rafmacalaba/data-use-mentions-extended huggingface dataset:
with the product level of ...nancing needs (FDc � sanitary_riskk) and control for product and country ...xed e¤ects. This allows to isolate the impact of ...nancial development on product survival after controlling for omitted variable bias at the country and product level. The level of ...nancial development is taken at the initiation of the sample period for each exporting country<sup>11</sup> . That is the year we ...rst have export data. All other explanatory variables take value at the initiation of the trade relationship. Our vector of Controls includes various product and ...rm characteristics as well as traditional bilateral gravity variables. The productrelated variables include the value of export in US dollars in the initial year of the trade relationship in logs, initial_exportcikj. This re‡ects the level of con...dence importers have in the pro...tability of their trading partner (Rauch and Watson, 2003; Albornoz et al., 2010). Additionally, we include the total number of destination markets served by ...rm i in country c with product k in the initial year of the trade relationship, NDestinationscik, in log terms. This allows to control for the experience the ...rm has in supplying the world market with product k. We control for the degree of export

> 10Sector is de...ned at HS2 level.

> 11We do this due to the short time span of our sample, and the poor reliability of the data for African countries. Additionally, we used the average level of ...nancial development over the sample period for each country, and ...nancial development measured at the beginning of each spell, results remain qualitatively the same.

11
to provincial level political ranking, while our measure represents government connection at all levels for all types of firms. Third, our sample is more representative, and thus we can examine how the effects of political connections on financial constraints differ across firm sizes. Fourth, we examine how political connections are associated with investment levels and CEO incentives. Fifth, we offer a simple theoretical model that spells out explicitly how government connections affect a firm’s investment and investment-cash-flow sensitivity. Finally, we provide a more comprehensive set of robustness checks. 8 Chow and Fung (1998, 2000) use a panel of manufacturing firms operating in Shanghai over the period 19891992. Héricourt and Poncet use data from 1300 firms from 2000 to 2002. Chen (2008) used listed firms, which are largely state-owned (see Wang, Xu and Zhu 2004) and arguably are less financially constrained than typical Chinese firms. Poncet, Steingress, and Vandenbussche (2010) use a sample of 22,300 observations from 15,000 large firms from 1998 to 2005. Those firms had at least 150 employees, US$10 million in annual sales turnover, and/or US$20 million in total assets. Similarly, Guariglia, Liu, and Song (2011) a data set with more firms than ours, but that covers large and medium-sized private firms (and all SOEs), and thus is not suited to assessing the financial constraints of small, young firms, which is a point of emphasis in what follows.

8
et al. (2021a). This index lists five questions on the laws related to the employment of refugees at the country-year level:<sup>21</sup>

1. Does the law or policy contain a provision guaranteeing the right to work?

2. Does the law or policy contain a provision guaranteeing the right to selfemployment and/or to start a business?

3. Does the law or policy contain a provision guaranteeing the right to work in professional fields provided an individual holds the requisite training or certification?

4. Does the law or policy oblige individuals to hold a work permit?

5. Does the law or policy place additional restrictions on individuals in terms of work, including restrictions on which industries they may work in, or where they may work?

We are grateful to the DWRAP authors for sharing these data for their sample in Africa and much of Asia. We supplement these data by coding laws in 20052018 for 33 additional LMIC countries that are not present in the original DWRAP dataset but have GWP data on attitudes. These laws are listed in Table A4, which complements Table A1 in Blair et al. (2021a). We follow the DWRAP methodology and create a 0 to 1 index that combines the five measures according to Anderson (2008). Higher DWRAP indices indicate laws that afford refugees more access to the labor market.

While the _de jure_ environment is important, it does not provide the complete picture. Laws, both liberal and restrictive, are sometimes ignored in practice. Our hypotheses are based on de facto access – what the refugees and host populations actually experience. We are therefore working to create an additional dataset, the 21Questions 1-3 are assigned 0 if no; 1 if yes, only for recognized individuals; 2 if yes, for all individuals. Question 4 is coded 0 if yes, and permits cost a fee; 1 if yes, but permits are free; 2 if no, work permits are not required. Question 5 is coded 0 if yes,
**B. Sensitive items:** To measure changes in the composition of respondents, we use items from the second set of questions in the midline survey to construct two sensitive outcomes.<sup>25</sup> We asked the same questions in baseline and endline surveys for the impact evaluation.

- _Stress:_ To measure participants’ perception of stress in the midline survey, we asked them, “How stressed did you feel last week?” The response options were _Very Stressed, Moderately Stressed, Mildly Stressed, Not Stressed at All_ . An individual has “High Stress” if she responds, “Very Stressed.” In the baseline and endline surveys, we measured participants’ stress using the Depression, Anxiety, and Stress Scale (DASS-21) instrument (Lovibond and Lovibond, 1996). We use the seven items from the stress subscale. Each item is measured on a scale of 0–3 points ( _Never, Rarely, Almost Always, or Always_ ). We estimate the total stress score following Lovibond and Lovibond (1996); that is, a participant has “High Stress” if the score is greater than 18.

