rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8202

# **Abstract**

Institutions are defined as the set of rules that govern human interactions. When these rules are discriminatory, they may disempower segments of a population in the economic spheres of activity. This study explores whether laws that discriminate against women influence their engagement in the economy. The study adopts a holistic approach, exploring an overall measure of unequal laws also known as legal gender disparities, and relates it to several labor market outcomes for women. Using data for more than 60,000 firms across 104 economies, the study finds that unequal laws not only discourage women’s participation in the private sector workforce, but also their likelihood to become top managers and owners of firms. Suggestive evidence indicates that access to finance and corruption are pathways by which legal gender disparities disempower women in the labor market.

This paper is a product of the Global Indicators Group, Development Economics. 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 aislam@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 6: GDP per head with and without the programme B.

|t|y<sup>′</sup>(B)|y<sup>′</sup>(D = 1)|diference|% change|present<br>r = 0.04|value<sup>1</sup><br>r = 0.05|
|---|---|---|---|---|---|---|
|1|0.48893|0.48801|0.00092|0.19|1.523|1.523|
|2|0.73379|0.73313|0.00066|0.09|0.817|0.674|
|3|0.96072|0.95633|0.00439|0.46|3.870|2.640|
|4|1.47587|1.47142|0.00445|0.30|2.434|1.371|
|5|2.36242|2.35664|0.00578|0.25|1.685|0.784|
|6|3.65845|3.65225|0.00620|0.17|0.876|0.337|
||||Beneft-c|ost ratio:|5.30|3.75|

> 1 Present values are reported in billions of dollars.

> Note yt′<sup>seriesfromTables5and2.</sup>

Table 7: Full income with and without the programme B.

|t|y<sup>′′</sup>(B)|y<sup>′′</sup>(D = 1)|diference|% change|present <br>r = 0.04|value<sup>1</sup><br>r = 0.05|
|---|---|---|---|---|---|---|
|1|0.51331|0.51220|0.00111|0.22|1.741|1.741|
|2|0.80387|0.80352|0.00035|0.04|0.416|0.344|
|3|1.03734|1.03307|0.00428|0.41|3.591|2.449|
|4|1.55136|1.54712|0.00424|0.27|2.207|1.243|
|5|2.43638|2.43074|0.00564|0.23|1.565|0.728|
|6|3.74082|3.73477|0.00605|0.16|0.814|0.313|
||||Beneft-c|ost ratio:|4.89|3.49|

> 1 Present values are reported in billions of dollars.

> Note yt′′<sup>seriesfromTables5and2.</sup>

39
**Table A4. Moldova’s import tariffs and import by** **<u>product groups</u>**

|**Product groups**||**WTO final bo**|**und dut**|**ies**|**MFN**|**applied du**|**ties**|**Im**|**ports**|
|---|---|---|---|---|---|---|---|---|---|
||AVG|Duty-free,<br>%|Max|Binding, %|AVG|Duty-<br>free, %|Max|Share<br>in total,<br>%|Duty-free,<br>%|
|**Animalproducts**|17.2|1.1|_30_|100|11.9|13.3|_24_|0.8|13.0|
|**Dairy products**|14.3|0|_35_|100|13.6|1.0|_34_|0.8|0|
|**Fruit, vegetables,**<br>**plants**|14.3|0|20|100|12.8|5.0|20|2.6|5.6|
|**Coffee, tea**|10.6|0|15|100|10.6|0|15|1.2|0|
|**Cereals**<br>**&**|13.2|0.6|20|100|11.0|14.1|15|2.9|21.9|
|**preparations**||||||||||
|**Oilseeds, fats &**<br>**oils**|10.7|2.7|20|100|9.9|9.1|20|1.5|3.0|
|**Sugars**<br>**and**<br>**confectionery**|56.3|0|75|100|15.0|0|15|0.7|0|
|**Beverages**<br>**&**<br>**tobacco**|14.4|0|_61_|100|13.4|0|_59_|2.8|0|
|**Cotton**|0.0|100.0|0|100|0.0|100.0|0|0.0|100.0|
|**Other agricultural**<br>**products**|8.9|10.4|20|100|7.6|14.9|20|0.7|22.8|
|**Fish**<br>**&**<br>**fish**<br>**products**|4.3|57.9|15|100|4.0|59.4|15|1.1|73.3|
|**Minerals & metals**|3.4|59.1|20|100|2.7|66.3|20|17.1|79.8|
|**Petroleum**|0.0|100.0|0|100|0.0|100.0|0|9.1|100.0|
|**Chemicals**|4.5|25.8|10|100|3.8|28.8|7|15.5|48.3|
|**Wood, paper, etc.**|5.9|32.6|15|100|4.5|41.6|15|5.6|47.2|
|**Textiles**|7.9|1.5|20|100|5.2|29.5|20|7.0|34.1|
|**Clothing**|12.0|0|12|100|11.9|0|12|2.3|0|
|**Leather, footwear,**<br>**etc.**|8.4|5.5|15|100|7.8|6.3|15|3.2|4.7|
|**Non-electrical**<br>**machinery**|7.9|16.6|15|100|2.2|68.8|10|7.7|59.0|
|**Electrical**<br>**machinery**|6.0|30.6|10|100|5.6|21.9|10|8.5|32.3|
|**Transport**<br>**equipment**|4.3|19.6|10|100|2.3|56.5|10|5.7|52.4|
|**Manufactures,**<br>**n.e.s.**|**5.8**|**31.1**|**15**|**100**|**5.8**|**29.4**|**15**|**3.0**|**56.0**|

