rafmacalaba/datause-encoder-data huggingface dataset:
Del Rey, Elena, Andreas Kyriacou, and José I. Silva. 2021. “Maternity Leave and Female Labor Force Participation: Evidence from 159 Countries.” Journal of Population Economics 34 (3): 803– 24. doi.org /10.1007/s00148-020-00806-1.

<mark>Dildar, Yasemin (2015), Patriarchal Norms, Religion, and Female Labor Supply: Evidence from Turkey,</mark> _<mark>World Development</mark>_ <mark>, 76, issue C, p. 40-61.</mark> <mark>Druedahl, Jeppe, Ejrnæs, Mette and Jørgensen, Thomas, (2019), Earmarked paternity leave and the relative income within couples,</mark> _<mark>Economics Letters</mark>_ <mark>, 180, issue C, p. 85-88.</mark> Duchin, R., O. Ozbas, B. A. Sensoy (2010). “Costly External Finance, Corporate Investment, and the Subprime Mortgage Credit Crisis,” _Journal of Financial Economics_ 97(3): 418–435.

<mark>Fallon, Kathleen M., Mazar, Alissa and Swiss, Liam, (2017), The Development Benefits of Maternity Leave,</mark> _<mark>World Development</mark>_ <mark>, 96, issue C, p. 102-118</mark> <mark>Fernández, Raquel, Fogli, Alessandra and Olivetti, Claudia, (2004), Mothers and Sons: Preference Formation and Female Labor Force Dynamics,</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>, 119, issue 4, p. 1249-1299.</mark> <mark>Gaddis, I., Klasen, S. (2014), Economic development, structural change, and women’s labor force participation:.</mark> _<mark>J Popul Econ</mark>_ **<mark>27,</mark>** <mark>639–681. https://doi.org/10.1007/s00148-013-0488-2</mark> <mark>Goldin C (1995) The U-shaped female labor force function in economic development and economic history. In: Schultz TP (ed) Investment in women’s human capital. University of Chicago Press, Chicago, pp 61–90.</mark> <mark>Goldin, Claudia. 2006. "The Quiet Revolution That Transformed Women's Employment, Education, and Family."</mark> _<mark>American Economic Review</mark>_ <mark>, 96 (2): 1-21.DOI: 10.1257/000282806777212350</mark> <mark>Heath, Rachel. 2017. “Fertility at Work: Children and Women's Labor Market Outcomes in Urban Ghana.”</mark> _<mark>Journal of Development Economics.</mark>_ <mark>126: 190-214.</mark> <mark>Hyland, Marie, Djankov, Simeon and Goldberg, Pinelopi, (2020), Gendered Laws and Women in the Workforce,</mark> _<mark>American Economic Review: Insights</mark>_ <mark>, 2, issue 4, p. 475-90</mark> <mark>İlkkaracan, İpek, (2012), Why so Few Women in the Labor Market in Turkey?,</mark> _<mark>Feminist Economics</mark>_ <mark>, 18, issue 1, p. 1-37.</mark> <mark>Islam, Asif, Silvia Muzi & Mohammad Amin (2019) Unequal Laws and the Disempowerment of Women in the Labour Market: Evidence from Firm-Level Data, The Journal of Development Studies, 55:5, 822-844, DOI: 10.1080/00220388.2018.1487055</mark> 44
almost bound to be curtailed very quickly.<sup>7</sup> The potential employment creation following greater trade integration can be significant. Thus, in the case of Madagascar, employment in the textiles export industry grew from 47,000 to some 200,000 between 1997 and 2001, with workers earning a 40 percent premium over the average income earned in the informal sector (Nicita 2004). In fact, even giant economies benefit from large overseas markets. China’s initial take-off was fueled by agricultural reform but kept running on manufactured exports usually from EPZs and township and village enterprises (Fu and Balasubramanyan 2005). India had a fiscal boom in the late 1980s, but kept growing in the 1990s via further reforms in which trade figured strongly. To trade openness, Kee and Hoon (2005) add the benefits of openness to foreign direct investment which brings technology and forward and backward linkages. As argued by Fu and Balasubramanyan (2005), inward FDI in China played an important role in generating demand for labor.

