rafmacalaba/datause-encoder-data huggingface dataset:
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 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
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deviation increase in the height-for-age score at age 2 increases annual income by 8% for both men and women. Hoddinott et al. (2013b) use data for Guatemala. They estimate that being stunted at age 2 decreases wages by 16% for men and 14% for women. When using an IV strategy to reduce endogeneity bias the impact becomes much larger for men (65%) and slightly larger for women (18%) but it is less precisely estimated. Finally, the comprehensive review provided by Galasso and Wagstaff (2016) confirms the large impact of stunting on income by performing a meta-analysis on program evaluations of nutrition interventions.

Despite the difficulty in measuring health investments and the problems in identifying causal impacts, returns to health are potentially a large component of individual human capital. If some of the earlier literature focused on dramatic impacts on health, such as disability, the most recent literature is working on outcome variables more susceptible to marginal changes, such as body mass indexes.

# **4.2.3 Interpretation of the results**

Does the estimated 9.7% return in earnings for one additional year of schooling mean that if a policy manages to increase education by one year for the entire population the overall income will increase by 9.7%? Based on the estimate that a reduction in an individual's initial body mass by 10% leads to a 4% increase in wages, is it possible to conclude that a national nutrition policy able to achieve that objective for the entire population will lead to a 4% increase in overall wages? The answer is generally _no_ due to two main reasons: _biased estimates_ and _equilibrium effects_ . The first relates to the possible bias affecting the estimates of the parameters of interest. The second relates to the difficulty of building counterfactual policy experiments able to take into account behavioral responses.

**Biased Estimates.** In virtually all the settings described in the previous review of the empirical results the parameters of interest are at risk of estimation bias. In a regression context, the issue is frequently framed as lack of identification: the estimates of the parameters of interest are not really _causal_ but may simply represent a conditional correlation or a potentially spurious empirical association.  The two main sources of bias are _unobserved individual heterogeneity_ and _reverse causation_ .

_Unobserved individual heterogeneity_ refers to relevant individual-level variables affecting the relation of interest but not available in the data used in estimation. This omission generates endogeneity either directly as a standard _omitted variables bias_ or indirectly by implying not-random _sample selection_ . Unobserved individual heterogeneity may also mask important heterogeneous effects, i.e. the fact that the relation of interest is very different between individuals in the population. If this is the case, the estimated parameters may only be reporting a distorted average of them.

Consider once again equation [7] presented at the beginning of this section. Assume that the human capital variable of interest is the years of schooling completed _si_ : 20
**Table 5: Exploring Heterogeneity by Female Subgroups**

||Depen<br>|dent Variable: Log [Self-R<br>|eported Productivity (Bi<br>|rr/HA)]<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|**_Female and Marital Status Int_**|**_eractions_**||||
|Female x Married †|-0.0395|-0.0395|0.1591|0.1588|
||(0.1478)|(0.1479)|(0.1121)|(0.1121)|
|Female x Not Married †|-0.3015***||-0.2392**||
||(0.1117)||(0.1065)||
|Female x Single †||-0.6239***||-0.9145***|
|||(0.1261)||(0.3068)|
|Female x Divorced †||-0.2348<br>(0.1728)||-0.4350***<br>(0.1389)|
|Female x Widowed †||-0.3063**<br>(0.1245)||-0.1697<br>(0.1269)|
|**BaselineCovariates**|No|No|Yes|Yes|
|**Observations**|1,518|1,518|1,518|1,518|
|**R-Squared**|0.009|0.009|0.343|0.345|

Note: Clustered Standard Errors at EA (Enumeration Area) Level in Parentheses. *** p<0.01, ** p<0.05, * p<0.1 † Denotes a Dummy Variable 28
# **IV. ISLAMIC FINANCE AND SUSTAINABLE DEVELOPMENT: PRACTICE AND EVIDENCE**

Islamic financial assets increased at an annual rate of 17 percent from 2009-13, and are estimated to have exceeded USD 2 trillion in 2014 (The Economist 2014). Despite rapid growth, there is a view that Islamic finance has failed to fulfill social goals (Siddiqi 2004). Moving forward, the impact of Islamic finance on SDGs can be enhanced if the broader goals of _Shari’ah_ are integrated into its operations. This section focuses on the role of Islamic finance in promoting resilience, increasing social sustainability (financial inclusion and reducing vulnerability), achieving environmental and social goals, and facilitating sustainable infrastructure development. The role of these factors in affecting different SDGs is considered in Annex table 2. For each of these factors, the current and potential roles that Islamic financial institutions, the capital market and the social sector can play are discussed.

