rafmacalaba/data-use-mentions-extended huggingface dataset:
- 14.-

well. Although both sets of authors acknowledge the frailty of the underlying data, their conclusions are consistent with the view of a rural structural transformation involving increasing specialization and diversification out of agriculture.

For individual countries, time series data on trends in rural nonfarm employment are available only for Sierra Leone and Rwanda. O The Sierrf fLeone data are based on rigorous surveys of identical areas and firms, first in 1974 ard subsequently in 1980 (Chuta and Liedholm, 1982). Unfortunately, sample sizes in the 1980 follow-up survey were not sufficiently large to permit inferences on trends in the most dispersed rural settlements, those below 2,000 in population. Nor do they include data for comercial, non-repair service firms, or for any enterprise employing over 50 workers. In practice the firm size cutoff should not pose a problem of incomplete coverage except in Freetown, which falls in the size category of cities over 250,000.

The results from Sierra Leone, displayed in Table 7, lead to several important conclusions. First, they indicate that repair services and food processing have grown most rapidly, both overall and in small and medium rural towns. Second, while manufacturing in general appears to have declined in the smallest localities (2,000-20,000 persons), tailoring and welding have held constant, and they, along with carpentry, have grown very rapidly in the medium sized towns (20,000-250,000), These observations are consistent with the previously cited spatial evidence suggesting that repair services and food processing grow most rapidly with the development of rural towns.

The Rwanda time series data (Vanvali, 1985), tracing changes in rural nonfarm employment between 1978 and 1983, are more conjectural than those from Sierra Leone, since they splice together two very different data sets, the 1978 population census and the results of a more recent
Bank Competition and Industry Structure in Local U.S. Markets,” Journal of Finance 61(1), 437-461.

- Cho, I., S. Contessi, K.N. Russ, and D. Valderrama, 2019, “Financial Choice and International Trade,” Journal of Economic Behavior and Organization 157, 297–319.

- Chor, D., and K. Manova, 2012, “Off the Cliff and Back? Credit Conditions and International Trade during the Global Financial Crisis,”Journal of International Economics 87, 117-133.

- ˇCih ́ak, M., A. Demirg ̈u ̧c-Kunt, E. Feyen, and R. Levine, 2012, “Benchmarking Financial Systems around the World,” World Bank Policy Research Working Paper No. 6175.

- Claessens, S., and L. Laeven, 2003, “Financial Development, Property Rights, and Growth,” Journal of Finance 58(6), 2401–2436.

- Crin`o, R., and L. Ogliari, 2017, “Financial Imperfections, Product Quality, and International Trade,” Journal of International Economics 104, 63-84.

- Dell’Ariccia, G., D. Kadyrzhanova, C. Minoiu, and L. Ratnovski, 2020, “Bank Lending in the Knowledge Economy,” Review of Financial Studies, forthcoming.

- Demirg ̈u ̧c–Kunt, A., E. Feyen, and R. Levine, 2013, “The Evolving Importance of Banks and Securities Markets,” World Bank Economic Review 27(3), 476–490.

- Demirg ̈u ̧c–Kunt, A., and V. Maksimovic, 1998, “Law, Finance, and Firm Growth,” Journal of Finance 53(6), 2107–2137.

- Demirg ̈u ̧c–Kunt, A., and V. Maksimovic, 2002, “Funding Growth in Bank-Based and MarketBased Financial Systems: Evidence from Firm-Level Data
necessarily true. Likewise, in a fiscal system with more than one element, a pro-poor or progressive transfer (or a progressive tax) is not necessarily inequality reducing. This study incorporates every type of fiscal policy element listed in Figure 7 using consumption expenditure recorded in HIECS as the measure of _primary_ income. The team assumes that total consumption expenditures––including the value of imputed rent for those living in owner-occupied housing as well as the implied value of any auto-production/auto-consumption––are equal to the CEQ disposable income concept (approximately in the middle of the flowchart in Figure 7). The team then works “backward” and “forward” from disposable income to other CEQ income concepts to arrive at pre- and postfiscal measures.<sup>21</sup> Egypt’s prefiscal income measure includes income received from the public pension system. Market income reflects income before any transfers (including public spending on health and education, fuel and energy subsidies, and unconditional cash transfers) or taxes (including personal income taxes and GST) have been added. In treating contributory pensions, the CEQ methodology treats pension contributions and pension income received as deferred income. Egypt’s contributory pension system functions much as a nonwage salary payment; therefore, the team’s prefiscal income measure becomes Market Income + Pensions (top box in Figure 7).<sup>22</sup>

