is made of 1,750 households and 14,450 individuals, in 150 randomly drawn census districts. In the whole sample, 57.1 percent of the individuals are living in a rural area, 48 percent are males, and 95 percent are Muslim. The average household size is between eight and nine members.

The PSF survey data are rich and unique in that they intend to account for the complexity of household structures in Senegalese society. The questionnaire relies on the preliminary identification of household substructures, referred to as cells. Cells are defined as units that are semi-autonomous as regards resource allocation decisions, composed of a cell head and his or her direct dependents—in particular, children, foster children, or widowed mother or father. The average household is made of 2.4 cells of around three members each. Notably, expenditures data were collected at the cell level, with a distinction between expenditures specific to each cell or common to the whole household, and the identification of all contributors to each type of expenditures. This allows us to identify intrahousehold variations in consumption patterns and resources reallocation between spouses.

The PSF data are also particularly suitable for this analysis since savings stock, as well as the flows of transfers sent out and received from individuals out of the household in the past 12 months, were collected at the individual level, meaning in particular that each spouse reports his or her own savings and transfers. We have detailed information about savings; notably, we can distinguish between savings in formal institutions, informal associations, or Rotating Saving and Credit Associations (ROSCA, or _tontine_ in Senegal) and savings held at home.

# _Descriptive Statistics_

Our sample of interest in the first step of our empirical analysis is composed of all coresident monogamous and polygamous unions made of
as our crop losses estimates, per hectare variables are winsorized at the 0.01 percentile.

_Table 1:_ Data sources and sample sizes.

|**Country**|**Data Source**|**Sample Size**|**Survey Rounds**|
|---|---|---|---|
|Ethiopia|Ethiopian Socioeconomic Survey (ESS)|13,570plots|2013/14, 2015/16,<br>2018/19|
||||2010/11, 2013/14,|
|Malawi|Integrated Household Panel Survey (IHPS)|5,998plots|2016/17,2019/20|
|Uganda|Methodological Experiment on Measuring Maize<br>Productivity, Soil Fertility and Variety (MAPS2)|213 plots|2016|

> 19 Crop cuts have been termed the “gold standard” to obtain an objective measure of crop yield (Carletto, Gourlay and Winters, 2015).

> 20 https://microdata.worldbank.org/index.php/catalog/lsms

> 21 However, it is important to note that in case of urgent need of swift policy action in response to a sudden disaster, more aggregate approaches to measuring crop losses retain validity for the relative speed at which they can be conducted.

10
15|50|15|26|<br>29|
|Absolute (% of all employed in the age group)|-|33|-|-|17|-|-|40|<br>41|
|Relative to employedpersons aged 25-54(ratio)|-|0.67|-|-|0.50|-|-|0.81|<br>0.79|

Source: OECD older worker scoreboard, https://www.oecd.org/employment/ageingandemploymentpolicies.htm 41
the breakdown of domestic trade, and all periodic markets were forced to close. The self-employment sector, which accounts for 91% of the labor force, shed around 170 _,_ 000 jobs (with revenues for surviving enterprises falling 40%), and a further 9 _,_ 000 jobs were lost in wage employment [Thomas _et al._ 2014, 2015, Evans _et al._ 2015, Himelein _et al._ 2015, Casey _et al._ 2016]. Food security became an issue by December 2014. In terms of food price impacts, the best data comes from a series of tracker surveys conducted at 200 markets during the crisis [Glennerster _et al._ 2016]. They document relatively modest food price increases (e.g. for rice), but more pronounced impacts on price dispersion, with increases and decreases back to normalcy during the crisis. This is as expected given transportation restrictions but also offsetting government efforts to distribute rice.

The second key policy response related to the education system. Schools were closed in May 2014 and re-opened in April 2015 as the epidemic began to slow down (the school year runs from

> 11For the 2014-16 outbreak, the WHO estimates Liberia had 10675 cases and 4809 deaths (a 45% fatality rate), and Guinea had 3811 cases with 2543 deaths (a 67% fatality rate). WHO case data exists by district, but not any finer geography: this prevents us examining responses across villages impacted by the crisis at different times.

> 12As Adda [2016] notes, closing transportation networks or introducing quarantines is a common policy response dating back to plague epidemics in Europe and Asia during the Middle Ages. School closures are an equally common policy response to viral outbreaks, having been used in Japan, Bulgaria, China, UK and the US.