# **1. Enhancing Stability and Resilience of the Financial Sector**

Islamic finance can play a role in improving the stability of the financial sector. The estimated USD 15 trillion in losses from the global financial crisis (GFC) highlights the vulnerability of the financial sector and the potential for reducing output and welfare (Yoon 2012).<sup>13</sup> Though various factors contributed to the GFC, one key cause was excessive debt (Buiter and Rahbari 2015, Mian and Sufi 2014 and Taleb and Spitzagel 2009). There is also some evidence that high levels of debt are generally associated with lower growth. Arcand et. al (2012) find that the financial sector increases economic growth when private sector credit is less than 100% of GDP, but reduces growth when credit expands beyond this level. Similarly, Cecchetti et. al (2011) find that levels of corporate debt exceeding 90% of GDP, and of household debt exceeding 85% of GDP, tend to slow down economic growth. Thus, financial sector stability may be increased by raising the proportion of equity modes of financing relative to debt (Buiter; Rahbari 2015 and Taleb; Spitzagel 2009).

The extensive use of instruments such as mortgage backed securities (MBS), collateralized debt obligations (CDO) and credit default swaps (CDS) also contributed to the crisis. The value of the overall notional amounts of over-the-counter (OTC) derivatives contracts reached $596 trillion by the end of 2007, with CDS increasing by 36% during the second half of the year to reach $58 trillion (BIS 2008). While some of these instruments are used for hedging purposes, the fact that the notional amounts of derivative contracts were more than 10 times the size of global GDP ($54.3 trillion) indicates that most of them were used for speculation. Some of the derivatives do not have links to any real transactions or assets, creating risks that are complex and difficult to understand (LiPuma; Lee 2005). Furthermore, certain types of derivatives such as futures,

> 13 Researchers from the Federal Reserve Bank of Dallas estimate the losses from GFC in the US to be in the range of US$ 6 to US$ 14 trillion. See Atkinson et. al. (2013).

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criminal justice. The work by Hendley and Murrell (2003) is a rare exception to the lack of emphasis on the role of government in this context, though this paper's attention to this role is linked to the focus on the transition of Romania from socialism. Our data suggests that government officials do sometimes support the enforcement of agreements, even if that role is minor. In the _strong comprehensive governance_ class of supplier relations, government officials are prominent.

Second, given the amount of attention paid in the literature to non-paid private third parties, especially networks, one would expect them to be more important. Network approaches have been very popular in recent decades, following Granovetter's (1985) emphasis that transactions are embedded in a broader social structure, the historical-theoretical analysis of Greif (1989), and case studies on the importance of trading networks in varied settings (Bernstein 1992; Landa 1981). However, as we point out in Section II, the term 'network' is used in diverse ways in the literature, some of which would imply that a network is present even when the reference is only to a large grouping of similar agents who are not necessarily invoked in any type of enforcement process. In fact, our results suggest that, in aggregate, non-paid private third parties are arguably the least important of the six agents in enforcement activities (Tables 1, 2a, and 2b).

# **V. Variations in the Importance of Governance Structures**

In this section, we examine how the importance of governance structures varies with the environment of firms, focusing on the countries and regions in which they are located. This exercise and that in Section VI are exploratory, providing examples of how our LCA-generated data can be used to understand the links between governance structures and their determinants. Here, we simply explore patterns in the data and do not attempt to isolate ceteris paribus, causal effects of single variables. Nevertheless, this exercise adds evidence on the validity of the data we have produced: if there were no significant association between chosen governance structures and 37
11 Ukraine had received HUS subsidies. Because eligibility is based on both expected consumption and income, HUS subsidies are by design more targeted than the implicit subsidies received by natural gas users when the tariffs were artificially kept below the price of gas on the international market, transmission through pipeline and distribution costs.

