rafmacalaba/datause-encoder-data huggingface dataset:
**Table** **<u>9: Disentangling the effects of closures on the labor market by workers’ answers</u>**

||(1)<br>Emp|(2)<br>Wage|(3)<br>W_days|(4)<br>W_days|(5)<br>W_days|(6)<br>W_hours|(7)<br>W_hours|(8)<br>W_hours|
|---|---|---|---|---|---|---|---|---|
|_CP_<sup>_30min_</sup>|-0.0038***|-0.0645***|-0.0253***|-0.0275***|-0.0253***|0.0438***|0.0392***|0.0435***|
||(0.001)|(0.014)|(0.009)|(0.009)|(0.009)|(0.011)|(0.011)|(0.011)|
|_REG_<sup>_30min_</sup>|-0.0002|0.0003|-0.0030|-0.0029|-0.0026|0.0006|0.0008|0.0004|
||(0.000)|(0.003)|(0.002)|(0.002)|(0.002)|(0.003)|(0.003)|(0.003)|
|Absent due to closure (%)|-0.0019**|0.0110***|-0.0028*|||-0.0123***|||
||(0.001)|(0.004)|(0.002)|||(0.003)|||
|Less than 35 hrs due to||||-0.0069***|||-0.0124***||
|closure (%)||||(0.001)|||(0.002)||
|Absent due to closure|||||-0.0635***<br>(0.023)||||
|Less than 35 hrs due to<br>||||||||-0.4137***|
|closure||||||||(0.027)|
|Observations|92,335|82,005|89,952|89,952|89,729|85,118|85,118|81,981|
|R-sq. within|0.110|0.034|0.021|0.022|0.022|0.011|0.012|0.024|
|Nr. of workers|44,172|41,278|43,152|43,152|43,067|42,414|42,414|41,723|

_Robust standard errors (Huber-White method) clustered at individual level in parentheses; *significant at 10%; **significant at 5%; ***significant at 1%. All regressions include individual fixed effects, time effects, sector-time effects and a set of controls including years of schooling, age, age squared, marital status, tenure, urban area / refugee camp residence dummies, a set of work place dummies, Hebron district-time effects, locality’s number of fatalities in the preceding quarter, locality’s length of the West Bank wall, the locality’s share of workers employed in Israel the year before and sector-year effects._ 43
borrower’s credit rating, other institutions will be asked to follow suit<sup>22</sup> they may be reluctant to change ratings to accurately reflect a borrower’s situation. This is especially true if their exposure to the client is significant and they don’t want other banks to shut off credit. Small banks may also be tempted to just follow the lead of large institutions in assigning borrowers ratings and limit or forego independent risk analysis which both detracts from competition in the market and also introduces additional risk if these smaller institutions are not performing due diligence on their own lending portfolios.

# **_4_** _._ **_Banking regulation_**

Credit registry information can also assist in the determination or refinement of the regulatory rules themselves. Many public credit registries are intimately tied to provisioning rules. PCR information can be used to analyze if current provisioning is adequate. In some cases, the very design of the PCR reflects the provisioning rules in place so there is an important feedback between rules, the information that is generated and the subsequent refinement of those rules. However, PCR information can be used more widely. In particular it may be employed to assess, not only provisioning, but also capital and also complementary regulations on concentration and related lending. In short, a PCR can be a valuable tool to understand the portfolio credit risks of either individual institutions or the whole financial system and assess the overall level of reserving (capital and provisioning) in relation to those risks.

In relation to regulations regarding capital adequacy, a common view is that forward-looking provisions should reflect expected losses and capital should reflect unexpected losses. These are simply two statistics from the same probability of loss distribution curve and hence if PCR data is useful for considering expected loss it is surely useful to analyze unexpected loss as well. The sum of expected and unexpected loss (up to some statistical confidence limit) is normally referred to as the Value at Risk. As reviewed in a companion paper in this project, countries may be able to build on PCR databases in order to transition to the more advanced approaches of Basel II that uses an estimate of a loan’s Value at Risk to calibrate capital requirements.<sup>23</sup>

> 22 Many PCRs require that borrower ratings be uniform across institutions or at least similar, such as differing by only one rating category.

> 23 See Basel Committee for Banking Studies (2003) and in particular the Internal Rating Based Approach.

