rafmacalaba/datause-encoder-data huggingface dataset:
wished to settle in and integrate with their host community, mainly because of security concerns in their place of origin and better opportunities at their destination (IOM, 2013).

Evidence shows IDPs live in precarious conditions. The majority of settlements are self-built overcrowded houses or illegally occupied houses, with limited access to basic services and poor infrastructure, which may lead to poor health outcomes (IOM, 2013). Besides, IDPs might have difficulties finding a formal job due to lack of an identity document, language or cultural barriers. Additionally, forced displacement may have damaging consequences on IDPs’ family structure. For instance, family members may be separated, with working-age adults, and especially males, unable to return to their place of origin. Displacement may also disrupt children’s schooling. This suggests IDPs may be more likely to be deprived in multiple dimensions of well-being; which calls for an in-depth analysis of the deprivation of IDPs to identify the dimensions that demand urgent intervention and to help design policies that are best-suited to the needs of IDPs.

The variation of exposure to violence and insecurity between IDPs and non-IDPs provides a unique quasi-experimental setting, with the war representing an exogenous shock to multiple components of household well-being. The dimensions may be analyzed separately, or combined into a single index of multidimensional poverty, enabling a consolidated analysis of multiple dimensions of deprivation. In the following analysis, we apply the Multidimensional Poverty Index (MPI) methodology developed by Alkire and Foster (2011)<sup>1</sup> to the 2012-2013 Iraqi Household Socio-Economic Survey (IHSES-II)<sup>2</sup> to investigate the effect of forced displacement on multidimensional poverty.

Although there have been numerous works on the macro-level implications of wars and conflicts (e.g. Collier, 1999; Barron et al., 2004; Collier and Hoeffler, 2004; Miguel et al., 2004), 1 See also Alkire and Santos (2010).

2This survey is an outcome from the cooperation between the Central Organization for Statistics of Iraq and the Kurdistan Region Statistics Organization supported by the World Bank, which initiated in 2007 with the first IHSES. The IHSES-II is an update of the first round with additional data and information such as individuals’ past migration history.

3
In that case, the destruction by a disaster of a (marginal) amount of one type of capital would lead to a loss of output equal to: If  _i_ is really small, the marginal productivity of capital _Ki_ can be extremely high, much higher than the marginal capital productivity given by Eq. (A4). This case is somewhat extreme because the different categories of capital are assumed non-substitutable, but the qualitative result remain valid with higher substitutability: _considering disaggregated capital categories with imperfect substitutability_<sup>_33_</sup> _, a disaster would break the assumption that the total amount of capital is optimally distributed across these categories, increasing the marginal productivity of destroyed capital and the value of output losses (and as a result, the marginal productivity of reconstruction)._

# **7 Appendix B. Quantity and prices in disaster aftermaths**

For marginal shocks, impacts can be estimated using pre-disaster prices, i.e. assuming that the shock has a negligible effect on prices. But this assumption is not always valid. Post-disaster price inflation (also referred to as “demand surge”) is especially large in the construction sector, which sees final demand soar after a disaster. For instance, Figure B1 shows the large (and persistent) increase in wages for roofers in an area heavily affected by hurricane losses in Florida in 2004.

<!-- Start of picture text --> Annualized wage change in Miami, FL<br>20%<br>All<br>occupations<br>15% Roofers<br>10%<br>5%<br>0%<br>-5%<br>ug-03 A Oct-03 Dec-03 Feb-04 Apr-04 Jun-04 Aug-04 Oct-04 Dec-04 Feb-05 Apr-05 Jun-05 Aug-05 Oct-05 Dec-05 Feb-06 Apr-06 Jun-06 Aug-06 Oct-06<br><!-- End of picture text --> _Figure B1: Roofer wages in an area where losses have been significant after the 2004 hurricane season in Florida. Data from the Bureau of Labor Statistics, Occupational Employment Surveys in May 03, Nov 03, May 04, Nov 04, May 05, May 06, May 07._ Figure B2 is a classical quantity-price plot, showing the long-term demand and supply curves for a goods or service aggregated at the macroeconomic level. The green line is the demand curve: it shows how the

> 33 The only case where this result does not hold is when the production function is in the form: _Y=f(K1+K2+...+Kn)._

41
than the rise in current income by borrowing against future income or reducing current savings. Thus, relative volatility of consumption of a Ricardian household with respect to output volatility is greater than one. Relative volatility of total consumption, when total consumption is a weighted average of the relative consumption volatility of a Ricardian household and that of a liquidity-constrained household, may be smaller or higher than one depending on the size of λ .

