rafmacalaba/datause-encoder-data huggingface dataset:
# Appendix Table 4

Default Probability Model: Different Regressors and Samples

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|_Panel A: Estimation results_|||||||||
|Log(Net Worth)|_−_0_._010|_−_0_._009|_−_0_._009|_−_0_._007|_−_0_._010|_−_0_._007|_−_0_._010|_−_0_._009|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Log(Value Added/Number of Workers)|_−_0_._018|_−_0_._017|_−_0_._015|_−_0_._014|_−_0_._018|_−_0_._012|_−_0_._017|_−_0_._016|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Firm Age|_−_0_._001|_−_0_._002|_−_0_._001|_−_0_._001|_−_0_._001|_−_0_._003|_−_0_._002|_−_0_._003|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Log(Wage Bill)|_−_0_._008|_−_0_._007|_−_0_._007|_−_0_._006|_−_0_._008|_−_0_._005|_−_0_._009|_−_0_._008|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Log(Annual Sales)|0_._003|_−_0_._003|0_._005|_−_0_._003|0_._003|_−_0_._000|0_._008|0_._002|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Log(Debt Outstanding)||0_._013<br>(0.001)|0_._013<br>(0.001)|0_._012<br>(0.001)||0_._010<br>(0.001)||0_._012<br>(0.001)|
|Spread _Ex Ante_||0_._003|0_._003|0_._003||0_._001||0_._003|
|||(0.000)|(0.000)|(0.000)||(0.000)||(0.000)|
|Spread 2018||||0_._004<br>(0.000)|||||
|Default Probability||||||0_._189<br>(0.002)|||
|Sales Variation|||||||_−_0_._040|_−_0_._034|
||||||||(0.002)|(0.002)|
|Dependent Variable Mean|0.088|0.088|0.080|0.080|0.088|0.088|0.090|0.090|
|Dependent Variable Std. Dev.|0.284|0.284|0.271|0.271|0.284|0.284|0.286|0.286|
|Number of Firms|96,411|96,411|69,308|69,308|96,328|96,328|92,811|92,811|
|R<sup>2</sup>|0.073|0.111|0.068|0.117|0.073|0.256|0.091|0.123|
|Industry FE and Municipality FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|_Panel B: Predicted Default Probability_|||||||||
|With Previous Loans|0.088|0.089|0.079|0.079|0.088|0.089|0.090|0.091|
|Without Previous Loans|0.107||0.091||0.107||0.097||

This table reports probit estimations of the probability of a firm defaulting on a loan on a set of _ex ante_ firm-level characteristics for the default model sample. The dependent variable is a dummy variable equal to one if the firm defaults on a loan during 2019 (has payment past due over 90 days) and zero otherwise. Each model is first estimated using real regressors, and then with real and financial regressors. Columns 1 and 2 are also displayed in Table 1, and Columns 4 and 8 are used as a benchmark. Firms with previous loans are those that either have bank credit outstanding in December 2019 or receive a bank loan over the period 2012–2019. Conversely, firms without previous loans are those with no credit records in the banking system throughout the same period. Spread _ex ante_ is calculated as the average spread charged on bank credit obtained by each firm over the period 2012–2018. Spread 2018 is the spread charged on bank credit obtained by each firm during 2018. Columns 1–8 include industry and municipality fixed effects, and a different set of controls. Standard errors, shown in parentheses, are clustered at the industry and municipality level.

22
including gender, age, previous work experience, job search duration, employment type, seniority, job type,<sup>9</sup> and geographic area. We also control for cognitive skills as measured by the language (Bangla) and math test scores of workers in the sample. The OLS model also includes control variables for firm size and industry.

# _e) Estimation Strategy_

The analysis is based on an OLS model and a firm-level fixed-effect model. Both models use sampling weights to account for sampling design effects. The OLS model provides correlations between wages and personality traits, which can serve as a basis for comparisons with studies in other countries. They capture the average correlations between wages and personality traits across the sampled firms, controlling for occupation, industries and various individual characteristics.

