rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Marriage penalty by husband’s education

|Dependent variable|||Working|||
|---|---|---|---|---|---|
|Sample|SAR|BGD|IND|MDV|NPL|
||(1)|(2)|(3)|(4)|(5)|
|_Panel _|_A: No child _|_sample_||||
|Post-marriage|-0.129***|-0.144***|-0.175***|-0.107*|-0.053**|
||(0.010)|(0.023)|(0.012)|(0.062)|(0.023)|
|Post-marriage _×_ Husband higher education|0.066***|0.037|0.092***|0.093|0.036|
||(0.019)|(0.035)|(0.025)|(0.116)|(0.044)|
|No. of obs.|7848|1712|3779|434|1916|
|R-squared|0.277|0.152|0.164|0.174|0.132|
|Birth Cohort FE|Yes|Yes|Yes|Yes|Yes|
|Country _×_ Year FE|Yes|Yes|Yes|Yes|Yes|
|_Panel B: _|_Ignore chil_|_d sample_||||
|Post-marriage|-0.168***|-0.158***|-0.210***|-0.281***|-0.102***|
||(0.008)|(0.019)|(0.009)|(0.051)|(0.020)|
|Post-marriage _×_ Husband higher education|0.061***|0.060**|0.064***|0.184*|0.024|
||(0.016)|(0.025)|(0.019)|(0.094)|(0.038)|
|No. of obs.|9583|2163|4413|570|2434|
|R-squared|0.296|0.149|0.211|0.188|0.103|
|Birth Cohort FE|Yes|Yes|Yes|Yes|Yes|
|Country _×_ Year FE|Yes|Yes|Yes|Yes|Yes|

Note: Standard errors in parentheses clustered at the cohort level. All estimates weighted by cohort size. SAR - South Asia Region, BGD - Bangladesh, IND - India, MDV - Maldives, NPL - Nepal. No child sample refers matching post-marriage observations in the sample without children. Ignore child sample refers matching post-marriage observations in the full sample. Cohort FE refer to birth cohort. Husband education is the share of women in the pseudo-cohort whose husband has any post-secondary education. *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

26
- Billette de Villemeur, E. and Pineau, P.-O.: 2010, Environmentally Damaging Electricity Trade, _Energy Policy_ **38** (3), 1548–1558.

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35
learn about intrahousehold bargaining and its impacts can then inform program design and evaluation procedures.

The broader literature includes many papers that are less econometrically rigorous but that nonetheless provide additional useful information towards policy making. Given the convincing evidence that bargaining power is important in some specific cases, we should be more willing to accept the findings of less rigorous studies as well as those that simply demonstrate correlations.

Taken together, there are sufficient data already in place to support a greatly expanded focus on intrahousehold outcomes and bargaining power. Additional data at the individual level will allow for further and more detailed research. A growing literature is exploring the current conventional wisdom -- namely, that the patterns of evidence suggest that women’s education, incomes, and assets all are important aspects of women’s bargaining power.

# **References**

Adam, Christopher S. and Hoddinott, John and Ligon, Ethan. 2011. “Dynamic Intrahousehold Bargaining, Matrimonial Property Law, and Suicide in Canada” _._ CUDARE Working Papers 1113. University of California, Berkeley Departmentn of Agricultural and Resource Economics, Berkeley, CA.

Agarwal, Bina. 1997. “‘Bargaining’ and gender relations: Within and beyond the household.” _Feminist Economics_ 3 (1): 1-51.

Alderman, Harold, Pierre-Andre Chiapori, Lawrence Haddad, John Hoddinott and Ravi Kanbur. 1995. “Unitary versus Collective Models of the Household: Is It Time to Shift the Burden of Proof?” _World Bank Research Observer_ 10 (1): 1-19 Allendorf, Keera. 2007. "Do Women's Land Rights Promote Empowerment and Child Health in Nepal?," _World Development_ 35(11): 1975-88.

