rafmacalaba/datause-encoder-data huggingface dataset:
opened between May 2017 and May 2018 and operated from their opening onwards. Summary statistics Table A1 (collected after various months of implementation through the endline survey described in Section 4) reveals the effectiveness of the program components 1 and 2. First, each preschool had on average 4 instructors and 75-79 children enrolled in 2018 and 2019, respectively. An average of 47 children attended the preschools a small effective student-teacher ratio. Most were built daily, implying fairly preschools with a playground (94%) and provided educational games for children (76%). Most preschools also had notebooks or writing paper (56%), pens and pencils (55%), and drawing books or cards (54%). They also had leisure games (56%) and storybooks for children (57%). Instructors had, on average, 8.6 years of schooling, and for most of them, this was their only job. The preschools were open for three hours a day for ten months every year. However, component 3 was not implemented as planned. Although the plan was to hold monthly parenting education sessions, they were held every three months on average.

In terms of delivery modality, the DICIPE program was delivered through a partnership between the Ministry of Education and Human Development (MINEDH) and thirdparty providers. By the time of the program launch, the MINEDH’s implementation capacity was constrained by a parallel rapid expansion in access to primary education and its almost no experience with ECD programs. Fortunately, there was substantial local capacity to implement ECD programs within international and local NGOs. In this sense, the delivery of the ECD package was operated by third-party providers, which were competitively selected and supervised by MINEDH.

# **3 Experimental Design**

The experimental design started with the selection of eligible communities. First, three provinces (out of five provinces participating in the overall DICIPE program) and the two districts within each province—Macomia and Chiúre (Cabo Delgado); Eráti and Memba (Nampula); Angonia and Changara (Tete)—were selected in collaboration with the MINEDH based on their vulnerability, relevance for the intervention, and the extent to which they 8
# **The Regional Dimension of North-South Trade-Related R&D Spillovers***

**Maurice Schiff**<sup>**a**</sup> **World Bank**

## **and**

## **Yanling Wang Carleton University**

JEL Code: F15, O33, O47 Keywords: North-South Trade; R&D; Regional Spillovers a. Corresponding author: M. Schiff, World Bank, 1818 H St, NW (MC3-303), Washington, DC 20433. Tel.: 202-473-7963. Email: <mschiff@worldbank.org>.

* The views expressed in this paper are those of the authors and should not be attributed to the World Bank, its Board of Executive Directors or the governments they represent.
employees work in production. Only professionals and commercial workers each account for less than 10 percent of the total sample.

**Table 3. Gender and Occupation (percent)**

|Occupation|Full<br>sample|Female|Male|
|---|---|---|---|
|Administration|11.8|_25.4_|8.8|
|Commercial|6.7|7.6|_6.5_|
|Manager|10.0|5.4|11.0|
|Production|54.9|54.1|_55.1_|
|Professional|_3.5_|1.6|3.9|
|Support|13.2|6.0|14.7|
|Total||||
|Percent|100|100|100|

_Note:_ The values show the percentage of each category of workers with each level of education. For example, 25.4 percent of all female manufacturing workers in the sample work in administration. _Source:_ Round Three of the 1994 Regional Program on Enterprise D)evelopment for Ghana.