> 21 Consumption expenditure is the team’s primary income measure. Moreover, all other income concepts. including market income. are derived from consumption expenditure. For these reasons, the team does not create a _taxable income_ concept. Other CEQ assessments do produce this income concept when relevant. Creating a taxable income concept requires knowledge of the composition of market income. An Egyptian household’s expenditure profile in the HIECS cannot provide any information on the composition of its income. For the same reason, the team is unable to say anything about the savings or current asset profiles of HIECS households: a current consumption expenditure
for countercyclical finance by financially constrained SMEs that otherwise tend to resort to internal funds to finance their operations and investment.<sup>6</sup> The compounded difficulties that SME face in accessing bank finance during an economic slowdown or, worse, in a crisis are well documented in the literature, reflecting banks’ risk aversion against a backdrop of declining profitability and eroding capital. However, this is often counterbalanced by government interventions aimed at maintaining access to finance for SMEs through public guarantees or directed lending, as seen in the wake of the global financial crisis and more recently in the context of the COVID-19 pandemic.

On the other hand, price stability (low inflation), a key sign of macroeconomic stability, is negatively correlated with SME financial inclusion, that is, higher inflation impacts negatively on private sector expectations and incentives, reducing loan supply to riskier borrowers such as SMEs. The share of SME employment in total employment, a proxy for the economic structure of a country, enters the model with a negative sign but is not statistically significant at standard intervals. The relationships between our FCS dummy and the SME financial inclusion index show a strongly negative correlation, suggesting that on average SMEs in FCS countries are relatively more constrained in terms access to and usage of formal financial services. Finally, estimates on income level dummies suggest that SMEs financial inclusion tends to increase with economic development, i.e., higher income levels.

Turning to the macroeconomic environment, our results show the statistically significant importance of informality. SMEs in countries with a sizeable informal economy (as a share of official GDP) tend to have less access and usage of financial services, in light of the negative sign of our proxy for informality. Another statistically significant correlate with SME financial inclusion is natural resources rents (as a share of GDP), our measure of economic diversification. The coefficient for natural resources rents enters our regression with a negative sign, implying that SMEs in countries with higher economic rents find it more difficult to access bank
**Figure 5: Poverty Rates by Region of Uzbekistan (2018)**

<!-- Start of picture text --> 40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>$3.2/Day (Inc) $3.2/Day (Cons)<br><!-- End of picture text --> _Source: The Listening to the Citizens of Uzbekistan baseline survey, Author’s calculations_

**Turkmenistan** alone has no data available at all to estimate internationally comparable estimates. For this reason, it is excluded from the analyses that follow.

In each of the four countries for which data are available, the survey data that are used to generate poverty estimates are usually considered to be representative at the level of regions (rayons) only. When disaggregated further, sampling errors are sufficiently large that confidence intervals substantially overlap. The resulting maps can potentially be misleading. Point estimates indeed suggest a specific value for the poverty rate in a given district, but if the precision of these estimates is ignored, it is easy to miss that the true value of the poverty rate may be much higher or lower. Such interpretations can lead to inappropriate policy decisions. Figure (6) presents harmonized poverty estimates for the four countries in which survey data are available at the level that the underlying surveys are usually considered to be representative. The task for the remainder of this report is to estimate and report welfare indicators, including poverty rates, that are representative at the district level, one step lower than those presented here.

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consumption and total fertility data from the World Development Indicators. In our sample of 85 countries, the average value of children is around 37% of household utility.<sup>28</sup> The value of children as a fraction of household utility in our model is computed as where _u_ ( _·_ ) is the pre-pandemic steady-state level of utility of a household in state _k_ ; _k_ = 1 is the state in which all the members are alive, and _k_ = 4 is the state in which the children are deceased (see Appendix Table B.1 for the list of household states). This exercise yields _σ_ = 2 _._ 5.

Finally, we could assess sensitivity by treating _σ_ as a free parameter. Appendix Table B.6 compares the basic results from using the baseline _σ_ , the fertility literature-based _σ_ , as well as a wider range of _σ_ ’s from 1 to 5. As expected, a lower _σ_ implies that both the reference and the optimal lockdowns lead to fewer children’s lives lost per COVID-19 fatality averted (top panel). This is because a lower _σ_ implies a stronger private labor supply response, making the government-imposed lockdown less needed (bottom panel). Qualitatively the results are unchanged. Even with _σ_ very close to 1, there is a substantial number of children’s lives lost per COVID-19 fatality averted in the lower-income countries.<sup>29</sup>

**Simple adjustments to the reference lockdown** While the optimal lockdown involves a high-dimensional global optimization problem, an interesting question is whether governments could have done better than the reference lockdown by varying either the duration or the onset timing of the reference lockdown. Appendix Table B.7 investigates this. It summarizes our key statistic, the ratio of children’s deaths per adult COVID-19 fatality averted under alternative reference lockdown scenarios. The middle panel shows what happens when

> 28We abstract from the distinction between skilled and unskilled children in Doepke (2004) and compute the
# **1. Introduction**

The traditional microeconomic textbook model assumes that the level of employment and capital used by firms is optimal at any point in time. But since adjusting employment and capital is costly to firms, they often deviate from what would be optimal in the absence of frictions. Understanding the way firms react to employment and capital shortages can shed light on many issues of microeconomic and macroeconomic nature. In this paper we analyze such adjustment process, based on a panel of manufacturing establishments in Uruguay.