- _Perceptions of effectiveness of physical violence toward children:_ We asked participants, “Do you think physical punishment is an effective method to discipline children?” The response options were _No, it is never effective; It is rarely effective; It is sometimes effective; It is effective most of the time; It is always effective_ . The same question and response options were included in baseline, midline, and endline surveys.

**C. Consistency in self-reports of sensitive items:** To measure consistency in the reporting of either of the two sensitive outcomes at baseline or in the endline survey, we generated dummy indicators that take the value of 1 if the answer in the midline survey is equal to the one reported at baseline and in the endline survey, respectively.<sup>26</sup>

**D. Sociodemographic
||[0.22]|[0.23]|[0.22]|[0.21]|
|---|---|---|---|---|
|Female headed household|0.09|0.05|0.10|0.10|
||[0.29]|[0.22]|[0.30]|[0.29]|
|Household size|7.40|6.48|6.99|7.83|
||[3.60]|[2.53]|[3.27]|[3.91]|
|IDP|0.07|0.12|0.14|0.02|
||[0.25]|[0.32]|[0.35]|[0.12]|
|Observations|1500|540|300|660|

Note: Standard deviations are in brackets. _Data source_ : Rapid Welfare Monitoring Survey (SWIFT) 2017-18.

_Table 2. Common variables between the SWIFT and the SEVAT model_

||**(1)**<br>**Iraq**<br>Mean[SD]|**(2)**<br>**Kurdistan**<br>Mean[SD]|**(3)**<br>**North**<br>Mean[SD]|**(4)**<br>**Center-South**<br>Mean[SD]|
|---|---|---|---|---|
|Home: not owned by HH|0.27|0.30|0.29|0.26|
||[0.45]|[0.46]|[0.
Two limitations of this study are that it only applies to 10 districts in Malawi and is based on a 20 percent extract of the census. Additional work could demonstrate that similar results hold in different contexts and when using a full census. A third limitation is that the hypothetical partial registry is taken from the census, and therefore assumed to match the census exactly. In reality, measurement error in data collection for the partial registry will reduce its performance relative to census-based predictions. Indeed, a partial registry could suffer from some of the same issues in data collection experienced when collecting data for the UBR. A project that pilots the collection of a partial registry for the purpose of training a geospatial model would provide a more realistic test of the partial registry approach and could shed new light on whether such a partial registry would be prone to systematic bias. Finally, future research could leverage household level information on geocoordinates if they can be obtained in census data. This would enable estimating models relating predicted welfare to geospatial indicators at the household level, which may perform better than the village-level models considered in this analysis. Despite these caveats, the results convincingly demonstrate both the limitations of existing methods, and the potential for partial registries to add massive value when using survey and geospatial data to identify the poorest villages in a very low-income setting.

25
D. N. 1978. Prediction of School Failure From Third-Grade Data: Educational and Psychological Measurement. https://doi.org/10.1177/001316447803800442

- Nicola, M., Alsafi, Z., Sohrabi, C., Kerwan, A., Al-Jabir, A., Iosifidis, C., Agha, M., & Agha, R. (2020). The socio-economic implications of the coronavirus pandemic (COVID-19): A review. International Journal of Surgery (London, England), 78, 185–193. <u>https://doi.org/10.1016/j.ijsu.2020.04.018</u>

- Macdonald, K. A. D. 2012. Indigenous Peoples and Development Goals. In G. H. Hall & H. A. Patrinos (Eds.), Indigenous Peoples, Poverty, and Development (pp. 17–72). Cambridge University Press. https://doi.org/10.1017/CBO9781139105729.002

- Marcotte, Dave E. and Steven W. Hemelt. 2008. Unscheduled School Closings and Student Performance. Education Finance and Policy, Volume 3 | Issue 3 | Summer 2008, p.316-338. Available online at https://www.mitpressjournals.org/doi/pdf/10.1162/edfp.2008.3.3.316

- Maria A. Cattaneo, Chantal Oggenfuss, Stefan C. Wolter. 2016. The More, the Better? The Impact of Instructional Time on Student Performance.  IZA DP No. 9797 March 2016. <u>http://ftp.iza.org/dp9797.pdf</u>

- McClain-Nhlapo, Charlotte. 2020. An inclusive response to COVID-19: Education for children with disabilities. Global Partnership for Education. Available online at <u>https://www.globalpartnership.org/blog/inclusive-response-covid-19-education-childrendisabilities.</u>