Table 3 divides the employees into six groups by occupation: manager (encompassing managers, supervisors, and foremen); administration (administrative workers and clerks); commercial (commercial workers, salespeople, maintenance workers, and technicians); support (support staff); production (production workers); and professional (engineers, accountants, and others). The regression analysis reveals that only the impact of managers is statistically significant anid positive in the wage equation, controlling for other individual and firm characteristics. The reference group for the analysis is workers in maintenance, skilled production workers, other production workers, support staff, and trainees. The calculated occupational wage premium is 33 percent for managers and -40 percent for support staff (see appendix table B-1). Surprisingly, the estimated productivity of managers is not statistically significantly different from that of 19
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Turk, James L. and Norman W. Bell. "Measuring power in families." _Journal of Marriage and the Family_ , 34 (1972), 215-223 Twyman, Jennifer, Pilar Useche, and Carmen Diana Deere. 2015. "Gendered Perceptions of Land Ownership and Agricultural Decision-making in Ecuador: Who Are the Farm Managers?" _Land Economics_ 91(3): 479-500 Valientes, Rodger M. 2015. "Male-female wage gap decomposition in agriculture-based employment in the Philippines." _Journal of Economics, Management, & Agricultural Development_ 1(1): 45-62 World Bank. 2012. Republic of the Philippines: Gender and Development Mainstreaming. Country Gender Assessment 30
kilometer, were also estimated. The coefficients were insignificant, and the coefficients for per capita GDP, shares of services and industry, and urbanization were left largely unchanged.'9 The major limitation of this analysis is that the shares of each sector in a department may be jointly determined with education levels of men and women. For example, services may require more educated women than educated men, so provinces that have relatively more educated women will tend to have a larger share of services in GDP. The issue of causality between share of services and the demand for education of women is left unresolved. This is a crippling limitation for purposes of deciding policy.

I address this issue by estimating regressions by cohorts. Assuming that the sectors' shares in GDP are relatively stable across departments, the correlation between schooling and sectoral share should be stronger for younger cohorts if a higher share of services and industry _leads to_ a higher demand for education. On the other hand, if education intensive activities are concentrated in areas with high exogenous education levels, this would imply uniformly strong correlations between education levels and structure of the economy across _all_ age groups. The regressions discussed above indicate that the correlations are weak for older cohorts, implying support for the view that causality runs from economy structure to schooling levels, and not vice versa.

The results in this section are similar to those found in Gill and These results, which are not reported here, are available from the author.

45
# **<u>1. Introduction</u>**

Women’s bargaining power within the household is generally recognized as an important development outcome (Duflo 2012). It is commonly measured through survey questions on women’s participation in decision-making. However, research comparing the answers of husbands and wives to these survey questions has uncovered widespread and systematic discrepancies between their reports, raising concerns with this measurement approach (e.g., Anderson, Reynolds & Gugerty 2017; Annan et al 2021; Ghuman, Lee, & Smith 2006; Jejeebhoy 2002). Pinpointing the mechanisms underlying this discrepancy is crucial for understanding the relationship between self-reported decision-making and bargaining power and, consequently, the reliability of decision-making as a measure of bargaining power. However, very little quantitative work has been done to illuminate the reasons behind these disagreements.

Using data from a spousal survey of 421 farmer couples in the Philippines, this paper tests for the three potential mechanisms underlying spousal response discrepancy that have been advanced in the existing literature. First, we test for asymmetric interpretation: measurement error caused by men and women understanding survey questions in different ways. Spouses may provide diverging reports of household decision-making if they have different interpretations of what it means to make a decision, to be a decisionmaker, or what qualifies as a “major” decision, leading to diverging answers to questions on general decisionmaking. These interpretations are asymmetric if they systematically fall along gendered lines. Second, we test for asymmetric information: spouses may have different perceptions of the overall balance of decision-making within the couple because they may not be privy to decisions made in activities in which they do not participate. Third, and finally, we test for enumerator effects—specifically the effect of enumerator gender—on reports of decision-making.

Comparing spouses’ reports of agricultural decision-making, we find that discrepancies are widespread and systematic, echoing studies from a multitude of different regions and contexts. Overall, 50.2 percent of spouses disagree on who makes decisions in any given area, although rates of discrepancies vary somewhat by decision. Most discrepancies occur when one spouse reports that decision-making was made by both husband 2
“Other” and then hand-matched bank names from the survey with bank names from Fitch Connect. Handmatching was necessary because the survey included spelling mistakes and in several cases the name of the bank was in local language in the survey and in English in Fitch Connect. After dropping observations with banks for which Fitch Connect does not report any financial information, we were left with a dataset of approximately 13,000 firms and more than 500 banks. While we use the matched dataset to analyze the effect of state-ownership on firms’ access to finance, we retain all the unmatched firm records to test for potential statistically significant differences in firm characteristics between matched and unmatched firms.

