rafmacalaba/datause-encoder-data huggingface dataset:
# 1. Introduction

Over time, an increasing number of countries have been mandating maternity leave provisions that meet or exceed the standard of 14 weeks set by the International Labor Organization (ILO).<sup>2</sup> In tandem, more countries have introduced legislation to grant a certain amount of paid leave to fathers following the birth of a child. Policy movements in this area recognize the growing body of evidence that granting parents a period of leave following the birth of a child can be beneficial for both children and their parents. However, the consensus on the impact of maternity and paternity leave on women’s labor market outcomes remains equivocal and while the empirical evidence on the link has been growing in depth, it has not necessarily been growing in breadth. Indeed, much of the research has been based on data from a select set of mostly high-income economies. Through this research, we present a global overview of how family leave policies have evolved using a novel data set covering 190 countries over 52 years and consider the relationship between maternity and paternity leave and women’s labor market outcomes.

The literature on family leave policies and their effects considers different types of leave—maternity, paternity and parental; paid, partially-paid and unpaid—and a range of outcomes, including wages and employment for both parents, child development, and fertility rates. The pioneering study of Winegarden and Bracy (1995) shows that periods of paid maternity leave may be associated with several positive outcomes. Using data from 17 OECD countries, results from the authors’ structural estimation show that extending the duration of maternity leave is associated with lower infant mortality rates, higher rates of female labor force participation and increased fertility. Results from a reduced-form estimation, however, show that when the direct and indirect effects are combined, the positive association with fertility rates disappears, but the positive correlation between female labor participation and lower infant mortality remains significant. Ruhm (1998) considers the impact of parental leave on the gap between female and male labor market outcomes, using data from nine countries in the European Union (EU) from 1969 to 1993. The author finds that paid parental leave is associated with higher levels of female employment, but that excessively long durations of leave are associated with a decline in women’s wages. Leave for a period of three months is associated with increased employment without a corresponding wage penalty. On the other hand, Klerman and Leibowitz (1999) consider the United States’ Family and Medical Leave Act of 1993 and show that maternity-leave legislation is unlikely to have a major effect on female job continuity, because new mothers already presented high levels of job continuity before its introduction.

2 ILO Maternity Protection Convention, 2000 (No. 183), Article 4.
analysis methodology. Based on the existing literature, we categorize so called governance institutions in three broad clusters as “Administrative Quality”, ( _QA_ ), “Public Accountability” ( _PA_ ), and “Political Stability” ( _PS_ ). We also generate a global indicator of governance _(GOV)_ which summarizes these three aspects<sup>15</sup> . This method allows in a second step to evaluate the contribution of the initial indicators to the private investment’s decisions. This information will be useful to understand which factors explain the low investment’s performances of the region. In this paper, human development and economic policy variables have been similarly processed.

Our empirical approach relies also on panel data (cross section-time series analysis) which is suitable -- contrary to previous studies -- to jointly assessing the impact of economic policies and governance institutions on private investments: the time series dimension captures the variability of policies through time and the cross section dimension covers the governance variables which tend to evolve slowly. This paper also acknowledges the deficiencies of existing data on governance as being subjective and outcome-based rather than representing the quality of actual institutions. However, this paper points out that these deficiencies do not constitute a severe problem in analyzing the effects of governance on private investment. Whether actual or not, what is important for the private investors is the perceived quality of governance at the time of investment.

This paper also benefits from a newly constructed data set on private investment. The ultimate purpose of the existing paper on investment is to determine which factors are central in the decision-making process of private entrepreneurs to invest. However, due to the lack of comparable data on private investment, most of the earlier studies use aggregate investment as a proxy for private investment. Later, the International Finance 6
sequent financial development, though it is not statistically significant.<sup>5</sup> The fact that the positive effect of trade on financial development is not strong in the OECD sample is not surprising, as a large share of OECD trade is with other OECD countries, and the financial comparative advantage is not likely to be especially important in this trade relative to other determinants, such as increasing returns. By contrast, in developing countries trade openness has a negative effect. Column 2 replicates the regression from Column 1 in the non-OECD sample. The coefficient on the trade variable is negative and significant, with a p-value of 8%.

