rafmacalaba/data-use-mentions-extended huggingface dataset:
percentiles (Columns (5) to (8)). Again, our results on the effects of temperature remain consistent while the effects of rainfall appear inconclusive.

# **4.3. Temperature Effects on Poverty Dynamics**

# _Chronic Poverty_

We turn next to investigate climate change impacts on chronic poverty. Chronic poverty, characterized by prolonged and persistent poverty conditions, can have severe and lasting consequences on individuals and communities, affecting multiple generations and hindering overall socio-economic development. Addressing chronic poverty is therefore essential for sustainable poverty reduction, as transient poverty alleviation measures might not effectively target the root causes of long-term poverty.

We present our analysis of the effects of warmer temperatures on chronic poverty using three measures: (i) the number of times each subnational unit is in poverty (i.e., the subnational unit’s mean consumption per individual in 2017 falls beneath the $3.65/day or $6.85/day poverty thresholds) from when it was first observed until it was last observed in the SPID database; (ii) the share of time that a subnational unit is in poverty during the same period. The measures are then regressed on temperature and rainfall variables using the same period of measurement.<sup>17</sup> Alternatively, we also employ a third measure, which is the average poverty rate using a window of 5-year period, conditional on each subnational unit having at least two data points during a particular period.<sup>18</sup> We then apply a fixed-effects model for the last measure. These three measures offer different but complementary aspects (i.e., absolute and

> 17 We also include the number of data points we have for each subnational unit in the database to control for potential differences due to data availability for each subnational unit. 18 For example, a subnational unit with data available from 2010 to 2019 was divided into two 5-year periods, with poverty and weather variables being averaged during each period.

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surveys can be considered representative. The <u>Statistical Addendum</u> gives the OLS regression results for the Basic PMT. The simple average R<sup>2</sup> is 0.53, with a range from 0.32 (for Ethiopia) to 0.64 (Burkina Faso); Table 3 provides summary statistics for the Basic PMT (as well as for the extended version discussed below). This explanatory power is typical of past studies.<sup>42</sup> We did not try to prune this model, by either _ad hoc_ or more systematic methods (such as stepwise regression). This would reduce the number of predictors but (of course) also reduce R<sup>2</sup> and probably increase targeting errors. However, we do consider stepwise regression as an option in Section 5 when using a much larger set of explanatory variables.

> 39 We also considered the option of using log consumption per equivalent single adult using the scales provided by the LSMS. We focus on the “per capita” case in this paper although the Addendum also gives regressions and key results using scales.

> 40 We use nominal consumption for Burkina Faso.

> 41 Using the World Bank’s international line of $1.90 a day at 2011 purchasing power parity, 43% of the population of SubSaharan Africa are found to be poor in 2013 (based on <u>PovcalNet).</u>

> 42 A seemingly representative set of studies is Grosh and Baker (1995) (R2 from 0.3 to 0.4), Ahmed and Bouis (2002) (R<sup>2</sup> =0.43), Narayan and Yoshida (2005) (R<sup>2</sup> =0.59), Sharif (2009) (R<sup>2</sup> =0.57), Stoeffler et al. (2015) (
and wholesale/retail trade. These firms were largely involved in services (other than retail), which were probably substantially impacted by social distancing restrictions introduced to reduce the spread of COVID-19. Larger firms were far less likely to report a decline in sales by June 2020, which may be because they have greater stability in operations. In addition, firms that reported changing their business practices in response to the pandemic were less likely to experience a decline in sales. In terms of employment, firms that faced large or very severe obstacles to access to finance immediately prior to the pandemic (according to the baseline ES) were more likely to reduce their number of employees. Firms that had reported introducing new products in the three years prior to the pandemic were far less likely to report reducing their number of employees.