22
84 public services **are** associated more with female than male productivity, and vice versa. But administrative records, and hence quantitative analys iS, rarely consider the gender of the recipient of a public service or for that matter the gender of the administrative staff who provides the service to the public It has been observed that public services that favor female productivity are given relatively low priority in m*Ot low income countries. This **may be** traced to our undervaluation of home production. For example, priority **is** often given to supporting cash crops, which are often managed by males, because of the commercial and governmental interests in these potential export crops that provide tax revenue. Regardless of why private markets fail to meet social needs, the economic issues of effectiveness and distribution of benefits tend to be hard to evaluate empirically. They involve measuring the substitutability between male and female labor in many production processes, the interaction of modern productive inputs and extension with male and female labor, and the functioning of interconnected factor markets as they impinge on the marginal product of both types of labor.

# Home Production and Nev Technologies

Women stand to gain from the development and spread of new technologies that improve their efficiency in home production. There is relatively little systematic investment by the public sector in low-income countries designed to improve labor productivity in the home, yet this is a large share of total production in these countries. Only a few international assistance agencies have encouraged research and development projects that seek small scale technologies that would enhance production possibilities in the home. Systematic evaluation of these undertakings **is** rarely attempted. Women in the lowest incone countries spend much tiLe fetching water and
(2000), _Dominican Republic_ (1997), _Rwanda_ (1998), _Senegal_ (1995), and _Viet Nam_ (1989). The classification of fragility and the date of exit are based broadly on the CPIA index. Appendix 1 summarizes the experience of each of these countries and the circumstances of their exit from fragility.

# **The selection of countries was based on four criteria** :

- _Sustainable exit_ from fragility, i.e., the countries continued to be classified as non-fragile for at least ten years after exit.

- Broad _geographical representation_ .

- _Diversity_ of fragility experience, i.e., the sample covers cases where fragility emerged from a combination of different circumstances, such as an economic crisis, breakdown of the political system, violent conflict, etc.

- _Complexity_ of fragility experience, i.e., fragility did not emerge _solely_ as the result of war, while beforehand the state was otherwise stable and effective.

The purpose of these criteria is to ensure that any lessons learned would have the broadest possible applicability. The rationale for the last criterion, in particular, is to exclude cases (e.g., Cyprus in the 1970s or Lebanon in the 1980s) with functioning states and financial systems that descended into fragility only because of war, displacement, or the arrival of UN peacekeepers. In cases like this, financial reforms and policies would play little or no role in the exit from fragility, and there would be no lessons to be drawn for financial sector policy makers elsewhere.

**Data and information for the financial sector policies and reforms around the time of exit from fragility are sourced from World Bank and IMF documents for these countries** . Financial sector reforms are often undertaken with support of the World Bank and the IMF. Even when they do not form a major part of their respective adjustment programs, these country documents provide fairly detailed information about these reforms and policies. The main source documents for each country are listed at the end of the paper.

# **This approach is subject to three caveats** .

- Although the sample of countries is as diverse as possible, it is still small. There are not many countries that (i) experienced fragility relatively recently (so that it is captured by the CPIA index, which is used for measurement and consistency) and (ii) meet the conditions for inclusion in the sample—especially the requirement to have remained stable for at least ten years after exit from fragility. The reader should be cautious about generalizing from the review of the experience of seven countries to all other cases of fragility.

- The World Bank and the IMF did not focus in depth on financial sectors in the 1980s and 1990s. Their policy advice and conditionality was concentrated on fiscal, monetary, and exchange rate policies—and, for the World Bank, poverty alleviation and other structural

9
14 Table 6: Funding Structure of AAA and A firms

|.<br>.|A|AA-Firms|||A Firms|I|
|---|---|---|---|---|---|---|
|SOURCE|1990|1991|1992|1990|1991|1992.|
|Working Capital Loans|192.8|115.7 |1|05.1|95.8|56.2|46.9|
|Investment Loans|35.8|49.3|54.7|25.4|18.4|19.6|
|Capitalized Interest|0.2|2.5|S.6|1.3|1.8|1X9|
|Tax Arrears|11.4|10.4|15.7|7.5|15.4|24.0|
|Interfirm Borrowine|-140-1|-77.8|-84.1|-30.0|8.2|7.6|
||* 100.0 I|<br>100.0|1100.|100.0}|I1000|100.0|

The next most important debt holders (in terms of seniority) were commercial banks (cf Table 6. the items Working Capital Loans. rnvestment Loans and Capitalized Interest). With such a dominant position on the firms7 liability side. commercial banks were clearly in a position to exercise control both direcdy and through their abilitv to grant or deny new loans. And finally cash-rich enterprises accumulated claims on cash-poor ones through interenterprise arrears (LEAs). In wha; follows we will investigate control group by control group the extent to which they played an active role in establishing enterprise governance."

