rafmacalaba/datause-encoder-data huggingface dataset:
Table F.4. Heterogeneous Effects on Social Capital by Social Desirability Score

||Positive<br>Reciprocity<br>Index|Negative<br>Reciprocity<br>Index|Altruism|Trust|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_β_1=I(Treat)_×_I(High SDS)|-0.054|-0.069|-0.000|0.043|
||(0.061)|(0.061)|(0.061)|(0.061)|
|_β_2=I(Treatment)|0.019|-0.023|-0.017|-0.036|
||(0.061)|(0.061)|(0.061)|(0.061)|
|SDS Diff. Effect=_β_1+_β_2|-0.035|-0.092|-0.017|0.007|
||(0.087)|(0.086)|(0.086)|(0.086)|
|R-squared|0.031|0.066|0.027|0.019|
|Observations|1,040|1,040|1,040|1,040|

_Notes:_ This table reports an OLS estimate interacting the treatment variable with the Social Desirability Score (SDS). The Social Desirability Score (SDS) is a measure of the individual’s propensity to report socially desirable answers. High SDS refers to having an above-median score among all participants. The variable in column (i) is an index constructed using the methodology of Kling et al. (2007) and the answer on a 1 to 5 scale of the approval of the statement: _when someone does me a favor, I am willing to return it_ , and the answer regarding the hypothetical money the respondent may give to a stranger as a thank-you for helping him on the street; (ii) is an index constructed using the methodology of Kling et al. (2007) and the reported answer on a 1 to 5 scale of the approval of the following statements: _How willing you are to punish someone who treats you unfairly, even when there are risks to you of personal consequences_ ; _How willing are you to punish someone who treats others unfairly, even when there are risks to you of personal consequences_ ; and _If I am treated very unfairly, I will take revenge on the first occasion, even if I have to pay a cost for it_ ; (iii) is the standardized reported answer on a 1 to 5 scale of the approval of the statement: _How willing are you to donate to charitable causes without expecting anything in return_ ; (iv) is the standardized reported answer on a 1 to 5 scale of the approval of the statement: _I always assume that people have only the best intentions_ . All the columns control for sex (female and male), two age groups (21–28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

91
1*811 10: ASSUMPT IONS 1989 1990 1991 1992 1993 1991.

1995 OMS YEAR; 1988 CLOSURE: RE

# A) COUDIIIOIiS OF NIEW DEST:

|Ni<br>GI<br>|1. IDA:<br>Nativity<br>Grace Period|30.00<br>5.00|30.00<br>5.00|30.00<br>5.00|30.00<br>5.00|30.00<br>5.00|30.00<br>5.00|30.003<br>5.00|
|---|---|---|---|---|---|---|---|---|
|gin|Interest<br>Rate|0.12|0.12|0.12|0.12y|0.12|0.71|0.12|
|TI|Time Profile<br>of Wiln|252|_25%_|_252_|2S2|02|02|02|
||2. GTHEE MULTILATERAL<br>COU|CESSIOUAL:|||||||
|_112_|maturity|38.00|38.00|38.00|38.00|38.00|38.00|31.00|
|62|<br>GracePeriod|10.00|10.00|10.00|10.00|10.00|10.00|10.00|
|*2n|Interest<br>Rate|1.1.02|1.1.02|1.1.02|1.10.0|1.1.02|1.1.02|1.1.02|
|_12_|lime Profile<br>of GWZn|_22_|92|_112_|222|192|132|102|
||3. IURD:<br>A) Noncomcessional||||||||
||Mediun and Slow Disb. loa|ns|||||||
|131S|Maturity|17.00|17.00|17.00|17.00|17.00|17.00|17.00|
|GiNS<br>|<br>Grace Period<br>|4..00|4.00|1..00|4.00|4.00|4.00|4.00|
|ailsa|Interest<br>**Rate**|7.12|7.71|7.12|7.12|7.12|7.12|7.72|
|Ti11S|Time Profile<br>of GD3NSn|12|192|_212_|_112_|132|12|42|
||lastDilxseurent||||||||
|N3NF|maturity|_17.00_|17.00|17.00|17.00|17.00|17.00|17.00|
|G311l|Grace Period|4.00|4.00|4.00|4.00|4.00|4.00|4.00|
|RuI1n|Interest Rate|7.12|7.12|_7.712_|7.12|7.12|7.12|7.12|
|1311F|**Time**Profile<br>ofG03NFn|332|332|332|ox|02|02|02|
||Concessionai||||||||
|_NuC_|maturity|_17.00_|17.00|_17.00_|17.00|17.00|17.00|17.00|
|G3C|<br>Grace Period|4.00|4.00|4.00|4.00|4.00|4.00|4.00|
|W3n|Interest Rate|0.12|0.12|0.12|_0.12_|_0.12_|0.72|0.12|
|isc|lime Profile of GD3Cn|51.2|102|112|92|12|32|22|
Table 5: Technology adoption is associated with access to knowledge and information