# **3. The Data**

# _3.A. Construction of the Sample_

We have five waves of Ukraine’s Household Budget Survey (from 2014 to 2019). A summary of the sample size for each wave is displayed in table 1. The expenditure and income information is provided in each HBS data set for the full year, even though Ukrstat, the statistical service of Ukraine, collects the HBS information from each household on a quarterly basis. We appended the national as well as oblast-specific consumer price indices,<sup>15</sup> information about utility tariffs from NERC and Ukrstat, and oblast-specific heating and cooling degree days in each year, since the HBS does not indicate exactly in which city, town or village the household lives.

Heating and cooling degree days are important because the demand for heating and cooling, and the related fuels, depends crucially on outside temperature (Summerfield et al., 2015). Ukraine averages about 4,000 heating degree days a year (base: 18 ° C). The heating

> to commingle industrial and residential customers.  Dodonov et al. (2004) consider non-payment widespread and cite official estimates that households pay for only 70%-80% of the electricity they consume. Fankhauser et al. (2008) examine the “stock” and “flow” of arrears for all utilities in the various regions of Ukraine in 2003 and 2004. Fankhauser and Tepic (2007) report that “payment discipline” among residential customers has improved over recent years, and that “many countries now have collection rates close to 100%.”

> 15 We normalized the CPIs so that it is equal to 100 in 2014. With the national CPI, for example, 2014=100, 2015=148.7, 2016=169.4, 2017=193.8 and 2018=214.9.
- **Longer Time Horizon:** Eight studies concluded that money demand in Egypt was stable during various intervals; many of which missed key events mentioned in the theoretical motivation section above. This research will encompass previous studies through analyzing over a longer period to ensure robustness of the analysis and soundness of tests for cointegration and quality of inference in both the short and the long runs.

- **Financial Market Imperfection:** This paper will incorporate a proxy for market imperfection in the money demand function. This was overlooked in earlier money demand studies in Egypt despite the existence of a significant parallel exchange rate market.<sup>11</sup> The premium of the parallel exchange rate over the official market rate is considered as a proxy for market imperfection and currency substitution. This is relevant to the less developed economies that suffer distorted financial markets (Osokee et. all 2011).

# **_4.  Hypothesis to Be Tested_**

In view of the theoretical motivation, we can represent the general model in the following specification: _M_ 2t<sup>12</sup> = _f_ ( _gdp_ t,,Interest Ratet, Discount Ratet, Inflationt, Exchange Ratet) The null hypothesis H� that demand for real broad money (M2� in Egypt positively relates to real GDP and/or real consumption as portrayed by theory will be tested.

However, when it comes to price levels, some ambiguity emerges depending on the position of the economies within the business cycle and the prevailing level of inflation rate. When inflation is

> 11 See IMF article IV consultation report for Egypt ; various issues; accessible at <u>http://www.imf.org/external/country/egy/</u>

> 12 Expected signs between brackets based on theory. Keynesian theory identified three motives for holding real money balances and liquidity preference including transactions, precautionary and speculative motives. The rationale behind the later was questioned by several authors paving the way for more emphasis on transactions demand (Baumol, 1952; Tobin, 1956) and the asset motive (Tobin, 1958; Friedman, 1956).

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The paper is organized as follows. Section 2 describes the survey and earth observation data. Section 3 presents the empirical methodology. Section 4 discusses the results and section 5 concludes.

# **2. Data**

# **2.1. Survey data**

We use nationally representative, multi-topic household survey data collected in Malawi and Ethiopia by the respective national statistical offices over the period of 2018-2020 with support from the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) initiative. The key variables that drive each survey’s sampling design is household consumption expenditures and poverty. However, the surveys do provide large samples of agricultural households and extensive data on their agricultural activities. Maize is the primary crop grown in Malawi, while in Ethiopia, small grains are more prevalent, but maize still plays an important role as a staple crop. More details regarding the survey data are provided below.

# **2.1.1. Malawi**

The survey data in Malawi stem from the Integrated Household Panel Survey (IHPS) 2019 and the Fifth Integrated Household Survey (IHS5) 2019/20. The surveys were implemented concurrently by the Malawi National Statistical Office.

