rafmacalaba/datause-encoder-data huggingface dataset:
9

**Expectations of counterpart** Think now for a moment about your husband/ father/ brother, and his views. Does he think or speak badly about women who work outside their homes?

## 2.3 **<mark>Limitations of the Study</mark>**

<mark>The study has several limitations. First, the Social Norms survey sample is representative of urban women and men in KRI; due to the lack of availability of more work options for women in rural areas, we did not include rural women and men in the study. Second, while we were able to oversample working women and were able to speak with a sufficient number of women working in the public sector, we were unable to find larger numbers of women working in the private sector. Future surveys should include more women working both in the formal and informal private sector. Finally, all regression results presented using the Social Norms survey data are correlations and do not bear any causal interpretation.</mark> <mark>In the Rwanga sample, while we started the study with a large number of female j</mark> ob seekers (1,780) registered at the jobs agency, we faced difficulty in regular follow-ups with this sample, and high attrition rates by the end of the study. As such, we only report findings that help to better explain some of the findings from the Social Norms survey.

# 3. Results

## **3.1 Descriptive Statistics**

## **3.1.1 Social Norms Survey**

Table 2 shows descriptive characteristics of the social norms sample broken down by working women, non-working women, men with a working woman counterpart, and men with a nonworking woman counterpart. In total, 1,150 respondents were interviewed, out of which 577 or 50.2% were women, and the rest were men. Nearly all respondents (99.6%) identified themselves as Kurdish. Thirty-six percent of the sample were from Erbil, 36.4% from Sulaymaniyah and 27.5% were from Duhok. Nearly 94% of the sample was women and men from the same household, out of which nearly 82% were spouses (the remaining were brother-sister, 15%, and fatherdaughter relationships, 3%). We successfully oversampled and interviewed working women among our female respondents (32%), close to our target of a third. Women on average were slightly younger than men, with an average age of 35 years, compared to men who were 39 years old on average.

The table also shows that working women are indeed different from non-working women. Working women in the public sector, on average, had the most education even when compared to men, including almost twice as many years of education when compared to other women in
<mark>Restructuring, Preventive Reconciliation and Bankruptcy in 2018. Jordan established a credit bureau in</mark> 2015 and passed the insolvency and secured transaction laws in 2018. West Bank and Gaza passed the Secured Transactions Law and set up a new collateral registry in 2017. Djibouti created a credit information system by law in 2016. Other countries (e.g. above-mentioned and Tunisia) expanded the scope of information collected and reported by their credit bureaus, and credit bureaus in Morocco, Qatar and United Arab Emirates (UAE) recently started to provide credit scores.

Credit guarantee and subsidized programs are common but issues regarding designing, targeting, and displacement of private credit providers distort their effects. The use of interest rate caps is less prevalent. Credit guarantee schemes have been recently used by several MENA countries, but those programs suffer from cumbersome process, poor targeting, and design issues related to eligibility criteria, pricing mechanism, and sustainability, resulting in limited impacts of the programs. In addition, subsidized programs are common in the region, run by large state-owned banks, and especially focused on MSMEs and housing, which create market distortions and crowd out private creditors willing to finance at market-based rates. Finally, some countries have interest rate restrictions, while others have interest caps on loans benefiting from government incentives or subsidies, with potential distortions in credit markets (Ferrari et al.,2018).

<u>South Asia</u><sup>21</sup>

# **Major Trends**

In comparison with other regions, median lending rates and lending-deposit spreads marginally declined in the South Asia Region (SAR) but both rates are above the global median of developing countries. Median nominal lending rate in SAR declined to about 9.8% in 2017 from 12% in 2003, and it remained above the global median of developing countries. The decline in median lending-deposit spreads was the lowest among regions (-1%) during the reference period but median lending-deposit spreads in SAR remains above the global median of developing countries. In 2017, countries with the highest lending rates are as follows: Bhutan (14.2%), Sri Lanka (11.5%) and Maldives (10.1%). In terms of lending-deposit spreads, Bhutan, Maldives, and Bangladesh have the highest spreads in descending order.

# **Major Challenges**

Challenges to interest rate levels in SAR differ depending on the countries’ macro-financial health and the appetite of their public sectors for national savings. Some countries continue to have large stocks of non-performing loans (NPLs) which by necessity add to higher lending rates for the less risky customers. Extensive informality of many SAR economies keeps the pool of eligible borrowers much below potential, while small capital markets concentrate most of the real sector financing risks in the banking sector which is dependent on retail deposits – all contributing to elevated lending rates and spreads.

