rafmacalaba/datause-encoder-data huggingface dataset:
**Table 16. Sales and Profits: Reduced Form Regressions**

||**Sa**|**les**|**Profits**||
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|**_Activity & Age_**|||||
|<br>Firm age (years)|0.019**|0.015**|0.017*|0.013|
||(0.008)|(0.008)|(0.009)|(0.009)|
|-Food and beverages,<br>|-0.307|-0.466*|0.683|0.407|
|brewing/distilling|||||
||(0.272)|(0.258)|(0.440)|(0.386)|
|-Grain milling|-1.458*<br>|-1.275<br>|1.386<br>|1.635<br>|
||(0.759)|(0.856)|(0.980)|(1.140)|
|-Hotels and restaurants|<br>-0.174|<br>-0.374|<br>0.892*|<br>0.615|
||(0.358)|(0.316)|(0.491)|(0.415)|
|-Retail trade via stalls and<br>markets|-0.429|-0.472|0.226|-0.027|
||(0.357)|(0.330)|(0.443)|(0.430)|
|-Services|-2.106***|-1.965***|-0.599|-0.721|
||(0.418)|(0.376)|(0.638)|(0.584)|
|-Whole sale trade|0.959**|1.004**|0.630|0.625|
||(0.482)|(0.384)|(0.977)|(0.896)|
|-Transport services|-1.581***|-1.679***|-0.040|-0.290|
||(0.439)|(0.503)|(0.618)|(0.705)|
|-Manufacturing (excl: grain|-1745***|-1434***|-0462|-0257|
|milling, food and|.|.|.|.|
|<br>beverages, distilling)|(0.252)|(0.238)|(0.394)|(0.335)|
|**_Characteristics of household_**|||||
|**_head_**|||||
|Manager's Age|-0.023|-0.010|-0.041|-0.008|
||(0.026)|(0.025)|(0.037)|(0.034)|
|Manager's Age Squared/1000|0.136<br>|0.008<br>|0.273<br>|-0.049<br>|
||(0.270)|(0.268)|(0.395)|(0.361)|
|Manager is a male|<br>1.129***|<br>1.064***|<br>0.833***|<br>0.914***|
||(0.207)|(0.191)|(0.287)|(0.271)|
|Years of schooling|-0.100<br>|-0.133<br>|0.032<br>|0.009<br>|
||(0.093)|(0.085)|(0.121)|(0.120)|
|Years of schooling squared /<br>1000|17.009*|16.959*|1.981|-0.159|
||(9.850)|(9.655)|(12.194)|(13.360)|
|**_Local Demand_**|||||
|WRSI- 2006|0.081**|0.079***|0.079*|0.091**|
||(0.032)|(0.026)|(0.043)|(0.036)|
|**_Geography_**|||||
|Remote location||0.208||0.655|
|||(0.359)||(0.446)|
|Location in rural town||<br>0.711**||<br>1.429***|
|||(0.306)||(0.473)|
|Distance to nearest financial<br>institution||-0.110||0.102|
|||(0.142)||(0.177)|
|Distance to nearest all weather<br>||0106||0101|
|road||.||.|
|||(0.119)||(0.157)|
|Distance to nearest market||<br>-0.186||<br>-0.238|
|||(0196)||(0245)|
|Financial institution||.<br>-0.076||.<br>-0.355|
|||(0.281)||(0.389)|
|**_Availability of Electricity_**<br>|||||
|proportion of firms excl firm i|||||
|<br>who do not use electricity||0.333||0.753**|

-36-
# 6 Conclusion

Using firms’ balance-sheet information for 69 Emerging Markets and Developing Economies (EMDEs), this paper introduces the Corporate Vulnerability Index (CVI) which provides a framework to monitor financial conditions of the non-financial corporate sector. The CVI employs seven indicators for which data are readily available that capture four aspects of firms’ financial vulnerabilities: debt service capacity, leverage, rollover risk, and return-to-market value.

The Corporate Vulnerability Index suggests that vulnerabilities have increased sharply since 2013, but have stabilized in 2016. At the global level, increased leverage ratio and deteriorations in profitability and debt service capacity were the main drivers behind corporate vulnerability. But global trends mask regional diversity. Corporate vulnerability in Eastern Europe has been elevated since 2007, while Latin America experienced a steep increase in vulnerability since 2013. Corporate vulnerability in EMDEs has been stable in 2016, but the Middle East and Sub-Saharan Africa have shown deterioration. Relative to 2009, financial conditions in the non-financial corporate sector have deteriorated in several EMDEs. However, if currently relatively benign global funding conditions and higher commodity prices endure, companies may have an opportunity to strengthen their balance sheets.

