rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

Since the early 1990s, many countries in the Arab world have embarked on significant financial and economic reforms, involving internal and external financial liberalization, as well as efforts to increase the depth, scope, and efficiency of their financial systems. At the same time, Arab financial systems have shown considerable improvements over the last two decades. Although policy makers in the region have realized the importance of expanding the breadth of their financial systems and operating active capital markets, Arab financial systems are often perceived to be underdeveloped. In particular, financial markets are still highly bank-based, thin, tightly regulated, and dominated by government ownership. Within the Arab world, financial systems in the Gulf Cooperation Council (GCC) tend to be more developed and globally integrated than those of other countries in the region.<sup>1</sup> This paper uses a unique data set to provide a first documentation of the extent to which firms in the Arab region use capital and syndicated loan markets to obtain financing and grow. We address three main questions as follows. (1) What is the total amount raised in equity, bond, and syndicated loan markets by firms in the Arab region and how does their issuance activity stand with respect to the rest of the world? (2) How many and which firms actually issue equity, bonds, and syndicated loans? (3) How do the assets, turnover, and number of employees evolve for issuing relative to nonissuing firms?

To address these questions, we assemble a comprehensive transaction-level data set on equity, corporate bonds, and syndicated loans issued in domestic and international markets over the 19912014 period. The data include 719,242 individual security issuances conducted by 138,091 (listed and non-listed) firms from 96 countries. For Arab countries, we then match these data with balance sheet information on publicly listed firms. Our matched data comprise 1,462 firms over the period 2003-

> 1 T he Gulf Cooperation Council states include
error terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. The imputed poverty rates for TA 1 use the estimated parameters based on the 2019/20 data. 1000 simulations are implemented. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2020/21 deflated to 2022 prices using the annual WB deflator. The estimates in bold or with an “a” respectively fall within the 95% CI or one standard error of the true poverty rate. The underlying regression results are provided in Appendix A, Table A.6.

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such as wage, education, age and gender.

> 8 _Secretaria de Com ́ercio Exterior_ (SECEX) from the Ministry of Development, Industry and Trade (MIDC). A list of exporting firms by year is publicly available on www.mdic.gov.br.

> 9The information of the treatment is available at the firm level. Nonetheless, in Peiex’s cases, the majority of treated firms are relatively small with a unique plant.

> 10The Peiex program targets manufacturing firms. We used CNAE 2.0, 2 digits, from 10 to 33, which defines manufacturing.

> 11For some of the estimations, it was necessary information from firms with two lags. Then, it was kept firms that are in RAIS in at least three subsequent periods ( _t-2_ , _t-1_ and _t_ ).

> 12The concept of competence adopted by CBO has two dimensions: (1) It is a function of the complexity, scope and responsibility of activities in employment or another working relationship; (2) It is related to the characteristics of the work context as an area of knowledge, function, economic activity and production process.

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Policy Research Working Paper 9658

# **Abstract**

The COVID-19 pandemic has resulted in border closures in many countries and a sharp reduction in overall international mobility. However, this disruption of legal pathways to migration has raised concerns that potential migrants may turn to irregular migration routes as a substitute. This paper examines how the pandemic has changed intentions to migrate from The Gambia, the country with the highest pre-pandemic per-capita irregular migration rates in Africa. A large-scale panel survey conducted in 2019 and 2020 is used to compare changes in intentions to migrate to Europe and to neighboring Senegal. The data show that the pandemic has reduced the intention to migrate to both destinations, with approximately one-third of young males expressing less intention to migrate. The largest reductions in migration intentions are for individuals who were unsure of their intent pre-pandemic, and for poorer individuals who are no longer able to afford the costs of migrating at a time when these costs have increased and their remittance income has fallen. This paper also introduces the methodology of priming experiments to the study of migration intentions, by randomly varying the salience of the COVID-19 pandemic before eliciting intentions to migrate. There is no impact of this added salience, which appears to be because knowledge of the virus, while imperfect, was already enough to inform migration decisions. Nevertheless, despite these decreases in intentions, the overall desire to migrate the backway to Europe remains high, highlighting the need for legal migration pathways to support migrants and divert them from the risks of backway migration.