|**VARIABLES**|**GBF Ext**|**GBF Int**|**SBF Ext**|**SBF Int**|
|---|---|---|---|---|
|Loan for Machine|0.024|-0.014|0.049|0.028|
||(0.023)<br>|(0.014)|(0.035)<br>|(0.029)<br>|
|Similar Products in 50km|0.039*|0.010|0.093**|0.056*|
||(0.023)|(0.016)|(0.039)|(0.032)|
|Supplier and Buyers in 50km|0.049**|0.008|0.042|-0.002|
||(0.023)|(0.015)|(0.044)|(0.034)|
|Supplier or Buyer MNCs|0.076***|0.033|0.060|0.072|
||(0.027)|(0.020)|(0.052)|(0.045)|
|Manager Experience in Large Firms|0.038<br>(0.024)|0.040**<br>(0.015)|0.081*<br>(0.042)|0.049<br>(0.036)|
|Use of External Consultant|0.093***|0.088***|0.081|0.083*|
||(0.028)|(0.024)|(0.049)|(0.043)|
|Beneft from Government Support|-0.071*|0.069**|-0.015|0.023|
||(0.038)|(0.033)|(0.060)|(0.056)|
|Manager with University or More|0.137***<br>(0.033)|0.113***<br>(0.026)|0.116**<br>(0.053)|0.101**<br>(0.043)|
|% of Worker with College|0.003***|0.003***|0.003**|0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|
|Ln (Employment 2018)|0.041***|0.050***|0.032*|0.027*|
||(0.011)|(0.009)|(0.017)|(0.016)|
|Informality|-0.133***<br>(0.030)|-0.124***<br>(0.024)|-0.122**<br>(0.055)|-0.049<br>(0.045)|
|Constant|0.461***<br>(0.056)|0.132***<br>(0.041)|0.649***<br>(0.093)|0.221***<br>(0.080)|
|Observations|1,111|1,111|677|674|
|R-squared|0.494|0.509|0.348|0.290|
|Sector FE|YES|YES|YES|YES|
|Region FE|YES|YES|YES|YES|

_Note:_ Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 35
Table 5: Loan-to-new toilet conversion

||Toilet o<br>Subsidy ine|wnership<br>ligible HHs|Toilet o<br>Subsidy el|wnership<br>igible HHs|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||OLS|IV|OLS|IV|
|_Second stage_|||||
|Sanitation loan uptake|-0.001|0.8595***|0.2478***|0.4462***|
||(0.1037)|(0.3322)|(0.0783)|(0.1657)|
|Strata FE|Yes|Yes|Yes|Yes|
|Interviewer FE|Yes|Yes|Yes|Yes|
|Household covariates|Yes|Yes|Yes|Yes|
|Ratio sample clients/GP size|Yes|Yes|Yes|Yes|
|_First stage_|||||
|SL - First stage||0.2288***||0.2040***|
|||(0.0633)||(0.0469)|
|F-stat||13.07||18.88|
|N|343|343|616|616|

_Notes_ : OLS = Ordinary Least Squares regression; IV = Instrumental Variable regression; First stage = OLS results for the first stage of the IV regression; Second Stage = OLS results for the second stage of the IV regression; SL stands for sanitation loan treatment arm. Sample restricted to matched client households not having a toilet at survey baseline. Standard errors clustered at the village level are shown in parentheses. *, **, *** indicate significance at the 10, 5 and 1 percent level. Dependent variable: Toilet ownership as observed by interviewer observation during endline survey in August-Sept 2017; Covariates: Toilet ownership at baseline, indicator for presence of a child aged 0 - 2 at baseline, ratio of number of sampled clients to village size, indicator for whether there is any uncertainty around the matching quality. Strata and interviewer included.