- **=**

- Table 6 shows that AAA firms reduced working capital loans but also inter-firin lending (interfirm borrowing payab[Ls **minus** receivables). Capitalized interest goes up. but so do investment loans **(capitalization** was.permitted only for such loans). Tax arrears go up slightly, but not as **a** percentage of taxes due. Hence- good firms began monitoring accounts receivable. For A **firms.** the **fall** in working capital loans was accompanied by a switch from **being an** inter firm lender to an inter firm borrower and ax **arrears** increased direefold.
# **References**

Adelino, Manuel, Igor Cunha, and Miguel Ferreira, 2017, “The Economic Effects of Public Financing: Evidence from Municipal Bond Ratings Recalibration.” _The Review of Financial Studies_ , 30, 3223-3268.

Alesina, Alberto, Carlo Favero, and Francesco Giavazzi, 2019a, “Effects of Austerity: Expenditure- and Tax-based Approaches.” _Journal of Economic Perspectives_ , 33, 141-162.

Alesina, Alberto, Carlo Favero, and Francesco Giavazzi, 2019b, “Austerity: When It Works and When It Does Not.” _Princeton University Press_ .

Banerjee, Abhijit and Esther Duflo, 2014, “Do Firms Want to Borrow More? Testing Credit Constraints Using a Directed Lending Program.” _Review of Economic Studies_ , 81, 572-607.

Banerjee, A., Breza, E., Duflo, E. and Kinnan, C., 2019. Can microfinance unlock a poverty trap for some entrepreneurs? (No. w26346). National Bureau of Economic Research.

Banerjee, A.V. and Newman, A.F., 1993. Occupational choice and the process of development. Journal of political economy, 101(2), pp.274-298.

Beck, Thorsten, Asli Demirgüç-Kunt, and Vojislav Maksimovic, 2008, "Financing Patterns Around the World: Are Small Firms Different?” _Journal of Financial Economics_ , 89, 467-487.

Becker, Bo, and Victoria Ivashina, 2018, “Financial Repression in The European Sovereign Debt Crisis.” _Review of Finance, Forthcoming_ .

Belo, Frederico, Vito Gala, and Jun Li, 2013, “Government Spending, Political Cycles, and the Cross Section of Stock Returns.” _Journal of Financial Economics_ , 107, 305-324.

Blanchard, Olivier, 2019, "Public Debt and Low Interest Rates." _American Economic Review_ , 109, 1197-1229.

Braga, Breno, Diogo Guillen, and Benjamin Thompson, 2017, “Local Government Spending and Employment: Regression Discontinuity Evidence from Brazil” Available at SSRN: https://ssrn.com/abstract=2966187.

Broner, Fernando, Aitor Erce, Alberto Martin, and Jaume Ventura, 2014, “Sovereign Debt Markets in Turbulent Times: Creditor Discrimination and Crowding-out Effects” _Journal of Monetary Economics_ , 61, 114-142.

Buera, Francisco, Joseph Kaboski, Yongseok Shin, 2011, “Finance and Development: A Tale of Two Sectors” _American Economic Review_ , 101, 1964-2002.

Chakraborty, Indraneel, Itay Goldstein, and Andrew MacKinlay, 2018, “Housing Price Booms and Crowding-Out Effects in Bank Lending” _Review of Financial Studies_ , 7, 2806-2853.

Chodorow-Reich, Gabriel, 2019, “Geographic Cross-Sectional Fiscal Multipliers: What Have We Learned?” _American Economic Journal: Economic Policy_ , 11, 1-34.