8
than formal debt relief programs. However, similarly to ad hoc debt relief policies, allowing higher inflation in the present may increase expectations of future inflation. Such expectations discourage lending in the present, reducing credit access.

Moreover, attempts to provide debt relief through inflation have severe distributional implications. Inflation is a regressive tax that places a disproportionate burden on low-income households that spend a larger share of their income on consumption. The benefits of inflation as a form of debt relief are similarly regressive and accrue primarily to debtors with larger loans that have longer maturities. Given that access to finance is limited and long-term financing is scarce and only available to households at the top of the income distribution in most developing economies, inflation is an extremely unattractive instrument to provide debt relief in these settings.

# **4 Debt Relief and Borrower Expectations: Survey Evidence**

The effect of debt relief on borrower expectations is an important channel through which relief programs can affect credit market outcomes. However, there is still little empirical evidence on how beneficiaries update their expectations in response to debt relief.

To shed light on the link between debt relief and borrower expectations, this section presents evidence from a survey of microfinance and agricultural credit borrowers in India. Our survey takes advantage of a unique policy setting that exposed borrowers in two Indian states to both debt forbearance and forgiveness programs. Specifically, borrowers across India were eligible for a sixmonth _debt forbearance_ program covering both microfinance and bank loans as a result of the Covid-19 crisis. Additionally, the Indian states of Assam and Maharashtra enacted _debt forgiveness_ programs for microfinance and agricultural credit borrowers, respectively, after the end of the national Covid-19 debt moratorium.<sup>9</sup> We surveyed a total of 1,630 borrowers who had either benefited from debt forbearance, debt forgiveness, or neither of these programs. The sample consists of 810 microfinance borrowers and 820 agricultural loan borrowers residing in states of India that had offered debt forgiveness for microfinance and agricultural loans, respectively. The survey was administered
. (2019). A survey of Nigerian households and small businesses: What do Nigerians think about taxes? Lagos: The Nigerian Economic Summit Group.

> 28 Torgler, B. (2003). Tax morale, rule-governed behaviour and trust. _Constitutional Political Economy_ , 14(2):119-140.

> 29 Kangave, J., Nakato, S., Waiswa, R, & Lumala Zzimbe, P. (2016). Boosting revenue collection through taxing high net worth individuals: The case of Uganda. _ICTD Working Paper 45_ . Brighton: International Centre for Tax and Development; Kangave, J., Nakato, S, Waiswa, R, Nalukwago, M, & Lumala Zzimbe, P. (2018). What can we learn from the Uganda Revenue Authority's approach to taxing high net worth individuals? _ICTD Working Paper 72_ . Brighton: International Centre for Tax and Development; Moore, M, & Prichard, W. (2018). Tax reform for low-income countries: Five ideas for simplifying tax systems to fit local realities. _ICTD Summary Brief 17_ . Brighton: International Centre for Tax and Development.

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Construction|0.05|0.04|0.02|0.08|0.03|
||Motor vehicle services|0.03|0.05|0.02|0.03|0.02|
||Transportation|0.05|0.03|0.03|0.05|0.06|
||Wholesale|0.09|0.09|0.02|0.12|0.08|
||Retail|0.18|0.24|0.07|0.24|0.12|
||Hotels & Restaurants|0.06|0.12|0.05|0.04|0.06|
||IT,Professional services|0.02|0.01|0.02|0.02|0.01|

Note: Based on estimation sample covering 53 countries.

27
recovery scenarios. The total potential short-term costs associated with privatization are equal to the sum of any capitalization costs borne by the province and residual entity liabilities, minus the price paid for the privatized entity (column 5). Final privatization costs will depend on the extent to wlhich provinces recover residual assets. Column 6 lists total privatization costs assuming that 20% of residual assets are recovered; Column 7 assumes a 50% recovery rate. Clearly, total costs will be quite sensitive to the success of the recovery effort. The total cost estimates in Table 7 assume that the province pays off all of the residual liabilities. To the extent that they are able to negotiate their way out of some liabilities, the total cost figures in columns 5-7 should be reduced. The bulk of the short-term fiscal implications will, thierefore, derive from the terms on which the residual entity is liquidated. The Fondo Fiduciario loan data, moreover, should be some guide as to the post-privatization financing assistance that provinces might expect.