The Uruguayan economy evolved from inward looking, based on state interventionism and import substitution protectionist policies, towards an outward looking orientation, with more reliance on markets as resource allocation mechanisms and exports as the growth engine. A first phase of trade reform took place in the 1970s, accompanied by a quick financial liberalization process. Later, trade liberalization was deepened in the 1990s, combining a gradual unilateral tariff reduction with the creation of Mercosur, an imperfect customs union between Argentina, Brazil, Paraguay and Uruguay.

A by-product of the trade liberalization process in Uruguay was that manufacturing firms switched to more capital intensive technologies. Specifically, in the 1990s, there were high job destruction rates as reported in Casacuberta, Fachola and Gandelman (2004). We focus on the adjustment process, and our main concern is with the effect of protection levels and trade liberalization on the adjustment functions for capital and white and blue collar employment.

The objectives of the paper are: i) to present an estimation of the adjustment functions for labor (blue and white collar) and capital, ii) to study the differences in the adjustment process for each factor of production and iii) to analyze the impact on these adjustment functions of tariff barriers, trade liberalization and international exposure.

Our main results confirm the lumpy and discontinuous nature of firms’ adjustment process. Large shortages of one factor lead to less responsiveness in adjustment in the creation side of other factors but larger adjustment in the destruction side. Adjustment costs reduce the volatility of factor usage
# _i. Literature on environmental technology transfer_

In the broadest sense, environmental technological change is addressed in literature on trade and the environment. There, economists decompose the effect of international trade on environmental quality in developing countries into three components. First, scale effects account for increased pollution levels due to the greater wealth and increased economic activity that follows international trade. Second, composition effects refer to reductions in pollution resulting from a preference for cleaner goods that develops as countries become richer. Third, technique effects refer to emission reductions that occur because trade expands access to cleaner technologies (Esty 2001, Copeland and Taylor 2003). Attempts to identify this technique effect can be seen as examples of technology transfer.

Fisher-Vanden and Ho (2010) consider the interaction of scale and technique effects in a simulation of increased science and technology (S&T) capabilities and energy use in China. They note that improving S&T capabilities has two offsetting effects. While technological development can lead to the use of cleaner technologies (the technique effect), increases in S&T also lead to larger energy intensive industries (the scale effect). Their paper simulates the effect of S&T growth in China, with R&D intensity reaching 2.5% by 2020, as stated in China’s longterm policy goals. They note that China’s R&D intensity has already increased from 0.6% in 1996 to 1.3% in 2003. Calibrating their model based on econometric results from 1500 industrial enterprises, they find that the S&T takeoff should have an energy-saving bias, resulting in lower energy prices. However, this leads to more economic growth and greater energy consumption by households, so that the net effect of the S&T takeoff is greater energy use and more carbon emissions. Fisher-Vanden and Sue Wing (2008) develop an analytical model that finds similar results.

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# **1.  Introduction**

The relationship between corruption and business activity has long been a contentious area of research. Corruption can impose a tax on business, which is not only costly to individuals and firms—it can be arbitrary and unpredictable. It can also affect the incentives of firms and investors, increasing rent seeking activity. Yet, corruption can also increase economic efficiency, where it greases the wheels of business, either as a collusion between public officials and firms, or simply a mechanism that increases the efficiency of business by using informal mechanisms to overcome formal barriers to economic activities.

In this paper we examine the extent of business corruption in a survey of firms in Bangladesh. Isolating our study to a single country allows us to sidestep many of the difficult cross-national comparisons of individual responses to survey questions. Yet our research project is confronted with the same empirical research design issue as literally decades of scholarship: the measurement of corruption.

The most serious problem with corruption research is the ability to directly measure corruption. Corruption often entails illegal and unethical activities where one or all parties have incentives to conceal corrupt acts. Thus one response to this measurement problem is to rely on the opinions of experts on the levels of corruption. Unfortunately, this fails to uncover the vast differences in experiences with corruption across firms, and still suffers from the same measurement problem. If these experts aren’t directly observing “corruption” what information are they using to evaluate the level of corruption?

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refugee waves as political violence.