- McClain-Nhlapo, Charlotte Vuyiswa
# **1. Introduction**

COVID-19 triggered one of the most precipitous and dramatic shocks to households and regional economies since the end of World War II. Within weeks of the World Health Organization’s declaration of COVID-19 as a global pandemic on March 11, 2020, the virus had spread across most of the world.<sup>2</sup> As the crisis unfolded, there was an immediate need for fast, accurate data on the economic effects of the pandemic. The World Bank Group worked with national statistical offices to field phone surveys in dozens of member countries, with the goal of expeditiously measuring how the pandemic was impacting households. These surveys asked respondents about directional income changes, both for total and various sources of income. In addition, they asked about access to critical services and resources, food security, employment status, behavioral adjustments, and overall well-being. Insights from these survey data have proven to be invaluable for documenting the impact of the shocks on households in many developing countries and informing policy responses: they are enabling more precise targeting in assistance and recovery programs, whether in the form of direct cash transfers to residents or interventions to assist firms in the hardest-hit sectors. However, fielding phone surveys takes critical time, which is not always an available resource in the midst of a major crisis, and requires substantial resources to sustain over longer periods.

Real-time indicators – like satellite images, pollution data, and Google mobility data – are immediately available at low cost. Hence, it is not surprising to see that, for example, mobility data from Google has become popular for understanding community responses to the pandemic. When lockdowns were implemented, Google mobility data shows corresponding declines in activity in commercial areas and increases in time spent at residential spaces. These data are available in close to real time and can be used to document compliance with mobility restrictions. Similarly, near real-time data on nitrogen dioxide pollution showed less pollution in some areas in the aftermath of the shock (Masaki et al
# **_A.2. School Enrollment-Income elasticities_**

We estimate the income elasticity to schooling using data from 130 household surveys, using the latest available Global Monitoring Database (GMD) for all available countries. We estimate this relationship by welfare quintile, which has the advantage of allowing for non-linearities. We estimate non-parametrically the following relationship, O ... qq=1,aa,cc qq=1,cc �O OO × WW where, qq=5,aa,cc qq=5,cc OO × WW O , is the share of out-of-school by welfare quintile _q_ , for age group **_a_** , in country **_c_** (see figure 3 for the out of school variation across welfare quintile, per country income group) OO W, is the share of children in welfare quintile **_q_** , in country **_c_** We apply the income shock to all children by multiplying the per capita welfare of all children by the available macro projections of contraction in 2020. In our reported estimates, we use the latest published Macro Poverty Outlook (MPO) projections for GDP per capita growth, with the regional average imputed if no country value was available. Preserving the baseline cutoff values for each welfare quintile, we observe how this shock changes the distribution of children across the original quintiles. The total out of school children is obtained by reweighting the number of children on each welfare quintile, and assigning them the observed shared of out of school children ( _OoSq,ac_ ). O ... qq=1,aa,cc qq=1,cc O OO × WW′ � where, qq=5,aa,cc qq=5,cc OO × WW′ where, O , is the share of out-of-school by welfare quintile _q_ , for age group **_a_** , in country **_c_** (see figure 3 for the out of school variation across welfare quintile, per country income
>(7)<br>|
|How many onsite examinations per bank were performed in the last 5 years (2006-2010)?|4<br>(3.5)|2<br>(2.5)|5<br>(4)|

Source: Calculations based on the 2011–12 Bank Regulation and Supervision Survey, which reflects values as of 2010. Values in parentheses refer to the 2007 Bank Regulation and Supervision Survey, which captures data as of 2005. ADV: advanced economies, EMDEs: emerging and developing economies (see Table 1 provides the complete list of countries).

47
The model picks at least one variable in each of the key pre-identified categories (table 2). This suggests that there is empirical validity that country-level measures of performance corresponding to these categories have a relevance to assessing the investment climate. This broadly validates approaches like the Doing Business and Competitiveness indicators.

One surprise is the relative absence of indicators linked with infrastructure (only one indicator of transport connectivity) and financial services. In the specific context of the data used for the estimation, this may be explained by the fact that large investments (FDI captured in the database are presumably the decision of large investors) may be able to internalize these types of constraints. Another point of note is that the FDI data sample (unlike the trade data one) includes services and goods sectors and that we cannot exclude that results would vary in a specification differentiating goods and services sectors.

Natural resources rents pose a specific conundrum as they may influence market readiness for investments in opposite directions. Resource rents are obviously specific to resource sectors and play positively on FDI prospects in these sectors. They also impact other sectors, with a mix of potentially positive and negative effects. Resource windfall through induced demand effect, potential Dutch disease and resource curse effects that should favor non-trade sectors and thus favor market seeking FDI. However, these effects also come at the expense of efficiency in other productive sectors thus detracting potential investment in these. Resource-curse effects may negatively affect the overall attractiveness of investing in a country.