Many studies indicate that absorptive capacity in the host country is crucial for obtaining significant spillover benefits from trade or FDI. For example, using data from industrialized countries to sixty-nine developing countries Borensztein, De and Lee (1998) tested the effect of FDI on growth in host countries and found that FDI contributes _more_ to domestic growth than domestic investment but this happens only when the host country has a minimum threshold stock of human capital. Similarly, Keller (1996) argues that access to foreign technologies alone does not increase growth rates of developing countries and he shows that if a country's absorptive capacity (measured by its stock of human capital) remains unchanged, a switch to an outward orientation may not lead to a higher growth rate. The ability of local firms to absorb new technologies is a determinant of whether better access to trade as well as the labor turnover associated with greater competition is a means of technology diffusion—in turn an important channel for growth. This suggests a priority for any country is to pursue general complementary policies such as education, efficient infrastructure and measures to reduce entry barriers for local firms into new activities. The latter is important for a number of reasons, including employment creation. To the extent that prevailing policies (e.g., taxes, restricted access to finance, etc.) discourage such investments, they should be reformed to encourage more innovation. The same

> 7 The elasticity of demand for exports is typically high even if foreign markets are restricted by tariffs. Tariffs cut sales, but not necessarily sensitivity to price changes.

6
VAR (1969q3 and 1982q4). We can then conclude that only the break-dates 1969q3 and 1982q4 can be associated with a change in the impact of house prices on the macroeconomy, when GDP is included in the VAR specification. In a final step, we use the _SupLRT_ test to detect the location of the strongest rupture in the system during the considered period. To do this, we apply the _SupLRT_ test with the null hypothesis of no structural break against the alternative of one break. The _SupLRT_ test allows us to reject the null hypothesis of no structural break against the alternative of one break, as the value of the _SupLR_ statistic is 25 _._ 08 while the critical value at the 5% level is 21 _._ 37. The test gives us 1984q4 as a break-point, and the confidence interval associated to this break, 1981q4-1987q4, covers the second break-date obtained in the no restricted VAR (1982q4). Thus, we conclude that the strongest rupture in the house prices macro-link, for the considered VAR specification, occurred in the mid-1980s.

**Table 4. The** _SupLRT_ **Test for a VAR with Linear Restrictions with X=GDP**

||Statistic|Critical value<br>at 5% level|Signifcant break|
|---|---|---|---|
|_SupLRT_ test ( 1_|_0|):<br>25.08|21.37|1984q4(1981q4-1987q4)|
|_SupLRT_ test ( 2_|_0|):<br>29.54|28.17|1971q2 (1968q2-1973q2);|
||||1983q1 (1980q2-1985q4)|
|_SupLRT_ test ( 3_|_0|):<br>29.92|33.71|-|

We now test the robustness of the three break-dates estimated in a VAR including private consumption as an endogenous variable (i.e, X=C). Results (Table 5) show that the _SupLRT_ test does not allow us to reject the null hypothesis of no structural break against the alternative of three breaks, as the value of the _SupLRT_ statistic is 24 _._ 40 while the critical value at the 5% level is 33 _._ 70. The _SupLRT_ test also does not allow us to reject the null hypothesis of no structural break against the alternative of two breaks, as the value of the _SupLRT_ statistic is 24 _._ 24 while the critical value at the 5% level is 28 _._ 17. Finally, the _SupLRT_ test allows us to reject the null hypothesis of no structural break against the alternative of one break. The test gives us 1984q3 as the only break in the system. The confidence interval associated with this break -1981q1-1988q1- covers the second break-point obtained in the non restricted VAR (1985q1). We can then conclude that only the break-point 1985q1 can be associated to a change in the effect of house prices on the macroeconomy, when private consumption is included in the VAR system.

Results in this section suggest that the housing prices impact on the selected US macro-aggregates experienced an important rupture in the mid-1980s. Even though the economic agents experienced a progressive relaxation on credit constraints specially during the last 30 years, the modification of the housing market in the mid-1980s seems to have been a determining factor of the change of the house prices macro-relationship.<sup>9</sup>

> 9Our results also suggest that the break estimated in the end of the 1960s is not due to a
Transparency itself is also significant at the .05 level, and each 0.5-point improvement on its 1-6 scale is associated with an increase of 4 percentage points in use of country PFM systems.