Financial development reduces the share of liquidity-constrained households in the economy and hence allows more people to respond to the permanent income shock by raising current consumption more than the rise in current income. As a result, volatility of total consumption relative to output volatility increases with financial development.

Combining these observations, the main theoretical prediction of the model can be stated as follows:

**Main prediction:** Other things unchanged, under the occurrence of permanent income shock, financial development leads to a rise in the volatility of consumption in the economy relative to output volatility.<sup>4</sup>

The main prediction is tested by calibrating the model economy to Indian data. The hypothesis is tested for an emerging economy where relative consumption volatility shows an increase after witnessing of financial sector development.

CASE STUDY: EVIDENCE FOR INDIA The model is calibrated for India, an emerging economy which has witnessed financial sector reform. Ang (2011) finds that financial liberalisation

> 4 It follows from the implications of the main prediction of the model that in response to a negative permanent income shock, Ricardian households reduce current consumption by more than the decline in current income and raise investment in order to smooth consumption over the lifetime. Financial development will allow more people to respond to the negative income shock by reducing current consumption more than the fall in income. Volatility of total consumption relative to output volatility thus increases with financial development under negative trend growth shocks as well.

18
financial (or business) services and the financial (or business) services-intensity of each manufacturing sector. We find that domestic services development has a mixed effect on manufacturing export RCA: in manufacturing sectors with low embodied services, services development reduces manufacturing export RCA; however, in sectors with a high degree of embodied services, services development increases manufacturing RCA. Figure 2 provides a visual illustration of this relationship in the case of financial services. We see a negative association between manufacturing RCA and a measure of financial development for a sector with low embodied financial services, but a positive association for a sector with high embodied financial services.

In the second hypothesis, we consider the role of services imports in helping overcome the limitations of domestic services markets. We begin by showing that a country’s access to foreign services markets measured by the share of foreign embodied services is negatively correlated to countries’ services trade barriers. Using the World Bank Services Trade Restriction Indexes (STRI) (Borchert, Gootiiz and Mattoo, 2012), Figure 3 shows a negative relationship between financial services trade barriers and the share of embodied foreign financial services among embodied domestic and foreign financial services for the textile sector of 40 countries in 2000, using data from the World Input-Output Database (WIOD).<sup>4</sup> A similar pattern holds for other manufacturing sectors and years. We then find that in countries with lower levels of services development, manufacturing exports benefit more from access to foreign services inputs. Our result suggests that lower services trade barriers may help developing countries to bypass their inefficient domestic services provision and promote their manufacturing exports through inter-sectoral linkages.

The rest of the paper is organized as follows. We review the relevant literature in Section II. In Section III, we present our hypotheses and carry out the empirical analysis. We conclude in Section IV.

# **II. Literature review**

This paper is related to at least two strands in the literature: one is on the estimation of services embodied in traded goods; the other is on the role of services in economic development.

> 4 See Dietzenbacher et al. (2014) and Timmer et al. (2015) for more information on the construction of the WIOD.

4
Table 8

# Percentage Share **of** CACU **Export. Facing Mon-Tarift barrieor** in OECD Markets. 1960 **(By Country**

||U.S.|E.C.|Japan|Canada|
|---|---|---|---|---|
|Costa RICa<br>(of which|28.2|1.2|93.7|98.1|
|<br>Hard Coro**NThs)**|(10.9)|(1.1)|(0.1)|(0.4)|
|El Salvador<br>(of which|7.0|0.2|91.8|98.9|
|<br>Hard Core NTBa)|(6.2)|(0.1)|(0.0)|(0.1)|
|Guatom la<br>(of which|11.4|1.4|82.6|99.2|
|<br>Hard Core NT.)|(8.1)|(0.8)|(0.0)|(0.6)|
|Honduras<br>f hih|11.6|6.6|98.8|93.9|
|(o wc<br>Hard Core NTBs)|(1.8)|(6.1)|(2.3)|(0.1)|
|Nicaragua<br>|2.4|1.6|38.7|43.1|
|(of which<br>Hard Core NTBs)|(O °0)|(1.)|(0.2)|(10.1)|
|All Countries<br>(Weighted Average)<br>|15.0|2.0|82.8|89.4|
|(of which|||||
|<br>Hard Core NTB.)|(7.1)|(1.8)|(0.9)|(1.8)|

> **a** For a definition, **see** Table 7 and Footnote 9.

