rafmacalaba/datause-encoder-data huggingface dataset:
coe ffi cient of the interaction e ff ect implies that wealth allows farmers to gain access to market facilities easily and the resulting bene fi ts largely outweighs its e ff ect on opportunity cost of leisure. The regression results thus indicate that the e ff ect of market access is also dependent on the wealth level of the farmers, thus pointing to possible distributional consequence of an improvement in access to market. In this section, we simulate the e ff ect of an improvement in the market access index by 20 percent. Given the average market access index of around 5 in our sample, a 20% increase translates into adding about an extra facility in each market. As our sample consists mainly of tomato and mango farmers, the bene fi t estimates are only relevant for these farmers. Because of lack of data, we also do not attempt to account for the general equilibrium e ff ects that may arise due to change in the cropping pattern of all farmers. Moreover, the estimated bene fi ts are one-time bene fi ts which ignore the bene fi ts that may accrue over the lifetime of a facility. Thus the estimated bene fi ts can be construed as the lower bounds of bene fi ts from an improvement in market facility and market access.

One di ffi culty in the estimation of the welfare consequences of an improvement in market facilities is that we need data that would represent household welfare of the farmers such as consumption expenditure. However, the India agricultural marketing survey did not attempt to collect these information due to cost and time consideration. In the absence of household expenditure, we took total sales revenue as an indicator of the farmers’ bene fi t from crop production and sales. At the fi rst stage of the simulation exercise, a median regression is run on the log of total value of sales using the following speci fi cation.

where Si, the decision to sell at the market is determined by equation (2) reported in the 24
less effective in developing countries. Maloney and Taskin (2020) analyze the determinants of social distancing and economic activity across countries. They conclude that much of the social distancing behavior was voluntary rather than a result of repressive restrictions. Azevedo et al. (2020) and Psacharopoulos, Collis, Patrinos and Vegas (2020) focus on school closures. They estimate significant negative effects on years of schooling adjusted for quality, which will significantly depress future earnings of affected cohorts. Demirg ̈u ̧c-Kunt, Lokshin and Torre (2020) estimate the effects of non-pharmaceutical interventions across countries in Europe & Central Asia and find that countries that implemented restrictions sooner had better short-term economic outcomes. Similarly, the International Monetary Fund (2020) concludes that mitigation measures have been successful in bringing down infections and set the stage for an eventual recovery from the downturn.

The analysis most closely related to this paper is International Labour Organization (2020), which specifically monitors labor market impacts, in particular the effect of workplace closures, working hours losses and labor income losses derived from labor force surveys. These reports find that a large share of the world’s workers live in countries with workplace closures (with a peak of 97 percent in April 2020). In their analysis, working-hour losses are high and translate into substantial losses in labor income. This work complements that analysis in two ways. First, it looks at a wide variety of labor market outcomes, including work stoppage, income loss, income and job changes. Second, this study considers a larger set of developing countries, particularly in Sub-Saharan Africa, that are not reporting labor statistics during the pandemic. Considering the literature as a whole, this paper makes three main contributions. First, it considers developing countries instead of developed countries, which have been the focus of most existing studies. Second, it compares the labor market impacts across a variety of developing countries spanning multiple regions. This paints a richer and more accurate picture of how the crisis affected workers than existing global or regional studies based on firm data, in large part because the phone survey data include informal sector workers who make up a large share of workers in low-income contexts. Finally, 9
<!-- Start of picture text --> 120<br>100<br>80<br>60<br>40<br>20<br>0<br>English Spanish French<br>Google Trend Index<br><!-- End of picture text --> Note: It shows the trend for the expression “online shopping” in English, Spanish or Portuguese, and French.

Figure 1: Google trend for the expression “online shopping” This wave of digitalization comes, however, with an important risk; the deepening of the digital divide. Literature has shows that significant differences in the use of digital technologies across firms existed pre-pandemic (Cirera et al. (2022)). These can be explained by several factors such as access to infrastructure and finance or capabilities to successfully implement these technologies. While the shock on digital demand for businesses can offer an opportunity for smaller firms and those less exposed to digital technologies to catch up, it can also lead to an increasing gap, if more capable firms are disproportionately able to respond faster to this shock. For example, in retail and wholesale, the shock may lead to an increasing market share towards well-established e-commerce platforms.