At the industry level, we find that energy-linked sectors in particular have experienced rising financial vulnerabilities; especially since the 2014 peak in global oil prices. We also find that debt of non-financial listed firms in EMDEs has increased both in level and in riskiness: EMDEs’ corporate debt rose by 46 percent, from US $2.6 trillion in 2010 to US $3.8 trillion in 2016. Over the same period, debt in the hands of firms that are considered financially vulnerable in at least one indicator grew by 120 percent, from $1 trillion to $2.2 trillion. In 2016, approximately 58 percent is considered at risk according to at least one indicator.

Logit regressions suggest that the CVI has some leading indicator qualities: an increase in the CVI is positively associated with a future rise in unemployment and an economic recession. Results are robust to controlling for macroeconomic conditions. The CVI also outperforms the commonly used Debt at Risk measure based on the Interest Coverage Ratio (ICR).

26
other papers. We find similar results with the two data sets, but see Kravis and Lipsey (1990) for a comparison of the two data sets.

III. aA_Basic Set of Variables for Growth Regressions and Some Firs. Results The choice of the included variables, ° was based on past empirical studies and economic theory. When the dependent variable is the average annual growth rate in GDP per capita (GYP), the I-variables are the investment share of GDP (INV), the initial level of real GDP per capita in 1960 from Summers-Heston (RGDP60), the initial secondary school enrollment rate as a proxy for human capital (SEC), and the average annual rate of population growth (GPO). Although few empirical studies include all of these variables, most studies control for some subset. Of 41 growth studies surveyed in Levine and Renelt (1990), 33 included the investment share, 29 included population growth, 13 included a human capital measure, and 18 included a measure of initial income. In addition, Mankiw, Romer, and Weil (1990) empirically show that the variables we term *included-variables" enter with the signs predicted by their human-capitalaugmented neoclassical growth uodel, and the included variables are consistent with a variety of "new" growth models that rely on constant returns to reproducible inputs or endogenous technological change [e.g., Barro (1990, 1991) and Romer (1989, 1990a)]. Furthermore, with these I-variables, we can confirm the findings of a large assortment of empirical studies; and, in recognition of the issues raised by McAleer, Pagan, ard Volker (1985), we show that small changes in the included variables do not alter this paper’s conclusions. There are statistical and conceptual problems with using these I- variables. In keeping with this paper's focus on assessing the statistical fragility of past findings, we discuss these problems only triefly. Measurement 7
in _ex post_ better project outcomes. Taken together, we find that such country-level variables can account for about 40 percent of the cross-country variation in project outcomes.

However, enthusiasm for this finding on the importance of country-level variables for project outcomes needs to be tempered by the observation that roughly 80 percent of the variation in project outcomes in our sample occurs across projects _within_ countries, rather than _between_ countries. While country-level variables explain a respectable fraction of the cross-country variation in average project outcomes, country-level variation comprises just 20 percent of the total variation in project outcomes. This basic observation suggests that there are very large returns to gathering and studying potential project-level correlates of project outcomes, which have largely been overlooked in the cross-country literature on aid effectiveness. The primary difficulty in doing so lies in obtaining such variables. To meet this challenge, we draw extensively on the World Bank’s internal databases to extract three categories of such variables, all of which we discuss in greater detail below: (1) basic project characteristics such as the size and sector of the project, and the amount of resources devoted to the preparation and supervision of the project, (2) potential early-warning indicators of project success retrieved from the World Bank’s institutional processes for monitoring and implementing active projects (the Implementation and Status Results (ISR) reports); and (3) information on the identity of the World Bank task manager associated with the project.

We find that several project-level variables, such as project size, project length, and whether the project was flagged as a “problem project” early in the life of the project, are important correlates of project-level outcomes. While these findings are encouraging, they too however are limited by the fact that these variables account for only a very small fraction of the within-country project-level variation in the data (on average around 6 percent of the within-country variation). Since the within-country variation in project performance is so large, however, this is still a substantial contribution to the overall explanatory power of our regressions: country-level variables account for about 8 percentage points of the total R-squared, while project-level variables account on average for about 5 percentage points of the total R-squared.