Figure 3: Toilet coverage over time <!-- Start of picture text --> All<br>2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Date of toilet construction<br>Control - nonVG Treatment - nonVG<br>Control - VG Treatment - VG<br>N = 1,274; Outlier toilet ownership observations prior to 2010 are dropped from the sample;<br>Date of toilet construction is obtained through respondent recall report during endline household surve<br>.6<br>.4<br>.2<br>% of client households with toilet<br>0<br><!-- End of picture text --> 33
Policy Research Working Paper 9434

# **Abstract**

This paper provides a comprehensive assessment of the short-term impact of the COVID-19 pandemic on businesses worldwide with a focus on developing countries. The results are based on a novel data set collected by the World Bank Group and several partner institutions in 51 countries covering more than 100,000 businesses. The paper provides several stylized facts. First, the COVID-19 shock has been severe and widespread across firms, with persistent negative impact on sales. Second, the employment adjustment has operated mostly along the intensive margin (that is leave of absence and reduction in hours), with a small share of firms laying off workers. Third, smaller firms are disproportionately facing greater financial constraints. Fourth, firms are increasingly relying on digital solutions as a response to the shock. Fifth, there is great uncertainty about the future, especially among firms that have experienced a larger drop in sales, which is associated with job losses. These findings provide a better understanding of the magnitude and distribution of the shock, the main channels affecting businesses, and how firms are adjusting. The paper concludes by discussing some avenues for future research.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at Liacovone@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Table A2: Effects of a Workerʹs Unexpected Illness on the Labor Supply of Other Household Members, Based on Characteristics of the Absent Worker**

|Dependent Variable|1(Work)|Days worked|Hours worked|
|---|---|---|---|
|_Panel A: Absent Work is Ill Him/Herself vs Caretaking_||||
|Other worker ill him/herself|0.077**|0.344*|4.299**|
||[0.037]|[0.236]|[2.227]|
|Other worker caretaking|0.192***|0.896|8.808|
||[0.060]|[0.671]|[6.827]|
|Female X Other worker ill him/herself|‐0.304**|‐1.422*|‐11.745*|
||[0.134]|[0.908]|[7.885]|
|Female X Other worker caretaking|0.192|1.026|6.640|
||[0.142]|[0.925]|[8.323]|
|P‐value for equal responses: Males|0.120|0.400|0.482|
|P‐value for equal responses: Females|0.101|0.170|0.209|
|Mean dependent variable|0.807|4.279|37.660|
|Observations|3,951|3,948|3,948|
|R‐squared|0.507|||
|_Panel B: Gender of Ill Worker_||||
|Other worker ill|0.037|0.325|3.468|
||[0.063]|[0.461]|[3.776]|
|Other worker ill X That worker is female|‐0.039|‐0.231|‐4.297|
||[0.079]|[0.572]|[5.539]|
|Female X Other worker ill|‐0.077|‐0.556|‐7.764|
||[0.111]|[0.558]|[4.991]|
|Female X Other worker ill X That worker is female|‐0.074|0.094|3.005|
||[0.122]|[0.672]|[6.592]|
|P‐value for equal responses: Males|0.795|0.825|0.840|
|P‐value for equal responses: Females|0.962|0.655|0.294|
|Mean dependent variable|0.807|4.279|37.660|
|Observations|3,951|3,948|3,948|
|R‐squared|0.507|||
# I. Introduction

International evidence shows that total factor productivity growth accounts for most of the income and growth differences across countries. Research shows that both physical and human capital accumulation are persistent processes, while growth is volatile and varies widely across countries (Easterly and Levine, 2001). Hence, identifying the determinants of productivity is essential to understanding growth.