35
**Table 6: Heterogeneous Effects of Larger Loan Uptake**

**OLS**

|||**Male**|||**Femal**|**e**|
|---|---|---|---|---|---|---|
||All Members<br>(1)|All Business<br>Owners<br>(2)|Matched Business<br>Owners<br>(3)|All Members<br>(4)|All Busines<br>Owners<br>(5)|s<br>Matched Business<br>Owners<br>(6)|
|Female Brochure (FB)|-0.132**|-0.134*|-0.156|-0.01|-0.071*|-0.077|
||(0.051)|(0.071)|(0.113)|(0.034)|(0.038)|(0.054)|
|Business Training|0.061|0.097*|0.075|0.027|0.013|0.033|
||(0.045)|(0.054)|(0.070)|(0.026)|(0.032)|(0.052)|
|FB x Business Training|-0.042|-0.064|0.002|0.03|0.022|0.054|
||(0.065)|(0.080)|(0.108)|(0.035)|(0.044)|(0.071)|
|High Decision-making power (1=yes)|-0.017|-0.010|-0.039|-0.022|-0.053*|-0.101*|
||(0.024)|(0.031)|(0.068)|(0.024)|(0.030)|(0.054)|
|FBx High Decision making power|0.008<br>(0.005)|0.008<br>(0.007)|0.006<br>(0.014)|0.023<br>(0.031)|0.077**<br>(0.039)|0.138**<br>(0.068)|
|Digit Span Recall|-0.005|-0.014|-0.016|0.008|0.008|0.015|
||(0.006)|(0.010)|(0.017)|(0.005)|(0.007)|(0.013)|
|FB x Digit Span Recall|0.017**|0.024*|0.023|-0.009|-0.013|-0.019|
||(0.008)|(0.012)|(0.021)|(0.006)|(0.009)|(0.016)|
|Yrs. Education of Spouse|-0.038|-0.063*|-0.111|-0.011|-0.01|0.034|
||(0.028)|(0.038)|(0.069)|(0.025)|(0.033)|(0.064)|
|FB x Yrs. Education of Spouse|0.039|0.09|0.123|0.043|0.041|-0.012|
||(0.040)|(0.056)|(0.101)|(0.032)|(0.039)|(0.071)|
|Business owner (Yes=1)|0.031<br>(0.030)|||0.045**<br>(0.022)|||
|FB x Business owner|0.033<br>(0.042)|||-0.057*<br>(0.031)|||
|Mean Dependent Variable|0.20|0.24|0.25|0.08|0.08|0.11|
|N. Obs.|1880|1149|363|1,571|916|363|
|R-squared|0.19|0.19|0.18|0.12|0.13|0.18|

Note: The dependent variable takes value 1 if the member applied for a larger loan. All regressions are estimated using OLS methods and control for eligibility, own education, landholdings, membership in a mixed group, age, number of children, risk tolerance, as well as branch fixed effects. Standard errors are clustered at the borrower group level.The following symbols *, * * and ** * denote significance at the 10 , 5, and 1 percent level, respectively. See Appendix B for definition of variables.
34 -

-

Table 11: Proportion of Less-Developed Countries Offering Instruction in Selected Subjects of the Primary School Curriculum by Gross Primary Enrollment Rate, 1960s and 1980s (Constant Cases)

