rafmacalaba/data-use-mentions-extended huggingface dataset:
15 The Sachs and Warner index, based on the recent update by Wacziarg and Welch (2008), is equal to one when a country is considered open and zero otherwise on the basis on the following criteria: an aggregate tariff rate greater than 40 percent, a nontariff barrier covering more than 40 percent of trade, a black market exchange rate of less than 20 percent relative to the official exchange rate, and a state monopoly in major exports.

16 An alternative means of correcting for the potential problem of inconsistent standard errors would be to follow a residual-aggregation procedure, as suggested by Bertrand et al. (2004). In our case, where we consider approximately 40 reform episodes, this could be problematic because the power of this procedure is quite low and diminishes rapidly with sample size.

17 Hausman tests also confirm this correlation. Please note that in the random effects model the key result (i.e., the effect of democratization on the _NRA_ and _RRA_ ) is positive and strongly significant and is virtually identical in magnitude to the results reported in table 2 (additional results are available upon request).

> 18 As emphasized by Papaioannou and Siourounis (2008), the _democracy_ indicator behaves as a trend because countries that switch to democracy seldom revert to autocracy.

19 We use total government consumption instead of government spending owing to data limitations. For our broad country sample and the 1955–2005 time period, this is the most widely available measure of government spending.

20 See de Gorter and Swinnen (2002) for a survey. A positive impact of GDP per capita is in line with the so-called development paradox. A negative impact of agricultural employment is in line with Olson’s (small) interest group story and the reduced per capita 33
through two-part payment terms, such as early payment discounts to incentivize buyers to pay early (e.g., Ng et al., 1999).

In sum then, we see three important factors driving trade credit: 1) As a way for suppliers with cheaper access to credit to finance buyers; 2) As a means for the buyer to exercise market power and obtain favorable price discrimination; and 3) As a warranty assuring buyers of product quality. Given these three factors, terms may be influenced by the supplier‘s need to contain financial risks.

Our data set is unique in that we know some characteristics of the parties on either side of the contract. So we can take a closer look at these rationales for trade credit, focusing on how contract terms vary with characteristics of the parties to the contract. The limitations of our data set are that we do not have detailed characteristics on the firms, and we have no way of getting more data. Therefore our tests are reduced-form in nature, allowing us to document associations but not identify causality. Nevertheless, what we can tease out is intriguing.

# **3. Data, Summary Statistics, and Variance Decomposition Analysis**

We use a novel database of trade credit contracts for nearly the universe of suppliers of 56 large buyers.<sup>5</sup> The data are provided by PrimeRevenue, an online network that links large, global companies, their suppliers, and third-party financial institutions, via the Internet. PrimeRevenue provides software and an IT platform for buyers to post their invoices directly. Suppliers choose whether to be paid at the maturity of the contract or to have the contract ―factored out‖ and be paid immediately at a discount. PrimeRevenue is a leading provider of such ―open platform supply-chain finance (SCF)‖ solutions, allowing multiple banks to participate directly in a buyer‘s SCF program.

The buyers in our dataset are PrimeRevenue‘s clients. The data on the suppliers is collected from the
example, at the time of the first survey wave in May 2020, approximately 82% of countries in Europe had a school closure policy in place due to COVID-19 (fully closed or partially closed); 18% of countries had schools fully open and no countries were on Academic Break. In July 2020, most of the countries surveyed in Europe were on a scheduled Academic Break. By the time of the final survey wave in October 2020, the school closure policies within Europe had changed whereby approximately 19% of European countries had a school closure policy related to COVID-19 in place and 81% of European countries had schools fully open. However, the pattern is not the same in all regions. For example, in the South Asia region the policies show a more constant trend whereby schools were closed in the majority of countries throughout the six month study period. Notably, in Latin America, Sub-Saharan Africa and South Asia there was a higher average rate of school closures due to COVID-19 (approximately 80% of countries had schools fully or partially closed due to COVID-19 averaged across the survey period) compared with Europe, Middle East and Asia and Oceania (approximately 40% of countries had schools fully or partially closed due to COVID-19 averaged across the survey period).