Most of the productivity analysis in Nigeria is based on aggregate macroeconomic data and points to four main findings: 1) total factor productivity increased in the period 2000-2014; 2) growth in Nigeria is negative in periods of fast capital accumulation (Sala-i-Martin, et. al. 2012; Lennon, 2016: World Bank, 2017); 3) periods of faster positive productivity growth can be associated with episodes of market-oriented reform and development of more inclusive political institutions (World Bank, 2017); the 4) the most important determinants of labor productivity growth are investment and education of the labor force (Lennon, 2016).

Yet, aggregate macroeconomic figures do not capture heterogeneity of productivity across sectors or firms. Despite the importance and need of disaggregating the analysis of productivity, firmlevel productivity has been an under-researched topic in Nigeria. However, the World Bank’s Enterprise Survey (ES) is a useful source of both quantitative and qualitative information of firms in Nigeria. Between 2007 and 2014, the Bank performed three ES for the manufacturing and services sectors. The ES contains quantitative and qualitative information of over 7,500 firms. The survey contains balance sheet information as well as production details on output (sales) and factor utilization. It also collects information on variables related to how infrastructure affects the firm, on the firms’ relationship with government officials, finance and corporate governance, labor skills, and other topics. However, evidence on the productivity of Nigerian firms is sparse and limited to a descriptive analysis of the investment climate (McKinsey Global Institute 2014; World Bank 2008, 2011, 2015, 2016).

The objective of this paper is to examine the impact of the investment climate and other factors on Nigerian enterprise productivity. We consider four main dimensions of investment climate that are insecurity, bribe or informal payment to get things done, the amount of time that management spends dealing with government regulation, and the costs of power outage. Besides the investment climate, other factors that can determine firm productivity are its size, the quality of the workforce, the availability of credit, and the sector of operation.

The remainder of the paper is structured as follows. Section II describes the data and main stylized facts of the production, productivity, resource utilization, and investment climate in Nigeria. Section III presents the empirical strategy and shows econometric results. Section IV concludes.

2
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23
# **II. Secondary Education in Indonesia**

The secondary education system in Indonesia is divided into junior and senior high school, each taking three years to complete. The country has two different school systems, secular and Islamic, and in this paper we focus exclusively on the former.<sup>6</sup> In the secular school system, children graduating from junior high school must choose whether to enroll in a vocational or general high school.<sup>7</sup> These school types are distinct, and only a small portion of the curriculum used by these school types overlap, mostly with regards to subjects such as English and Indonesian. In addition, general high schools do not usually offer vocational subjects, such as carpentry or machinery.

With regards to specialization, the general stream offers three majors: natural science, social science, and language. On the other hand, the vocational stream offers many vocations. A vocational school usually focuses on just one or two majors. The available majors are business management; technical, which includes machinery and information technology; agriculture and forestry; community welfare; tourism; arts and handicraft; health; and marine studies. In addition, there are very specialized vocational high schools that focus on aviation and shipbuilding. Of all of these choices, the first two, business management and technical, are the most popular.<sup>8</sup> The public cost of vocational education is at least as high as general education. As shown in Figure 1, Ghozali (2006) finds that a public vocational student is 28% more costly for the government to educate, annually, than a public general student.<sup>9</sup> Meanwhile, the amount of per student public funds spent in private schools is lower, and private vocational schools receive the same amount of public funds as private general schools. With regards to household out of pocket costs, meanwhile, private schools are more expensive than public schools. Comparing the four school types, households report that private general schools are the most expensive, followed by private and public vocational schools respectively, with public general schools being the least expensive.

> 6 In 2007, the National Socioeconomic Survey ( _Susenas_ ) shows that only 8.4% school-age children are enrolled in the Islamic system.

> 7 Better senior secondary schools also select applicants based on their test scores.

> 8 Information on vocational majors is taken from the National Labor Force Survey ( _Sakernas_ ). Unfortunately, the Indonesian Family Life Survey (IFLS), which is used for the analysis presented below, does not collect data on high school major.

> 9 Public cost is defined as the amount of government spending on each school type.

5
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19