35
- 10 -

# 111. Data and Vatriable Description

**The** data used in this paper are drawn from 85 districts of India for a period of 9 years beginning from 1972/73 to 1980/81. The number of observations vary depending on the data available for particular dependent variable. Thus, **765** observations (9 years x 85 districts) are used for the output supply and wage equations, 738 (9 year x 82 districts) observations for the fertilizer equation, 228 (3 years x 76 districts) observations f the investment equations, and only 170 (2 years x 85 districts)

observations for the farm and nonfarm employment equations. The investment data are computed frc livestock censuses of 1966, 1972, 1976 5nd 1982, while fertilizer, crop output, and wage data are from yearly fertilizer, wage, and agricultural statistics published by the Ministry of Agriculture of India. Crop output is the aggregate index of 20 crops using 1975/76 as the base year, fertilizer is measured in nutrient tons of nitrogen, phosphate and potash, and the wage rate is the daily wage rate of agricultural field workers. The investment variables are the net additions over each census interval to the stock of draft animals (male bullocks and male buffalos), milk animals (female bullocks and female buffalos), small stocks (sheep and goats) and pumps (both diseal and electric).<sup>3</sup> Employment data are dravn from the population censuses of 1970 and 1980 which are comparable with agricultural census years of 1971 and 1981. By employment we mean here the number of persons who were employed in farm or nonfarm activities for atleast 183 mandays in one year.

The government infrastructure variables include road length, regulated markets, primary school density, rural electrification and canal irrigation. All the infrastructure and dependent variables are normalized
# **Household Business Performance in Ghana: The Role of Personality Traits and Gender Role Attitudes**

Nkechi S. Owoo<sup>1</sup> , Akuffo Amankwah<sup>2</sup> , Pauline Castaing<sup>2</sup> , Amparo Palacios-Lopez<sup>2</sup> 1Department of Economics, University of Ghana; 2Living Standards Measurement Study, World Bank;

**Ke** **<mark>y</mark> words:** business <mark>p</mark> erformance <mark>;</mark> informal businesses <mark>; g</mark> ender norms <mark>; p</mark> ersonalit <mark>y</mark> traits; Ghana.

**WB Thematic Area:** Jobs Strategies; Gender and Economic Empowerment
# **Appendix B: Main results, without controls**

Table B1: Effect of Self-interviewing (SI) on Placebo Questions, no individual controls

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Farm work|Market visit|Maize/Rice|Meat|
||(past year)|(past week)|(next week)|(next week)|
|**Panel A. Malawi**|||||
|SI _×_ Screen Pass (_β_)|-0.01|0.08***|0.11***|0.11***|
||(0.01)|(0.03)|(0.03)|(0.03)|
|SI _×_ Non-Pass (_γ_)|-0.03|0.09**|-0.02|0.04|
||(0.02)|(0.04)|(0.04)|(0.04)|
|Screen Pass|0.02|0.07*|0.04|0.02|
||(0.02)|(0.03)|(0.04)|(0.04)|
|FTFI _×_ Non-Pass mean|0.93|0.43|0.52|0.28|
|_p_-value (_β_ =_γ_)|0.478|0.808|0.019|0.189|
|Observations|1,718|1,713|1,345|1,228|
|_Efect of SI for the average _|_respondent_||||
|Pooled SI efects|-0.02|0.08|0.06|0.09|
|_p_-value|0.150|0.001|0.020|0.001|
|**Panel B. Liberia**|||||
|SI _×_ Screen Pass (_β_)|0.01|0.07**|-0.03**|0.01|
||(0.03)|(0.03)|(0.01)|(0.03)|
|SI _×_ Non-Pass (_γ_)|0.04|0.11**|-0.06**|-0.03|
||(0.04)|(0.05)|(0.03)|(0.05)|
|Screen Pass|0.05|0.08**|0.03**|0.06|
||(0.04)|(0.04)|(0.02)|(0.04)|
|FTFI _×_ Non-Pass mean|0.77|0.61|0.95|0.65|
|_p_-value (_β_ =_γ_)|0.515|0.546|0.299|0.541|
|Observations|1,259|1,260|1,226|1,101|
|_Efect of SI for the average _|_respondent_||||
|Pooled SI efects|0.01|0.09|-0.04|-0.00|
|_p_-value|0.500|0.001|0.002|0.948|

Note: Regressions are at the respondent-question level. “Screen Pass” is defined by selecting “yes” to the first five questions in Table 2. Standard errors clustered at individual level in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively.