Equation 2.4 adds a dummy variable for 12 “fragile states” (listed in OECD, 2011a: 104). In 2007 the OECD-DAC adopted a set of “Fragile States Principles” reflecting a view that they had different problems requiring special treatment in comparison with better-performing aid recipients. One of the 10 principles calls for “a focus on state building as the central objective,” suggesting that use of country PFM systems may be higher in fragile states than otherwise expected, based on income, size, etc. On the other hand, donors might rely less on country systems in fragile states, if these countries are perceived to involve extra risks to donors, beyond the risks measured by PFM Quality and Civil Liberties. Results in equation 2.4 suggest that the latter effect dominates. Use of country PFM systems is 10 percentage points lower on average in fragile states, other things equal, and this effect is significant at the .05 level. Controlling for the fragile states dummy, coefficients for PFM Quality and Civil Liberties are somewhat smaller than in the baseline specification of equation 1.3, but they remain significant at the .01 level.

Equation 3.1 adds the percentage of aid for each donor-recipient pair that is in the form of direct budget support (DBS), as reported in the PDMS. Budget support (which comprises 12.5% of aid on average in our sample) uses country PFM systems, by definition. When this variable is included, the regression in effect is testing for relationships between the regressors and use of PFM systems for project aid and other non-DBS aid. Coefficients for PFM Quality and Civil Liberties are again somewhat smaller in 3.1 than in 1.3, but they remain highly significant.

Results for PFM Quality are potentially weaker for donors other than the World Bank. The CPIA assessments are conducted by Bank staff, so they are particularly likely to affect decisions by the Bank on aid implementation. Equation 3.2 therefore replicates the base 20
# **<<B>>** Outcome Collection and Measurement

The outcome measures come from administrative data from PRISMA. These data specify whether the family took up the program when solicited by PRISMA marketers and whether the household turned in recyclables in each of the following four weeks.<sup>12</sup> On the basis of these data, we consider three outcome variables to represent a household’s participation in the recycling program: “Participates at any time” is an indicator equal to one if the family participated in the program and gave at least once.

“Participation ratio” is the ratio of the number of times a household turned in residuals over its number of opportunities to turn in residuals. This variable measures the strength of commitment to the program. In constructing this measure, the denominator, opportunities to turn in residuals, includes instances in which there was no one at the household when the residual collector visited.

“Participates during either of last two visits” is an indicator of whether the household turned in residuals during one of the last two canvassing weeks (weeks _n_ and _N_ of the marketing/enrollment campaign). It measures the initial persistence of households’ commitment to the program.

During the data collection process, we were unable to obtain outcome data for 1,468 houses owing to coordination problems with PRISMA, household migration, and households choosing to combine their recyclables with other households, which were excluded because we had no way of determining what percentage of recycling came from each participant. We were therefore unable to construct accurate outcome values for these households. We tested for differential attrition rates between treatment groups and found no significant differences. Our final sample for the participation study comprised 5,250 households.<sup>13</sup> 15
**Table A6: Regression results for hunger**

||(1)|(2)|(3)|
|---|---|---|---|
|VARIABLES|Hunger|Hunger|Hunger|
|Survey year: 2016||||
||-0.822***|-0.322*|-0.525**|
|Survey year: 2017|(0.255)|(0.187)|(0.198)|
||-0.726***|-0.718***|-0.403***|
|Inflation*Post|(0.199)|(0.255)|(0.148)|
||0.430***|||
|Food price inflation*Post|(0.145)|||
|||0.329**||
|Non-food price inflation*Post||(0.143)||
||||0.193*|
|Conflict|||(0.108)|
||0.00362**|0.00563***|0.00424**|
|Female household head|(0.002)|(0.002)|(0.002)|
||(0.036)|(0.020)|(0.042)|
|Education level of household head_Primary|(0.072)|(0.074)|(0.073)|
||-0.151*|-0.146*|-0.152**|
|Education level of household head_Secondary|(0.077)|(0.075)|(0.075)|
||-0.209*|(0.195)|-0.213*|
|Education level of household head_University|(0.121)|(0.119)|(0.123)|
||-0.437***|-0.449**|-0.450***|
|Education level of household_Other|(0.154)|(0.169)|(0.162)|
||0.011|(0.025)|0.034|
|Land ownership|(0.122)|(0.127)|(0.123)|
||-0.264**|-0.235**|-0.253**|
|Household head Unemployed|(0.104)|(0.109)|(0.107)|
||0.246|0.222|0.251|
|Household size|(0.182)|(0.182)|(0.191)|
||(0.008)|(0.012)|(0.010)|
|Household size-squared|(0.021)|(0.022)|(0.022)|
||0.000|0.000|0.000|
|2nd welfare quintile (Ref=1st quintile)|(0.000)|(0.000)|(0.000)|
||0.036|0.040|0.030|
|3rd welfare quintile|(0.151)|(0.147)|(0.146)|
||(0.182)|(0.164)|(0.186)|
|4th welfare quintile|(0.153)|(0.155)|(0.152)|
||-0.388***|-0.407***|-0.408***|
|5th welfare quintile (Richest)|(0.082)|(0.079)|(0.082)|
||-0.507***|-0.532***|-0.515***|
|Constant|(0.091)|(0.083)|(0.090)|
||2.499***|2.488***|2.513***|
|Observations|(0.118)|(0.115)|(0.115)|
|R-squared|702|702|702|

*** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses.

30
European Union in its goal of creating a monetary union through the provisions of the Maastricht treaty established ceilings on national deficits and debts and supporting provisions that there should be no bailout of any government by member central banks or by the European Central Bank. The European Union is also prohibited from providing an unconditional guarantee in respect of the public debt of a member state (Pisani-Ferry, 1991). These provisions were subsequently strengthened by the Growth and Stability Pact provisions (legislated fiscal rules adopted by the European Parliament). Most mature federations also specify no bailout provisions in setting up central banks with the notable exception of Australia until 1992 and Brazil until 1996. In the presence of an explicit or even implicit bailout guarantee and preferential loans from the banking sector, printing of money by sub-national governments is possible thereby fueling inflation. European Union guidelines provide a useful framework for macro coordination in federal systems but such guidelines may not ensure macro stability as the guidelines may restrain smaller countries with little influence on macro stability such as Greece but may not restrain superpowers like France and Germany as demonstrated by recent history. Thus a proper enforcement of guidelines may require a fiscal coordinating council. Recent experiences with fiscal adjustment programs suggest that while legislated fiscal rules are neither necessary nor sufficient for successful fiscal adjustment, they can be of help in forging sustained political commitment to achieve better fiscal outcomes especially in countries with divisive political institutions or coalition regimes. For example, such rules can be helpful in sustaining political commitment to reform in countries with proportional representation (Brazil) or multi-party coalition governments (India) or in countries with separation of legislative and executive functions (USA, Brazil). Fiscal rules in such countries can help restrain pork-barrel politics and thereby improve fiscal discipline. Von Hagen (2005) based upon a review of EU experiences with fiscal rules concludes that budgetary institutions matter more than fiscal rules. The EU fiscal rules may have encouraged European countries to strengthen budgetary institutions which in turn had welcome effects on fiscal discipline and fiscal outcomes.

|**Table 6**<br>**Fis**|**cal Rules at**|**a Glance**||||
|---|---|---|---|---|---|
|Country/Province|Budgetary<br>balance<br>controls|Debt<br>restrictions|Tax or<br>expenditure<br>controls and<br>establishment of<br>stabilization<br>funds|Referendum<br>for new taxes<br>and<br>expenditures|Penalties for<br>non-<br>compliance|
|EU- GSP|yes|yes|||Yes but<br>ineffective<br>for large<br>states|

15
# **_Table 1b: Gender Gap in Business Outcomes: Pooled Specification – Accounting for Firm Sector, Size and Age_**