# **Policy Overview**

> 21 This regional section is based on substantive inputs provided by Marius Vismantas (FCI-SAR), and Muhammad Waheed (MTI-SAR).

31
- Williams, A. 2001. "Science or marketing at WHO? A commentary on 'World Health 2000'." _Health Economics_ 10(2).

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- World Bank. 2001. "Jamaica Survey of Living Conditions (JSLC) 1988-98: Basic Information." DECRG, The World Bank. Processed.

- Wouters, A. 1993. "The cost and efficiency of public and private health care facilities in Ogun State, Nigeria." _Health Economics_ 2(1): 31-42.

- Wouters, A. M. 1991. "Essential National Health Research in Developing Countries: Healthcare Financing and the Quality of Care." _International Journal of Health Planning and Management 6:_ 253-71.

- Ye, X. and S. Canagarajah. 2001. "Tracking public resource flows in Ghana's schools and clinics." Africa Region Working Paper Series. World Bank, Washington, D.C. Forthcoming.

- Zuckerman, S., J. Hadley, and L. Iezzoni. 1994. "Measuring hospital efficiency with frontier cost functions." _Journal of Health Economics_ 13(3): 255-80; discussion 335-40.

47
**Table 4: Input intensity by total firm size**

||**Total**|**Small**|**Lan**<br>**Medium **|**d size**|**Large**||
|---|---|---|---|---|---|---|
|Used chemical fertilizer|0.17|0.12|0.15|*|0.21|***|
|Value of chemical fertilizer used in USD per ha|39|82|30|***|22||
|Used manure|0.73|0.66|0.75|***|0.80|***|
|Manure used in kg per ha|4814|8333|4307|***|2504|***|
|Used pesticides|0.13|0.09|0.12|**|0.16|***|
|Value of patricides used in USD per ha|27|69|20|***|8|***|
|Used improved seed|0.26|0.19|0.25|***|0.31|***|
|Used extension advice|0.19|0.15|0.20|***|0.21|***|
|Male family labor days, per ha|136|234|138|***|71|***|
|Female family labor days, per ha|326|592|291|***|127|***|
|Used hired labor|0.43|0.26|0.40|***|0.62|***|
|Hired labor days, per ha|146|71.56|77.33||61.15||
|Total labor days, per ha|488|948|537|***|276|***|
|**Total Observations**|3493|1124|1185||1184||

**Source:** Own computation from WB survey 2011. **Note:** T-test is performed using as a reference the immediate lower tercile for land size: * significant at 10%; ** significant at 5%; *** significant at 1%

21
- 30 -

# APPBNDIX

Estimation of equation (7) in the text requires measures, for each household member j, of the predicted household income in the absence of migration by the household member (Y0) and of the level of relative 0 deprivation associated with this predicted income (RDJ). In this Appendix we 0 outline the method used to obtain, first, an instrument (YJ) for household 0 income in the absence of migration by person j and, second, an instrument (RDJ) for relative deprivation associated with this income. 0