# **4.3.2 Factors associated with recovery in sales and employment levels by 2021**

Table 3 shows that a combination of structural and firm-specific factors were associated with firms ex- periencing a recovery in sales and/or employment by July 2021. Recovery in sales was more common among larger firms and those in manufacturing (these factors were also associated with less likelihood of a decline in sales initially). In addition, the top manager’s level of experience was positively associated with a recovery in sales. A lack of recovery was more common among firms that were in a partic- ularly competitive market (immediately prior to the pandemic they stated more than five competitors had entered their market in the previous two years). In regard to a recovery in employment, this was positively associated with top managers having more experience and receiving government support. A lack of recovery in employment was more common among older firms according to the survey data (the opposite was the case according to the tax administrative data) and those outside of manufacturing and retail trade.

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Bhattacharya, & Nguyen, 2005; Johansson, 2010). Additionally, improvements in child health from HIPC debt relief through these channels may be linked to the emphasis on and strong oversight of pro-development policies in HIPCs at the different stages of the initiative.

The impacts of debt relief in general and the HIPC Initiative in particular on child health are not well known. Thomas and Giugale (2015) show that the general economic and social development in African HIPCs has been positive since the initiative was rolled out and Marcelino and Hakobyan (2014) argue that economic growth has gone up after HIPC debt relief. In a World Bank report on HIPC debt relief, Schmid (2009) finds that the infant mortality rate declines after a HIPC reaches Decision Point of the program. However, this study relies on country-level data on child health and was performed only a few years after the initiative came into effect. There may thus be problems with confounding factors which are difficult to account for in cross-country analyses. There are also drawbacks with the analysis in Schmid (2009) related to the relatively short time frame. In the same report Crespo Cuaresma and Vincelette (2009) find increases in educational attainment and expenditures after HIPC Decision and Completion Points. Again, however, there are concerns with regard to endogeneity and the short time frame of the analysis, and we currently lack reliable knowledge and evidence of the social effects of debt relief.

3
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|Country,<br>L<br>economy, or<br>territory|ast<br>lu<br>P<br>Date|btished<br>censu<br>opulation<br>(1000)*|s<br>Source for population<br>(mid-1985)|Source for fertility<br>(1985-90, unless indicated)|Source for mortality<br>(1985-90, unless indicated)|
|---|---|---|---|---|---|
|Thailand|Apr 80|44825 F|Official<br>est., National<br>Economic and Social<br>|Official<br>est., National<br>Economic<br>and Social|Official<br>est., National<br>Econfmic<br>and Social|
||||Development<br>Board|Development<br>Board|Development Board|
|Togo|Nov 81|2705 F|Bank projection from census|Bank assessment<br>of 1961<br>and 1971 censuses|U.N. 1988 revision (prelim.|
|Tokelau|Oct 82|2 F|UNPVSR<br>4/87 (official<br>est.)|--||
|Tonga|Nov 76|<br>90 J|UNPVSR<br>4/87 (official<br>est.)|Bankest.|Bank est.|
|Trinidad**and**|May80|1080 F|U.N. 1988 revision (prelim.)|Based on 1987DHS|1980-85, U.N. 1984|
|Tobago|||||assessment; 1985-90,<br>Bank projection|
|Tunisia|Mar84|6966 F|Bank projection from census|Bank est.|BasedonU.N. 1984<br>assessment|
|Turkey|Oct 85
0.064|−0.274||−0.188|||

Notes:

1. OLS estimates. Sample is cross-section of 5-digit HS agricultural products for 93, 96, 99. NTM data (from TRAINS database) available for those years.

2. NTM=Coverage of imports with some Price-type (PRICE), Quant-type or Threat NTM (THREAT).

3. **Bold** and underline implies that corresponding estimate is statistical significance at 5%, **bold** implies that corresponding estimate is statistical significance at 10%.

4. %PACEXP=PAC spending as a fraction of total PAC spending for the cross-section. RANKPACEXP=Ranking by PAC spending (high rank implies relatively high PAC spending) scaled by sample size ( _N_ is the highest rank). So 1 ≥ RANKPACEXP ≥ 0 and monotonically increases with PAC spending.