The objective of this paper is to describe the use of digital technologies during the pandemic and the heterogeneity in its use for 57 (mostly developing) countries, where the evidence is very scarce. Specifically, the paper analyzes the gap in digital responses to COVID-19 across businesses. It relies on three waves of the World Bank’s Business Pulse Surveys (BPS) and COVID-19 Followup Enterprise Surveys (WBES), which provide comprehensive information on the impact of the COVID-19 pandemic on businesses worldwide, including digital adjustments made by firms in response to the shock.<sup>2</sup> The paper aim to address three key questions: i) What is the share of firms that started or increased the use of digital technologies in response to COVID-19 and for what business purpose? ii) How has the digital response varied across sector, size, and digital readiness of the firms? and, iii) Has the inequality in the digital sales markets increased over this period? This analysis is complemented by testing if these patterns on digitization are associated with firm

> 2See Apedo-Amah et al. (2020) for more details on the BPS and www.enterprisesurveys.org for more details on the WBES.

3
# **Table A11: Heterogeneous Effects by Household Demographics**

|||Days|Hours||Days|Hours|
|---|---|---|---|---|---|---|
|Dependent variable|1(Work)|worked|worked|1(Work)|worked|worked|
|_Panel A: Working age adults_|||||||
|Other worker ill|0.132|0.39|4.394|0.094|0.166|2.362|
||[0.080]|[0.459]|[4.959]|[0.060]|[0.403]|[4.015]|
|Other worker ill X Female|‐0.226|‐0.28|‐2.002|‐0.147|‐0.023|‐0.667|
||[0.142]|[0.661]|[6.875]|[0.100]|[0.550]|[5.432]|
|Number of Adults 18 to 64 in household|‐0.007|‐0.004|‐0.066||||
|X Other worker ill|[0.010]|[0.051]|[0.529]||||
|Number of Adults 18 to 64 in household|0.014|‐0.021|‐0.371||||
|X Other worker ill X Female|[0.016]|[0.067]|[0.715]||||
|Number of Adults 18 to 64 out of LF in HH||||‐0.005|0.095|0.774|
|X Other worker ill||||[0.026]|[0.150]|[1.293]|
|Number of Adults 18 to 64 out of LF in HH||||0.009|‐0.18|‐1.818|
|X Other worker ill X Female||||[0.036]|[0.179]|[1.676]|
|Net effect on Male 1 sd above mean|0.065|0.351|3.755|0.073|0.551|5.513|
|P‐value|0.131|0.164|0.079|0.264|0.127|0.040|
|Net effect on Female 1 sd above mean|‐0.030|‐0.127|‐1.832|‐0.038|‐0.206|‐2.553|
|P‐value|0.423|0.569|0.399|0.449|0.439|0.322|
|Mean dependent variable|0.807|4.279|37.660|0.807|4.279|37.660|
|Observations|3,786|3,783|3,783|3,786|3,783|3,783|
|R‐squared|0.516|||0.516|||
|_Panel B: Older adults_|||||||
|Other worker ill|0.093**|0.385*|4.008**|0.092**|0.381**|4.282**|
||[0.038]|[0.259]|[2.332]|[0.036]|[0.240]|[2.232]|
|Other worker ill X Female|‐0.138**|‐0.463|‐5.222*|‐0.134**|‐0.439|‐4.972*|
||[0.059]|[0.357]|[3.522]|[0.057]|[0.335]|[3.288]|
|Number of Adults 65+ in household|‐0.085|‐0.187|‐0.624||||
|X Other worker ill|[0.057]|[0.429]|[5.277]||||
|Number of Adults 65+ in household|0.099|0.138|2.676||||
|X Other worker ill X Female|[0.066]|[0.764]|[9.499]||||
|Number of Adults 65+ in household out of||||‐0.143**|‐0.326|‐5.771**|
|labor market X Other worker ill||||[0.065]|[0.541]|[10.325]|
|Number of Adults 65+ in household out of||||0.137**|‐0.173|‐0.703|
|labor market X Other worker ill X Female||||[0.060]|[1.077]|[20.743]|
|Net effect on Male 1 sd above mean|0.0421|0.273|3.633|0.0331|0.248|1.923|
|P‐value|0.173|0.251357|0.265247|0.3|0.33267|0.663156|
spectively, are plotted in the results section. The coefficients are interpreted as percentage changes having been transformed into elasticities. Coefficients _βj_ were transformed into elasticities as follows _βj_ =(exp _βj_ -1)*100 (see Ullah et al. (2021) and Clarke and Tapia-Schythe (2021) for a guide on conducting event-study analysis in Stata). All regressions are done in sub-samples of imports and exports for: overall trade, type of product, type of transportation and products affected by specific non-tariff measures. We later conduct additional analysis to establish the sensitivity of our results to the type of lockdown policy and the effect of lockdowns on export and import prices.