> 4 Please refer to the methodological note “Global Monitoring of School Closures caused by the COVID-19 Pandemic”

48
_(iii)_ _<u>Gender</u>_ <u>. The base case scenario does not distinguish between genders. However,</u> women generally tend to earn less than men. The gains from education therefore differ across genders. We include two alternative scenarios with expected income profiles and unemployment rates for each gender.

_(iv)_ _<u>Income tax</u>_ <u>. We use tax-rates from 1997, the mid year for the years of our labor</u> market data. Colombia's top income tax rate was 35 percent, which was payable for yearly incomes over Col$ 50,200,000 (US$ 24,000), (Price Waterhouse, 2002). The marginal rate for the average taxpayer ranged from 0.35 percent to 23 percent. For purposes of calculation of extra income a marginal tax rate of 12 percent is applied, whereas high taxation is set to 23 percent. Workers with annual income less than Col$ 48,600,000 (US$ 23,300) may choose to deduct either mortgage interest payments, or prepaid medical assistance and educational expenses.<sup>6</sup>

# **Costs from additional education**

The costs of education stem from foregone income while studying, which depends upon the length of study time, and tuition fees: _(i) Foregone income from studying_ <u>. Studying takes up time, which will reduce time for</u> work. According to the household survey, 75 percent of adult students are employed. On average, they work 6 hours less per week than adults not attending education. Annex table 4 presents the average wage per hour for studying workers and non-studying workers. This suggests that time for studying is predominantly taken out of leisure time as opposed to working time. Based on this information, the base case scenario assumes that foregone income for time dedicated to studies amounts
harvests would reduce the price effect of export bans. 1 and 1 measure the extent of association between price and harvest when there is a ban ( _b_ ) and no ban ( _nb_ bb ). We would expect a negative sign for nnbb 1 since, αα� αα� if there is a ban, surplus output does not leave the country, and hence domestic prices are likely to reduce. bb αα� When there is no export ban, increased harvest will have a lower impact, if any, on domestic prices since any surplus has the potential to be exported to neighboring markets, and the sign on 1 is likely to depend on the market conditions in export destination markets. nnbb αα� We finally examine the seasonal variation in the association of export ban with domestic prices and harvest by extending the specifications to include interactions with seasonal ( ) dummies in equations 6 below: i i + ∑ 1 B 2 B 3 2Bqq 3 (6) QQ qq=4 qq qq qq qq=1 qq ii ii ii ii ii ii IIii PP = λλ [QQ × (αα� BB × HH + αα� BB + αα� HH + 1)] + αα� BB + αα� HH + εε

# _3.2 Difference-in-difference estimation: Cross-border price dispersion and export bans_

> 2 We convert all prices from domestic currency to USD using exchange rate data for comparison.

9
home-consumption including owner-occupied housing,<sup>18</sup> treatment of publicly-provided health and education benefits, and the use of top-coding of high incomes (where all incomes above a certain ceiling are coded as equal to that ceiling).<sup>19</sup> For different countries and at different levels of development, differences in the treatment of these categories are not equally important. For poor countries, it is the problems of valuation of own consumption and self-employed income that are the most difficult and that can make individual incomes often move up by a factor of 2 or more; for the rich countries, it is the treatment of publicly-provided health and education benefits that is of most concern. Swedish disposable income with them or without them is quite different. For countries with extravagantly rich individuals it is the underestimation of capital incomes which is of concern.