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Having the managers of firms rate constraints on the firms’ operation and growth is a useful start for identifying important obstacles in the business environment. We analyze these obstacles not only by using econometric tools but also by examining the importance of these obstacles across regions and sectors.

# **4.1 Understanding Obstacles to Firms’ Operation**

In the World Bank Enterprise Survey, as noted, firms rate 15 obstacles in their business environment. These are access to finance, practices of competitors in the informal sector, electricity, corruption, crime, inadequately educated workforce, labor regulations, business licensing and permits, political instability, tax administration, tax rates, transport, customs and trade regulations, courts, and access to land.

A review of firm responses shows that the biggest reported obstacles differ across regions and countries (see appendix). Using model 2 (as explained in greater detail in the next section), we find that different sectors also confront different obstacles. For example, in the manufacturing sector access to finance, informal sector competition, tax rates, and labor regulations matter the most, while in the sales and services sectors only access to finance and informal sector competition are negatively and significantly correlated with firm growth (see table 6). Estimation results for the same model show that each country faces its own set of significant obstacles.<sup>8</sup> So does each region (see table 5).

Many of these obstacles are linked directly or indirectly to poor firm performance. In an ideal world a country would address all these problems in order to improve firm performance. But governments in developing countries have limited financial and human resources and, as argued by the growth diagnostics approach, should therefore prioritize reform efforts to remove the most important constraints.

The top three obstacles to firms’ operation emerging from the survey data for our sample are electricity, access to finance, and tax rates (figure 1). But we do not know whether these are the top obstacles to employment
observation is therefore that the South African real exchange rate is depreciated because of dismal productivity performances that have characterized this economy over the past 20 years.

In practice, the Balassa-Samuelson reasoning focuses our attention on the role of productivity in the export sectors as the source of productivity growth and wage increases. Either in the traditional perfect competitive framework or in the most recent models of monopolistic competition we need to first consider the sectoral composition of South Africa’s traded sector. As far as trading in goods is concerned, the following graph from the Atlas of Economic Complexity shows the composition 9
The first measure is the **basic UHCI** **_,_** which captures the income gains from employing all potential workers. The basic utilization measure is the employment-to-working-age-population ratio, drawn from the ILO and the World Bank’s JOIN database. The basic UHCI has the advantage of simplicity, ease of construction and measurement. It is also available for a wider variety of countries and helps to fix concepts.

# (2) Utilization (basic measure) = Employment/population (working age)

Second, I produce the **full UHCI** which, in addition to the gains from higher employment rates, also takes account of the income gains from moving workers to jobs where they can better use their human capital to increase productivity (“better employment”). The full utilization measure also has the desirable property that it is measured relative to potential: countries with high levels of human capital are penalized more heavily if people are not in the types of jobs where their human capital can be used to increase productivity. I define “better employment” as nonagricultural employees plus employers, which excludes the lowest productivity jobs in developing countries where human capital could have less effect on productivity: subsistence own-account/family agriculture, non-farm selfemployment (which is often working in household microenterprises, as own-account workers or contributing family workers), and landless agricultural laborers.<sup>3</sup> This definition is not intended as a value judgment, but rather is based on the type of jobs that are relatively rare in low-income countries, but are common in high-income countries— suggesting they are associated with higher productivity (Merotto et al. 2018). The better employment rate (BER) is defined as the fraction of the working age population in better employment: (3) Better employment rate (BER) = (Non-agricultural wage employees+ employers)/Population (working age) The full UHCI is a weighted average of the HCI score for those in the better employment (who are as
economies after the GFC (Mc Cauley et al., 2015). For example, in the period 2009-12 emerging market debt funds received flows of $86 million, more than twice the $39 million received in the period 2003-06. As flows into emerging market funds increased, emerging market corporate bond yields declined, and the number of corporate bond issuances increased. In particular, there was substantial growth in the issuance of large denomination bonds. Investors tended to favor large, indexeligible bonds because they are more liquid and limit the risk of underperforming market benchmarks (Calomiris et al., 2019).