The firm-level fixed-effect model captures variations within firms. Including firm-level fixed effects eliminates the possibility that differences in form characteristics will compromise the model. The entry-level wage of each worker surveyed in the ESS is modeled by the (1) OLS and (2) fixed-effect regressions as follows: where Wi is the monthly wage of individual i and Wif specifies firm f, Non-cogi is one of eight personality traits, Xi is a vector of covariates that includes individual characteristics, such as age, gender, education level, seniority, employment type, occupation, and industry, as well as cognitive skills as measured by language and math scores, αf is the firm-specific constant term in the regression model, β0 is a constant reflecting the intercept, β1 is the coefficient that measures the returns to personality traits, and εi is the error term for the OLS model and εif specifies firm f in the fixed-effect model.

> 9 Job type is based on the International Standard Classification of Occupations (ISCO).

9
countries face logistics costs that are significantly higher than those in the OECD and the United States<sup>4</sup> .

**FIGURE 3: LOGISTIC COST AS PERCENTAGE OF PRODUCT VALUE, 2004.**

<!-- Start of picture text --> 35<br>32<br>30<br>27<br>26<br>25 23<br>20<br>20 18<br>15<br>9.5 9 8.5<br>10<br>5<br>0<br>Peru Argentina Brazil Colombia M exico Chile U.S.A. OECD Singapure<br>Source: Guasch and Kogan, 2006<br>Percentage<br><!-- End of picture text -->

- **World Bank, Logistics Perception Index** . The World Bank is developing an innovative questionnaire to be distributed to shippers and logistics operators with the aim of identifying the areas that require immediate intervention if logistics costs are to be lowered. Those receiving the questionnaire have to assign points to each question, on topics that include: effectiveness and efficiency of the cargo shipment process, available infrastructure for logistics operations, and the quality of infrastructure in use for logistics operations. In a preliminary round of this questionnaire<sup>5</sup> , the first place corresponded to the Netherlands, the United States was in 19<sup>th</sup> place, South Korea was 25<sup>th</sup> , while in the LAC region Chile was 29<sup>th</sup> , Argentina ranked 40<sup>th</sup> , followed by Mexico in 45<sup>th</sup> place. Ecuador was in 46<sup>th</sup> , Brazil ranked 51<sup>st</sup> and Peru in 55<sup>th</sup> , among 70 countries included in the sample.

# **3.3 Role of Inventories and Other Determinants of Logistics Costs**

Inventory levels are usually studied to assess the reliability of infrastructure services, in particular transport. When a country has poor transport infrastructure, firms need to have high levels of inventories, to account for contingencies. Maintaining such levels is expensive because it ties up capital, which has a high cost in the region. This significantly increases unit costs, lowering competitiveness and productivity. Estimates show that, assuming an interest rate of 15

> 4 The estimation of logistics costs for Argentina was updated in a recent World Bank report (2006). Logistics costs, in part as a result of the devaluation of the domestic currency and the improvement in the terms of trade account for 16% of product value.

> 5 http://www.gfptt.org/uploadedEditorImages/00000325.pdf

10
In column (2), we allow heterogeneity in the price effect by including its interactions with the initial landholding size and wealth level. If land is used as a collateral, small farmers are likely to face borrowing constraint and, as a result, small farmers tend to invest less. It is also possible that large agricultural land owners have higher profitability per acre (scale economy) and the return to investment is scale-dependent.<sup>11</sup> Although we cannot perfectly separate borrowing constraint from the scale effect since land is a collateral as well as the most important factor determining the scale of production (thus, the efficiency of investments since, e.g., machines are more effectively used on large farms), we can test whether the size of land affected the effect of price shocks on the investment decision.

The theory also predicts that rich farmers invest more than poor farmers since their borrowing cost is lower. On the one hand, if this prediction is true, poor households will absorb the positive income shock by increasing savings and consumption. However, it could also be the case that poor farmers have a stronger incentive to invest since the marginal return to capital is higher than for rich farmers. It all depends on which activity – investment or consumption (current or future) – was constrained. With higher agricultural profit caused by the positive price shock, poor households might take this chance to invest since they do not have to borrow with a high interest rate. To test which hypothesis holds in our context, we interact the price change and durable assets in 2007 _(_ A0), a measure of the initial wealth level.. This measure does not include agricultural land but include residential land.