# **IV** PRIVATIZED PROVINCIAL BANKS: INDICATORS OF FUTURE SOLVENCY

Owners of privatized provincial banks should face the same incentives as other private bankers in Argentina. To the extent that prudential regulations and bank supervision are adequate -- and all indications are that both have improved substantially in the 1990s -- neither privatized nor private banks should be so crisis-prone that they pose a substantial bail-out risk, especially when one considers that Argentina's present system of deposit insurance has a disciplining effect on member banks.29 In a country with a strong private deposit insurance

> 2') That system is partly privately managed and imposes high premia (.36-.72% of deposits, depending on the class of deposit) by international standards on member banks. In Denmark, another country with privately managed deposit insurance, premia payments are only .2% of deposits. In Colombia premia are .15% of deposits; in Mexico they are .3% -- and coverage per depositor is unlimited. Membership in Argentina
have low pension accumulations upon retirement.<sup>15</sup> Only 37 percent of women were projected to have a pension above the MPG level, in comparison with 67 percent for men. The average projected replacement rate for women under the pre-reform pension system was 28 percent of the last wage in comparison to 51 percent for men.

An important factor underlying gender gaps in projected pensions is that labor force

> 13The pension must also be equal to or greater than 50 percent of the average taxable income for the last 10 working years.

> 14In August 2007, the minimum wage was 159,000 pesos per month, while the PASIS was 44,186 pesos for retirees between 65 and 70 years of age, 47,103 pesos between 70 and 75 and 51,503 pesos if older than 75.

> 15The micro-level data on pension contribution histories were obtained from a database of the pension fund regulatory agency, the _Superintendency of Pensions_ or SP. These are the same data as used in this paper.

7
# **6.2 Progressivity**

**The** **_Kakwani_ Index is a summary statistic of progressivity.** For taxes, it is calculated by subtracting the concentration coefficient of the tax from the Gini coefficient of a reference income (in this case Market Income);<sup>71</sup> for transfers, the index is calculated by subtracting the Gini coefficient from the concentration coefficient of the transfer. A value greater than zero represents a progressive tax or transfer, while a value below zero represents a regressive tax or transfer. The minimum and maximum values of the Kakwani Index depend on the size of the Gini coefficient. With a Gini coefficient at Market income of 35.1, the maximum value of the Kakwani is 64.9 for taxes (100-35.1), and 135.1 for transfers (35.1+100). The values of the Kakwani Index for transfers (Figure 6-3) are therefore not comparable with those of taxes (Figure 6-4).

**Most fiscal instruments in the West Bank and Gaza are progressive at Market income except for fuel subsidies (direct and indirect effects) and pension income. The direct transfers and in-kind benefits from health and education are the most progressive instruments in the West Bank and Gaza.** The CTP and near-cash transfers score the highest on the Kakwani index (at 100.3 and 73.0, respectively). The inkind health and education benefits are substantially less progressive than the direct transfers – which is unsurprising given that the latter directly target the poor, particularly through the CTP program.<sup>72</sup> Hospital and primary healthcare both score 47.9 on the Kakwani Index, while basic and secondary education score 45.3 and 39.0 respectively.

**Fuel subsidies and pension income are regressive (they have a negative score on the Kakwani Index).** This is not unusual in either case since fuel subsidies generally benefit the wealthy who spend
**Figure 5.** Probability of being persistent at risk of poverty and persistent materially and socially deprived (2021), Bulgaria

# **Persistent at risk of poverty**

<!-- Start of picture text --> 25.0<br>15.0<br>5.0<br>-5.0<br>-15.0<br>-25.0<br>-35.0<br>location vulnerable households household structurehousing head of the household individual characteristics<br>Persistent materially and socially deprived<br>25.0<br>15.0<br>5.0<br>-5.0<br>-15.0<br>-25.0<br>-35.0<br>location vulnerable households household structure housing head of the household individual characteristics<br>Roma student inactive retired age male<br>rural areas towns and suburbs hh with pensioner recipient hh with +5 members hh with unemployed hh unemployed<br>hh with social aid hh with disability hh with children single-elderly hh female headed number of >64 number of 18-64 number of <18 crowded house tertiary educ. secondary educ. home ownership<br>rural areas recipient Roma student inactive retired unemployed age male<br>towns and suburbs hh with social aid hh with pensioner hh with disability hh with +5 members hh with children single-elderly hh hh with unemployed female headed hh number of >64 number of 18-64 number of <18 crowded house tertiary educ. secondary educ. home ownership<br><!-- End of picture text --> Note: Average marginal effects from the Probit regressions are expressed as percentage points. See Annex A4 for further details on the regression analysis. See Table A4.10 for regressions results. Source: Own estimates based on 2021 Panel EU-SILC. We report the survey year and not the income year.

