rafmacalaba/datause-encoder-data huggingface dataset:
country variations). This finding is shared by the World Bank’s _Africa Competitiveness Report 2009_ , which shows that as a country’s income rises, its set of constraints changes.

All these studies share a common result: business environment variables affect firms’ growth, in the expected direction. The results are heterogeneous by firm size, and they are robust.

# **2.2 Financial Development and Firm Size Distribution**

A common finding in the literature is that the firm size distribution in developing countries is skewed toward small and medium-size firms. Small firms are often credit constrained and cannot borrow to engage in productive investments, which limits their growth and can prolong the skewness. If lack of access to finance prevents small firms from growing, the allocation of resources will be distorted. Capital and labor will not be able to flow to where they are most productive, and growth will suffer.

Cooley and Quadrini (2001) and Cabral and Mata (2003) present different models of firms’ growth, showing that capital constraints can cause a skewness in the firm size distribution. Their prediction is verified empirically. Cabral and Mata (2003) find that the size distribution of firms is skewed toward small firms and that the skewness decreases with firm age. Many subsequent papers confirm the skewness of the firm size distribution, such as Angelini and Generale (2008), Beck, DemirgüçKunt and Maksimovic (2005), and Desai, Gompers, and Lerner (2003).

Desai, Gompers, and Lerner (2003) find that in countries with less developed capital markets, the firm size distribution is significantly more skewed. They also find that a better legal environment favors entry (more small firms will enter) while the growth of small firms reduces the skewness. Angelini and Generale (2008) and Beck, Demirgüç-Kunt and Maksimovic (2005) find that capitalconstrained firms grow more slowly than their counterparts.

# **2.3 Growth Diagnostics Approach**

The growth diagnostics approach proposed by Hausmann, Rodrik, and Velasco (2005) (hereafter, the HRV approach) provides a theoretical framework to identify the most binding constraints on economic growth in general. This methodology recognizes that constraints on the growth of a developing economy are numerous and that previous approaches to reforms and growth are either unrealistic (as with wholesale reform that attempts to eliminate all obstacles at the same time) or wrong (by hoping to do as many reforms as possible, the current prevailing approach goes against the principle of second best).

The HRV approach is based on the theory of second best (Lipsey and Lancaster, 1956). According to this theory, if there are many distortions in the economy, fixing any one distortion would not necessarily lead to a better Pareto outcome. The HRV approach shows that if there are many distortions, whether removing one growth constraint will have a positive effect on growth depends 5
of 2020 than in 2019:Q4. The P90 for nonperforming loans is 1.42 percent in 2019:Q4, and is lower than that for every subsequent quarter except 2020:Q2, when it reaches 1.51 percent, which is only slightly larger.

It is likely the case, as argued elsewhere (e.g., Beck and Keil, 2021), that reported loan performance would have been significantly worse were it not the case that the bank portfolios included hundreds of billions of dollars of Paycheck Protection Program (PPP) loans. These loans are largely not intended to be paid back, so should contribute little to nothing to charge-offs and nonperforming loans. As discussed in more detail below, this suggests that part of the “financial surprise” of a lack of banking crisis or significant financial distress is likely due to government stimulus during COVID-19 in addition to prior fortification of the industry in response to the GFC. Some of the improvements may also be illusory due to the forbearance rules discussed above, but it is not possible to determine how much this may be the case. Nonetheless, it is reassuring that reported loan performance problems did not significantly worsen during the crisis.

Checking next for insufficient loss allowances for the industry, this also does not appear to be problematic. The mean allowance of 0.81 percent in 2019:Q4 is slightly exceeded in every quarter of 2020, ranging between 0.82 and 0.84 percent. Similarly, the P10 values of the allowance also modestly exceed the 0.36 percent in 2019:Q4 in 2020, ranging between 0.37 and 0.38 percent. We cannot say whether the increase in allowances in 2020 are sufficient to cover the increased risks from the pandemic, but the banks at least made some effort to maintain slightly higher reserves to absorb these risks.

Turning to the credit performance data for the G-SIBs, the story is much the same – no substantial problems. Mean charge-offs are low in 2020, between 0.05 and 0.14 percent, and remain below the 2019:Q4 peak of 0.20 percent. There is some upward movement in 22
- Bottini, Novella, Mohamed Ali Marouani and Laura Munro (2001), “Service Sector Restrictiveness and Economic Performance: An Estimation for the MENA Region,” _The World Economy_ , 1652-1678.