31
||Series code|Variable|Definition|Units|
|---|---|---|---|---|
|||Contracts with Incentives|and promotion of managers are determined<br>by their performance||
|||Governance: Penalties for<br>Poor Performance|Whether there are penalties for poor<br>performance of managers|1=yes, 0=no|
|||Governance: Monitoring|Whether there is a periodic formal monitoring<br>of managerial performance|1=yes, 0=no|
|||Governance: Third-party<br>Monitoring|Whether there is a performance monitoring<br>by an independent entity (private sector<br>auditor)|1=yes, 0=no|
|||Governance: Billing and<br>Collection|Whether the enterprise contracts out billing<br>and bill collection|1=yes, 0=no|
|urcing||Governance: Meter<br>Reading|Whether the enterprise contracts out meter<br>reading|1=yes, 0=no|
|Outso||Governance: Human<br>Resources (HR)|Whether the enterprise contracts out human<br>resources|1=yes, 0=no|
|||Governance: Information<br>Technology (IT)|Whether the enterprise contracts out<br>information technology services|1=yes, 0=no|
|||Governance: Restriction to<br>Dismiss Employees|Framework guiding restrictions to dismiss<br>employees|0=public service guidelines, 1=<br>corporate law, 2=performance<br>contracts|
|market discipline||Governance: Wages|Whether the enterprise’s wages are<br>comparable to those in the private and public<br>sector|0=comparable to public sector<br>salaries, 1=comparable to<br>private sector salaries,<br>2=somewhere in between public<br>and private salaries|
|Labor||Governance: Benefits|Whether the enterprise’s benefits are<br>comparable to those in the private and public<br>sector|0=comparable to public sector<br>benefits, 1=comparable to<br>private sector benefits,<br>2=somewhere in between public<br>and private benefits|
|iscipline||Governance: No<br>Exemption from Taxation|Whether the enterprise is exempt from any<br>form of taxation (for example, value added<br>tax, VAT)|1=yes, 0=no|
|al market d||Governance: Access to<br>Debt|Whether the enterprise has access to debt at<br>preferential rates|0=below market rate, 1=equal to<br>market rate, 2= above the<br>market rate|
|Capit||Governance: State<br>Guarantees|Whether the enterprise holds guarantees by<br>the state|1=yes, 0=no|

_Source_ : Adapted from Vagliasindi (2009).

32
made the most progress had done so with a strategy with two equally important elements: “The first element is to promote the productive use of the poor’s most abundant asset—labor. It calls for policies to harness market incentives, social and political institutions, infrastructure, and technology to that end.

The second is to provide basic social services to the poor. Primary health care, family planning, nutrition, and primary education are especially important.

The two elements are mutually reinforcing; one without the other is not sufficient.” During 1965‐1987 in countries like Brazil and Pakistan growth had raised incomes but governments had failed to provide social services. In Morocco and Sri Lanka, primary education and health indicators were impressive but the ranks of the poor had swelled instead of shrinking because income growth had been anemic. But in Indonesia and Malaysia, where both parts of the strategy were implemented, the number of poor people had been cut in half, along with impressive improvements in health and education and other social indicators.

The best evidence in favor of the strategy came from Taiwan and Korea. Between 1965 and 1987, Korea’s infant mortality fell from 62 to 24 per 1,000 births, the fertility rate fell from 4.9 to 1.8, and life expectancy at birth rose to more than 70 years. Secondary school enrollment for girls rose from 25 percent in 1965 to 86 percent in 1987, and for boys from 35 to 88 percent. Rigorous implementation of this two‐part strategy—made potent by a swift reform that transferred more than half of all cultivated land to poorer households—had slashed extreme poverty to under 3 percent by 1990. Taiwan’s story is similar.

The 1997‐98 crisis would convince the Koreans that these two elements needed a complement: social insurance. The 1990 WDR had anticipated this.

“Even if this basic two‐part strategy is adopted, many of the world’s poor—the sick, the old, those who live in resource poor regions, and others—will continue to experience severe deprivation. Many others will experience temporary setbacks owing to seasonal variations in income, loss of the family breadwinner, famine, or adverse macroeconomic shocks. A comprehensive approach to poverty reduction, therefore, calls for a program of well‐targeted transfers and safety nets as an essential complement to the basic strategy.” The 2.5‐point strategy proposed by the WDR can be seen as a policy package to increase the incomes and the assets of the poor, and to provide basic insurance to the poor against bad luck or the vagaries of the economy. It can also be seen as a public‐private partnership: private initiative would increase the incomes of the poor, a blend of public and private actions would help them add to their assets, and government‐sponsored safety nets would aid the most destitute and unfortunate. For the countries that had put this approach into practice in the 1970s and 1980s, it proved to be a winning game plan. During the next 25 years, as global extreme poverty rates continued to fall, it would display its durability (Figure 2).

10