It is clear from these results that much more could be done to understand why project outcomes vary so much within countries. While we cannot provide a full accounting, in the final section of the paper we explore in a preliminary way one set of candidate explanations: the role of differences in task manager quality in explaining variation in project performance. We study this question in a reduced sample of projects where we have information on the identity of the task manager, and we also 3
same period of the previous year (World Bank (2003)).World Bank estimates indicate that the urban headcount poverty index rose from 6.2 percent in 1994 to 17.2 percent in 2001. Credit to the private sector (particularly in the nontradables sector) fell sharply as a share of GDP and recovered only slowly–a typical pattern in the aftermath of financial crises in developing countries, as documented for instance by Tornell and Westermann (2003) and Schneider and Tornell (2004).

The development of the public debt overhang and the consequent need for continuous refinancing of the debt has led to a very close link between financial market participants’ perceptions of credibility of the Government’s program, key macroeconomic variables such as interest rates, exchange rates and inflation, and real variables such as employment and growth. Our premise in this paper is that a proper modeling of the structure of the labor market in Turkey, and a proper account of the linkages between the financial and real sides of the Turkish economy, are essential steps to understand the impact of the disinflation program on the evolution of output and employment. Accordingly, we develop a dynamic computable general equilibrium (CGE) model with a relatively aggregated productive sector, a segmented labor market and a full-blown financial sector.<sup>3</sup> By itself, this endeavour is not new; over the years, a number of CGE models have been developed for Turkey. These include Lewis (1992), Yeldan (1997, 1998), Diao, Roe, and Yeldan (1998), Karadag and Westaway (1999), De Santis (2000), Voyvoda and Yeldan (2003), and Elekdag (2003). Those of Lewis (1992), Yeldan (1998), and Elekdag (2003) include a financial sector, whereas the others are “real” models focusing on tax and trade policy issues. In all of these models, however, the treatment of the labor market is fairly rudimentary and some important channels through which the real and financial sectors interact are absent. Indeed, as far as we are aware, some of these channels have been either ignored or improperly addressed in the previous literature; our framework should therefore be of independent interest to researchers focusing on other countries with large market-financed debt overhangs.

We pay particular attention to financial sector issues such as a high degree of exchange rate flexibility, risk premia in the banking sector, dollarization of

> 3The model developed in this paper is based on the IMMPA (for Integrated Macroeconomic Model for Poverty Analysis) framework developed by Agénor (2003a), Agénor, Izquierdo, and Fofack (2003), and Agénor, Fernandes, Haddad, and Jensen (2003). This class of models captures real and financial features that are common to many developing countries.

5
Column 3 includes a dummy for whether the household received advice on how to cultivate and sell tobacco.<sup>14</sup> This measure is highly correlated with tobacco adoption and it further reduces the coefficient of interest to .032, no longer significant. Column 4 adds in two dummies for whether the farm used fertilizer and pesticide (only 21% and 7% do so, respectively). Farms growing tobacco are significantly more likely to invest in these inputs, and controlling for that further reduces the coefficient of interest to .029. Column 5 includes the log of number of farm tools owned. This measure is insignificant and does not affect the coefficient of interest.

Overall, we find that almost half of the total effect of female non-cognitive skills on adoption can be accounted for by differences in investments in farm family labor, nonlabor inputs (fertilizer and pesticides), and specific knowledge on how to grow and sell tobacco. As a corollary, these findings suggest that the _de facto_ resource constraints to adoption faced by households are a function of female non-cognitive skills.

# **3.3.3. Intrahousehold Bargaining Power and Health**

We investigate two other possible mechanisms mediating the effect of female noncognitive skills on adoption: intrahousehold bargaining power and health status. Low bargaining power vis-à-vis her husband can limit a woman’s control over how their income is spent, which in turn can reduce their monetary (and thus extrinsic) incentives to pursue high-value cash crops. It is possible that female non-cognitive skills help shape the balance of power within the household: perseverance and optimism could provide the woman with the psychological aid needed to cope with the potential conflict that can ensue during intrahousehold bargaining. If so, the mechanism driving the effect of female non-cognitive skills on adoption might not be increased (intrinsic) personal motivation, but instead increased bargaining power, that in turn, raises her expected monetary rewards from adoption.

> 14 The three main sources of advice are: (i) radio (58%), (ii) government agricultural extension service (21%), and (iii) neighbor/relative (10%).