Source: World Trade Organization.

# Benefits

# Social insurance pensions

# Old-age pensions

The pensions system in Moldova is organizes as a PAYG system. The contributions are paid on the account of both employer and employee. Until the 2018 tax reform, the employer’s contribution for mandatory social insurance was 23%, which since 1 of October 2018 has been brought down to 18%. The employees’ share is 6%. Due to the large number of beneficiaries (533 thousand end of 2017), the old-age pensions are the main item of the social insurance part of the SSIB (70%). Moreover, the growth rate of the number of pensioners has been much smaller than in 2016, mainly due to the increase in the retirement age and mandatory contribution period.

The monthly pension is rather small (around 85 USD, see Table A5) and the pension-wage replacement rate is only 26%. Compared to general old-age pension, there are a number of privileged categories receiving much higher pensions.

39
period fluctuated between 37.4 and 34.7 percent. The best performance was achieved by **Colpatria,** followed by **Protecci6n** and Colmena.

In real terms, gross rates of return of the mandatory pension funds were 14.4 percent during May-December 1994, i.e. during the first nine months of the system, 15.9 percent in 1995, and 15 percent in 1996. Returns net of commissions are reported only for affiliates who joined the system at the beginning, i.e. in May 1994.

For affiliates who joined the private system later, the returns would be lower since their balances are lower. Table 11 shows that real net returns were highly negative for the affiliates during the first six months of the new pension system and slowly increased to more than 10 percent during the third quarter of 1996.2

**Table 11**

**Real Rates of Return Net of AFP Commissions** **_(%)_**

|||**5/94-**|**1/95-**|**7/95-**|**1/96-**|**5/94-**|
|---|---|---|---|---|---|---|
|||**12/94**|**6/95**|**12/95**|**9/96**|**9/96**|
|**Real**<br>**return***|**net**<br>**II**|-16<br>|0|4.1<br>**II**|10.5|-3.4|

* only for affiliates who contributed from 5/94-9/96

Source: Superintendencia Bancaria

# **_Old_** _Age_ **_Benefits_**

The benefit entitlement in the Colombian pension system differs between the public and the private system. Workers still have the choice of switching systems before retirement if they find that the public system offers a more attractive benefit than the private system.

In the private system, the official retirement age is 57 years for women and 62 years for men. But early retirement is possible as soon as the balance in the individual account is sufficient to finance a pension of at least 110 percent of the minimum wage. Any excess capital may be withdrawn and used for purposes other than retirement. As mentioned earlier, affiliates whose balance is sufficient to finance a minimum pension may also choose other investment plans for the excess capital. An affiliate retiring in the private system has essentially the same type of options as in the Chilean pension system: Upon retirement, the worker can choose to convert the accumulated balance in the individual account plus the recognition bond to pension payments under a programmed withdrawal program, use the amount to purchase an annuity from an insurance company, or opt for a combination of the two.