35
|||0.008<br>(0.006)||0.018*<br>(0.008)|
|CB Index||||||-0.016<br>(0.010)|-0.008<br>(0.010)|
|Suffering (%)|||||||0.026**<br>(0.010)|
|Struggling (%)|||||||0.013<sup>+</sup><br>(0.007)|
|Constant|4.535|4.380|1.910|0.526|4.381|4.898|1.190|
||(4.312)|(4.488)|(4.260)|(4.467)|(4.424)|(4.147)|(4.359)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Control Var|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R-squared|0.270|0.267|0.266|0.258|0.258|0.259|0.293|
|Observations|847|855|861|788|856|847|814|
|Countries|118|118|118|118|118|118|118|

Note: Results are estimated with an OLS model. Robust clustered standard errors in parentheses. *** p<0.001, ** p<0.01, * p<0.05,<sup>+</sup> p<0.10 In the last column of Table 7 (Column 7) we include all
the 14-day diary approach used in the Household Budget Survey (HBS) and the 7-day recall approach used in the National Panel Survey (NPS), both surveys implemented by the Tanzania National Bureau of Statistics (NBS) and the Office of the Chief Government Statistician (OCGS) Zanzibar. This study is vital and timely, especially as regional and global bodies push for the harmonization of poverty statistics to ensure comparability. While Beegle et al. (2012) provide general guidance on how the diary and recall methods work, the data used is not nationally representative in nature. This study builds on their findings but uses existing modules and new methods of data collection, following the evolution of methods in this area.

We use nationally representative household survey data from a randomized experiment in Tanzania to explore differences in household food consumption expenditure between 14-day diary and 7-day recall designs. We employ both descriptive and regression analytical approaches. The results provide evidence of differences in food and total consumption expenditure across the diary and recall arms. We observe very little significant differences in non-food consumption expenditure, allowing for attributing variations in total consumption expenditure to differences in food expenditure emanating from the disparities in methodological designs. The results show that while the diary method captures more diverse food consumption items, the overall consumption expenditure appears significantly lower than the recall arms, even at different percentiles. Poverty rates varied significantly across the designs and are very much conditional on the poverty line chosen.

The rest of the paper is organized as follows. In Section 2 we present country context for the study. Section 3 provides a description of the data and study design. Section 4 presents the analytical framework employed to generate the results of the study, while Section 5 contains the discussion of results. The conclusion and implications of the study are presented in Section 6.

4
Policy Research Working Paper 9797

# **Abstract**

As more and more Venezuelans leave their country, fleeing the economic and social crisis, the number of Venezuelans in Brazil has risen steadily since 2016, constituting about 18 percent of Brazil’s 1.3 million refugee and migrant population as of October 2020. Although the economic gains of immigration are well-documented in the literature, the impacts of forced displacement on the labor market and government budget are mixed and have mainly focused on developed countries. This paper extends the previous literature by exploring the short-run fiscal impact of Venezuelan refugees and migrants on the public expenditure and revenue of Roraima, the state bordering the República Bolivariana de Venezuela at the north and the main gateway of the Venezuelan refugees and migrants entering Brazil, and by investigating their impact on its labor market. Using various administrative and survey data and a regression discontinuity framework, the paper finds that the population shock caused by the influx of forcibly displaced Venezuelans in the short-run did not have any statistically significant effect on the fiscal variables of Roraima. On the labor market, the paper finds that the population shock translated into an increase in unemployment among women and a decrease in employment among women and low skilled workers in the short-run. The effects on earnings are heterogenous across industries, but mainly positive for the high skilled and male workers, suggesting a need for cross-cutting policies that target the most vulnerable host population as well as the forcibly displaced.

This paper is a product of the Social Protection and Jobs 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 pacosta@worldbank.org and rschwengber@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