Note that WBES also includes data from 5 advanced European economies, but we do not use these data in our empirical analysis which focuses on emerging and developing economies. After dropping these five countries we are left with 11,840 records with a bank-firm match.

# Summary Statistics

We report summary statistics of firm characteristics in Table 2, 3, and 4. Table 2 includes the full sample of firms, while Table 3 and Table 4 report the sub-samples of firms matched to private banks, and to stateowned banks, respectively. There is a good degree of similarity among these 3 groups. The average firm size, measured by the number of workers employed by the firm, is 75 for the full sample, 81 for the subsample of firms matched to private banks, and 108 for the subsample of firms matched to state-owned banks. The average age is 18 years for the full sample, and 20 for the 2 subsamples of matched enterprises. Financing conditions faced by firms also appear to be similar across the three samples. The reported value of collateral needed for a loan, measured as percent of the loan value, is 167 for the full sample, 188 percent for firms that have a relationship with private banks, and 183 percent for firms that are financed by government-owned banks. Similarly, the proportion of loans requiring collateral is 76 percent when considering the full sample, 75 for firms matched to private banks, and 74 for firms matched to governmentowned banks. Interestingly, the number of years that firms report to have operated without formal registration is 0.4 for firms that are financed by private banks, 0.6 for firms financed by state-owned banks, and 1 for the full sample of firms. This difference seems plausible, as informal firms are less likely to apply for credit from the commercial banks sector, and commercial banks are less likely to extend credit to the informal sector.

We then report summary statistics of bank characteristics in Table 5 (full sample of matched banks), Table 6 (private banks only), and Table 7 (state-owned banks only). A simple comparison of these three groups shows that the average state-owned bank is more than twice as large as the average private-owned bank, is 7
<mark>WPS3559</mark>

# Decomposing Changes in Income Inequality into Vertical and Horizontal Redistribution and Reranking, with Applications to China and Vietnam

## by

## Adam Wagstaff

Development Research Group and East Asia & Pacific Region, The World Bank, Washington DC, USA

### **Abstract**

It is acknowledged that the lack of any systematic link between growth and income inequality does not necessarily mean that economic growth is not accompanied by major changes in the underlying income distribution. This paper employs a method devised to decompose the redistributive effect of a tax to analyze the extent to which vertical redistribution associated with changing incomes over time is offset or reinforced by horizontal redistribution and reranking. The paper uses panel data from China and Vietnam over a period when both countries grew spectacularly as they transitioned from planned to market economies, and yet experienced smaller annual percentage increases in income inequality. The results suggest that substantial amounts of horizontal redistribution and reranking in both China—and to a lesser extent Vietnam—more than offset pro-poor vertical redistribution. Without the horizontal redistribution and reranking, the Gini coefficient for China might have fallen between 1989 and 1997—substantially so.

**Author’s contact details:** The World Bank, 1818 H Street NW, Washington, D.C. 20433, USA.  Tel. (202) 4730566. Fax (202)-522 1153. Email: awagstaff@worldbank.org.

**Keywords:** Income inequality; vertical redistribution; horizontal redistribution; China; Vietnam.

**Acknowledgements:** My thanks to Will Dow for his income data from the China Health and Nutrition Survey, and to Bert Hofman, Indermit Gill, Peter Lambert, Magnus Lindelöw, Branko Milanovic and Martin Ravallion for comments on earlier drafts.