Source: UNCTAD Data Base on Trade Control Measures.
**Table 6:  Truncation Bias in the Intercepts (13-60 Years)**

||**(δ0)**|**(ρ0)**<br>|**(β0)**||
|---|---|---|---|---|
||**Full**<br>**Coresident**|**Full**<br>**Coresident**|**Full**<br>**C**|**oresident**|
|**All Children Sample**|**BA**|**NGLADESH**|||
|**Father's Schooling**|0.247<br>0.26|0.741<br>1.016|3.225|3.955|
|**Bias**|**4.79%**|**27.05%**|**18%**||
|**Mother's Schooling**|0.232<br>0.256|0.869<br>1.134|3.813|4.435|
|**Bias**|**9.52%**|**23%**|**14.04%**||
|**Average Schooling**|0.234<br>0.247|0.751<br>0.999|3.303|3.896|
|**Bias**|**5.32%**|**25%**|**15.22%**||
|**Father-Sons**|0.297<br>0.262|0.847<br>0.980|3.994|3.999|
|**Bias**|**-13.38%**|**14%**|**1.37%**||
|**Mother-Daughters**|0.170<br>0.237|0.770<br>1.200|3.002|4.218|
|**Bias**|**28.39%**|**35.82%**|**28.83%**||
|**All Children Sample**||**INDIA**|||
|**Father's Schooling**|0.276<br>0.295|0.838<br>1.037|4.113|4.987|
|**Bias**|**6.53%**|**19.18%**|**17.53%**||
|**Mother's Schooling**|0.273<br>0.309|1.056<br>1.25|5.185|6.012|
|**Bias**|**11.58%**|**15.49%**|**13.76%**||
|**Average Schooling**|0.266<br>0.290|0.855<br>1.062|4.199|5.110|
|**Bias**|**8.23%**|**19.48%**|**17.83%**||
|**Father-Sons**|0.350<br>0.326|1.097<br>1.179|5.321|5.581|
|**Bias**|**-7.32%**|**6.95%**|**4.67%**||
|**Mother-Daughters**|0.146<br>0.164|0.776<br>0.878|3.603|4.137|
|**Bias**|**11.06%**|**11.68%**|**12.90%**||

NOTES: (1) **( δ0 )** is the intercept of rank-rank, **( ρ0 )** is the intercept of IGC regression, and **( β0 )** is the intercept of IGRC regression.
technology. At the same time, lower dismissal costs decrease firms’ costs of retaining workers by diminishing their bargaining power.

<u>Anesi and De Donder (2013)</u> study the political economy of combining employment protection and unemployment insurance policies. They show that the election process and the formation of political parties could influence the relative weights of the policies. In the direct citizen-candidate model, low-skilled workers are decisive and push for maximum employment protection and unemployment benefits. Allowing for the party formation results in policy equilibria in which high-skilled workers form a coalition with the unemployed and support a combination of policies with high unemployment benefits and lower job protection levels.

Several recent papers analyze the impact of the COVID-19 pandemic on the labor market and its relationship to social policies. <u>Eymeoud et al. (2021)</u> contrast US and European labor market trends during the pandemic. They demonstrate that the EU countries avoided a sharp increase in unemployment in the early stages of the pandemic by funding short-time work programs. However, these programs may slow the reallocation of workers from shrinking to growing sectors of the economy during the recovery.

<u>Aidukaite et al. (2021) and Beland et al. (2021)</u> study the labor market response to the COVID-19 crisis in Hungary, Lithuania, Poland, the Slovak Republic, and the OECD countries. Both papers find that labor market policies in these countries depended on pre-pandemic social policy choices and focused mostly on job protection measures. Finamor and Scott (2021) document a negative association between the generosity of unemployment insurance and employment in the US during the pandemic. Notably, the employment gap remained constant even after the expansion of benefits expired, indicating that fear of infection and childcare options could be important factors explaining the persistent rates of labor inactivity in the US.