IHPS 2019 is the fourth follow-up to a national sample of households and individuals that had been interviewed for the first time in 2010, and later in 2013 and 2016. At baseline, the IHPS was designed to be representative at the national-level and separately for rural and urban domains.<sup>6</sup> Starting in 2013, the IHPS attempted to track all household members that were interviewed in the last survey round and that were projected to be at least 12 years of age and were known to be residing in mainland Malawi during the follow-up survey round.<sup>7</sup> Once a split-off individual was located, the new household that he/she may have joined vis-a-vis the prior survey round was brought into the IHPS sample. Based on these protocols, the dynamically expanding IHPS sample included 3,181 households in 2019, which can be traced back to 1,491 original households that had been interviewed in 2010. The IHPS 2019 fieldwork was conducted from April to December 2019, and the households that were determined to have owned and/or cultivated land during the 2018/19 rainy season were attempted to be visited twice, once in the post-planting period and once

> 6 For more information on the IHPS rounds, please consult the publicly-available basic information document for each survey - IHPS 2010: https://bit.ly/ihps2010; IHPS 2013: https://bit.ly/ihps2013; IHPS 2016: <u>https://bit.ly/ihps2016; and IHPS 2019: http://bit.ly/ihps2019.</u>

> 7 The individuals who were residing in prisons, police compounds or army barracks were excluded from tracking.

5
Moreover, the intensive use of land has caused a large soil erosion problem for farmers. Sixty-six percent of extreme poor farmers and 46 percent of the 20 percent richest farmers reported that soil erosion is a severe problem on their land in 2001. Poor farmers have little access to productivity enhancing plant nutrients and therefore see soil erosion as a more severe problem than rich farmers. Moreover, lack of titles or land ownership is another problem faced by poor farmers. Of the 20 percent poorest farmers 81 percent have a title or own their land compared to 92 percent of the 20 percent richest farmers (Table 7). Data reveal little difference in travel time to plots between rich and poor farmers (Table 8).

Table 6: Reporting on Soil Erosion in Haiti, 2001 (percent)

||||Quintile<br>|||
|---|---|---|---|---|---|
||1 (poorest)|2|3|4|5 (richest)|
|Yes, important|65.5|65.82|63.92|62.9|45.63|
|Yes, minor|7.82|7.47|8.73|6.47|7.32|
|Noproblem|26.67|26.71|27.34|31.63|47.05|

_Source:_ Own calculations based on HLCS 2001.

Table 7: Legal Title or Deed to Home and Land , 2001 (percent)

|||1(poorest)|2|Quintile<br>3|4|5 (richest)|
|---|---|---|---|---|---|---|
|Plot|Yes|80.45|86.29|87.67|88.27|91.83|
||No|19.55|13.71|12.33|11.73|8.17|
|Home|Yes|56.71|58.95|58.06|56.03|54.63|
||No|43.29|41.05|41.94|43.97|45.37|

_Note:_ Plot: No. observations: 4473 with access to land. Yes, includes all owned, but missing legal titles, and owned with titles for at least one plot. No, includes all plots sharecropped or rented. Deed status is for largest accessible plot. Home deed: No. observations 7177. Yes, includes those owned by deed, sales receipt, customary rights or other. No, includes those rented, long term rent, other, no reply, no legal title or rights.

_Source:_ Own calculations based on HLCS 2001.

Table 8: Average Travel Time to Plot in Haiti, 2001 (minutes)

|1(poorest)|2|Quintile<br>3|4|5 (richest)|
|---|---|---|---|---|
|38.76|39.17|39.02|36.15|38.08|
|(63.26)|(54.57)|(75.14)|(85.41)|(64.74)|

_Note:_ Standard deviations in parentheses. Only households with access to land included. Time to largest plot if several owned (rest excluded). No. observations: 4473.

_Source:_ Own calculations based on HLCS 2001.

Poor farmers have less access to productivity enhancing technology than nonpoor farmers. Of the 20 percent poorest farmers 43 percent apply less than 3 tools compared to 26 percent of the 20 percent richest farmers (Table 9). Only 19 percent of the poorest apply more than 5 tools compared to 30 percent of the richest farmers. Moreover, of the poorest 20 percent of farmers only 4 percent have access to irrigation (mechanical, handpump, or ditches), 2 percent use pesticides or insecticides, 7 percent use fertilizers (chemical, natural by people and animal, compost and other), and 11 percent have 19