Because of the importance of investments that are linked to extractive industries (in poorer countries in particular), we ran a specification of the model omitting investments in the resource-based sectors (mining and oil sectors). We find that that results are robust to the omission maintaining an overall good fit for the model, and with most variables being stable to the specification. The model also assigns the same significance to rent variables in both specifications. Taken together these results suggest that the drivers of investments are not determined by investment behavior in the extractive sectors and
areas related to health care with systematically lower infant mortality levels.

We evaluate the potential impact of individual and collective inputs by using data from participatory budgeting, where participants have the right to select infrastructure projects, social programs, or some combination of the two. We argue that the presence of participatory budgeting directly incorporates citizens’ voice into budgetary and policymaking processes. In turn, these programs lead to greater support for state and government activities because governments using participatory institutions produce better well-being outcomes (see paragraph above). The process of incorporating citizens’ voice and improving well-being then generates broader support for tax compliance.

<u>Collective Oversight: Groups that are involved in the selection of specific projects</u> have vested interests in working to ensure that these projects are implemented well. Participatory institutions typically establish oversight committees to monitor project design, project bidding, as well as actual project implementation. These committees generate close working relationships among civil society leaders, civil servants, and contractors as they seek to ensure that scarce public resources are properly used.

12
of Cross-Country Growth Regressions”. The American Economic Review 82(4): 942-963.

Mensah, A., Hutchet-Bourdon, M, and Latruffe, L. (2014). « Infrastructure Access and Household Welfare in rural Ghana”. _African Development Review_ 26(3): 508-519.

Narayan, A., Vander Weide, R., Cojocaru, A., Lakner, C., Redaelli, S., Mahler, D., Ramasubbaiah, R., and Thewissen, S. ( 2018). _Fair Progress? Economic Mobility Across Generations Around the World_ , Equity and Development. Washington: The World Bank.

Prydz, E., Jolliffe, D. and Serajuddin, U. (2022). “Disparities in Assessments of Living Standards Using National Accounts and Household Surveys”. _The Review of Income and Wealth_ 68(S2): S385S418.

Ram, R. (2011). “Growth elasticity of poverty: Direct estimates from recent data”. _Applied Economics_ 43(19).

Ravallion, M. (2001). “Growth, Inequality and Poverty: Looking Beyond Averages. _World Development_ 29(11): 1803-1815.

Ravallion, M. (2003). “Measuring Aggregate Welfare in Developing Countries: How Well Do National Accounts and Surveys Agree?”. _The Review of Economics and Statistics_ 85(3): 645-652.

16
2

# **I. INTRODUCTION**

Most economists accept that, in the long run, open economies fair better in aggregate than do closed ones, and that relatively open policies contribute significantly to development. Many commentators fear, however, that in the shorter run, one of the steps towards openness – trade liberalization – harms the poorer actors in the economy, and that even in the longer run successful open regimes may leave some people behind in poverty. Africa remains the poorest continent of the world. Yet, at the same time, African countries have experienced significant improvements in trade liberalization. It seems that the large gains expected from opening up to international economic forces have, to date, been limited in Africa, especially for poor people.

While the traditional trade theory predicts welfare gains from openness at the country level through specialization, investment in innovation, productivity improvement, or a better resource allocation, the theoretical impact of trade on the poor remains uncertain. Besides, empirical results do not converge on this point and it seems that developing countries are not equally able to make use of the opportunities arising out of increased access to markets in the developed world.

The contribution of this paper lies in providing new cross-country empirical evidence focused on Africa on how the poverty reduction effect of greater trade openness depends on a variety of structural characteristics, including some that are subject to reform. Using a panel of African countries over the period 1981-2010 and testing for non-linearities in the trade-poverty relationship, this paper explores the empirical link between trade openness and poverty. Its results uncover an interesting pattern of reform complementarity: trade openness tends to reduce poverty in countries as their financial sector grows deeper, their education level higher and their governance stronger.

Our concern is with poverty, not inequality. Since trade liberalization tends to increase the opportunities for economic activity, it can very easily widen income inequality while at the same time reduce poverty. Consequently, statements about its effects on inequality cannot be translated directly into statements about its impact on absolute poverty
at a financial institution, with this proportion decreasing to 16% for the poorest forty percent of the country. While 14% had borrowed from a financial institution during 2014, 32% had borrowed money in general, with the difference being made up by borrowing from friends and families as well as informal lenders (Demirgüç-Kunt et al. 2015). Part of the reason for these low rates of financial inclusion could be because there are important information asymmetries– only 2.5% of the population is covered by the country’s public credit registry, making it difficult for lenders to assess credit risk (World Bank Group 2016). Microfinance institutions are often seen as one avenue through which countries can increase access to financial services.

In Jordan, 97% of the population of 7.5 million identify as Muslim. The World Bank estimates that 14% of the population lives in poverty. Religion plays a large role in the country. Most of the populace, 85%, say that religion 8