Both linear and square terms of land size are positively signed, which implies that the effect of agricultural land on investment is increasing with convexity. Therefore, regardless of price shocks, large farmers tend to invest more. However, the interaction term of the price shock

> 11 Foster and Rosenzweig (2011) found the positive relationship between farm size and productivity using panel data in India.

19
<!-- Start of picture text --> Figure7<br>External Private Credit to Private SectoriDomestic Private Credit<br>17 countries, 1990-2000<br>su<br>50<br>40<br>5<br>2 40<br>a<br>LX<br>20<br>10<br>o<br>1990 1991 {gg 1993 19944 1995 1996 1997 1998 1999 2000<br><!-- End of picture text -->
_Table 2 Impact of nitrogen fertilizer on water pollution (grid cell-level analysis)_

||Lo<br>|g (Total nitro<br>|gen in wat<br>|er)<br>|
|---|---|---|---|---|
||1|2|3|4|
|log(Nitrogen in grams per sq m)|0.1686**|0.2745**|0.3437*|0.3466**|
||(0.073)|(0.108)|(0.197)|(0.169)|
|N|1461|1343|1037|1037|
|Grid Cell FE|y|y|y|y|
|Country-Year FE|y|y|y|y|
|Share Cropland||y||y|
|Log NPP|||Y|Y|
|Adj. Rsq|0.703|0.706|0.759|0.759|
|RMSE|0.606|0.609|0.553|0.553|

Notes: Dependent variable is log of total nitrogen water pollution in a grid cell. All regression models include grid cell fixed effects; country-year fixed effects; controls for precipitation, temperature, runoff, and log population. In some specifications both the extensive (share of cropland) and intensive margins of agriculture (Log of NPP on cropland) are included as controls. Statistical significance is given by +P < 0.10, *P < 0.05, **P < 0.01, and ***P < 0.001.

Finally, Table 3 and Figure 3 demonstrate the overall impact of subsidies on total nitrogen in water. The results show that coupled producer support has a positive and significant impact on nitrogen pollution levels across various definitions of coupled support. Further, the results show that even when the _Table 3 Impact of agricultural subsidies on water pollution (country-level analysis)_

|X: Share of Value of Production|Y: IHS(Tota|l nitrogen in w|ater)|||
|---|---|---|---|---|---|
||1|2|3|4|5|
|Decoupled PSE|-0.0122|-0.0254+|-0.0245+|-0.0249+|-0.0264|
||(0.016)|(0.014)|(0.015)|(0.014)|(0.016)|
|Decoupled PSE [t+1]|||||0.0011<br>(0.016)|
|Coupled PSE (def1)|0.0065*<br>(0.003)|||||
|Coupled PSE (def2)||0.0143*<br>(0.006)||||
|Coupled PSE (def3)|||0.00572+<br>(1.71)|||
|Coupled PSE: Variable input support||||0.0733**<br>(0.022)|0.0861**<br>(0.032)|
|Coupled PSE: Variable input support [t+1]|||||-0.0192<br>(0.034)|
|N|117|117|117|117|117|
|Country FE|Y|Y|Y|Y|Y|
|Year FE|Y|Y|Y|Y|Y|
|R-sq|0.957|0.956|0.956|0.958|0.958|
|AdjR-sq|0.937|0.934|0.935|0.937|0.936|

Notes: Dependent variable is inverse hyperbolic sine of total nitrogen water pollution in a gridcell. All regression models include country fixed effects and year fixed effects. Def1 includes all coupled support, Def2 includes coupled support minus market price support, Def3 includes only market price support. Statistical significance is given by +P < 0.10, *P < 0.05, **P < 0.01, and ***P < 0.001. Following Bellemare and Wichman (2020), the coefficients are converted into elasticities as follows: �=y ~~<u>�</u>~~ <u>y</u> +1 εε<sup>2</sup> ββ̂yy yy 12
of the income distribution is well represented in the GEIH, which collects data monthly and provides information at the national, urban, and rural levels as well as at the departmental level. In order to compare annual values from the administrative data with those from the household survey, we multiply by 14 the total individual monthly income (12 months plus a bonus equivalent to 2 months’ pay).