# Household Income without Migration by Person j.

A household's predicted income in the absence of migration by household member j (Y') is the sum of predicted income from other househol memb j 0 sources (YJ) and the expected contributioni by the household member as a nonmigrant (WJ). Estimates of household income (from sources other than 0* person j) were obtained by regressing observed 1982 income from sources other than person j on household assets at the start of 1982. The estimated equation is yi= 1376.8 + 0.29A + 821.92TED - 96.29NADS (4.94) (5.59) (8.87) (-1,98) t 558.08USNET - 397.96MEXNET (2.81) (-1.85) (A.1) R<sup>2</sup> = 0.28 N = 423,
||**Table**|**7: US/EU/EF**|**TA IPAs (Bil**|**ateral Trade,**|**58 IPAs)**||||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Low-IP|(2)<br>AI|(3)<br>BIO|(4)<br>CHEM|(5)<br>ICT|(6)<br>MED|(7)<br>PT|(8)<br>Other|
|**Exports**<br>log(GDPi)|0.352***<br>(0.083)||||||||
|Sector × log(GDPi)||0.485***|0.144*|0.309***|0.267**|0.504***|0.424***|0.330***|
|||(0.098)|(0.085)|(0.092)|(0.106)|(0.084)|(0.119)|(0.081)|
|Sector × LI × IPAi|–0.072|0.316|1.661*|0.016|0.010|1.171*|0.452|0.432|
||(0.318)|(0.555)|(0.969)|(0.620)|(0.745)|(0.639)|(0.765)|(0.471)|
|Sector × LMI × IPAi|0.041|0.466|1.875***|0.227|–0.665|1.016|0.827*|0.430|
||(0.217)|(0.572)|(0.392)|(0.343)|(0.567)|(0.703)|(0.470)|(0.341)|
|Sector × UMI × IPAi|–0.178|0.923|1.877***|0.205|–0.110|1.554**|0.362|0.339*|
||(0.303)|(0.581)|(0.392)|(0.388)|(0.727)|(0.633)|(0.430)|(0.188)|
|Sector × HI × IPAi|0.033|0.479*|2.456***|0.653***|0.106|0.853***|0.108|0.160|
||(0.233)|(0.268)|(0.331)|(0.249)|(0.217)|(0.260)|(0.319)|(0.178)|
|**Imports**|||||||||
|log(GDPj)|0.475***<br>(0.040)||||||||
|Sector × log(GDPj)||0.119***|0.103**|0.016|–0.063|0.113***|0.069**|0.032|
|||(0.025)|(0.044)|(0.026)|(0.065)|(0.009)|(0.033)|(0.040)|
|Sector × LI × IPAj|–0.224|–0.122|1.581***|0.113|–0.611*|0.836***|0.169|0.351|
||(0.240)|(0.222)|(0.544)|(0.464)|(0.321)|(0.275)|(0.269)|(0.214)|
|Sector × LMI × IPAj|–0.064|–0.513**|–0.122|–0.282|–0.604*|–0.069|–0.587***|–0.318*|
||(0.134)|(0.251)|(0.324)|(0.211)|(0.334)|(0.277)|(0.223)|(0.173)|
|Sector × UMI × IPAj|–0.032|0.101|0.111|–0.243|0.335|0.005|0.135|0.073|
||(0.181)|(0.161)|(0.256)|(0.278)|(0.313)|(0.233)|(0.181)|(0.162)|
|Sector × HI × IPAj|<br>–0.121|<br>0.273*|<br>0.805***|<br>0.332*|<br>0.121|<br>0.266|<br>0.295*|<br>0.205|
||(0.222)|(0.142)|(0.272)|(0.181)|(0.249)|(0.182)|(0.171)|(0.172)|
|Observations||||||||923,632|
|Number of country pairs||||||||24,322|
|Pseudo_R_<sup>2</sup>||||||||0.945|

_Notes_ : Robust standard errors two-way clustered by exporter and importer in parentheses. Includes exporter- and importer-specific time trends, exporter- and importer group-sector year fixed effects, and bilateral pair fixed effects. TRIPS compliance by country included not reported. Observations at 5-year intervals: 1995, 2000, ... 2015. *** _p_ < 0.01, ** _p_ < 0.05, * _p_ < 0.1.

27
countries, due, among others, to permanent differences in educational attainment and institutions.<sup>12</sup> In this context, the inclusion of access costs is quite crucial since one of the main regulatory issues in the telecom sector is the design of interconnection access rules and its implications for access costs. We also test if a model of technological diffusion based on epidemic theories of diffusion may explain internet access in Latin America. In our tests, we use two samples of data. The first is for the whole World, testing for any regional specificity for Latin America. The second focuses on Latin America only, making full use of the additional institutional and technological information available for that region. The number of explanatory variables is drastically restricted by the data available, however we do feel that the data bases are good enough to generate some useful policy insights.

The specific model we use for the whole world is the following:

- (1) InHit = ao + a,lnYit + a2lnPit + a3lnL1 t + a4lnC1 t + a5lnH1t1l

where, for a generic country i in year t: H: per-capita number of Internet hosts (Internet users in the second set of regressions), Y: per-capita GDP,

- P: internet access cost, defined as the sum of 30 3-minutes local calls plus monthly phone subscription rates,

- L: per-capita digital fixed telephone lines,

- C: per-capita Personal Computers.