The key effect that our model illustrates is that trade affects financial development differently depending on how strong are the forces of financial comparative advantage, as proxied by per capita income. Thus, we augment the basic specification by including an interaction term between trade and per capita GDP: This allows us to pool the sample and test for the differential impact of trade. While now we are agnostic about the sign of the main effect of trade, β1, we are interested in whether the coefficient on the interaction term, β2, is positive and significant. Column 3 presents the results of estimating equation (17). To ease interpretation of the interaction coefficient, all variables have been demeaned. The main effect of trade openness on financial development is positive but not significant in the full sample. The trade-income interaction term, by contrast, is positive and significant at 1% level. Column 4 presents a specification that includes a full set of GDP interaction terms, allowing the effect of other regressors on financial development to be affected differentially for rich and poor countries. The coefficients of interest are virtually unchanged in the more flexible specification.

The results show that trade affects financial development differentially based on a country’s level of income. In particular, for a country at the mean of the per capita GDP distribution, the effect of trade on financial development is given by the coefficient on the main trade term, slightly positive in this sample, though not statistically different from zero at 0.3. A country which is in the 25th percentile of per capita income distribution is affected by trade negatively, with the derivative of financial development with respect to trade being -0.2. In a country in the 10th percentile of the per capita income distribution, that negative effect has magnitude of -0.7. By contrast, in a country that is in the 75th

> 5 The coefficient in the OECD sample is not significant, however, if outlier Japan is dropped from the sample, the coefficient increases by a factor of more than 1.5 and becomes significant at 2% level. Japan is indeed an exception, as it is relatively closed but experienced a very strong financial sector expansion in the last 30 years.

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calendar, in a third crop season, which is irrigated if there is sufficient water. In Southeast Asia, the introduction of short-season rice varieties allowed farmers to plant the second rice crop with a short-season variety and follow it in the dry season with high-yielding corn or soybeans, which have longer growing seasons (100 days or more) than those of traditional varieties. In north India, farmers were able to follow a short-season rice variety with a short-season wheat variety, thus growing two major grain crops a year.<sup>15</sup> In such instances, nev, rice varieties have allowed farmers to adopt improved varieties of other crops. Improved facilities for local **research** and extension could play a major role in adapting crop varieties **to** fit particular niches in the crop rotation and to develop varieties resistant to local pests and diseases.1 6 3.22 Improvement or modernization of irrigation systems to permit diversification into non-rice crops has been complicated by the sharp drop in rice prices in the early 1980s. Until that time, most rice irrigation projects, including those for which all the infrastructure down to the onfarm water delivery works was built, were viable. Under the economic conditions of the 1980s, the only viable investments in rice irrigation projects are those that take advantage of sunk costs in existing infrastructure, and a detailed analysis of each project would be needed because of the sensitivity of the rate of return to low rice prices. Investment in improving the tertiary system and the distribution system may not be justified unless there is, as a result of using the water saved through more efficient operation, either a substantial increase in rice yields or an increase in cropping intensity, or both. Increased production of rice alone may not economically justify such improvements; the land may have to be planted to higher-value crops. Investment required for crop 15/ See Barker and Herdt, pp. 26-27.

- 16/ This section draws on field surveys conducted in Indonesia and reported in Stephen D. Mink, Paul A. Dorosh, and Douglas H. Perry, "Corn Production Systems," and in Stephen D. Mink, 'The Economics of Input Use," in C. Peter Timmer, ed., The Corn Economy of Indonesia (Ithaca: Cornell University Press, 1987), pp. 62-104.
Haddoud, M. Y., A. K. E. Onjewu, W. Nowiński, and P. Jones (2021). “The determinants of SMEs’ export entry: A systematic review of the literature.” _Journal of Business Research_ 125: 262–278.

