rafmacalaba/datause-encoder-data huggingface dataset:
International Country Risk Guide (ICRG) produced by the country-risk rating agency Political Risk Services Group. This variable has broader scope of coverage than the previous one in that it catches a broader notion of compliance with legal provisions. This index has previously been used by LLSV (1998), among others, as a proxy for the quality of the legal system and the enforcement on legal contracts.

We use the average of the monthly index for the year 2000. The scale of the index ranges from 0 to 6, with higher scores for more tradition for law and order. As potential instruments for our judicial efficiency variables we use the legal origin variables ORIGIN first considered by LLSV (1998). We expand their list of countries and update the legal origins for a number of transition countries using the World Bank’s Doing Business Database.

Several control variables will be considered to account for countries’ macroeconomic and structural features and for the average level of bank efficiency. First, we control for the rate of inflation INFL using data from the IMF’s International Financial Statistics (IFS) database. The rationale for introducing inflation among the determinants of bank lending spread needs to be related to the fact that, in equilibrium, net interest income should be such as to provide a constant real rate of return on banks’ capital and therefore should be increasing in the level of inflation. Second, we control for a number of bank regulatory characteristics using the Barth, Caprio, and Levine (2001) database, such as whether the country imposes liquidity requirements, whether banks are allowed to engage in non-bank financial activities, and on the degree of entry into banking. All these features may affect the degree of competitiveness prevailing in the banking and financial markets or introduce quasi-fiscal elements that may affect the level 10
**_Section 4_**

**_Potentialproblems associated with free entry_**

An example of the value of exclusivity clauses is given by Hong Kong. In 1998 the Hong Kong government paid US$866 million (HK$6.7 billion) to Hong Kong Telecom to buy out the last eight years of its exclusive international license.<sup>5 2</sup> It is important to unravel why exclusivity is granted. In many cases it is to preserve cross-subsidies. This was the case in South Africa telecommunications for example, and it may have been a factor in Buenos Aires water.

If the private investor is not required to preserve cross subsidies, the main benefit of exclusivity is to remove the demand risk created by competitive entry. As discussed in Section 4.1, there may also be occasions in which it can increase efficiency by reducing the risk of inefficient duplication of facilities.

# **4.3.1 Circumstances in which exclusivity may be important**

If a government is intent on maintaining cross subsidies (and will not adopt a competitively neutral method for cross subsidisation as outlined in Section 4.2 above), then it is very likely that an exclusivity clause will be required.

If exclusivity is granted to remove demand risk, the value of the exclusivity clause will vary across industries. We distinguish between these three cases:

- competitive entry is unlikely, such as in water distribution (with no cross subsidy requirements);

- competitive entry is likely, for example in bus services or cellular phones, and consequently exclusivity is valuable to the incumbent; and

- e competitive entry will only result from a positive decision by Government. For example planning approvals and land acquisitions for urban rail, toll roads or airports, are often so complex that new entrants require special legal support from Government.

In the first case, where exclusivity has a very low value, not granting exclusivity should not affect the willingness of the private sector to invest.

In the second case where exclusivity is valuable, it is also very also costly to the economy as a whole. Governments need to weigh up whether the benefits of private participation outweigh the costs of the hidden subsidy, and also whether exclusivity is the least cost way to deliver the subsidy. Often there will be better options, such as accepting a lower sale price for the asset.

In the third case, the Government has private information about its likely future action which is highly valuable to an investor. A toll road to the airport may be worth

> 52 31 March 1998 Reuter News Service - Far East Reuter Textline (Q2:60). March 1998 Exchange rate US$1:IK$:7.74 as at March 1998.

**_48_**
in the case of high quality arabicas, were often at a considerable discount (estimated at 30-50%) to ICO prices. It was subsequently agreed that any new agreement would use "universal quotas'".