But there is no agreement that “income” in global inequality studies should be income at all. Many people think that rather than income, one should look at consumption or expenditures as the true indicator of the standard of living. This debate mirrors the debates that are waged in individual countries since obviously inequality can be measured using either income or consumption. What lends this debate an added importance in the case of global inequality is that in many countries household surveys ask income questions only, while in other countries they ask for both, or for expenditures only. Then, a global study of inequality has to do what all national studies try to avoid, that is to mix household survey data that use two different concepts of “income”: disposable income and consumption. This introduces an error whose direction and magnitude cannot be estimated. Although in the last few years, there has been a trend toward the use of consumption measures (not the least through the efforts of the World Bank and the influence it exerted on the choice of survey instruments in the former communist countries and Africa), we are still far from unanimity on this issue. In the
A renewed emphasis on poverty reduction began in 1990. The understanding of poverty, under the influence of Sen and others, broadened from a narrow focus on income and consumption to a more complex vision of the linkages between growth and poverty reduction. Richer countries tend to have better social indicators. From this undeniable fact, the Bank drew the conclusion that human development promotes income growth and income-poverty reduction. Not all economists share this view.<sup>26</sup> The emphasis on poverty in the 1990s was not new. What was new from the late 1980s onward was the articulation of a more complete strategy, combining growth with delivering social services (education, health and social protection) to the poor. In other words, it was the recognition that it was not growth alone that would do it—which was implicitly the view of the 1980s. At the World Bank, the change was spearheaded by the _1990 World Development Report_ on Poverty, which included the first standardized global estimates of the prevalence of poverty, and by a shift in the institution’s emphasis after the arrival of James Wolfensohn as President in 1995. There was an expansion in analytic tools and capabilities, in both data and methods, linking micro data to aggregate outcomes, and with a view toward assessing policies and programs. This was built on the rapid expansion in household survey data production, which started in the mid-1980s.

Since then, development economics—moving away from macroeconomic explanations à la Keynes or Harrod/Domar—has increasingly emphasized the microeconomic foundations of development. Researchers and policymakers have realized how crucial micro-level decisions are for economic growth. Examples are the role of women in household decision-making; the effects of the proportion of household resources controlled by women on the health and nutrition of their children; the role of microeconomics in poorly functioning land, labor, and credit markets; and the role of informal networks and institutions in dealing with market failures. The aim of this micro development literature is to understand what institutions may arise at
/sup> Viewed over the long run, this is a remarkable development. Bourguignon and Morrisson (2002) find that global inequality rose steadily between 1820 and the 1990s, by some 15 percentage points. Therefore, as Bourguignon (2015) and Milanovic (2016) point out, global inequality has stabilized or even declined for the first time since the Industrial Revolution.

The decline in inequality has been driven by falling inequality between countries. Figure 2 decomposes global inequality into differences within and between countries. The total height of the bars shows total global inequality as measured by the GE(0) (or mean log deviation, Theil-L) inequality index.<sup>8</sup> The striped bars depict that part of total inequality that is due to differences within every country. The solid bars capture the contribution of differences in mean incomes across

> 5 The baseline results use the largest possible sample of countries in every year. A closely connected issue, which cannot be addressed, is the unavailability of surveys for some countries. This problem is most acute in the Middle East and the African continent. The affected countries account for around 5% of global GDP and 10% of global population.

> 6 Anand and Segal (2015) use a different approach to account for missing top incomes, which results in an upward adjustment to the level of the global Gini index that is approximately half of the difference presented in Figure 1. They assume that the household survey fails to capture the top 1%. They then append the top 1% income share either directly from tax record data or predict it from a cross-country regression that uses the survey-based top 10% share and the survey mean income. While their procedure also increases a country’s mean, this effect is smaller than the nationalaccounts-based adjustment in Lakner and Milanovic (2016) (shown in Figure 1). Regarding the time trend, Anand and Segal also find a fall in the most recent period (between 2002 and 2005 in their data),
1997 are defined as the outcome of the offspring (G2) or parents (G1) observed in 2017 or 1997, respectively. The outcomes of interest considered are: (1) years of schooling; (2) a durable asset index formed with a principal component that considers ownership of cars, vans, TV, radio, sound equipment, blender, refrigerator, electric grill, gas stove, microwave oven, firm floor, electricity, drainage and water access in the property; and (3) monthly income. For column (1) the level of years of schooling is used by itself. For columns (2) and (3), the percentile of each outcome is calculated relative to all the individuals inside the same 10-year window age range. The unit of observation are individuals in relevant age groups, as described in section IV and their parents. Controls include (from ENCASEH 1997): HH head age, education, speaking a dialect, HH size, % members under 15, dummies for home, productive land and draft animals ownership, and locality marginalization index. Standard errors clustered at the locality level. Asterisks indicate significance at the ***1%, **5%, and *10% level.