2 These figures are not fully consistent with the gross rates of return published by the Superintendencia Bancaria.

34
||40–44|4.1|4.6|82.3|75.7|88.2|-12.5*|59.9|50.9|68.0|-17.1*|
|---|---|---|---|---|---|---|---|---|---|---|---|
||45–49|3.3|3.8|83.8|77.4|89.3|-11.9*|60.6|52.3|67.8|-15.6*|
||50–54|2.9|3.7|83.9|76.4|89.8|-13.4*|61.3|52.1|68.5|-16.3*|
||55–59|2.4|2.3|86.6|80.3|92.9|-12.6*|62.7|54.5|71.1|-16.6*|
||60–64|1.9|1.9|89.5|86.7|92.5|-5.8*|64.0|59.5|68.6|-9.1*|
||15–19|0.6|1.1|44.6|13.0|62.8|-49.8*|28.0|8.3|39.3|-31.0*|
||20–24|2.2|5.3|43.1|24.3|51.0|-26.7*|26.3|14.7|31.1|-16.5*|
||25–29|6.3|10.5|36.2|26.9|41.8|-14.9*|22.2|16.4|25.7|-9.3*|
||30–34|7.5|7.6|32.4|27.1|37.6|-10.5*|19.4|16.4|22.5|-6.1*|
|**go**|35–39|7.6|9.1|32.8|26.5|38.2|-11.7*|20.0|15.9|23.4|-7.5*|
|**To**|40–44|7.1|6.4|34.1|28.7|40.0|-11.3*|20.6|17.2|24.4|-7.3*|
||45–49|5.4|5.6|31.0|24.4|37.4|-13.0*|18.8|14.9|22.5|-7.7*|
||50–54|4.6|3.8|31.9|29.0|35.4|-6.4|18.6|17.0|20.5|-3.5|
||55–59|2.6|2.4|37.9|39.1|36.7|2.3|22.4|23.6|21.1|2.5|
||60–64|2.2|2.1|39.9|39.7|40.1|-0.5|23.2|23.4|23.1|0.3|

(*) and (**) mean that the differences are significant at 5% and 10% level respectively.

Concerning Togo, gender differences are observed for most age groups, except for 55 to 64 years for whom some equality, even a slight inequality in favor of women, can be noted. The highest gender inequalities occur in the youngest age groups, especially among individuals under 30 years. The deprivation headcount gaps in terms of percentage points are -49.8, -26.7 and -14.9, respectively for age groups of 15–19 years, 20–24 years and 25–29 years. This tendency remains the same with the MPI since the gaps are -31.0, -16.5 and -9.3, respectively. These results are unexpected insofar as, with all women empowerment programs implemented during recent decades, we expected rather more inequality for oldest age groups than for youngest ones, especially for dimensions such as _education_ and _employment_ .

It may be interesting to check whether the relative contributions of dimensions to gender inequality vary by age group. Figure 7 illustrates the case of Burkina Faso when the cutoff is equal to 3. It is clear from this figure that the _employment_ contribution increases sharply with age. In fact, it varies from about 20% among 15–24 year olds to 40% among 25–39 year olds and to around 50% among 40–59 year olds, before jumping to 80% for people aged 60 and over. An inverse and less pronounced correlation is obtained when considering the _education_ contribution. It gradually decreases between the youngest group (15–19 years) where it represents 40% and the age group of 30–34 years where it drops below 20% before stabilizing for older groups. The contribution patterns are almost 29
small North-South capital flows financed primarily through loans holds in this dataset as well.<sup>28</sup>

> 28 The only other notable difference between the two datasets is that South’s portfolio equity liabilities are relatively larger and increase faster in the Lane and Milesi-Ferretti dataset.  This is likely due to the fact that they use stock market valuations for portfolio equity which increased sharply in the 1990s, while in our data we use domestic investment deflators to measure changes in the value of domestic capital.

28
**_<u>Table 3.  Welfare losses associated with electricity tariff increases</u>_**<sup>**_1_**</sup>

||**A**|**Electricit**<br>**ll Househol**|**y price inc**<br>**ds**|**rease to 7 c**<br>**Househol**<br>**onl**|**€/kWh**<br>**ds using e**<br>**y for heati**|**lectricity**<br>**ng**|
|---|---|---|---|---|---|---|
|**Elasticity**|0|-0.5|-1|0|-0.5|-1|
|**1 (Poorest)**|3.41|3.04|2.66|4.96|4.41|3.86|
|**2**|2.35|2.09|1.83|3.24|2.88|2.52|
|**3**|2.31|2.05|1.79|2.97|2.64|2.31|
|**4**|1.99|1.77|1.55|2.48|2.21|1.93|
|**5 (Richest)**|1.68|1.50|1.31|1.86|1.66|1.45|
|**ALL**|2.39|2.13|1.86|2.84|2.52|2.21|

Notes:<sup>1</sup> Welfare losses expressed as a percentage of household expenditures. Calculations based on data from the 2004 ISSP Montenegro Household Survey.