On the producers' side, despite the recognition that the ICoAs had generally achieved a combination of high and stable prices, there was disagreement over the division of the spoils. The "other milds" group of producers perceived the existing ICoAs as acting primarily for the benefit of Brazil, which produces robustas and the lower quality unwashed arabicas, and the African robusta producers. Rotemberg and Saloner (1989) have emphasized the role that stocks can play in strategic behaviour between cartel members. This possibility was dramatically illustrated by the (not altogether successful) attempt by Saudi Arabia to discipline the other OPEC members in 1986, and although this threat was never implemented in the ICoA, in the early agreements Brazil had the capability of doing this. However, as the result of depletion of her stock levels after the 1975 frost and the 1985-86 drought, Brazil's dominant position as coffee stockholder was seriously weakened by the late eighties, This is illustrated in Figure 4 which graphs Brazil's share of total gross producer stocks at the October start of the coffee years 1967-92.16 Perceiving this diminution of Brazil's implied threat to disrupt the coffee market if her quota were not maintained, the other milds group were only prepared to agree to a 5th ICoA if the quota allocation were reallocated in their favor.

Practice under the first four ICoAs was that the allocation of quotas was the responsibility of the Council of the ICoA, which was required to take into account a number of factors including the market position. In practice, the quota allocation had only moved marginally in favor of the other milds producers during the 4th ICoA - the total quota for Colombian and other milds stood at 44.3 % when quotas were suspended in July 1989 against 43.1 % nine months earlier as the result of a redistribution of part of the Angolan quota. This redistribution, which also increased the Brazilian quota, did little to address the perceived pro-Brazilian bias in the 16 Source: International Coffee Organization, _Supply of Coffee,_ (ICO, London), various issues.

20
# **8 Tables and Figures**

Table 1: Summary Statistics and Balance Tests of Sample Characteristics

||(1)<br>|(2)<br>Fu|(3)<br>ll Sample|(4)<br>|(5)|(6)<br>WhatsApp|(7)<br>Phone|(8)<br>P-val.Dif.<br>|
|---|---|---|---|---|---|---|---|---|
|Variable|Obs.|Mean|St.Dev.|Min|Max|Mean|Mean|(6)vs(7)|
|_Panel A: Characteristics of full sample_|||||||||
|Age (years)|597|31.22|6.65|16|45|31.24|31.21|(0.960)|
|Female (%)|599|0.52|0.50|0|1|0.572|0.473|(0.016)**|
|Oldest child age (years)|599|4.56|2.70|0|13|4.602|4.517|(0.699)|
|Shares phone (%)|599|0.23|0.42|0|1|0.234|0.220|(0.681)|
|Has Tigo Money (%)|599|0.30|0.46|0|1|0.291|0.300|(0.809)|
|Completed baseline survey (%)|599|0.60|0.49|0|1|0.589|0.607|(0.653)|
|Completed endline survey (%)|599|0.44|0.50|0|1|0.431|0.453|(0.590)|
|Observations||||||299|300||
|_Panel B. Characteristics of subsample_|||||||||
|Education level|||||||||
|Basic (1-9 grades, %)|358|0.21|0.41|0|1|0.239|0.176|(0.143)|
|High school (10-12 grades, %)|358|0.43|0.50|0|1|0.426|0.429|(0.963)|
|Bachelor or higher (%)|358|0.37|0.48|0|1|0.335|0.396|(0.237)|
|Employment status pre and post pandemic|||||||||
|Always unemployed (%)|358|0.20|0.40|0|1|0.190|0.203|(0.746)|
|Always employed (%)|358|0.49|0.50|0|1|0.449|0.522|(0.165)|
|Lost job (%)|358|0.08|0.28|0|1|0.098|0.071|(0.370)|
|Foundjob(%)|358|0.23|0.42|0|1|0.264|0.203|(0.175)|
|Observations||||||176|182||

_Notes:_ This table shows descriptive statistics of samples’ characteristics in columns (1)-(5). Balance tests comparing mean characteristics of control and treatment groups and p-value for the difference in means are presented in columns (6)-(8). Panel A presents descriptive statistics using the full sample of the experiment and Panel B shows descriptive statistics only for the sub-sample of participants that completed baseline survey. Description of each variable is presented in table A10 in the Appendix. “WhatsApp” refers to the self-completion survey treatment and “CATI” to the phone-based survey. Robust standard errors are in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