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Francois, Joseph (1999), “Estimates of Barriers to Trade in Services,” Erasmus University, mimeo.

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- Francois, Joseph, Bernard Hoekman and Julia Woerz (2007). “Does Gravity Apply to Nontangibles? Estimates of trade and FDI openness in services,” mimeo. Available at: <u>https://www.gtap.agecon.purdue.edu/resources/download/3407.pdf.</u>

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17
Table 6. Impacts of Proximity to Refugee Camps on Labor Market Outcomes

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|_A. Labor Market_|||||||
||Labor Force|Employed [=1]|Monthly Wage<br>(Sin*)|Weekly<br>Hours Worked<br>(Sin*)|Written<br>Contract [Yes=1]|–|
|SID|0.002|-0.001|0.020|0.061|0.046***|–|
||(0.003)|(0.003)|(0.032)|(0.057)|(0.005)|–|
|FDR q-values|[1.00]|[1.00]|[1.00]|[1.00]|[0.001]|–|
|R-squared|0.066|0.031|0.027|0.079|0.033|–|
|Observations|10,989|6,915|6,913|6,889|1,620|–|
|_B. Type of Job Prov_|_iders_||||||
||Work for a<br>Government<br>Organization|Work for a<br>Private<br>Company|Work for an<br>NGO|Work for a<br>Household|Work on<br>Self-employed|Work for<br>a Mill|
|SID|-0.002|-0.019**|0.080***|-0.018**|-0.029**|-0.012**|
||(0.004)|(0.005)|(0.009)|(0.006)|(0.009)|(0.003)|
|FDR q-values|[0.141]|[0.024]|[0.001]|[0.029]|[0.029]|[0.024]|
|R-squared|0.016|0.013|0.099|0.029|0.032|0.013|
|Observations|1,857|1,857|1,857|1,857|1,857|1,857|
|Outcome Level|Individual|Individual|Individual|Individual|Individual|Individual|

_Notes:_ The table presents results from equation 3. SID represents standardized values of the inverse weighted distance from the respondent’s household location to refugee camps. Dependent variables are available for the 2019 and 2023 waves, with detailed explanations provided in Appendix B. In Panel A, the dependent variables include:(i) Labor Force, defined as all individuals aged 14 to 60 years categorized as employed or unemployed; (ii) Employed, indicating if respondents worked for remuneration for at least one hour in the last seven days; (iii) Monthly wage, represented by the inverse hyperbolic sine transformation of reported last wage payment in Takas; (iv) Weekly hours, represented by the inverse hyperbolic sine transformation of reported average weekly work hours; and (v) Written contract, indicating the presence of a written contract. Panel B delineates the type of work provider, with variables (i) to (vi) corresponding to an indicator set to one if respondents have worked for wages or payment (either in cash or in-kind) for the cited providers. (Sin*) represents the inverse hyperbolic sine transformation, interpretable as a logarithm transformation. Estimates control for individual characteristics including male=1, age, household size in 2019, household head=1, married=1, and household income sufficiency in 2017. Wave fixed effects are included and clustered standard errors at the _upazila_ (subdistrict) level are presented in parentheses, and False Discovery Rate (FDR) q-values are reported in brackets. Significance levels:<sup>_∗∗∗_</sup> at 1%,<sup>_∗∗_</sup> at 5%,<sup>_∗_</sup> at 10%.