<mark>Hanousek, Jan, Anastasiya Shamshur, and Jiri Tresl (2019). “Firm efficiency, foreign ownership and CEO gender in corrupt environments.”</mark> _<mark>Journal of Corporate Finance</mark>_ <mark>59: 344-360.</mark> <mark>Hsieh, Chang‐Tai, Erik Hurst, Charles Jones, and Peter J. Klenow (2019), The allocation of talent and U.S. economic growth.”</mark> _<mark>Econometrica</mark>_ <mark>87(5): 1439-1474.</mark> <mark>Hurley, D. and A. Choudhary (2016). “Factors influencing attainment of CEO position for women.”</mark> _<mark>G</mark> ender in Management_ <mark>31(4): 250-265. https://doi.org/10.1108/GM-01-2016-0004</mark> <mark>Islam, Asif and Mohammad Amin (2016). “Women managers and the gender-based gap in access to education: Evidence from firm-level data in developing countries.”</mark> _<mark>Feminist Economics</mark>_ <mark>22(3): 127-153. DOI: 10.1080/13545701.2015.1081705</mark> Islam, Asif, Isis Gaddis, Amparo Palacios López, and Mohammad Amin (2020). “The Labor Productivity Gap between Formal Businesses Run by Women and Men.” _Feminist Economics_ 26(4): 228—258.

<mark>Islam, Asif, Silvia Muzi, and Mohammad Amin (2019). “Unequal Laws and the Disempowerment of Women in the Labour Market: Evidence from Firm-Level Data.”</mark> _<mark>The Journal of Development Studies</mark>_ <mark>55(5): 822-844. DOI: 10.1080/00220388.2018.1487055</mark> Jayachandran, Seema, and Ilyana Kuziemko (2011). “Why do mothers breastfeed girls less than boys? Evidence and implications for child health in India.” _Quarterly Journal of Economics_ 26(3): 1485–1538.

Jayachandran, Seema and Rohini Pande (2017). “Why are Indian children so short? The role of birth order and son preference.” _American Economic Review_ 107 (9): 2600–2629.

<mark>Klasen, S. (2020). “From ‘MeToo’ to Boko Haram: A survey of levels and trends of gender inequality in the World.”</mark> _<mark>World Development</mark>_ <mark>128: 1-10.</mark> <mark>Klasen, Stephan (2019). What explains uneven female labor force participation levels and trends in developing countries?</mark> _<mark>The World Bank Research Observer</mark>_ <mark>34(2): 161–197.</mark> <mark>Klasen, Stephan, TU THI NGOC Le, Janneke Pieters, and Manuel Santos Silva (2021). What drives female labour force participation? Comparable micro-level evidence from eight developing and emerging economies.</mark> _<mark>The Journal of Development Studies</mark>_ <mark>57(3): 417-442.</mark> <mark>Levine, O. and M. Warusawitharana (2021). “Finance and productivity growth: Firm-level evidence.”</mark> _<mark>Journal of Monetary Economics</mark>_ <mark>117: 91–107.</mark> 31
# **_Annex B:  Estimation of GDP Projections to 2030_**<sup>**_3_**</sup>

# <u>Estimation Method</u>

- Data from 1995 to 2020 are obtained from the World Economic Outlook database.

- Data from 2014 to 2020 are IMF estimates.