This paper is a product of the Development Research Group, Development Economics. 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 dmckenzie@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
Policy Research Working Paper 10963

# **Abstract**

This paper develops a tractable general equilibrium model to quantify the aggregate productivity gains from adjusting the public sector premium and the size of the public sector to their optimal levels. In the framework, the optimal size of the public sector is contingent on the efficiency level of public goods in increasing the productivity of the private sector. The model also incorporates an endogenous decision between market and non-market activities for women. The model is calibrated using data from the Arab Republic of Egypt, a country that exhibits a disproportionate share of workers, and women especially, in the public sector. The findings show that, under a conservative value for the efficiency of the public sector, aligning the public sector premium with its optimal level, thus lowering the share of employment in the public sector, results in aggregate efficiency gains of 12 percent for output per worker and 8 percent for total factor productivity. For lower values of the elasticity of private output to public goods, the productivity gains are almost twice as large. The optimal premium is positive for women and approaches zero for men, preventing a shift of mid-high-level skilled women from the public sector to non-market activities and also a contraction of the male entrepreneurial sector. Notably, a reduced female public sector premium fosters greater female labor force participation in market activities through an expansion of the female entrepreneurial sector, which increases the demand for production labor and drives wages up.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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 francisco.parro@uai.cl and jtorrescoronado@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange
003)|
|_Panel E: Major city vs regional Australia_|||
|Standardized mental health scores*Residence area|0.772|-0.141|
||(28.106)|(0.416)|
|_Panel F: Social network before arrival_|||
|Standardized mental health scores*No network|-0.098***<br>(0.031)|-0.031***<br>(0.008)|
|_Panel G: Government benefit received_|||
|Standardized mental health scores*Not received|-0.001***<br>(0.0002)|-0.0002*<br>(0.0001)|
|_Panel H: Local community support_|||
|Standardized mental health scores*No support|-0.011|-0.00005|
||(0.008)|(0.0006)|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of IV-Logit-FE model (marginal effects) in Column (1) and IV-FE model in Column (2). Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6-item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one.

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As expected, child’s age, mother’s height, household wealth, and mother’s education are significant determinants of a child’s nutritional status. We included the mother’s height to control for a child’s genetic endowment and the high significance of this coefficient would suggest that there is indeed an intergenerational effect. However, one would not expect the mother’s height to directly influence the development of a child, and hence the child’s nutritional status, which explains the small impact of this variable. The probability of a child being underweight or stunted increases significantly as the child gets older. Children between 12–23 months of age are at the highest risk of being malnourished and that risk remains very high as the child ages, particularly for stunting. The coefficient on the child’s gender is not significant in any of the regressions, suggesting there are no gender bias affecting the nutritional status of children in Ethiopia.<sup>14</sup> The impact of household wealth, as measured by the wealth index created, is to decrease the probability a child is underweight by about 4 percent and stunted by about 3 percent. We would generally expect a lower impact of current household wealth on stunting, as stunting is a measure of chronic malnutrition reflecting the impact of the households’ current as much as past wealth.

Our proxy for household food insecurity, whether the mother’s BMI is less than 18.5, is positive and significant in the equation for underweight, but not significant in the equation for stunting.<sup>15</sup> Since a low BMI is a reflection of current nutritional status, it may not be a surprise that this measure of household food insecurity only affects the probability a child is underweight. As an indicator of household food insecurity, the coefficient on the BMI variable suggests that children in a household where the mother has a low BMI are about 6 percent more likely to be underweight. Children living in a female
Most overseas migrants eventually return to Kerala. International migration, which has been primarily to the countries of the Arabian Gulf, is governed by work contracts that stipulate the duration of employment—typically two years, although contracts are renewable at the employer’s discretion. In contrast, as citizens of India, Keralite migrants to other Indian states could much more easily decide to settle permanently outside Kerala. According to the KMS data, the overall number of domestic migrants who had returned to Kerala as of 2003 was, indeed, lower—approximately 42 percent of the number of overseas returnees.

Overseas remittances from international migrants played an important role in Kerala’s economy over the 1998-2003 interval: In 1998, these transfers were estimated to be around 25.5 percent of the state’s net domestic product (NDP); in 2003, they had fallen to 22 percent (Zachariah and Rajan, 2012), but this is still a notable portion of total NDP. According to estimates from the 2003 KMS data, households with one or more overseas members were receiving an average of US$1,058 per year in remittances. We know less about the magnitude of domestic remittances sent to Keralite households from migrant family members within India during this period, as the KMS data sets prior to 2014 did not record this category of financial transfer.

In the 64<sup>th</sup> wave (conducted between July 2007 and June 2008) of India’s National Sample Survey (NSS), a large nationally representative household survey, information on remittances was collected for the very first time. In 2007-2008, the average amount of annual overseas remittances received by households in Kerala with at least one member working abroad was US$1,467. In comparison, the mean amount of domestic remittances received by Keralite households with at least one member working elsewhere in India was US$641 for the same period. In absolute terms, it appears that domestic remittances per migrant household in Kerala are about half the size
strong statistical association with IDP presence with a F-statistic of 61. Both of the endogenous variables are strongly correlated with 2006 ethnic diversity at the household level as Columns 3 and 4 show. We have also considered the number of conflicts and fatalities within 50km outside of each commune’s boundary as an instrumental variable to IDP presence. As Table 8 in the online appendix shows no meaningful statistical relation between nearby conflicts and IDP presence. Thus, we only use the ethnic diversity index as an instrument in this section.