The empirical research shows that as interest rates declined in developed economies (particularly in the United States) since 2009, bond issuance activity by emerging market nonfinancial corporates expanded (Burger et al., 2018). Some calculations conclude that, without the monetary expansion in the United States, nonfinancial corporate bond issuances by emerging economies would have been about half of the actual issuance volume (Lo Duca et al., 2016). Firms’ balance sheet data unveil the same type of pattern. Overall, a 1 percentage point decline in the United States policy rate seems to increase leverage in emerging market firms by 9 basis points (Alter and Elekdag, 2020). Consistent with these patterns, other empirical evidence suggests that economy-specific factors (such as better macroeconomic fundamentals or institutional quality) became less relevant in explaining corporate leverage in the post-GFC period, whereas global financial conditions gained importance (Feyen et al., 2015; Ayala et al., 2017; Herwadkar, 2017).

Although there is some evidence that the low interest rates and abundant liquidity in bond markets might have relaxed corporate borrowing constraints (Alter and Elekdag, 2020), there is also evidence that speculative investment opportunities, such as carry trade, fueled bond issuance activity by nonfinancial corporates in emerging economies. Traditionally associated with the financial sector, carry trade is the activity of profiting from interest rate differentials in different markets by borrowing at low interest rates in one
spillover effects and perceived equity, the World Food Programme and the Lao Ministry of Education determined that implementation of the interventions should be done at the district level and type-of-program assignation given randomly across the three districts in Phongsaly province. Interventions were assigned as follows: Phongsaly District (Phongsaly Province): On-site feeding Khua District (Phongsaly Province): On-site feeding and take-home rations Nhot Ou District (Phongsaly Province): Take-home rations Ngoi (Luang Prabang District): Control district The design of the take-home rations in the evaluation study differed from that used in other districts in northern Lao PDR. Following WFP guidelines, take-home rations in other districts were targeted at girl students and at informal boarders. For the impact evaluation study, it was determined that take-home rations would be provided to both girls and boys, with a separate additional ration also provided to informal boarders.

# **_4.2 Sample_**

Data for the study come from a longitudinal survey of approximately 4,500 households with school-aged children in rural villages in the four sampled districts of northern Lao PDR. The region is ethnically very diverse, with over 50 distinct ethnic groups. At standard levels of significance (.05) and power (.8), the study size is sufficient to detect a difference in enrollment of 5 percentage points post-intervention based on baseline attendance data, as well as sufficient enough to detect a change in anthropometric z-scores of approximately 0.3 standard deviations. Eligible households (those with at least one school-aged child, defined as children aged 6-10) 13
of country, debt, financial development, access to infrastructure such as mobile subscriptions and telecom come from the World Development Indicators (WDI) of the World Bank. Indicators on institutional quality are from the Country Risk Project and Doing Business World Bank data projects. Other gravity model variables such as measures of bilateral distance between SSA countries and the US, a set of dummy variables including common language between countries and the US obtains from the GeoDist database (Mayer and Zignago, 2011). Table A1 in the appendix presents a list of variables used in the study, their definitions and sources, while Table A3 provides basic summary statistics.

# **6 Discussion of Results**

The first section below presents a discussion of the findings from the SCM, whereas the next section discusses the underlying factors explaining the variations in the gains from trade due to AGOA.