## World Bank Policy Research Working Paper 3559, April 2005

_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._
According to this measure, in the case of Morocco, none of the pension funds provide true incentives for delayed retirement. Changes in pension wealth are negative for all retirement ages (see Figure 2). Even for the RCAR, the results suggest that current adjustments to the accrual rate would not be sufficient to motivate individuals to delay retirement. The CMR is a peculiar case. The system provides strong incentives to individuals who are 59 years old to delay retirement by one year. This is because at age 60 the accrual rate increases to 2.5% from 2% for all previous years of service.

**Figure 2:   Changes in Net Pension Wealth by Retirement Age**

<!-- Start of picture text --> 1<br>0.8<br>CMR<br>0.6<br>0.4<br>0.2<br>RCAR<br>0<br>55 57 59 61 63 65 67 69<br>-0.2<br>CNSS<br>-0.4<br>-0.6<br>-0.8<br>-1<br>Retirement age<br>Same assumptions as for Figure 1. In addition, the discount rate used for present value calculations<br>is set at 2%.<br>Source: Authors’ calculations.<br> (proportion average earnings)<br>Gain in pension wealth of delaying retirement<br><!-- End of picture text --> In practice, however, we do observe that in all pension funds individuals retire at various retirement ages. The question is really in which pension fund the incentives for early retirement are stronger. It is here where the maps ngu(R, α , λ ) of each of the pension funds become helpful. These maps were calculated under the assumption that wages grow in real terms at 3% per year and the rate of time preference ρ is 0.98 annually. One hundred combinations of the parameters α and λ were used: α ∈ [0.2,0.7] and λ ∈ [0.5,5]. The results are presented in Figure 3.

The map for each fund is very different and characterizes the benefit formulas and eligibility conditions in a unique way. Each “line” within the map is associated with a given combination of the parameters α and λ . In all funds, the lines can be grouped into three categories. First, lines which are always below the horizontal axis. These are associated with utility functions that never generate incentives to delay retirement. The gain in expected utility from delaying retirement is always negative and individuals retire as soon as possible – in our example at age 55. The second type of lines involves lines that are always above the horizontal axis indicating that the gains from delaying retirement are always positive. These lines correspond to individuals who never have incentives to retire in the period of analysis (they could retire afterwards). Finally, there are lines which cross the horizontal axis. The more relevant are the ones that cross the 8
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38
# **<u>Table 2: OLS Amount of Crop Sales (in local currency units)</u>**

||<br>Kenya Coffee|Malawi Tobacco|Rwanda Coffee|
|---|---|---|---|
|n|270|498|362|
|Long recall|2,764.26|**2,543.84***|-5,093.12|
||(3,821.65)|(1,378.03)|(5,081.49)|
|Long recall|2,958.60|**3,743.56*****|-5,053.94|
||(3,737.30)|(1,098.12)|(5,014.92)|
|Long recall x (land – mean land)|1,465.13|**-2,491.21*****|-50.55|
||(1,182.63)|(677.00)|(2,065.05)|
|Long recall|2,880.11|**2,696.19***|-4,853.44|
||(3,780.22)|(1,405.35)|(5,018.99)|
|Long recall x (head’s education – mean<br>head’s educ)|-167.33|-165.11|793.73|
||(398.42)|(254.48)|(1,423.65)|
|Long recall|5,566.68|**2,511.70***|-7,363.24|
||(4,179.17)|(1,437.95)|(6,544.00)|
|Long recall x female head|**-7,019.12*****|328.68|7,970.89|
||(2,591.88)|(1,815.00)|(10,359.69)|
|Mean of sales amount|7,598|10,174|14,811|
|Mean standard error|1,112|441|2,264|
|Median value|3,563|8,348|6,000|

Note: *** p<0.01, ** p<0.05, * p<0.1. Samples are restricted to households with any sales. Robust standard errors are in parentheses. “Long recall” is a binary variable that indicates that the interview took place 8-11 months after harvest. The omitted category is 0-3 months after harvest. Other covariates included but not presented are acres of land, female headship, age and age squared of head, years of education of head, household head is main decision maker, geographic and season dummies, and the original unit in which the harvest was reported.

20