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which corresponds to the ratio of credit market instruments to total non-financial assets in the business sector in the Flow of Funds data.

In our benchmark exercise, we assume _φ_ = 10% of firms are non-essential and shut them down for one quarter. This matches the fall in the US GDP in the second quarter of 2020. As for the unanticipated demand or preference shock accompanying the shutdown shock, we calibrate the preference shock _ξ_ 1 so that the ratio of the fall in consumption to the fall in investment in period _t_ = 1 is similar to the one observed in US data in the second quarter of 2020 (see Figure 2 )— i.e., _I_ 1 _/I_ 0 = 0 _._ 92 _C_ 1 _/C_ 0.

# **4 The Ripple Effect of a Lockdown**

In this section, we show the transition dynamics following an unexpected shock that shuts down _φ_ = 10% of firms for one period and a concurrent unexpected demand shock that lowers the marginal utility of consumption during the lockdown. Unemployed workers that were employed in these firms receive full unemployment insurance benefits financed with government debt and can be reemployed directly by their previous employers without going through the matching process in period 2.<sup>15</sup> During the shutdown, non-essential entrepreneurs have to pay the rental cost of capital, which can be viewed as a fixed cost, and decide how much to draw their assets down and how much to consume.

We first present the aggregate implication of the lockdown, and then dive into the microlevel implications for employment and consumption dynamics across different firms and individuals, respectively.

## **4.1 Aggregate Variables**

Figure 4 shows the trajectory of aggregate variables. The left panel shows GDP, investment, consumption, and total factor productivity, while the right panel illustrates the dynamics of unemployment. The most salient feature of the figure is the swift recovery of the aggregate output. After falling by 10% on impact, GDP bounces
**Figure 4:** Perception of households' social level in comparison with own social environment

<!-- Start of picture text --> (a) By classes (b) By quintiles<br>Poor Vulnerable Lower M-class M-class U-class Q1 Q2 Q3 Q4 Q5<br>Very Rich Rel Rich Middle Rel Poor Very Rich Rel Rich Middle Rel Poor<br>Very Poor N/A Very Poor N/A<br>100 100<br>80 80<br>percent60 percent60<br>40 40<br>20 20<br>0 0<br>2006 2013 2006 2013 2006 2013 2006 2013 2006 2013 2006 2013 2006 2013 2006 2013 2006 2013 2006 2013<br><!-- End of picture text --> _Source:_ Authors’ calculations based on Morocco’s household surveys.

In both rounds of the survey, the majority of households did not consider themselves as richer than the average households belonging to the social contexts in which they lived. It is worth noting that the share of households that considered themselves as poorer than other households was particularly high in the first quintile, and then declined quasi-monotonically through to the top one. Nonetheless, there are important nuances to this general trend. For example, while the (largely pessimistic) subjective perception recorded in 2013 among the poorest classes had slowly declined, as compared to 2006, something had clearly changed among the newly graduated vulnerable and the lower-middle class households. In both cases the (correct) perception of ranking in the middle declined considerably, proportionally raising their awareness of being relatively poorer than other households. Also, the middle classes considered that their perceived status had decreased; however, 70 percent of respondents from this social class did not feel poorer than other households. Finally, the “class-consciousness” of upper-class households declined slightly in 2013 but still stood above 80 percent.

The sense of frustration and perception of non-improvement shared by many Moroccans is further corroborated by an analysis of specific issues
and 4.1 produce worse estimates, specifically 29.7–30.1 percent in TA 2 and 40.9–45.8 percent in TA 3. The results are again robust to the choice of the use of the normal regression model versus the empirical distribution of the error terms.

For the within-year imputation, we follow a similar approach as the across-year imputation mentioned earlier, b y standardizing the distribution of the variables in TA 2 and TA 3 by those in TA 1. The variables’ distributions become statistically similar to those in TA 1, as shown in Appendix A, Table A.4. Similar to the previous findings with across-year imputation, the standardization of variables results in a significant improvement of imputation accuracy for the Model 3.2 in TA 2 (Table 5) and Models 3.2 and 4.2 (Table 6), producing estimates that fall even within one standard error of the true rate.