**_Comparing the welfare losses of the poor across different socioeconomic groups._** We are particularly interested in the welfare losses incurred by the poorest households. Table 4 presents the estimated welfare losses for households in the lowest income quintile, categorized according to different socioeconomic characteristics. We focus on households using only electricity for heating. In this scenario it is interesting to compare the current budget share spent on electricity and the welfare losses expressed as percentage of total household expenditures. The sum of the budget share of expenditures and the associated welfare loss when the price elasticity is zero represents the total electricity expenditures for the poor at the new electricity prices, assuming that households do not switch to other energy sources for heating. This is a reasonable approximation if we believe that the household demand function is fairly inelastic, since households are likely to be consuming the minimum amount of energy necessary to withstand the cold winter temperatures. If households are already at their minimum electricity consumption level and they have no possibility to switch to other heating fuels, then the impact of higher electricity prices on household budgets would indeed be severe. Some poor households, such as those with disabled household members or receiving family material assistance, are spending 13 percent of their budget on electricity. Assuming a zero price elasticity, they would be spending nearly 20 percent of their budget on electricity if prices increase to 7 c€/kWh.

If we assume households do respond to higher electricity prices by reducing consumption of electricity, we cannot add the total welfare losses to the share of electricity expenditures to obtain the new share of electricity expenditures. This is because when the elasticity is not zero, the welfare losses include additional expenditures on electricity, as well as the consumer surplus loss incurred on the value of units not consumed at the higher price. Thus, when we calculate the welfare losses for an elasticity of -0.5, we present the consumer surplus loss and financial loss (or would induce households to invest in more energy efficient electrical appliances and better insulation in their homes in the long run. After such adjustments households would be able the same level of comfort using less energy.

8
# Notes

1. The research done for the project was presented at a conference held in June 1992, then papers were revised for the book, Public Spendin,e and the Poor: Theory and Evidence, edited by Dominique van de Walle and Kimberly Nead, to be published by The Johns Hopkins University Press.

2. For evaluations of the poverty impact of targeting correlates of poverty see Ravallion (1993), Ravallion and Sen (1994), and Datt and Ravallion (1993).

3. Note that from Alderman and Sahn's study, it is not clear how the released work time is allocated. One would have to first ascertain whether leisure has indeed increased. Whether the poor's leisure increases more so than the rich's would also be germane. Other issues arise. Parents may be devoting more time to their children, in which case there may also be important externalities to take account of.

4. The best known standard benefit incidence studies for developing countries are the early ones by Meerman (1979) for Malaysia and Selowsky (1979) for Colombia. Selden and Wasylenko (1992) review the literature.

_5._ For a review of results and a critique see Litvack and Bodart (1993).

6. A summary of incidence results for developed countries from existing studies is given in Selden and Wasylenko (1992).

7. Note that this is subject to the underlying welfare objective as discussed earlier.

45
- 13 -

suffic ieit coridi tiotn for poverty al leviationi. Both the WDR anid the IIDR strengthen the case for this strategy by looking at country experience.