# **3.2 Results**

In general, all individuals whose incomes exceed a certain threshold appear as a tax unit in the administrative data.<sup>6</sup> Individuals with incomes below this threshold are not captured by the tax records, but they are represented in the household survey. In 2010, this threshold corresponded to 81 million Colombian pesos per year. As explained above, this income level is used to determine the parameter _b_ of the formulas presented in Propositions 1 and 2: the bottom conditional distribution in the propositions will be obtained from the household survey by focusing on all individuals with incomes below _b_ , and the top conditional distribution will use all available tax records (since the threshold _b_ is chosen to be the minimum income level that appears in the tax record).

Figure 1 depicts the distribution of total individual incomes in the household survey.<sup>7</sup> Since tax records are based on individual returns, our estimation only considers the total individual income data collected through household surveys. If more than one person per household files a tax return, they will appear separately, each with their own individual income. We define as a control for population all individuals age 20 and above, since few individuals under 20 years of age<sup>8</sup> contribute income tax revenue; excluding them from the denominator does not significantly affect the results (Atkinson 2007).

The red vertical line in Figure 1 indicates the minimum total individual income needed

> 6A caveat is worth mentioning here: the level of tax evasion and its changes over time can affect the results obtained for the Gini coefficient.

> 7We calculate the total income distribution considering all personal incomes: labor income, transfers, remittances, and capital gains, among others. We also include pension claims, since in Colombia pension payments are considered labor income.

> 8The control for population uses population projections data for 2010. The total population is 28,104,576.

12
**<u>Table 15 Trade Orientation and R&D Interaction</u>**

||EmployGrowth|Prod Innov|
|---|---|---|
|Export/Import|0.028|0.163|
||(0.005)***|(0.024)***|
|Export Only|0.016|0.101|
||(0.008)**|(0.037)***|
|Import Only|0.009|0.129|
||(0.004)**|(0.019)***|
|Export/Import*R&D|0.015|0.056|
||(0.006)**|(0.033)*|
|Import Only*R&D|0.012|0.007|
||(0.006)**|(0.026)|
|Export Only*R&D|0.008|0.081|
||(0.010)|(0.048)*|
|R&D Inv Indicator|0.010|0.117|
||(0.004)**|(0.024)***|
|Foreign (≥%10)|0.014|‐0.021|
||(0.004)***|(0.016)|
|Observations|8355|9311|
|R2/Pseudo R2|0.122|0.158|

Robust standard errors clustered by country, industry, and year are in parentheses. The regressions control for Log(investment/worker), Nonproduction worker share, training, age, Log(Labor)t-3, 2-digit industry, survey year, and country fixed effects. Coefficients for the probit regression shows the marginal effects at mean values. *** p<0.01, ** p<0.05, * p<0.1.

The results are given in Table 15. R&D contributes significantly to the growth of two-way traders and only importers. For the results on product innovation, R&D significantly contributes to growth of two-way traders and only-exporters. These results show that R&D contributes to growth of trading firms more than it contributes to non-traders. Aw, Roberts and Winston (2007) who analyze the determinants of future profitability get similar results. They get a positive and significant coefficient on the interaction term between R&D and export variable. However, they do not incorporate the importing decision of the firm in their study.