In sum, the results in Tables 2–6 show that using total food consumption, total non-food consumption or non-food consumption components as predictors in Models 3-7 does not significantly improve on the accuracy of imputation in Models 1, 2 or 9, and may come at the cost of accuracy. The light questionnaire modules for deriving “imperfect” measures of food and nonfood consumption do not end up providing the requisite predictors that permit the reliable estimation of Models 3 and 4. However, the standardization procedures for both TA 2 and TA 3 exhibits a substantial improvement in imputation accuracy.

Comparing the results in Tables 5–6 to those in Tables 2–4 suggests that using TA 1 as the base survey offers slightly stronger statistical signifance than using the 2020/21 TZNPS as the base survey. But the improvements do not appear consisent or noticeably large for all the models, perhaps due to the closer time interval between the 2020/21 TZNPS and the 2022 experimental data.

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**Table 5. Socio‐economic characteristics of sub‐samples with private connections, shared** **<u>connections and all sample, (Addis Ababa, 2016)</u>**

||**P**<br>**(ex**|**rivate co**<br>**electric**<br>**clusive u**<br>**mem**|**nnectio**<br>**ity & w**<br>**se of ho**<br>**bers) ***<br>|**ns for**<br>**ater**<br>**useho**<br>|<br>**ld**|**or**|**Shared**<br>**no-wate**|**connect**<br>**r conne**<br>|**ions**<br>**ction****|||**All s**|**ample**<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Mean**|**Median**|<br>**St.**<br>**Dev**|**p10**|**p90**|**Mean**|**Median**|**St.**<br>**Dev**|**p10**|**p90
# **I.** **<u>Vulnerability</u>**

Estimating how vulnerable countries are to changes in energy prices is a complicated task, not only methodologically but also due to the lack of publicly available data even for basic analysis as performed here. We have discussed already some of the methodological choices we have made in this paper in, for example, performing a static analysis and shying away from general equilibrium effects. We define energy-intensive industries as per the European Union Emission Trading Scheme list discussed in the previous section. We measure vulnerability as the combination of three main indicators: 1) estimated energy price increase after regulation; 2) the share of employment in energy-intensive industries; and 3) the share of value-added in the same energy-intensive industries<sup>12</sup> . In ECA, a significant share of employment and value added is in energy-intensive industries – around 30 percent of employment and around 40 percent of value added (Figure 3 and Figure 4, respectively).

**Figure 3 – Energy-Intensive Sectors Take Up to 30 Percent of Employment in ECA**

Cumulative percentage of employment, by sector, 2008 <!-- Start of picture text --> 100%<br>energy intensive<br>50%<br>industries<br>19.05%<br>7.95%<br>2.16%<br>1.52%<br>0%<br>Mining Elec, Gas, Water Transport Manuf Finance<br>Hotel & Rest Real Estate Health Public Admin Constr<br>Education Retail Agriculture<br><!-- End of picture text --> Source: Authors’ calculation based on UN Data Note: The figure is a representation of unweighted averages across ECA countries. The names of the sectors had been shortened to fit the axis. The full names are Mining and Quarrying; Electricity, Gas and Water Supply; Transport, Storage and Communications; Manufacturing
Policy Research Working Paper 8980

# **Abstract**

This paper investigates the sources of growth in manufacturing productivity in Cote D’Ivoire, Ethiopia and Tanzania in comparison with the case of Bangladesh. Based on the analysis of establishment census data since the mid-1990s, it finds that reallocation of market share between firms contributed substantially to productivity growth in each of the four countries, although to a varying extent. In Ethiopia, the impact of market share reallocations among survivors tended to be larger than those associated with increases in within-plant productivity. In addition, plant closure (or exit) boosted productivity more than new plant openings (or entry) did in the sense that the relative productivity of survivors (or continuing plants) was higher relative to that of closing plants (or exit cases) than it was relative to the productivity of newly opening plants (or new entrants).

Reallocation of market share plays an important role in raising aggregate productivity in Côte d’Ivoire as well. But the pattern here is opposite to that in Ethiopia in that in Côte d’Ivoire entering (or newly opening) plants have larger impact on aggregate productivity growth than closing (or exiting) plants. Unlike the case with Cote D’Ivoire and of Ethiopia, the reallocation of market share among surviving plants is a smaller source of manufacturing productivity growth in Tanzania than the new plant openings and plant closure. The data suggest that the reallocation of market share among surviving plants and exiting plants has larger impact on productivity growth in Bangladesh than the productivity gap between new plants and survivors, as in the case of Ethiopia.

This paper is a product of the Office of the Chief Economist, Africa Region, commissioned as part of the _‘Industrialization for Jobs in Africa’_ regional study. 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.