# **4 Results**

## **4.1 Imports**

### **Overall imports**

We do not find that domestic lockdown policies affected import values and quantities (Figure 7 a) except for the second month following their implementation. Values declined by 171% while quantities slumped by 102% two months after Indonesia put in place lockdown measures. All other changes in import values and quantities cannot be attributed to domestic lockdowns as the coefficients are not statistically significant. Part b of Figure 7 shows that due to external lockdowns, imports dropped persistently, by 11% a month into the lockdown, and continued to deteriorate, reaching a drop of 30% in the twelfth month and 37% by the nineteenth month. Similarly, the quantity of imports fell by 11% a month into external lockdowns, followed by a 36% drop in the twelfth month and 43% decline by the nineteenth month. The average monthly drops in import quantities and values upon imposition of external lockdowns were 32% and 26%, respectively.

The results suggest that containment measures by Indonesia’s trading partners had a larger effect on the country’s imports than its own COVID-19 restrictions (given that all the 16
In all regressions, we include the following variables as controls: gender, education level, residence in the capital city, and the number of cars in the household (the latter to proxy for relative wealth).<sup>18</sup> These variables were chosen because they are strongly predictive of outcomes and, as a result, they improve the precision of the impact estimates.<sup>19</sup>

# 4 Main Results

## 4.1 Sample balance

Sample balance statistics are presented in Table 1, testing the outcome of the randomization process at the PSU level, and thus ensuring that observable characteristics of participants in the treatment groups were similar to those in the control groups. The standard errors of the mean difference between treatment and control groups are corrected for intra-cluster correlation at the level of the 21 PSUs. Panel A shows balance on individual characteristics, Panel B shows balance on household attributes and Panel C shows balance on baseline giving behavior. Overall, the experiment appears well balanced between the treatment and control groups over a broad range of outcomes (see column 4).

## 4.2 of the treatment

Table 2 describes the effect of the treatment on the probability of giving (i.e. choosing to accept a proposal and thus give up the fuel voucher), obtained via a treatment-effect regression controlling for participants’basic demographic and socioeconomic characteristics. The constant term in these regressions represents the mean in control groups, while the coeffi cient on the indicator variable for individuals’assignment to treatment groups represents the impact of the treatment. Table 2 reports the difference between treatment and control means in the giving rates (both aggregate and for each of the four proposals) as well as in the share of participants who gave up the voucher in more than two proposals. Column 5 shows full sample averages. Results indicate that mean giving (at the participant level) was 67 percent and that more than half of all participants in the experiment (61 percent) were frequent givers (i.e. opted to accept more than two proposals).<sup>20</sup> Across proposals, the unconditional food transfer proposal attained the highest acceptance rate (70 percent), closely followed by the unconditional cash transfer proposal (69 percent). Given the monetary value that the voucher represented for the subjects, the average giving rate of 67 percent was remarkable, compared with the giving rates found in other experiments, which ranged between 20 percent and 37 percent (DellaVigna, List, and Malmendier 2012;

> 18 Appendix Table A.1 presents the summary statistics.

> 19 Appendix Table A.2 presents more parsimonious results without control variables.

> 20 About half of all participants in the experiment opted to accept all four proposals (i.e. to give up their vouchers to the poor in each of the presented scenarios), 17 percent decided to reject all the proposals (i.e. never to give up their vouchers), while the remaining one-third decide to give up their vouchers for some but not all the proposals.