29
# **Table 2: Activities of Children Aged 6-15**

_limited to children in rural households in panel communities_

||Female<br>1993||1998||Male<br>1993||1998||
|---|---|---|---|---|---|---|---|---|
||-1<br>mean|-2<br>s.e.|-3<br>mean|-4<br>s.e.|-5<br>mean|-6<br>s.e.|-7<br>mean|-8<br>s.e.|
|Participation in Last Seven Days (%)|||||||||
|Wage Work|2.2|0.5|1.4|0.3|1.9|0.4|1.1|0.3|
|Agriculture|1.5|0.4|0.9|0.3|1.4|0.3|0.7|0.2|
|Non-Agricultural activities|0.7|0.2|0.5|0.2|0.6|0.2|0.5|0.2|
|Within Household Work|69.0|1.4|64.3|1.9|60.8|1.7|56.6|2.2|
|Agriculture|30.8|2.0|24.1|2.1|29.3|2.0|23.6|2.4|
|Household Enterprise|4.9|0.9|2.8|0.7|3.5|0.6|2.8|0.6|
|Household Production|62.6|1.4|62.7|1.9|47.7|1.7|50.6|2.3|
|Market Work|35.8|1.8|27.0|2.0|33.4|1.9|26.1|2.4|
|Any Work|69.4|1.4|64.6|1.9|61.9|1.7|57.2|2.2|
|Active|63.7|1.5|52.0|2.3|56.3|1.7|45.0|2.5|
|Hours Worked per Week|||||||||
|Wage Work|0.46|0.10|0.34|0.08|0.44|0.11|0.28|0.06|
|Agriculture|0.33|0.09|0.22|0.07|0.34|0.09|0.20|0.05|
|Nonagricultural activities|0.14|0.05|0.11|0.04|0.09|0.06|0.09|0.03|
|Within Household Work|14.58|0.59|10.63|0.59|11.22|0.56|8.47|0.55|
|Agriculture|6.17|0.50|4.51|0.47|6.08|0.49|4.35|0.42|
|Household Enterprise|0.85|0.23|0.46|0.12|0.41|0.09|0.23|0.05|
|Household Production|7.59|0.25|5.65|0.26|4.73|0.26|3.88|0.27|
|Market Work|7.48|0.50|5.31|0.45|6.94|0.47|4.86|0.41|
|Total Hours|15.05|0.60|10.98|0.58|11.67|0.55|8.75|0.53|
|Sample Size|2370||2287||2481||2456||

All means are weighted to reflect sample design and standard errors are corrected for PSU clustering. Market work includes work for wages, work in agriculture within the household, and work in household enterprise. Because people can paricipate in more than one activity the subcotegories of various participation variables do not necessarily add up to participation in wage work, within household work, and overall(any) work. Active is defined as participating in market work or participating in household production for at least 7 hours in the last week.
# **Table S6b: Reduced forms for Table S6a**

||Base<br>estimation<br>(Column 2,<br>Table 3)<br>|Excluding<br>exports<br>from Brazil<br>|Excluding<br>exports<br>from China<br>|Excluding<br>electrical<br>machinery<br>|Using a<br>2%/98%<br>winsorized<br>sample<br>|Using ISIC<br>2-digit level<br>industry<br>groups<br>|<br>Kerr (2008)<br>sample<br>using Imp-<br>ISIC2-Year<br>fixed effects<br>|<br>Kerr (2008)<br>sample<br>using Imp-<br>ISIC3-Year<br>fixed effects<br>|Using<br>exporter-<br>level<br>clustering<br>|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||The<br>P|dependent var<br>anel A: Weig|iable is Δ log<br>hting bilatera|bilateral exp<br>l routes by th|orts on the in<br>e interaction|tensive margi<br>of exporter a|n by exporter<br>nd importer tr|-importer-ind<br>ade in industr|ustry<br>y|
|Δ Log estimator for tech|0.938***|0.956***|0.698***|0.748***|1.044***|0.597|0.948***|0.794**|0.938**|
|flows from the United States|(0.298)|(0.299)|(0.220)|(0.288)|(0.304)|(0.387)|(0.265)|(0.325)|(0.434)|
|Observations|103,839|103,010|101,221|97,326|103,839|51,483|23,345|23,345|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|||||Panel B: E|xcluding sam|ple weights||||
|Δ Log estimator for tech|0.648***|0.664***|0.602***|0.545***|0.764***|0.259*|0.583***|-0.079|0.648***|
|flows from the United States|(0.112)|(0.112)|(0.110)|(0.110)|(0.123)|(0.141)|(0.180)|(0.200)|(0.177)|
|Observations|103,839|103,010|101,221|97,326|103,839|51,483|23,345|23,345|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: See Table 3.
# Motoshige Itoh and Shujiro Urata University of Tokyo and Waseda University