31
religious groups; *** implies significant at 1%, ** implies significant at 5% and * implies significant at 10%.

The identification strategies used so far assume that the war victims (as identified above) and control groups are exchangeable, such that they have identical distributions of variables. This can be confirmed by data using a randomized controlled trial; however, drawing causal inference using survey data requires a more careful analysis because selection biases and confounding invalidates the exchangeability assumption. In such cases the estimated causal effects are likely to be biased. Here we discuss a number of events that could be potential sources of bias in the estimated causal effect. First, education could be seen as a catalyzer of war in Côte d‟Ivoire. The distribution of educational facilities was unequal between the rebel-held North and the government-controlled South. This might produce an upward bias in the causal effect of war on education. Second, there were reasons other than the conflict that had a negative impact on 15
We first test the differences in medians between users and non-users of capital markets for a set of firm attributes by pooling all firm-year observations (Table 1). The results show that firms that use capital markets are indeed very different from non-users. Firms that raise capital through either equity or bonds are significantly larger (in terms of total assets, sales, or the number of employees) than the publicly listed firms that do not raise capital. The median equity (bond) user firm in China has total assets of $443 million ($1.2 billion), whereas non-users have $214 million in total assets.<sup>23</sup> In India, the typical firm in our sample is much smaller than that in China, though the differences between users and non-users are also large. For instance, total assets for the median firm without an issue are $9 million, which stands in stark contrast with the $56 or $597 million observed for users of equity and bond markets, respectively.<sup>24</sup> We obtain qualitatively similar differences between users and non-users of capital market financing if we focus on sales or the number of employees.

In terms of growth, the firms in our data set have had a performance that mirrors that observed for the whole economy. For instance, the firms in the data set had average total assets and sales growth of 11 and 17 percent per year in China and 7 and 12 percent per year in India between 2004 and 2010. GDP growth over the same period stood at 11 in China and 8 in India. Moreover, equity and bond market users grow faster than non-users and the difference in growth rates between these firms is statistically significant. In China, total assets growth (sales growth) for equity and bond users is on average 18 (20) percent per year, but only 9 (16) percent for nonusers. In India, total assets growth (sales growth) is 16 (17) percent per
Country Policy and<br>Institutional Assessment|
|Technological|Score on ‘Technological Readiness Index (9th Pillar)’|World Economic Forum,|
|readiness|(values from 1=low to 7=high readiness)|Global Competitiveness Report|

> 12 The GRD was launched in 2014 and combines government revenue data from various sources, including OECD Revenue Statistics, IMF Government Finance Statistics, and CEPALSTAT (revenue statistics in Latin America). It is arguably the most updated and comprehensive cross-country data set on government revenues available (for more detailed information on the construction and scope of the data set, see Prichard et al. 2014; McNabb 2017).

9
the sum of absolute values of the deviations between the share of countries in each of the 45 categories (five income categories x nine regions) in that source and in the world as a whole. By construction, this measure ranges from zero to one, with low values indicating more representative indicators. We report this number in the fifth column of Table 1. The six indicators covering the largest number of countries (DRI, EIU, FH, HFWSJ, PRS and WDR) are substantially more representative according to this measure than the others, with a value of this index of less than 0.25. We refer to these as representative indicators, and the remainder as non-representative indicators.

6
C. Ling, and S. Jha. 2010, The Effects of School-Based Management in the Philippines: An Initial Assessment Using Administrative Data, Policy Research Working Paper No. 5248, World Bank, Washington D.C.