17
00 TABLE 6.1 Sepegal l I(AR tzs) TV Bmutes of Augmpned Cobb-Douglhs Prodicti Funions - D **pedat** Varble - **LAg** of Comt Price Value **Addod**

**(Values** in Parentheses ae Standad Errors)

||Ba<br>i|sed on Value-<br>n Domestic Pri|Added<br>ces|Bas<br>i|ed on Value-A<br>n World Pric|dded<br>es|
|---|---|---|---|---|---|---|
||1|2|3|4|5|6|
|Intecept<br>(% Senegalese Private)|1836<br>(0.631)|1.183<br>(0568)|-0.066<br>(0.512)|0.532<br>(0.681)|03S2<br>(0.609)|-0.824<br>(0.550)|
|Ownmhio:|||||||
|% Senegalese State|0.317<br>(0278)|0.143<br>(02S4)|.0.063<br>(0.263)|0.169<br>(0296)|0.280<br>(0266)|-0.095<br>(02M)|
|% French|0.094<br>(0.112)|0.044<br>(0097)|0.119<br>(0.100)|0.002<br>(0.118)|-0.076<br>(0.103)|-0.003<br>_(0.105)_|
|% Syro-Lebanese|_-OAS_<br>(0.127)|40.471<br>(0.112)|40.452<br>(0.127)|-0382'<br>(0.133)|-. 516<br>(40.117)|-0.44?<br>(0.129)|
|% Other Foreign|0.028<br>(0.133)|-0.032<br>(0.119)|0.056<br>(0.120)|-0.086<br>(0.140)|-0.167<br>(0.123)|.0.076<br>(0.124)|
**Table 1. Descriptive statistics for transacted and non-transacted parcels**

||**Transactedparcels**|**Not transactedparcels**|**Difference**|
|---|---|---|---|
|Price per ha, USD (2024)|1097.99 (811.70)|||
|NMV per ha, USD (2024)|759.71 (354.02)|716.54 (916.75)|-43.168***|
|Plot size, ha|3.12 (2.67)|2.75 (2.81)|-0.374***|
|Dist. motorway, km|27.21 (23.15)|28.68 (24.09)|1.469***|
|Dist. primary road, km|12.09 (10.32)|12.52 (10.54)|0.430***|
|Dist. secondary road, km|6.85 (5.47)|7.01 (5.68)|0.157***|
|Dist. tertiary road, km|2.21 (1.88)|2.35 (2.21)|0.136***|
|Dist. port, km|338.48 (144.65)|344.34 (163.20)|5.867***|
|Dist. railway, km|10.05 (7.89)|10.35 (8.16)|0.301***|
|Dist. city, km|52.38 (33.06)|52.24 (33.16)|-0.139|
|Dist. elevator, km|11.86 (7.48)|12.64 (7.67)|0.785***|
|Built-up %|0.20 (3.13)|0.27 (4.05)|0.073***|
|Cropped %|86.90 (29.02)|79.87 (35.67)|-7.025***|
|Forested %|1.49 (8.62)|1.91 (10.65)|0.426***|
|Grassland %|11.02 (26.35)|17.21 (32.74)|6.185***|
|Soil density 0-60cm, cg/cm3|130.79 (4.74)|131.36 (4.84)|0.572***|
|Soil nitrogen 0-60cm, cg/kg|394.19 (82.00)|389.95 (87.79)|-4.241***|
|Soil pH 0-60cm|68.66 (3.14)|68.32 (3.51)|-0.341***|
|Soil organic content, t/ha|59.47 (8.30)|58.64 (8.70)|-0.831***|
|War (0|1)|0.79 (0.41)|1.00 (0.00)|0.213***|
|Price below NMV (0|1)|0.29 (0.45)|0.00 (0.00)|-0.292***|
|Missing NMV (0|1)|0.01 (0.08)|0.00 (0.00)|-0.007***|
|N subjects|1.00 (0.07)|1.00 (0.00)|-0.003***|
|No. of transactions (1-latest, 5-oldest)|1.02 (0.15)|1.00 (0.00)|-0.022***|
|Transaction number|1.05 (0.22)|1.00 (0.00)|-0.049***|
|Total area, 1000 ha|550.9|20,452.0||
|N non-missing NMV|175,386|1,130,661||
|No. of obs<br>_Note:_Statistics is reported as: Mean (SD), Co|176,609<br>lumn 3 reports the difference i|7,448,637<br>n means between parcels that w|ere and were not|

_Note:_ Statistics is reported as: Mean (SD), Column 3 reports the difference in means between parcels that were and were not transacted together with results of a two-sided Welch t-test assuming unequal variances for the significance of the difference. Significance levels are `*` < 0.05, '**' < 0.01, and '***' < 0.001 19
disagreement as to whether it is necessary to include strata dummies in the analysis after stratification – more than half the studies using stratification do not include strata dummies.