## **6.1 Impact of AGOA: Results from SCM**

Would trade flows have been different without AGOA? If Sub-Saharan African countries would have still experienced similar trends without AGOA, the trade creation or lack thereof in the post-AGOA period might not be fully attributed to AGOA. To answer these questions, we use synthetic controls - estimated country experiences of trade flows had countries not been treated with AGOA. The SCM procedure follows Table 1 in identifying the period countries became eligible to estimate the treatment effect. Most of the countries in the sample were eligible towards the end of 2000 or 2001 while a few others were eligible in different years afterwards. The estimation is based on specific years of entry into the AGOA framework, which may vary across countries. Hence, SCM employs this particular 21
<!-- Start of picture text --> Chart 15<br>Argentina and Brazil<br>Rate of Change of total real debt<br>0.3<br>0.2 l l<br>0.1<br>0.0<br>1991 1992 1993 1994 1995<br>_ - ARGTOTTD . BRGTOTTD<br><!-- End of picture text --> Sources: World Bank debt tables and Central Banks 33
on its face, can be even more demanding than the EU law. For example, the Cybersecurity Law seems to make consent the exclusive basis for information collection, unlike EU law which allows a variety of bases for collecting personal information, including a category of “legitimate interests.”<sup>46</sup> A draft proposal from the Cyberspace Administration of China would require network operators to inform “the local cyberspace administration when they collect important data or sensitive personal information;”<sup>47</sup> this would enhance the ability to regulate for security-related goals.

Security is also a key motivation for other aspects of the data regime. In comparison to the US’s all-permissive approach to cross-border data flow and the EU’s careful control on outward flows of personal data, China has moved towards highly restrictive policies to keep data within its own borders.<sup>48</sup> On June 13, 2019, China released a draft regulation on the outbound transfers of personal information, separating its treatment of “important data” and “personal information,” which it had previously categorized under one umbrella.<sup>49</sup> With regards to the outbound transfer of personal information, the 2019 draft permits domestic network operators to enter into contractual agreements with foreign data receivers to allow for the transfer of personal data.<sup>50</sup> In order to transfer or access personal data outside the China, data controllers must (1) inform the data subject of the overseas transfer, (2) obtain express consent from the data subject, (3) store a copy of the data within China, (4) conduct a security assessment and file this with the local CAC, 5) establish data transfer agreements with overseas data receivers, 6) submit an annual report to the CAC on all related data transfers, 7) keep a record of all cross-border data transfers for a minimum of five years and 8) to appoint an officer

>
however, Syrians were found to be significantly disadvantaged on both measures. Controlling for the type of employment showed that informal workers have seen their opportunities deteriorating in job separation and job finding compared to formal workers throughout the period regardless of nationality. The only exception was the second half of 2020 when Jordanian informal workers witnessed a significant recovery. Dissecting the Syrians by their residency in camps, we find that the Syrian disadvantage observed is completely driven by Syrians in camps.

These results highlight the importance of securing the right to work for refugees and dismantling barriers to formal employment to ensure their self-reliance and resilience in the face of shocks. Given the pressure on many host countries that are primarily developing countries with limited resources and social protection capacity, this calls for more coordinated efforts internationally to support refugees and alleviate the pressure on host country labor markets. Moreover, the results suggest the importance of revisiting camp settings, reconsidering camp closure policies, and providing job assistance to residents of camps.

# **References**

Abay, K. A., Tafere, K., & Woldemichael, A. (2020). Winners and Losers from COVID-19: Global Evidence from Google Search. _Winners and Losers from COVID-19: Global Evidence from Google Search_ , _June_ . https://doi.org/10.1596/1813-9450-9268 Alhawarin, I., Assaad, R., & Elsayed, A. (2021). Migration shocks and housing: Short-run impact of the Syrian refugee crisis in Jordan. _Journal of Housing Economics_ , _53_ , 101761. https://doi.org/10.1016/j.jhe.2021.101761

- Angelucci, M., Angrisani, M., Bennett, D., Kapteyn, A., & Schaner, S. (2020). Remote Work and the Heterogeneous Impact of COVID-19 on Employment and Health. _National_

_Bureau of Economic Research_ , 34.

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