# **3. Data**

The analysis in this paper relies on a new country-level data set of expenditure on social protection measures implemented in response to the COVID-19 pandemic put together by the authors. Our database uses information from the Global Database on Social Protection Responses to COVID19 (GDSPRC) (Gentilini et al. 2022). The GDSPRC contains information on the duration, target population, and other technical aspects of these measures. We collected detailed budget 7
# **_Fiscal Incentives and Firm Performance:_**

# **_Evidence from the Dominican Republic_**

Alessandra Amendola<sup>1</sup> , Marinella Boccia<sup>2</sup> , Gianluca Mele<sup>3</sup> & Luca Sensini<sup>4</sup>

**Keywords** : Corporate Income Tax, Firm Performance, Panel Data, Fixed and Random Effects

> 1 Department of Economics and Statistics, University of Salerno, Fisciano (SA), Italy

> 2 Department of Economics and Statistics, University of Salerno, Fisciano (SA), Italy

> 3 World Bank, Washington, USA

> 4 Department of Management and Innovation Systems, DISA-MIS, University of Salerno, Fisciano (SA) Italy
Policy Research Working Paper 8910

# **Abstract**

As one of the most pivotal ways that labor markets adjust to changing economic conditions, international migration is never far from the center of the national discussion in Uzbekistan. This paper summarizes the relationship between recent international migration trends and household well-being using a combination of administrative records and unique panel survey data from the Listening to the Citizens of Uzbekistan study. The panel design provides data on changes in well-being leading up to and following a migrants’ departure. This feature enables analysis that controls for unobserved time-invariant respondent and location characteristics. The findings show that weak local labor markets drive labor migration from Uzbekistan. Beginning to consider migration is associated with low life satisfaction, job loss, and unemployment. In contrast, actually migrating is associated with a remarkable improvement in labor market outcomes, alongside strong recovery in subjective and monetary measures of household welfare. The results further show that current migrants are more likely to send remittance payments when household members have deteriorating life satisfaction and/or subjective reports of worsening economic conditions at home. In the absence of remittance income, the poverty rate in Uzbekistan (measured at $3.2/day purchasing power parity) would be expected to rise from 9.6 to 16.8 percent, or to about 12.2 percent assuming (implausibly) that all current migrants were to find formal employment at the local prevailing median wage.

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 author may be contacted at wseitz@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
BOX 1.1 TEXT REFERENCE FOR THIS BOX How much have mobile money accounts driven growth in overall account ownership in African countries?

Among the 13 Sub-Saharan African countries where the share of adults with a mobile money account is 10 percent or more, South Africa was the first to have a GSMA MMU mobile money account service start operating within its borders, in 2004 (figure B1.1.1). Other countries in the group—including Kenya, where mobile money accounts first became common—began seeing the entry of mobile money account services in 2007 and 2008. The momentum began picking up in 2011, however, signaling that the market promised enough potential to attract competitors.

A comparison of 2011 and 2014 data for the 10 countries in the group for which data are available for both years shows different patterns of growth in account penetration (figure B1.1.2). In most of these 10 countries the growth was driven both by adults adding a financial institution account and a mobile money account and by adults adding a mobile money account only. But in Tanzania, where account penetration doubled to 40 percent in 2014, the growth was driven entirely by people adding a mobile money account only. In Botswana and South Africa, by contrast, the growth was due almost entirely to people adding both types of accounts.

Zimbabwe is one of the very few countries around the world where account penetration fell between 2011 and 2014. The country has suffered economic difficulties that have been accompanied by an erosion of trust in financial institutions since 2011,<sup>a</sup> and many people appear to have switched from an account at a financial institution to a mobile money account.

- a.  Based on results from a Gallup World Poll survey question asking respondents to rate their trust in banks and financial institutions. See also IMF (2014).

# FIGURE B1.1.1

# FIGURE B1.1.2

Mobile money account services operating in countries with mobile money account penetration of 10 percent or more B1.1.2 Number of services listed in GSMA MMU database Account penetration in countries with mobile money account penetration of 10 percent or more, 2011 and 2014 <!-- Start of picture text --> Adults with an account (%)<br>0 10 20 30 40 50 60 70<br>2011<br>Botswana<br>2014<br>Ghana<br>Kenya<br>Mali<br>Rwanda<br>South Africa<br>Tanzania<br>Uganda<br>Zambia<br>Zimbabwe<br>Mobile money account only<br>Financial institution and mobile money account<br>Financial institution account only<br><!-- End of picture text --> <!-- Start of picture text --> 2004 2006 2008 2010 2012 2014<br>1 2 3 4 5 6 Year service began<br>Number of services<br>Botswana<br>Côte d’Ivoire<br>Ghana<br>Kenya<br>Mali<br>Namibia<br>Rwanda<br>Somalia<br>South Africa<br>Tanzania<br>Uganda<br>Zambia<br>Zimbabwe<br>Source: GSMA MMU database.<br><!-- End of picture text --> _Note:_ While 13 countries have mobile money account penetration of 10 percent or more, the figure includes only the 10 countries for which both 2011 and 2014 data are available. The 2011 Global Findex survey did not collect data on ownership of mobile money accounts. _Source:_ Global Findex database.