Finally, we note that all but one of the papers in Table 1 present a table for comparing treatment and control groups, and carry out tests for imbalance.<sup>6</sup> The number of variables used for checking for imbalance ranges from 4 to 39. We examine the purpose and usefulness of such tests in more detail in Section 4.

# **2.2 Randomization in practice according to a survey of experts**

The long lag between inception of a randomized experiment and its appearance in at least working paper form means the results above do not necessarily represent how the most recent randomized evaluations are being implemented. We therefore decided to survey leading experts in randomized evaluations on their experience and approach to implementation. A short online survey was sent to 35 selected researchers in December 2007. The list was selected from members of the Poverty Action Lab, BREAD, and the World Bank who were known to have conducted randomized experiments. We had 25 of these experts answer the survey, with 7 out of the 10 individuals who did not respond having worked with those who did respond, ensuring our survey also covers the methods used by those non-responders in at least some of their experiments.

The median researcher surveyed had participated in 5 randomized experiments, with a mean of 5.96.<sup>7</sup> Three of those surveyed had only participated in one experiment, while three had participated in fifteen (or more). 71 percent of the experiments these researchers had been involved in had had baseline data (including administrative data) that could be used at the time when randomization to treatment was done, while in the remaining 29 percent no baseline data was available.

Preliminary discussions with several leading researchers established that in addition to stratified randomization and matched pairs, several other methods involving multiple random draws were being used in practice to increase the likelihood of balance

> 6 The exception is Field and Pande (2008), who are likely limited in space in the papers and proceedings format. They note that such a check was done and is available upon request.

> 7 This is after top-coding the number of experiments at 15, in order to not have the responses dominated by one researcher who had conducted more experiments than this.

- 8 -
distribution, though households with per-capita incomes above $20 per day (in 2017 PPP) were much less affected than the rest (World Bank 2022).

Within countries, evidence suggests the brunt of the burden from the COVID-19 labor market shock was borne by women, less educated, and urban workers (Bundervoet et al. 2022; Kugler, Viollaz, et al. 2021). The disproportionate impact on these population groups has also been corroborated in a number of countries that continued standard survey data collection, including T ̈urkiye (Aldan et al. 2021), South Africa (K ̈ohler et al. 2023), Brazil (World Bank 2022a), Vietnam (Dang et al. 2023), and India (Deshpande 2020). Similar patterns are also observed in advanced economies. For instance, harmonized data from the EU Statistics on Income and Living Conditions (EU-SILC) show that in Q2 of 2020 the risk of temporary layoffs or reduced hours declines with household income. Chetty et al. (2020), based on data from the United States, find that employment losses between January and April 2020 decreased monotonically with income, from 37 percent in the bottom wage quartile to 14 percent in the top wage quartile.

These differential impacts across population groups are in part because the ability of workers to continue jobs remotely is higher among better-educated workers, as a result of the digital divide and the cognitive nature of work for high-skill occupations. In the case of developing countries, some studies have analyzed task characteristics across occupations to estimate the share of jobs that can be done at home. Dingel and Neiman (2020) find the share to be increasing with country income levels, with fewer than 25 percent of jobs in Mexico and T ̈urkiye being amenable to working from home. World Bank (2020) uses data from India to estimate that less than 10 percent of jobs are amenable to working from home at the 70<sup>th</sup> and lower percentiles of the earnings distribution and suggests that the actual estimate is likely much lower because of constraints to accessing digital technology. Gottlieb et al. (2021) find that just 13.3 percent of urban workers in Brazil and 10.6 percent in Costa Rica were actually working from home during the pandemic. In addition to feasibility to work from home, there is also considerable evidence of the gender gap in the care responsibilities precipitated by the pandemic and accompanying school closures which may have led to a reduction in women’s labor force participation (Albanesi and Kim 2021; Bau et al. 2022; Levine et al. 2021; Lee et al. 2021; Miguel and Mobarak 2022).<sup>4</sup> Beyond the initial impact, there is also evidence that those who suffered the larger initial

> 4Evidence is more abundant in high-income countries. Sostero et al. (2020) find that in Europe threequarters of employees in the top wage quintile were able to work remotely, compared to only 3 percent of those in the bottom quintile. Adams-Prassl et al. (2022) similarly find that in the UK workers with gross labor income above GBP 70,000 can accomplish some 60 percent of tasks remotely, compared to 20 percent among workers with gross labor income below GBP 10,000.

4