- Data from 2020 to 2035 are projections based on the following assumptions:

1. Cobb‐Douglas production function with constant returns to scale: Y = A*K^(1‐ alpha)*L^(alpha)

2. Labor share (alpha) = 1/3 3. TFP growth (A) and Capital growth (K) are constant at steady state 4. Labor force growth is equal to population growth 5. Data on population growth are taken from the UN‐DESA World Population Prospects 2012 (medium fertility assumption)

# <u>Steps</u>

1. Derive the projections for annual growth rates of real GDP ‐‐> (World Economic Outlook data base (WEO_NGDP_RPCH)

2. Rebase the WDI constant GDP series (2011 GDP deflator = 100) in LCU ‐‐> WDI_GDP_constant_LCU2011 3. Derive the projections for real GDP at 2011 prices using projected growth rates (2021‐ 2035) to real GDP estimates from 2020 onward ‐‐> REAL GDP PER CAPITA 4. Apply WPP 2012 population projections based on medium fertility assumption to derive projected real GDP per capita (expressed in million LCU per capita) ‐‐> REAL GDP PER CAPITA 5. 2011 PPP factors are applied to convert constant GDP in LCU to international $ ‐‐> REAL GDP PER CAPITA 6. A multilevel model with country and region random effects is estimated to derive GNI projections from GDP projections ‐‐> REAL GNI PER CAPITA

> 3   GDP estimation for 2030 was prepared by Patrick Eozenou (Health Economist, World Bank) for the working paper by Olusoji Adeyi, Caroline Ly, Patrick Eozenou, Allyala Nandakumar, Ariel Pablos‐Mendez and Timothy Evans, "The economic transition of health in Africa: a call for progressive pragmatism to shape the future of health financing".

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countries, and are primarily the result of trade exacerbating income inequality. By contrast, consumption gains tend to be more evenly spread across households.

While trade-offs are prevalent, our findings also suggest that liberalization would be welfare enhancing in the vast majority of countries in our study: in a large part of the developing world, the current structure of tariff protection is inducing sizable welfare losses. Explaining what drives these patterns is beyond the scope of this paper but an interesting avenue for future research.

# **References**

Anderson, J. and P. Neary (1996). “A New Approach to Evaluating Trade Policy,” The Review of Economic Studies, vol. 63(1), pp. 107–125.

Andrew Young School of Policy Studies. (2010). Andrew Young School World Tax Indicators (Volume 1).

Antras, P., A. de Gortari, and O. Itskhoki (2017). “Globalization, Inequality, and Welfare,” Journal of International Economics, 108, pp. 387-412.

Arkolakis, C., A. Costinot, and A. Rodriguez-Clare (2012). “New Trade Models, Same Old Gains?,” The American Economic Review, vol. 102(1), pp. 94–130.

Arkolakis, C., A. Costinot, D. Donaldson, and A. Rodriguez-Clare (2015). “The Elusive Pro-Competitive Effects of Trade,” mimeo.

Artuc, E., S. Chaudhuri, and J. McLaren (2010). “Trade Shocks and Labor Adjustment: A Structural Empirical Approach,” American Economic Review Economics, vol. 100(3), pp. 1008–45.

Artuc, E., D. Lederman, and G. Porto (2015). “A Mapping of Labor Mobility Costs in the Developing World,” Journal of International Economics, vol. 95(1), pp. 28–41.

Atkin, D. and D. Donaldson (2015). “Who’s Getting Globalized? The Size and Implications of Intranational Trade Costs,” mimeo.

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Supplementary Pension) scheme, which is a mandatory public scheme, based on individual accounts, fully funded, and financed from employer and employee contributions (or the government for unemployed workers and those on parental leave). Another two schemes are also mandatory and fully funded, while two additional schemes are voluntary and have a much narrower scope. Though established by law and considered public, these schemes follow pure return/risk trade-offs in their investment strategies.

Coverage of the three pillars is very high. It is universal in the public pillar, reaches 80 percent of the labor force under occupational plans (outside the mandated supplementary schemes), and is close to 40 percent in the third pillar. It is estimated that over 90 percent of workers participate in either a second or third pillar plan.

The aggregate assets of the three pension pillars at the end of 2004 amounted to DKK 1,822 billion (€ 244 billion). This corresponded to 125 percent of GDP, up from 83 percent in 1995 (Table 1). The supplementary pension funds (ATP, SP and LD) accounted for 23 percent of total assets, while 77 percent belonged to second and third pillar institutions. The social pension scheme does not have accumulated assets.