# 1. Introduction and Overview

This paper has two main object;ves. The first is to analyze how Japan's small and medium enterprises ISMEs) developed over time, focusing on their acquisition of technological capability, marketing capability, and financial resources. The second is to examine the public sector's role in assisting SMEs to acquire these technological and marketing capabilities and financial resources.<sup>1</sup> We began by looking through published statistics and studies to analyze past SME performance and support policies. Then, between July and December 1992, we conducted intensive interviews with SMEs in three target industries based on common survey questions. At the national level, we interviewed officials from MITI, the Small and Medium Enterprise Agency under MITI, and public financial institutions. We also interviewed officials from local govemment, chambers of commerce, and industry associations. These discussions continued throughout the period of our research, in order to deepen our understanding of the industries involved and cross-check our findings.

- The three industries selected for this analysis the silverware industry, the synthetic fiber-weaving industry,, and the auto parts industry -- represent three distinct prevalent types of industrial organization in Japan. Despite differences in industrial organization, the industries share several common characteristics. First, each is geographically concentrated. This characteristic is important not only because SMEs in the same industry are often focused on a particular geographic area but also because SME policies are often applied by region. The following industry-region pairs were selected for our analysis: silverware-Tsubame, synthetic fiber-weaving-Fukui-Ishikawa, and auto parts-Ohta. A second similarity is that each industry has a close subcontracting relationship with parent firms or trading houses. As we shall see, close relationships of this kind are a pervasive feature of Japanese SMEs. The third common

'A large number of studies on SMEs in Japan have bean published in Japanese, but only a limited number have been published in English. See, for example, Kaneda (1980), Yokokura (1988), and Berry and Mazumdar (1991).

1
# **1. Introduction**

The shock associated with the COVID-19 pandemic has been large and persistent. As documented in Apedo-Amah et al. (2020) and Cirera et al. (2021), the private sector has experienced a large, persistent negative impact on sales across all regions of the world. Most firms had still not recovered the levels of sales in 2019 more than a year after the beginning of the pandemic (Cirera et al., 2021) and some sectors are experiencing structural changes in the way products or services are produced and demanded. These changes and the associated reallocation of resources have already been identified in the data from the United States (Barrero et al., 2021).

A critical question for policy is how these changes will affect firm growth and aggregate productivity. Are surviving firms coming out stronger from the pandemic? The answer to this question depends on many different channels that affect productivity, which in many cases are in opposing directions. For example, Harris and Moffat (2021) implemented a survey of UK firms and found that, on the one hand, 18% of firms have stopped and 45% reduced doing R&D but, on the other hand, 40% of firms have increased ICT investments. Andrews et al. (2021) find a positive reallocation effect for three OECD countries, mainly driven by technology readiness to face the shock and the additional investments in technology. This paper explores firms’ digitalization trends during the COVID-19 pandemic using a novel firm-level data set of 57 countries across three periods of time since the start of the pandemic. The paper identifies the digitalization effects on firms’ resilience, differences in adoption, and changes in market share distribution.

There are at least two forces associated with the pandemic, in opposite directions, driving the decision of businesses to invest in and expand the use of digital technologies. On the one hand, the reduction of economic activity and the increased uncertainty may reduce the overall willingness to invest. On the other hand, because a key source of the pandemic restrictions is associated with a lack of mobility of consumers and workers, digital technologies have the potential to minimize disruptions by facilitating home-based work and online sales<sup>1</sup> . So far, the evidence from different sources suggests that the increased demand for the use of digital technologies is prevailing. Highfrequency indicators suggest a significant jump in the demand for online sales. For example, Google trend indices for the expression “online shopping” in different languages suggest that interest in the use of digital technology reached a historical peak at earlier stages of the pandemic (Figure 1).

> 1For example Apedo-Amah et al. (2020) shows how digital sales channels helped firms coping with the first wave of the pandemic, and Kim (2020) shows how consumers preferences changed with the pandemic, allowing firms that digitalized to access a larger market

2
whether the husband sends his remittances to his parents or his wife, the reverse can also occur.