47
39

**Table 11. Marginal Effects for Labor Force Participation, Sri Lanka**

||1992|1993|1994|1995|1996|1997|1998|1999|2000|2001|2002|2006|2008|2011|2012|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Female dummy|-0.199***|-0.190***|-0.206***|-0.194***|-0.197***|-0.187***|-0.182***|-0.199***|-0.195***|-0.205***|-0.212***|-0.280***|-0.222***|-0.232***|-0.237***|
||(0.003)|(0.005)|(0.005)|(0.005)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|(0.005)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|
|Age (years)|0.056***|0.057***|0.059***|0.058***|0.062***|0.059***|0.059***|0.059***|0.060***|0.058***|0.059***|0.061***|0.054***|0.055***|0.055***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Age squared|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Education (years)|-0.000|0.002**|0.001*|0.001*|-0.003***|0.000|-0.000|0.002***|0.003***|0.006***|0.002***|-0.000|0.006***|0.005***|0.008***|
||(0.000)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Married dummy|-0.038***|-0.046***|-0.045***|-0.063***|-0.067***|-0.061***|-0.065***|-0.063***|-0.065***|-0.067***|-0.064***|-0.020***|-0.055***|-0.055***|-0.063***|
||(0.004)|(0.007)|(0.007)|(0.007)|(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|(0.007)|(0.006)|(0.006)|(0.006)|(0.007)|(0.006)|
|Widowed dummy|-0.030***|-0.035**|-0.020|-0.056***|-0.036***|-0.031***|-0.042***|-0.026**|-0.018|-0.013|-0.014|0.022*|0.012|0.014|0.014|
||(0.009)|(0.014)|(0.016)|(0.013)|(0.011)|(0.012)|(0.012)|(0.012)|(0.012)|(0.014)|(0.012)|(0.011)|(0.017)|(0.013)|(0.012)|
|Divorced dummy|0.043***|0.013|0.036|0.012|0.006|-0.009|-0.009|-0.006|0.005|0.043*|0.058***|0.084***|0.000|0.084***|0.051***|
||(0.016)|(0.026)|(0.032)|(0.028)|(0.021)|(0.020)|(0.019)|(0.020)|(0.021)|(0.025)|(0.021)|(0.019)|(0.011)|(0.022)|(0.018)|
|Observations|73,503|29,285|28,009|27,419|49,094|46,201|47,898|47,467|47,021|35,789|46,517|60,097|57,135|44,345|48,502|

_Source:_ Calculations based on the Sri Lankan 1992–2002, 2008, 2011, and 2012 Labor Force Surveys, and the Sri Lankan 2006 Household Income and Expenditure Survey. _Note:_ Marginal effects of the labor force participation are evaluated at the mean of the independent variables. Standard errors are in parentheses. Never married is an omitted category for marital status. Standard errors are reported below the estimated coefficients. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks respectively.
9 of manufactures using each of the defritions listed in a 'ble 1'3 In addition, an alternative proposal was tested that includes all _pmesc_ nonferrous products classified in S1TC 68 as manufactures (i.e., copper tubes, aluminum plate, zinc wire, etc.), but excludes the unwrought nonferrous metals. The rationale for this distinction is that many of the former have undergone a considerable degree of processing so they conceptually could be classified as manufactures.

Table 2 summarizes the empirical results when these alternative definitions of manufactures were used in connection with actual trade data for 72 developing countries. The table shows the value of each country's exports that would be classified as manufactures and the share of these products in total trade. To asr-' t in interpreting these statistics, the results for one definition (that used by IBRD/UNCIADIGATT for trade data) are arbitrarily used as a standard and trade values derived under alternative trade or production definitions flagged if they differed by more than 25 percent. Finally, similar computations were made (see the memo item) for France, Federal Republic of Germany, United Kingdom and United States to detennine the results from the alternative definitions of manufactures for industrial countries with a broader range of export products.

The results confirm the non-comparability of the tride and output series and the importance of the defnitional problem for manufactures, between the alternatives that hinge around a core group of products in SITC 5 through 8, and between core group and broader defimition **s** uch as those employed by

> '} Since many of the developing countries' export records were incomplete or out of data their export profiles were based on reported imports of partners. As such, the 1987 trade data are based on UN import statistics for the following group of countries which had complete reliable records: all developed market economy countries, Argentina, Bangladesh, Brazil, Cameroon, Colombia, Cyprus, Egypt, Faeroe Islands, French Polynesia, Greenland, Guadeloupe, Hong Kong, Hungary, Iceland, Israel, Jordan, Republic of Korea, Malta, Martinique, Reunion, Singapore, Thailand, Trinidad and Tobago, Tunisia, Turkey, Urguay and Yugoslavia. These countries, therefore, constitute the "world" to which the 72 developing countries are exporting.
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24
|2008|0.5903|0.4012|0.1924|
|---|---|---|---|
||(0.0219)|(0.0337)|(0.0249)|
|2009|0.5406|0.3873|0.1534|
||(0.0176)|(0.0287)|(0.0204)|
|2010|0.4830|0.3139|0.1658|
||(0.0182)|(0.0244)|(0.0187)|
|2011|0.4431|0.2725|0.1718|
||(0.0240)|(0.0340)|(0.0258)|
|2012|0.4368|0.3008|0.1285|
||(0.0252)|(0.0416)|(0.0330)|
|2013|0.4354|0.2260|0.2000|
||(0.0251)|(0.0328)|(0.0251)|
|2014|0.3823|0.2654|0.1145|
||(0.0233)|(0.0316)|(0.0216)|
|2015|0.4509|0.3776|0.0707|
||(0.0225)|(0.0304)|(0.0244)|