# **9 Conclusion**

Recent trade models with heterogeneous firms have shown that exporters are larger, more productive, more capital intensive, and pay higher wages than firms serving only the domestic market. However, the relation between firm growth and global engagement is less clear. In this study, using a detailed firm level dataset from 43 countries, I analyze whether firms with foreign exposure grow faster than domestic firms. In analyzing foreign exposure, in addition to exporting, I 29
<!-- Start of picture text --> 1500<br>1400<br>1300<br>1200<br>1100<br>1000<br>900<br>800<br>700<br>600<br>500<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>Hour<br>Load (MW)<br><!-- End of picture text -->

**Figure 9. Hourly electricity load curve (MW)**

_Note: The curve is for a typical day in October in 2016. Source. NEA (2017)_ This type of sharp peaky load may not favor the economics of addition of expensive hydropower capacities into the system. In other words, an assumption of absorbing more and more hydropower internally by encouraging cooking may not be a sustainable policy for Nepal. If electric cooking is to be promoted, Nepal’s power system needs to be further interconnected to a bigger system, like that of India. The better interconnection with the bigger system brings two benefits. First, addition of hydropower would not be needed to meet the peaky loads in Nepal because imported power from India could help meet those loads.<sup>11</sup> Second, even if hydropower capacities are added in Nepal to meet these loads, their economics would improve because the added hydropower plants would be running at all times as long as they are available to export electricity to India. Thus, cross-border interconnection would be further important and beneficial

> 11 This benefit, however, may not be realized at present because India also has evening peaking.

24
_Social Capital, Household Welfare and Poverty in Indonesia_ 11

# 2. **The Data** Set

The data set for this paper comes from the Local Level Institutions (LLI) Study, a comparative study of three countries (Bolivia, Burkina Faso and Indonesia), which aims to investigate the role of local institutions in providing service delivery and in affecting welfare and poverty outcomes.<sup>6</sup> Data were collected at the level of the community, the district and the household.

At the level of the _community,_ interviews with focus groups of households and with community leaders were held to establish a map of functioning institutions in the community. Three instruments were used:

- Infornation on community services was obtained through interviews with key informants such as village chief, teacher, health provider, etc. This was supplemented with information on the local economy (infrastructure and distance to markets), local society (ethnic/religious composition) and local institutions. Recent experience with selected development projects was also discussed.

- The community services were also discussed with groups of households, with an objective to learn the community's perspective on the quality of service, its experience with collective action, and its views on local institutions and development projects.

- 6 The objective of the Local Level Institutions study and the questionnaires are further discussed in World Bank (1998).
2000.) Since the data for the rich countries are, on average, more frequently available than for the poor, the cut-off off point neatly splits our sample into about two halves.

Table 4. Relationship between inter-occupational wage inequality and level of protection (average tariff rate) in poor and rich countries

|||Poorcountri<br>|es<br>||Richcountr<br>|ies<br>|
|---|---|---|---|---|---|---|
|Change in<br>average<br>tariff rate|Mean<br>change in<br>Ginioww<br>(Gini<br>points)|Standard<br>deviation<br>(Gini<br>points)|Number of<br>observations|Mean<br>change<br>in<br>Ginioww<br>(Gini<br>points)|Standard<br>deviation<br>(Gini<br>points)|Number of<br>observations|
|Decrease|+1.31|4.63|77|+0.44|2.56|82|
|No change|-2.71|6.64|18|-0.13|3.30|52|
|Increase|+0.29|4.21|30|+0.71|1.01|9|
|_Total_|_+0.49_|_5.02_|_125_|_+0.25_|_2.80_|_143_|

Note: poor countries are defined as those with GDP per capita less than $9000 at international 1995 prices. Rich, those above that threshold.

The table illustrates that the same regularity applies to both poor and rich countries: decreases in protection are associated with higher wage inequality, but so are (although the magnitudes are substantially lower) increases in protection. It is mostly when there is no change in mean tariff rate that we find shrinking occupational wage distribution. In effect, out of 122 cases when occupational inequality goes down, about one third (39) involve situations with no change in mean tariff rate. Poor countries display in all cases (decrease, no change, or increase in protection) greater variability in outcomes. This is illustrated in Figure 7 where we look at changes in occupational Gini when protection is reduced. The strongly spiked density function for the rich countries (dashed line) shows that reduced protection is accompanied by relatively small and very similar changes in rich countries’ Ginis; in contrast, in poor countries, Gini changes (solid line) are much more spread out. The hypothesis of equality of the two distributions is soundly rejected (the Kolmogorov-Smirnov test is significant at less than 0.1 percent).

38
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