This does not mean that violent crimes might not have a profound impact on the economy. There is a large related literature that calculates the cost of crime (See Soares (2009) for a review). The standard accounting approach is to estimate this cost simply by adding the costs and losses due to crime. For example, van Ours and Vollaard (2016) use estimates from an accounting approach to study the welfare gains from installing electronic engine immobilizers in the Netherlands. Their benchmark measures of the cost come from the UK Home Office. The methodology is based on Brand and Price (2000) which is a very detailed accounting exercise in which security expenditures, insurance costs, and damages are added up to derive a per case cost. Other approaches include individual valuations of counterfactuals, contingent-valuation, and changes in market prices to estimate the welfare costs. For example, Cook and MacDonald (2011) use both contingent-valuation surveys and jury awards to victims of violent crimes to calculate the welfare gains from crime reductions. And property prices are used to assess the cost of crime in London by Gibbons (2004).

Recent research on the economic effects of crime have shown that many of these studies will still constitute a lower bound of the cost of crime. Besley and Mueller (2018) show, for example, that twothirds of the economic impact of crime on firms comes through defensive measures taken by firms which might or might not be directly affected by crime. In other words, if one would focus on crime statistics to understand the impact of crime one would only capture one-third of its actual costs. Crime might disappear where firms are successful defending against it. But the effort exerted in defending against crime forms part of the cost.

There is therefore little doubt that crime will affect poverty rates through its impact on the allocation of jobs across firms and the arising disincentives for investment that arise from the lack of secure property rights. But this effect will only be visible in individual- and firm-level
for those with less education and with lower pre-COVID wage levels.

These predictions are not always borne out in the observed data, however. Overall, we find that the model predicts the level of further job losses fairly well. It predicts the distributional impacts by gender, nationality, and educational attainment, but it fails to predict the observed differences by age group and wage level. This is likely in part due to the lack of more detailed and specific data at the firm level on employment and employment losses by occupation. Results also align with observed high-frequency phone data from Georgia by age group and educational attainment, but the phone survey is not sufficiently comparable to reach conclusions on the methodology’s accuracy for that country. In addition, sector-level predictions are difficult to verify given definitional mismatches in the data, but the rates of job loss generally rank similarly to those observed. Overall, because our projections rely only on sector-, firm-, and broad occupational- class level shocks, we attribute the divergence between observed and projected outcomes at least in part to unobservable behavioral differences on the part of employers and workers that are correlated with gender, age, and unobserved skills or productivity, in addition to firm data limitations.

The rest of the paper is organized as follows. The next section summarizes the relevant literature on the jobs impacts of COVID-19. Section 3 presents the context in Jordan and Georgia, describes the data we use, and presents our methodology. Section 4 presents our econometric results for both countries. Section 5 presents the methodology for projecting job losses onto the labor force, projection results, and a comparison of projected future job losses with survey-measured future ones. It also presents PFPS job losses in the context

> 4 Although financial difficulties were correlated with permanent closures, there was no clear way to address possible simultaneity between these two outcomes.

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international (12 percent). Slightly over half the firms are in the service sector (53 percent), followed by manufacturing (27 percent) and retail (20 percent).

The ES COVID-19 Follow-up Surveys ask firms about changes in performance during the crisis, as well as expectations for the next months. Table 3 Panel B presents the summary statistics of firm performance by Round 3. Firms reported that their sales remained an average of 2 percent lower compared to the same month a year earlier. Round 3 surveys were typically conducted in mid-2021 (see Table A.1), meaning that firms reported their change in sales compared to mid-2020, a time when firms typically experienced steep declines in sales compared to pre-COVID-19 (Apedo-Amah and others 2020; Bruhn, Demirguc-Kunt, and Singer, forthcoming). Table A.1 also shows drops in sales reported in Round 1 and Round 2, which are all relative to the same month one year earlier. Taken together, the numbers suggest that, in mid-2021, sales of the average firm were still about 21 percent lower than before COVID-19. Unlike sales, employment outcomes in all rounds are measured compared to December 2019. The Round 3 data in Table 2 Panel B shows that the number of permanent full-time workers remained 15 percent lower compared to pre-COVID-19, with almost half of firms (47 percent) having decreased their number of workers. About 21 percent reported that they anticipated falling into arrears on outstanding liabilities in the next six months.

# **2.3 How are government support and firm characteristics correlated?**

Table 4 presents the correlation coefficients between firms that received government support and firm characteristics. Overall, not many firm characteristics are consistently or significantly correlated with having received government support across different rounds of government support. While many firm characteristics are significantly correlated with having received government support in Round 1 or 2, many of these correlations turn insignificant when we unbundle government support into Round 1 versus Round 2 support. Large firms, and those with foreign ownership are