# 4.2 What does country experience tell us?

HDR suggests a threefold typology of country experiences.<sup>First there</sup> is "sustained human development, as in<sup>Botswana, Costa Rica, Cuba, the</sup> Republic of Korea, Malaysia and Sri Lanka."<sup>Second, there is "disrupted human</sup> development, as in Chile, China, Colombia, Jamaica,<sup>Kenya and Zimbabwe."</sup> Third, there is "missed opportunities for<sup>human development, as in Brazil,</sup> Nigeria and Pakistan." While there are some<sup>differences of detail, the WDR</sup> emerges with a similar message. It certainly<sup>highlights Brazil and Pakistan</sup> as examples of countries that had high income<sup>growth but where poverty was not</sup> alleviated significantly because of uneven<sup>distribution of gains from growth</sup> and of social expenditure. While recognizing<sup>the reversals in the 1980s in</sup> some countries, they hold up the example of<sup>China, Costa Rica, Cuba and Chile</sup> and Sri Lanka as examples of what a purposive<sup>social expanditure strategy can</sup> achieve. Finally, the WDR highlights Indonesia<sup>as a leading example of a</sup> country that has seen dramatic reductions in<sup>poverty, as a result of a</sup> strategy of broad based growth and infrastructure<sup>expenditure.</sup> The HDR summaries the conclusions of its country<sup>analysis in terms of</sup> six lessons: First, growth accompanied by an equitable<sup>distribution of income</sup> appears to be the most effective means of sustained<sup>human</sup> development... Second, countries can make<sup>significarit</sup> improvements in human development over long<sup>periods--even in the</sup> absence of good growth or good distribution --<sup>througlh</sup> wellstructured social expenditures by governments...<sup>Third, well-</sup> structured government social expenditures can also<sup>generate fairlv</sup>
# Appendix Table S4. Model With Interaction Between Inequality and Initial Income (WDI Data)

|Dependent Variable is:|ln(yit)||
|---|---|---|
||(1)<br>(2)|(3)|
|Giniit|40.52***<br>21.88***|25.05***|
||(7.11)<br>(3.69)|(5.67)|
|Giniit|-5.48***<br>-2.98***||
|*ln(yi1980)|(0.99)<br>(0.50)||
|Giniit||-3.39***|
|*ln(yit-1)||(0.79)|
|ln(yit-1)|0.53***<br>(0.08)|0.43***<br>(0.10)|
|Kleibergen Paap F-Statistic|72<br>100|70|
|Country FE|Yes<br>Yes|Yes|
|Time FE|Yes<br>Yes|Yes|
|Observations|495<br>490|554|

Note: The method of estimation is two-stage least squares. Bootstrapped standard errors are shown in parentheses. The instrument for _Gini_ it is _Residual Giniit = Giniit – αln(yit)_ , where α measures the effect that ln(yit) has on _Gini_ it.
22

# foster innovation in this particular field in the operating divisions.

The slow dissemination of ideas was the result partly of inadequate communications between and within PHN divisions, but also, and more fundamenL-Ily, of a low priority given to staff training and to learning from experience within and outside the Bank. With regard to training, the operational work pressures which in all Bank sectors discourage staff from seeking training were not counteracted by management insistence that a certain number of weeks per year be devoted to this activity. With regard to learning from experience, a systematic review of experience outside the Bank with service delivery at the periphery was only commissioned in 1986, and no systematic effort was made by the PHN department to learn from the experience of other sectors within the Bank. Valuable (but very broad) internal reviews of operational work were carried out for each of the population, health, and nutrition ... ctors, but the latter two, like this review, were desk studies only. Primary reliance for learning from field experience--other than from routine supervision reporting--continues to be placed on the Bank's ex-post evaluation system, which, however, tends to produce its results 10-12 years after project appraisal. As of mid-1987, the two most recent PHN project performance audit reports dealt with Bangladesh Population 1 (1975) and Indonesia Population 2 **('077).**

# 4. CONCLUSIONS AND RECOMMENDATIONS

The Bank's work in the PHN sectors has put considerable emphasis on the development of IEC services and outreach delivery systems. Because formal evaluations of most projects have not yet been carried out, we do not yet know for certain what impact these activities are having. However, it appears that the Bank's knowledge of the determinants of both client and service provider behavior is in most operations too scanty for it to be sure whether the design of these IEC services and outreach systems is appropriate to cl.ents' needs and circumstances. It also appears that the Bank has not done enough to target project-financed services to priority clients, particularly the most disadvantaged clients at the periphery. These findings are worrying, in a situation where most Bank-supported PHN services are managed by public sector agencies with little incentive to reach the poor. Whereas, in the private sector, the firm that fails to be sufficiently responsive to its customers goes bankrupt and is replaced by a more effective company, in public health care the clinic wlhich fails to meet its clients' needs will survive indefinitely with low levels of utilization.

This review concludes that the Bank can and should do more to ensure that the PHN services it supports are responsive to clients' needs, that they are more driven by the market. In general terms, Borrowers and the Bank could move in this direction by

- learning more about clients' perceptions and behavior;

- doing more to target the clients most in need of services;