_Notes:_ bootstrapped standard errors in parentheses (200 replications) What industries drive these developments? The decomposition results suggest that in terms of the gender wage inequality within sectors (intra-industrial component), health care and education were the biggest culprits followed by trade and manufacturing (Table 1 in the Appendix). At the same time, these sectors also experienced the biggest drops in gender wage inequality, contributing the most to the contraction in the overall intra-industrial component. As for the role of industrial segregation (inter-industry component), finance sector is emerging as the sector in which women’s representation has increased in recent years, one of the privatesector developments potentially underlying the observed reduction in industrial segregation. Inter-industry component has also decreased in the education sector, although in that case the reduction reflects the growing (rather than diminishing) degree of gender segregation in already the most gender-segregated sector in the Georgian economy. Hence, in the case of the education sector the wage changes within this industry combined with the higher degree of gender segregation have jointly contributed to the contraction in the gap. Given the growing role of the private sector in education, these findings shed more light on the ways in which the private sector has contributed to the gender wage gap contractions in Georgia.

What portion of the gender wage inequality within sectors (intra-industrial component) is due to the gender differences in observed characteristics within industries and how has the role of these gender differences changed over time? We find that, unlike the public/private breakdown, in which about half of the gap within each sector (intra-sector component) could be explained by differences in the characteristics of men and women, a very small proportion of the intra-industrial component can be explained by differences in the characteristics (Figure 6). On average less than 10 percent of the intra-industrial component between 2004 and 2016 is due to observed factors, and in some years the composition effect is in fact negative (suggesting that women receive lower wages despite having stronger characteristics within 18
Table 4. Impacts of Refugee Camps on Land Use Outcomes

||Dense-Op|en Forests|Land C<br>Grass|over<br>land|Crop|lands|
|---|---|---|---|---|---|---|
||_Km_|Sin*|_Km_|Sin*|_Km_|Sin*|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_A. Main Specifcation_|||||||
|SID_×_I(Post 2017)|-0.008***|-0.007***|0.008***|0.007***|0.005***|0.004***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|FDR q-values|[0.001]|[0.001]|[0.001]|[0.001]|[0.001]|[0.001]|
|R-squared|0.650|0.657|0.724|0.732|0.960|0.959|
|Observations|47,840|47,840|47,840|47,840|47,840|47,840|
|_B. Controlling for differ_|_ential linear_|_pre-trends_|||||
|SID_×_I(Post 2017)|-0.008***|-0.007***|0.010***|0.009***|0.006***|0.005***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|FDR q-values|[0.001]|[0.001]|[0.001]|[0.001]|[0.001]|[0.001]|
|R-squared|0.650|0.657|0.725|0.732|0.960|0.959|
|Observations|47,840|47,840|47,840|47,840|47,840|47,840|
|Mean Dep. Variable|0.035|0.033|0.089|0.085|0.460|0.414|
|_Controls (All Panels)_|||||||
|Grid FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Panel A presents the results of our main specification, as outlined in equation 1. Panel B shows the results for the non-inferiority test proposed by Bilinski and Hatfield 2018. It shows the results of the main specification in equation, controlling for an indicator variable of differential linear pre-trends between the treatment and control groups. SID denotes standardized values of the inverse distance from each grid to the refugee camps, and I(Post2017) is an indicator variable with a value of 1 post-2017, marking the onset of the substantial Rohingya inflows to Bangladesh. Dependent variables are constructed using the MODIS Land Cover Type dataset, which captures Earth’s land surface imagery every 1 to 2 days, providing measurements of area in square km by land use type. (i) Dense-open forests refer to areas with dense canopy cover (¿40% tree cover), characterized by high values of vegetation indices such as the Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI), indicating vigorous and dense vegetation. (ii) Grassland refers to vegetated areas dominated by grasses, herbs, and other non-woody vegetation, typically with sparse to moderate tree cover or no trees present. (iii) Croplands refer to areas of land specifically designated or utilized for agricultural cultivation, including the cultivation of crops such as grains, vegetables, fruits, and other agricultural products. (Sin*) represents the inverse hyperbolic sine transformation, interpretable as a logarithm transformation. The estimates account for grid and year fixed effects, with clustered standard errors reported at the grid level in parentheses. False Discovery Rate (FDR) q-values are reported in brackets. Significance levels are denoted as follows:<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

45