_A 28-year old woman from Shyauli Bazaar in Gorkha district looks after her household and farm. Her husband is working in Dubai and sends his earnings to her. Though she lives in an extended family with her two children, grandmother, and father and mother-in-law, these days she is also the acting head of her household. Her grandmother and mother-in-law are not well, so she has taken charge of all household and farm responsibilities. Her father-in-law helps with the household chores but leaves most of the decision-making to her. Though her family and productive demands are many, she has become involved in community groups and is the treasurer of one savings and credit program_ (World Bank 2006) _._ In this paper, we develop a theoretical model in which a household decides to send a male migrant to work and whether the female participates in market activities. This model motivates our empirical strategy. Using data from the 2004 nationally representative survey of Nepali households, we applied the Full Information Maximum Likelihood method to estimate the effect of migration of men on market work participation (MWP) by the women left behind. The method took into account unobserved household characteristics that could simultaneously affect migration and MWP decisions. Our results indicate that male migration has a negative impact on the level of MWP by the women left behind.

The current paper contributes to the existing literature on the effects of migration and remittances in three important ways. First, to the best of our knowledge, this analysis is the first attempt to estimate the impact of remittances and migration on the labor market behavior of household members of sending households in Nepal. Second, in this literature, we used a new methodology—an econometric technique that controls for various forms of endogeneity and selection biases arising in our model. Finally, our results highlight the important gender dimension of the predominantly male worker migration on the well-being of sending households.

The rest of the paper is organized as follows: Section 2 describes data and defines the main constructed variables, Section 3 presents the descriptive results, and Section 4 4
Policy Research Working Paper 6854

# **Abstract**

Using data from 144 village-level governments in India’s Tamil Nadu state, this paper investigates political reservations for women and whether the gender of village government leaders influences the provision of village public goods. A knowledge test of village government presidents and a survey about the interaction between village presidents and higher-level officials reveal that female village government presidents have much lower knowledge of the village government system than do their male counterparts and have significantly less contact with higher-level government officials. Although male and female presidents provide similar amounts of some public goods, there is strong evidence that village governments led by a woman built fewer schools and roads—two public goods that require relatively more contact and coordination with higher-level officials.

This paper is a product of the Partnerships, Capacity Building Unit, Development Economics Vice Presidency. 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://econ.worldbank.org. The authors may be contacted at kgajwani@fas.harvard.edu.

_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
fiscal balance is the main indicator distinguishing both countries. Is it possible that this variable captures the essence of the difference between the two cases?

**Table 1 Summary of Public Debt Indicators in Argentina and Brazil 2000-2001**

||**Arge**<br>|**ntina**<br>|**Br**<br>|**azil**<br>|
|---|---|---|---|---|
||2000|2001|2000|2001|
|PublicDebt (% ofGDP)|50.0|62.0|49.6|53.3|
|Interest payments (% ofGDP)|4.1|5.4|7.2|7.3|
|Interest/ tax revenue (%)|22.7|30.9|30.9|29.8|
|Interest/currentrevenue (%)|16.6|22.9|18.0|16.5|
|Overall fiscalbalance (% ofGDP)|-3.6|-6.8|-3.6|-3.6|
|Primaryfiscalbalance (% ofGDP)|0.4|-1.4|3.6|3.8|

Source: Source: World Bank staff calculations based on official data for Brazil, Bacen and for Argentina, Minsterio de Economia.

14. To answer this question, it is necessary to examine in retrospect the relationship between primary balances and debt levels in both countries. While primary balances were similar until 1998, they diverged from 1999 onwards as Figure 1 shows. When debt ratios increased in 1999, the primary balance registered a significant surplus signaling Brazil’s commitment to adjustment. Statistical evidence supports the hypothesis that primary balances acted as signaling tools in Brazil (Herrera, 2002) and in other European countries that successfully overcame credibility problems (Drudi and Prati, 2000) unlike in Argentina’s case.

**Figure 1 Primary Balances and Debt Ratios in Argentina and Brazil 1995 – 2001 (% of GDP)**

<!-- Start of picture text --> Debt to GDP Ratios Primary Fiscal Balance<br>65 4<br>60 3<br>Argentina Brazil<br>55<br>2<br>50<br>1<br>45<br>0<br>40<br>35 -1 Argentina<br>Brazil<br>30 -2<br>1995 1996 1997 1998 1999 2000 2001 1995 1996 1997 1998 1999 2000 2001<br><!-- End of picture text --> Source: Herrera (2002) 7