The tests that focus on Latin America extend model (1) to include additional regressors, namely whether or not the incumbent operator is privatized in a given year and whether or not an independent regulator is present in a given year. In some specifications, we also control for the degree of income inequality as measured by the Gini coefficient.<sup>13</sup> In order to check for the robustness of our results, model (1) (and its extension for Latin America) is estimated twice, using two different dependent variables H. In the first one, we use Internet hosts, while in the second one we employ Internet users (taken from a

> 12 For a recent survey of the concept and empirical importance of international technology diffusion see W. Keller (2001)

> 13 Data on Gini come from the Wider database. Because data are not available each year, we have interpolated the actual series with a linear trend and used the estimated value as a regressor.

10
10 Table 3

# **Labor Force** Partolpatlon Rates of Older Workers, Selected Economles In TransIion

||Age 55-59|Men<br>Age 860-4|Age 65+|Age 55-59|Women<br>Age 60.64|Age 65+|
|---|---|---|---|---|---|---|
|Bulgaria<br>(1985)|80.9|39.2|15.2|32.0|16.5|4.3|
|Czechoslovakia<br>(1980)|84.2|46.3|19.5|40.8|21.5|6.5|
|Hungary<br>(1980)|72.2|13.2|3.9|18.8|8.7|3.2|
|Poland<br>(1978)<br>|81.5<br>|62.4<br>|34.9<br>|57.9<br>|37.4<br>|19.4<br>|
|(1988)|72.0|53.6|32.5|50.6|34.3|19.0|
|Romania<br>(1977)|78.7|44.7|15.2|52.5|25.2|10.0|

Source: OECD (1991). Tabe A.3 p. 17.

poor health, as compared to saying that *they got tired of working'. Furthermore, as shall be shown below, many countries offer income-support maintenance programs which require the applicant report he or she cannot work. It has been suggested, therefore, that at least some older people reply that they are in poor heafth as an ex-Dost raftonalization for retiring at relativel **y** oung ages (Fields and Mitchell 1984b; Sammartino 1987).

Good-quality evidence is difficul **t** o obtain on the effect of poor health on retirement. On the one hand we know that people retire earlier from physically demanding jobs, from jobs where they are exposed to heavy machinety and equipment, and from jobs with extremes of temperature, stress, and other unpleasant working conditions (Gustman and Steinmeler 1986; Mitchell 1990). On the other hand, these jobs also tend to offer above average pensions, so earier retirement may be due to pension opportunities rather than because the workers are forced out. There Is little suggestion that on-the-job
**Tables A.12 Individual-level regressions of norms within each index predicting women’s work**

**outcomes**

||||Full S<br>|ample<br>|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||Paid work|Paid work|Paid work|Paid work|Paid work|Paid work|
|PNB – Working woman|-0.11<br>(0.09)||||||
|SNE – Working woman|-0.11<br>(0.11)||||||
|PNB – Mobility freedom||-0.10<br>(0.11)|||||
|SNE – Mobility freedom||0.07<br>(0.10)|||||
|PNB – Honor|||-0.13<br>(0.11)||||
|SNE – Honor|||0.01<br>(0.11)||||
|PNB – Safety||||-0.09<br>(0.09)|||
|SNE – Safety||||-0.01<br>(0.10)|||
|PNB – Motherhood|||||-0.03<br>(0.04)||
|SNE – Motherhood|||||-0.01<br>(0.05)||
|PNB – Mixed-gender<br>workplace||||||-0.02<br>(0.08)|
|SNE – Mixed-gender<br>workplace||||||-0.05<br>(0.09)|
|Constant|-0.09|-0.14|-0.13|-0.13|-0.11|-0.12|
||(0.24)|(0.24)|(0.25)|(0.24)|(0.25)|(0.23)|
|Obs.|2,000|2,000|2,000|2,000|2,000|2,000|
|Adjusted R2|0.02|0.02|0.02|0.02|0.02|0.02|
|Controls|YES|YES|YES|YES|YES|YES|
|Regional FE|YES|YES|YES|YES|YES|YES|

_Notes_ : (1) Negative correlation means that more conservative beliefs and social expectations are associated with lower probability of women working for pay outside the house. (2) For all items, the higher the value, the more conservative the beliefs and expectations are. Outcome variable “Paid work” is binary with 1 if the female reported a “yes” to “I work outside the house for pay.” And if a male reported “yes” to “My wife works outside the house for pay.” (3) Standard errors clustered at the ward level. (4) Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.10. (5) The results for female caregiver and male breadwinner norms are reported in the main body of the paper (Table 6).