**Figure 1: Pension Assets**

||1995|2000|2004|
|---|---|---|---|
|Total Pension Assets (DKKbillion)|847.2|1507.2|1822.1|
|Total Pension Assets/GDP(%)|83.1%|116.5%|124.8%|
|ATP(% oftotal)|15.2%|16.4%|16.9%|
|SP(% oftotal)||1.4%|2.5%|
|LD(% oftotal)|4.3%|4.1%|3.2%|
|Life Insurance Companies (% of<br>total)|41.8%|43.1%|44.5%|
|Multi-Employer Pension Funds (% of<br>total)|17.2%|17.9%|18.6%|
|CorporatePension Funds (% oftotal)|4.0%|2.9%|2.2%|
|Banks (% oftotal)|17.5%|14.3%|12.2%|

Source: Statistics Denmark and the Danish Financial Supervisory Authority Total contributions to the supplementary schemes and second and third pillar institutions amounted to DKK 90 billion in 2004 (Table 2). This corresponded to 6.1 percent of GDP, up from 4.3 percent in 1995. Contributions to occupational pension plans absorbed 70 percent of the total and personal pension plans accounted for 22 percent. ATP contributions represented 8 percent of the total. SP contributions absorbed 10 percent in 2000 but were suspended in 2004. The share of occupational pension plans and of life insurance and multi-employer pension funds increased over time.

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that, in poor settings, individuals have come to treat sickness and doctor‘s visits as a regular part of their daily lives rather than an extraordinary event.

Figure 1 illustrates this effect in the observational data. The figure plots the number of acute illnesses reported in the second period of data collection on acute illnesses as reported weekly in the first period. The second period data is divided into weekly and monthly recall period. Clearly weekly recall is higher than monthly. Further, the higher the number of illnesses in the initial year, the higher the number of illnesses and the larger the gap between monthly and weekly reports is in the next period. Interpreting this gap as ―forgetting,‖ the results suggest that forgetting occurs in strict proportionality with the number of health episodes, a result that even extends into the unreliable area of the data where observations are sparse.

Finally, Table 5 presents a robustness check on our results to account for missing observations. Specifically, in the weekly survey, surveyors were instructed to visit every household on the same day every week to ensure that the reference period was benchmarked to the previous visit. One of the repercussions of this rigid survey schedule was that 15 percent of our weekly observations were missing. In around half of these cases, the observation is missing because the surveyor did not visit the household due to a national holiday and in the remaining half, it is missing because the house was locked or the respondent could not be found despite repeated visits during the day. These missing variables create a particular problem when we aggregate weekly reports into monthly data since in the monthly recall surveys, a week outside the house would not result in a missing observation. In our previous regression specifications, we took the extreme position that the missing variables correspond to a fully healthy report for the individual in the missing week—this ensured that we computed a lower bound for the recall effect. In Table 5, we present estimates from two alternate assumptions—that the data were ―missing at random‖ (which is certainly true for national holidays when surveyors did not visit the household) or that the missing data corresponded to a report of sickness with an associated doctor‘s visit. The estimates from these two alternate specifications are presented as the ―Missing at Random‖ (MAR) rows and the ―Upper Bound‖ (UB) rows. The ―Lower Bound‖ (LB) rows replicate the estimates from previous regressions for comparability. As seen in the table, the MAR estimates are somewhat higher but fairly close to the LB estimates, while the UB estimates are an order of magnitude higher. The true recall effects are likely somewhere between the MAR and UB estimates, and probably close to the MAR. This is because the simultaneous interviews by anthropologists on the team suggest that, in some cases, households would return to their villages in order to better cope with severe illness. If this is the case, absence may be correlated with illness, leading MAR to underestimate the true recall effect.

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