Lanzona, Leonardo A., 1998, Migration, Self-selection and Earnings in Philippine Rural Communities, _Journal of Development Economics_ 56: 27-50.

Maluccio, J. A., J. Hoddinott, J. R. Behrman, R. Martorell, A. R. Quisumbing, and A. D. Stein, 2009, The Impact of Improving Nutrition during Early Childhood on Education among Guatemalan Adults, _Economic Journal_ 119: 734-763.

Mani, Subha, 2008. Is there Complete, Partial, or No Recovery from Childhood Malnutrition? Empirical Evidence from Indonesia, Fordham Economics Discussion Paper Series dp2008-19, Fordham University.

Orbeta, Aniceto C., 2002, Education, Labor Market and Development: A Review of the Trends and Issues in the Philippines for the Past 25 Years, Manila: Philippine Institute for Development Studies.

27
# **1. Introduction**

The growth and prosperity of small businesses is vital for poverty reduction and for generating jobs for a young and rapidly growing labor force in emerging market economies (World Bank, 2013; de Mel, McKenzie, and Woodruff 2010). With self-employment rates of 40% on average and in some countries as high as 75% (compared to only 7% in the United States),<sup>1</sup> the economic policies surrounding small business promotion take on vital significance. Further, as multinational organizations look to emerging markets for new sources of growth, they discover that their fates are in many ways intertwined with the fates of millions of small firms and business owners on whom they have to rely as customers, suppliers, and distributors (Viswanathan, Rosa, and Ruth 2010; Prahalad 2005).

In search of policies that foster small business growth, the literature has long emphasized the importance of alleviating constraints on access to physical capital and external finance (Banerjee et al. 2015; Bruhn and Love 2014; de Mel, McKenzie, and Woodruff 2008). A growing body of work additionally stresses the significance of business skills and entrepreneurship training as key drivers of small firm growth and productivity (Bloom et al. 2010; Bruhn, Karlan, and Schoar 2010). However, attempts to identify the causal impact of business training programs among small firms in emerging markets have yielded disappointingly weak results (see McKenzie and Woodruff (2014) for a literature review). Even less understood are the mechanisms by which business trainings can succeed or fail to improve firm outcomes (Fischer and Karlan 2015). A better appreciation for which types of business practices can be affected by these trainings, and which outcomes these practices influence, can provide important insight on how to improve business performance.

In this paper, we study the causal links between business training, practices, and outcomes, and identify two separate pathways to profits through a randomized control study of 852 small firms in
for different groups. Figure 4 traces the averages of selected fiscal variables over time and separately for treatment and control groups. An implicit assumption behind the application of the DID estimator is that the time trend (or year effect) is identical between the treatment and control groups in the years prior to the elections. The graphs in figure 4 suggest that the assumption of common trends between the treatment and control groups prior to the elections (2001-2005) is justified, a fact that has also been confirmed by a variety of statistical tests. Average per capita total expenditure and

> 13 Special thanks go to Bambang Suharnoko (WB), Anstasia Soeryadinata and W. Paul Roland (NDI Indonesia), Natalia Warat, and Jeremy Gross (Asia Foundation).

> 14 SIKD is a facility provided by the Ministry of Finance to collect, validate, process, and analyze regional financial information. Regional governments have the legal obligation to report this data to the Ministry of Finance in a timely manner.

> 15 Before 2003, there were only two categories- revenues and expenditures with expenditure classified as either routine (recurrent) or development (investment). From 2003, the classification on the expenditure side changed from ―routine‖ and ―development‖ expenditures to ―public‖ and ―apparatus‖ expenditures. The other change introduced was the separation of ―financing‖ item from revenue and expenditure categories.  Approximately 60 percent of all regions used the new budget format for the 2003 realizations, while 40 percent continued to use the old format. By 2004, 90 percent of regions were using the new budget format.

15