||||**20**|**20**|||**2**|**021**|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||||Conditi|onal on Busin<br>100%|ess Beingin Op<br>Sales|eration<br>Lower|||
|||Time Spent||<br>|<br>|<br>||Time Spent|
||Business<br>closed<br>(1=Yes;<br>0=No)|<br>on Paid<br>Work<br>Activities<br>(Hours per<br>|Likelihood<br>reduced<br>workers<br>(1=Yes;<br>0=No)|employees<br>laid off in<br>last 30<br>days<br>(1=Yes;|reported to<br>be lower<br>than last<br>year<br>(1=Yes;|Sales %<br>lower<br>compare<br>to<br>previous|Business<br>closed<br>(1=Yes;<br>0=No)|<br>on Paid<br>Work<br>Activities<br>(Hours per<br>|
|||day)||<br>0=No)|<br>0=No)|<br>year(%)||day)|
|**_Pooled OLS_**|||||||||
|_Panel A: Gender Gap (Raw)_|||||||||
||0.040***|-0.888***|-0.005|0.039***|0.012**|0.129***|0.046***|-0.747***|
|_Female_|[0.00]|[0.05]|[0.01]|[0.01]|[0.00]|[0.03]|[0.01]|[0.13]|
|_Panel B: Gender Gap + Sector Controls_|||||||||
||0.037***|-0.849***|-0.006|0.033***|0.005|0.106***|0.042***|-0.727***|
|_Female_|[0.00]|[0.05]|[0.01]|[0.01]|[0.00]|[0.03]|[0.01]|[0.13]|
|_Panel C: Gender Gap + Sector + Firm Size + Firm_|||||||||
|<br>_Age + Role in Business Controls_|||||||||
||0.029***|-0.665***|0.004|0.024***|0.011**|0.076**|0.028***|-0.528***|
|_Female_|[0.00]|[0.05]|[0.01]|[0.01]|[0.00]|[0.03]|[0.01]|[0.14]|
|Constant|0.008|8.896***|0.349***|0.054***|0.562***|4.275***|0.063***|8.932***|
||[0.01]|[0.14]|[0.01]|[0.01]|[0.01]|[0.08]|[0.02]|[0.29]|
|Observations|130912|47346|64106|18156|81727|54112|19572|12025|
|Adjusted R-squared|0.05|0.06|0.06|0.04|0.03|0.02|0.01|0.05|
|Mean Male|0.17|7.83|0.44|0.08|0.68|4.68|0.10|7.08|
||Pooled|Pooled|Pooled|Pooled|Pooled|Pooled|Wave 7|Wave 7|
|Wave<br>Notes: * significant at 10% level, ** significant at 5% level, *** si|2020<br>gnificant at 1% le|2020<br>vel.|2020|2020|2020|2020|2021|2021|

<mark>[1] Female is a dummy variable where 1 = female-led business and 0 if male-led business</mark> <mark>[2] Regression adds controls for Business Sector (Panel B); and controls for Business Sector + Firm Size, Firm Age and Role in Business (Panel C).</mark>

[3] OLS regressions in Columns (1) to (6) Panels A-C using 2020 data include Region of the world Fixed Effects and controls for the 6 waves in 2020-time dummy variables.

- [4] OLS regressions in Columns (7) to (8) Panels A-C are for 2021 data and includes Region of the world Fixed Effects.

37
The propensity score function, therefore, includes a range of household and community level variables that would have influenced the allocation criteria above. Pre-intervention characteristics as reported in the IFLS 1997 survey are used throughout. Use of household and community level information reported in IFLS 2000, the post intervention period may cause endogeneity in the model. For instance, households’ familiarity with health care providers in 2000 may be a consequence of owning a healthcard. Similarly, household consumption reported in 2000 may be higher for households with healthcards than comparable households without cards, because of the latter’s reduced spending on health. Estimating the propensity score on the basis of pre-intervention attributes alone ensures that there is no endogeneity in the analysis.

For households, a wide range of variables were included that would not only indicate households’ eligibility for a healthcard to village committees but would also capture the households’ propensity to signal its needs to the committee. Socio-demographic characteristics of the head of household, such as sex, education level and employment status were included because they would indicate the level of social and economic deprivation faced by the household and influence what actions it took to obtain a healthcard. Households headed by females or by individuals who worked in sectors such as manufacturing and construction that were particularly affected by the crisis would have been regarded as more vulnerable. On other hand, heads of households with primary or a higher level of schooling would have placed greater value on access to health care, and consequently, made a conscious effort to obtain a healthcard. Household size and 26
# WPS4083

## Water Allocation Strategies for the Kat Basin in South Africa: Comparing Negotiation Tools and Game Theory Models

Ariel Dinar, World Bank, USA Stefano Farolfi, CIRAD and University of Pretoria, South Africa Fioravante Patrone, University of Genoa, Italy Kate Rowntree, Rhodes University, South Africa

**Abstract** . Governments and developing agencies promote participatory approaches in solving common pool resource problems such as in the water sector.  Two main participatory approaches have been applied separately, namely negotiation and mediation. In this paper we apply the RolePlaying Game that is a component of the Companion Modeling approach—a negotiation procedure, and Cooperative Game Theory (Shapley value and the Nucleolus solution concepts) that can be mirrored as a mediated mechanism, to a water allocation problem in the Kat watershed in South Africa.  While the absolute results of the two approaches differ, the negotiation and the cooperative game theory provide similar shares of the benefit allocated to the players from various cooperative arrangements.  By evaluating the two approaches, the paper provides useful tips for future extension for both the Role-Playing Game and the Cooperative Game Theory applications.

### World Bank Policy Research Working Paper 4083, December 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1