> THE GLOBAL FINDEX DATABASE **23**
# **Tables**

**Table 1: Learning cost-effectiveness results and sensitivity analyses**

|**Program**|**Additional SD**<br>**per $100**|**Impact on test**<br>**scores (Standard**<br>**Error)**|**Lower bound**<br>**impact**<br>**(90% CI)**|**Upper bound**<br>**impact**<br>**(90% CI)**|**Lower bound**<br>**additional SD per**<br>**$100 (90% CI)**|**Upper bound**<br>**additional SD per**<br>**$100 (90% CI)**|<br>**Reference**|
|---|---|---|---|---|---|---|---|
|Providing earnings information,<br>Madagascar|118.34|0.202 (0.106)|0.03|0.38|16.19|220.49|Nguyen 2008|
|Streaming by achievement, Kenya|34.78|0.176 (0.077)|0.05|0.30|9.75|59.82|Duflo, Dupas, & Kremer 2012|
|Linking school committee to local<br>government, Indonesia|34.62|0.165 (0.067)|0.05|0.28|11.50|57.75|Pradhan et al. 2012|
|Electing school committee and linking<br>to local government, Indonesia|13.34|0.216 (0.093)|0.06|0.37|3.89|22.78|Pradhan et al. 2012|
|Teacher incentives (year 2), Kenya|6.29|0.136 (0.071)|0.02|0.25|0.89|11.69|Glewwe, Ilias, & Kremer 2010|
|Textbooks for top quintile, Kenya|3.56|0.218 (0.096)|0.06|0.38|0.98|6.14|Glewwe, Kremer, & Moulin 2009|
|Remedial education, India|3.07|0.138 (0.047)|0.06|0.22|1.35|4.79|Banerjee et al. 2007|
|Camera monitoring, India|2.28|0.170 (0.090)|0.02|0.32|0.29|4.26|Duflo, Hanna, & Ryan 2012|
|Village-based schools, Afghanistan|2.13|0.588 (0.146)|0.35|0.83|1.26|2.99|Burde & Linden 2013|
|Extra contract teacher + streaming,<br>Kenya|1.97|0.248 (0.092)|0.10|0.40|0.77|3.17|Duflo, Dupas, & Kremer 2012|
|Individually-paced computer assisted<br>learning, India|1.55|0.475 (0.068)|0.36|0.59|1.19|1.92|Banerjee et al. 2007|
|Girls' merit scholarships, Kenya|1.38|0.270 (0.160)|0.01|0.53|0.03|2.73|Kremer, Miguel, & Thornton 2009|
|Read-a-Thon, Philippines|1.18|0.130 (0.050)|0.05|0.21|0.43|1.92|Abeberese, Kumler, & Linden 2013|
|Minimum conditional cash transfers,|0.06|0.202 (0.118)|0.01|0.40|0.00|0.12|Baird, McIntosh, & Özler 2011|

Source: Data from J-PAL 2014. Additional calculations by authors.

Notes: SD means the standard deviation of a given set of test scores, and measures how much individual test scores change as a result of a program compared to the average test score of the group. $ costs reported in 2011 USD.

17
- 23 -

On the other hand, during the process of reform, the efficiency of investment improved in all countries except Malawi. In fact, the most marked increase was in Tanzania where, according to this indi:ater, the efficiency increased more than five-fold. Despite that, however, the returns to investment remain low in Tanzania.

Figure 10 <!-- Start of picture text --> GDS AS RATIO OF GDP<br>(Annual average)<br>22<br>20.5<br>20<br>la<br>la<br>14.0<br>14 4.<br>12<br>* 10<br>4 1 .4 3. -<br>2<br>0<br>|GFna Keny. X Iawl Tanzania Lgpnda<br>1981-95 M 1986-90<br><!-- End of picture text -->

Source: IMF and World Bank data, and authors' estimates.

# V.3.3 Domestic Savings

Figure 10 presents data on the gross domestic :avings (GDS) rate as a ratio of GDP for the five eountries. It indicates that the impact of the structural adjustment programs on domestic savings is mixed. Given the problems of measuring the savings rate (usually calculated as a residual in the national accounts), and the problems that many of the SSA countries face with respect to the quality