11
<mark>effectiveness of decarbonization policies in terms of actual reduction of GHGs as well as on the growth</mark> <mark>prospect of a future without oil in MENA. The latter is important because it can counter the secular economic</mark> <mark>downturn for MENA oil producers associated with a potential stranded assets risk.</mark> <mark>Against this backdrop, the aim of this paper is (a), to gauge the e</mark> ffectiveness of GHG mitigation measures on CO2 emission reduction in MENA countries (b), to examine the pathway through which such GHG mitigation measure would work, and (c), to assess the impact of GHG mitigation measures on economic growth in MENA countries in both the short run and the long run. We do this <mark>by studying and comparing the effects of</mark> mitigation measures <mark>for three groups of countries: the MENA countries as a whole, the MENA oil producers, and the world.</mark> Certainly, removing fuel subsidies or imposing a carbon tax are two effective ways in which countries can reduce their carbon footprint. While a carbon tax has been discussed as the most efficient carbon mitigation policy among various other policy instruments, for example by Timilsina (2022), such a comparison does _not_ include comparing carbon taxes with the removal of fossil fuel subsidies as an alternative policy. Yet fossil fuel subsidies are the dominant fiscal instrument that results in underpricing of carbon, certainly among oil producers. So imposing a carbon tax alone will not be effective in changing the relative prices without eliminating massive underpricing of carbon as a result of fuel subsidies. For example, the International Energy Commission (2021) reports that most oil producers are the top subsidizers of fossil fuel (Figure 1). Given this background, the issue of eliminating fuel subsidies versus imposing carbon taxes raises efficiency issues and practical, policy sequencing issues. On one hand, subsidizing production of renewables and/or imposing a 4
Western Province (14.6%) among women aged 15-49 (Central Bureau of Statistics, 2004). The women scored very highly (with an average score of 94 out of 100) on a test of HIV knowledge that covered HIV transmission pathways, the relationship between HIV and AIDS, risk reduction methods, and misconceptions surrounding HIV/AIDS. Taken together, Panel B suggests that most sex workers in Busia are quite aware of the health risks related to HIV/AIDS.

Finally, Panel C presents summary statistics on access to savings. Though 37% of sex workers report having a savings account, almost all of these savings accounts are group arrangements (mostly arranged through SHCP) that do not easily allow for withdrawals. Indeed, among women with accounts, average savings withdrawals in the month prior to the survey were just 73 Kenyan shillings (Ksh), equivalent to about US $1.04. Instead of relying on formal mechanisms, women tend to save through Rotating Savings and Credit Associations (ROSCAs): sixty-four percent of women participate in ROSCAs, and the average sex worker that participated in a ROSCA saved over 7,000 Kenyan shillings (US $100) in her ROSCAs in the past year (not shown). However, nearly all of these ROSCAs have a predetermined payout schedules and so are not ideal for consumption smoothing purposes, as discussed in Gugerty (2007).

# 3.4.2 Shocks, Transfers, and Expenditures

In this study, we focus on three types of health shocks that are commonly experienced by sex workers. The ...rst is an indicator that is coded as 1 if the sex worker reported having a fever, cough, diarrhea, typhoid, malaria, cuts, burns, or other injuries or illnesses. The second is whether the sex worker reported that another member of the household su¤ered from any of these illnesses. The third is the occurrence of sexually transmitted infections (STIs), which should presumably a¤ect a woman’s ability to supply sex.<sup>14</sup> Since all of these health shocks require money in order to be treated, women may need to work and earn more in order to be able to a¤ord medicine.

Panel A of Table 2 presents summary statistics for these 3 shocks. Column 1 presents the daily averages. Women reported household sickness on 37% of days, own sickness on 34% of

> 14 We also collected information on other shocks, including the death of a friend or family member, but do not include them in the analysis as they have ambiguous e¤ects on labor supply. For instance, women may need to work more to a¤ord funeral contributions but may work less to attend the funeral itself.

11
Baker, Scott R., R.A. Farrokhnia, Steffen Meyer, Michaela Pagel, and Constantine Yannelis. 2020. “How does household spending respond to an epidemic? consumption during the covid-19 pandemic,” _National Bureau of Economic Research_ Working Paper 26949.

Barrett, Alan, Seamus McGuiness, Martin O'Brien and Philip O'Connell. 2013. “Immigrants and Employer‐Provided Training,” _Journal of Labor Research_ , 34 (1): 52–78.

<mark>Bartik, Alexander W., Marianne Bertrand, Feng Lin, Jesse Rothstein, and Matt Unrath. 2020. “Measuring the labor market at the onset of the COVID-19 crisis.”</mark> _<mark>National Bureau of Economic Research,</mark>_ <mark>Working Paper 27613.</mark> Bayer, Patrick, Stephen L. Ross, and Giorgio Topa. 2008. “Place of Work and Place of Residence: Informal Hiring Networks and Labor Market Outcomes,” _Journal of Political Economy_ , 116 (6): 1150-1196.