44
is by reducing the idiosyncratic risk faced by the firm owners. The impact of the legal system is greatest in sectors in which proprietorships dominate. Better legal systems appear to increase the investment of firm owners by reducing the risk faced by owners. These results stand up even when we use U.S. data on the distribution of legal forms of organization. Arguably, the quality of the legal system in the United States offers the basis for an optimal distribution of legal forms, and the U.S. data offer therefore an ideal benchmark of a sector-specific legal organization of businesses. Moreover, all of these findings are upheld when we instrument for the institutional variables using the log of indigenous population in 1900 and the active presence of the drug trade in the state.

The quality of contract enforcement is not the only variable thath affects firm size. Among other determinants, the size of the market appears to have particular impact. Large markets contain much larger firms. Regulatory costs and generalized trust also have an impact on firm size, though when all three institutional variables are included in the regression together, the impact of regulatory cost becomes insignificant.

The size distribution of firms in a cross section is the result of a combination of size at startup and a dynamic growth process. The results imply that the legal system affects the growth prospects of firms. Where courts are more efficient, capital is shifted from entrepreneurs with less entrepreneurial ability to those with more entrepreneurial ability. These results suggest that policies that improve the administration of courts and the enforcement of verdicts would be expected to result in the growth of firms, an increase in the demand for workers, and a reduction in the rate of self employment.

37
Productivity convergence: Is anyone catching up?<sup>_∗_</sup> Gene Kindberg-Hanlon<sup>_†_</sup> Cedric Okou<sup>_‡_</sup>

**Keywords** : Convergence, Macroeconomic Development, Productivity. **JEL classification** : E01, O11, O43, O47

> _∗_ This paper has benefited from many helpful comments from Alistair Dieppe, Graham Hacche, Ayhan Kose, Franziska Ohnsorge, Chris Papageorgiou, Jon Temple, and Chris Towe. With many thanks for research assistance provided by Khamal Clayton and Xinyue Wang. We gratefully acknowledge financial support from the PHRD fund.

> _†_ gkindberghanlon@worldbank.org, Prospects Group, World Bank

> _‡_ cokou@worldbank.org, Prospects Group, World Bank
proposals in the loans and the extent of implementation of those reform proposals by the recipient countries.<sup>1</sup> ' The countries on which implementation data was available were each assigned a rank as to tne level of their proposed reforms. In addition, the degree of compliance with reform proposals was assigned to one of the five levels of implementation: 80%, 60%, 40%, 20%, and 0% of proposed reforms. Zero implementation generally imply near complete reversals of or backtracking on trade reform. The product of these two series generates the index of trade liberalization, or, in other words, a measure of the change in openness of the economy.<sup>17</sup> If the proposals were strong and 80 percent of the proposals were implemented, then the Halevi-Thomas index would attain its highest leveL One problem with this measure is that the initial trade regime is not incorporated in the measure. Though weak reform proposals with 80 percent compliance would suggest low changes in openness, the reform proposals are weak because the economy may be very open initially. This problem is however likely to be of limited significance, since virtually all sample countries began their reform efforts in 1978 or after, from a highly restrictive trade regime.

The purchasing-power-parity based index of outward orientation index (Dollar 1991a) is based on prices for a common basket of consumption goods collected by Summers and Heston (1988) for the same set of benchmark countries. To obtain price of traded goods, the relative price of consumption goods' is regressed on variables like urbanization, GDP per capita (proxy for countys "this was developed on the basis of detailed information about trade reform proposals from Presidents' Reports on loans and about actual implementation of changes in import tariffs, bans, and licensing and changes in export taxes and restrictions from project completion reports and country economic reports (Halevi 1990, Thomas et. al. 1991).

"The intensity of trade reform proposals is categorized as weak(= 1), moderate (=2) and strong (=3) while the degree of implementation is categorized as _80%_ (=0.8), 60% (=0.6), 40% (=0.4), 20% (=0.2) and 0%. The product of the two is the index of change in openness.

"This relative price is (RP) a modified version of the Summers and Heston (1988) definition of relative price. The latter is: RP = CP/P,,,I x 100 while the former is: RP=epPDc x 100 where P,, is the price of consumption goods in the USA and PDC is the average price in 72 developing countries.

13
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28