Beaman, Lori A. 2012. “Social Networks and the Dynamics of Labor Market Outcomes: Evidence from Refugees Resettled in the U.S.” _Review of Economic Studies_ , 79 (1): 128-161.

<mark>Betcherman, Gordon, Nicholas Giannakopoulos, Ioannis Laliotis, Ioanna Pantelaiou, Mauro Testaverde, and Giannis Tzimas. 2020. “Reacting quickly and protecting jobs: The short-term impacts of the COVID-19 lockdown on the Greek labor market.”</mark> _<mark>IZA</mark>_ <mark>Discussion Paper No. 13516.</mark> Bonin, Holger, Amelie Constant, Konstantinos Tatsiramos, and Klaus F. Zimmermann. 2006. “Ethnic Persistence, Assimilation and Risk Proclivity.” _IZA_ Discussion Paper No. 2537.

Bonin, Holger, Amelie Constant, Konstantinos Tatsiramos, and Klaus F. Zimmermann. 2009. “Native-Migrant Differences in Risk Attitudes.” _Applied Economics Letters_ , 16: 1581-1586.

Borjas, George J. 1985. “Assimilation, Changes in Cohort Quality, and the Earnings of Immigrants,” _Journal of Labor Economics_ , 3 (October): 463–89.

______ 2015. “The Slowdown in the Economic Assimilation of Immigrants: Aging and Cohort Effects Revisited Again,” _Journal of Human Capital_ , 9 (4): 483-517.

Borjas, George J. and Hugh Cassidy. 2020. “The Adverse Effect of the COVID-19 Labor Market Shock on Immigrant Employment,” _National Bureau of Economic Research_ Working Paper 27243.

Brinca, Pedro, Joao B. Duarte, and Miguel Faria-e-Castro. 2020. “Measuring Sectoral Supply and Demand Shocks during COVID-19,” Working Paper 2020-011B, _Federal Reserve Bank of St. Louis_ .

Cadena, Brian C. and Brian K. Kovak. 2016. “Immigrants Equilibrate Local Labor Markets: Evidence from the Great Recession,” _American Economic Journal: Applied Economics_ , 8(1): 257– 290.

Carlsson, Magnus, and Dan-Olof Rooth. 2007. “Evidence of ethnic discrimination in the Swedish labor market using experimental data,” _Labour Economics_ , 14 (4): 716-729.

27
al., 2002; Levine and Schmukler, 2006). On the other hand, even when barriers to financial transactions are removed, frictions (such as information asymmetries, fixed transactions costs, or tax treatments) can still cause market segmentation to persist (La Porta et al., 1997; Karolyi and Stulz, 2003; Pirinsky and Wang, 2006; Bekaert et al., 2011; Colla et al., 2013). Therefore, market specific developments can be important for firms and investors participating in those markets.

We focus on East Asia for different reasons. First, corporate issuances in East Asia account for most of the capital market activity in emerging economies as a whole. The amount of bonds raised by East Asian firms during 1990-2016 accounted for about 70 percent of the total amount raised by firms from emerging regions in domestic and international markets. Second, East Asia weighs heavily in international benchmark indexes, which international investors widely use to allocate funds across economies (The Economist, 2019). This interest by international investors might push firms to raise capital in international markets in detriment of domestic markets. Third, following the 1997-98 Asian Financial Crisis (AFC), policy makers in the region have made a conscious effort to develop domestic bond markets as an alternative to international financing. By comparing bond issuance activity in East Asia with that in other regions, we can examine how international and domestic markets have developed over time, whether they act as substitutes or complements, and how the supply and demand of capital behave when financing takes place in different markets.

To perform the analysis on issuance activity and types of issuing firms, we use transactionlevel data on corporate bonds issued in domestic and international (cross-border) markets over 19902016. The data include 81,194 bond issuances conducted by 13,084 firms from East Asian economies. The analysis focuses on the largest 10 economies in the region in terms of gross domestic product (GDP). We account for the heterogeneity in these economies by computing the main patterns in the paper for different country groups and by estimating trends at the economy-industry level and using fixed effects, such that large economies do not drive the results. The data comprise the universe of 2