||Gr<br>Less th<br>(N|oss Primar<br>an 80 %<br>=36)|y Enrollmen<br>Greater t<br>(N=|t Rate:<br>han 80 %<br>39)|
|---|---|---|---|---|
||1960s|980s|1960s|1980s|
|Combined Language|100.0|100.0|100.0|100.0|
|National Lang.|86.1|88.9|89.7|89.7|
|LocalLang.|8.3|11.1|15.4|12.8|
|Foreign Lang.|38.9|47.2|23.1|30.8|
|Official Lang.|33.3|25.0|25.6|25.6|
|Mathematics|100.0|100.0|100.0|100.0|
|Science|86.1|97.2|94.9|100.0|
|Combined Social Science|94.4|100.0|97.4|100.0|
|Social Studies|19.4|52.8|33.3|66.7|
|History|75.0|52.8|71.8|41.0|
|Geography|69.4|47.2|71.8|38.5|
|Civics|27.8|25.0|38.5|28.2|
|Comb.MoralReligion|75.0|66.7|76.9|79.5|
|Religion|63.9|58.3|53.9|61.5|
|Moral Education|13.9|8.3|41.0|35.9|
|Aesthetic Ed.|94.4|100.0|100.0|97.4|
|Music|91.7|88.9|89.7|79.5|
|Art|91.7|100.0|97.4|89.7|
|Physical Ed.|91.7|100.0|97.4|92.3|
|Hygiene|19.4|25.0|48.7|48.7|
|Comb.Voc.Ed.|75.0|58.3|74.4|79.5|
|Manual Training|41.7|36.1|41.0|48.7|
|Agriculture|22.2|22.2|35.9|33.3|
|<br>Domestic Science|47.2|30.6|48.7|41.0|
|Vocational Ed.|5.6|0.0|7.7|10.3|
|Business|0.0|0.0|2.6|7.7|
|Other|27.8|38.9|30.8|41.0|
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- 16 -

**<u>Table 4. Comparison of household characteristics between treatment and control group</u>**

||_OPRC=_0|_OPRC=_1|Differen|ce|
|---|---|---|---|---|
||Mean|Mean|Mean|Std.Err.|
|HH size|5.24|<br>5.42|<br>0.18|0.13|
|Male HH head|0.80|<br>0.79|<br>-0.01|0.02|
|HH head age|42.64|<br>41.57|<br>-1.06|0.85|
|HH head educational|4.23|<br>4.33|<br>0.11|0.19|
|attainment|||||
|Concrete floor of the|0.11|<br>0.11|<br>0.00|0.02|
|house|||||
|Borehole use as a main|0.650|<br>0.586|<br>-0.064|0.024 ***|
|source of water supply|||||
|Tap water as a main<br>|0.008|<br>0.021|<br>0.013|0.006 **|
|source of water supply|||||
|Toilet use (pit latrine,|0.481|<br>0.447|<br>-0.034|0.024|
|ventilated, or flush)|||||
|Electricity or gas use as a<br>main source of lighting<br>HH assets:|0.065|<br>0.050|<br>-0.015|0.011|
|Solar panels|0.062|<br>0.080|<br>0.018|0.013|
|Vehicle|0.002|<br>0.006|<br>0.004|0.003|
|Motorcycle|0.024|<br>0.023|<br>-0.001|0.008|
|Bicycle|0.618|<br>0.635|<br>0.018|0.024|
|Mobilephone|0.256|<br>0.275|<br>0.019|0.022|

The summary statistics are shown in **Table 5** . The average value of crop production is US$801 or about ZKW4,000 per household. Recall that in Zambia about 78 percent of the rural population lives below the national poverty line.<sup>6</sup> In Chipata and Katete District, the poverty rates are estimated at 72.6 percent and 82.9 percent, respectively (World Bank 2015). The agro-climatic potential exceeds US$4,300, which is five times more than currently produced. The currently dominant production system is clearly subsistence farming, with little fertilizer and few pesticides. Average land cultivated is about 1.6 ha. Only about one-third of the households participated in market transactions.

> 6 In Zambia, the income distribution is highly skewed. According to the Government of Zambia, the threshold of poverty is defined by the nation’s 2010 food basket to meet minimal nutritional requirements, which is ZKW96,366 per household (6 household members). This is significantly higher than many households’ incomes. ZIPAR (2013), analyzing the income distribution in the country, indicates that the threshold of household income for the lower income group is ZKW510 in 2012. This group is estimated to account for about 24 percent of the total population.
- McFadden, Daniel. 1974b. ―The measurement of urban travel demand,‖ _Journal of Public Economics_ , 34: 63-82.

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World Bank 2012a. ―Bangladesh Poverty Assessment‖ _forthcoming._

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21
# **2 Overview of the Economy and the Emerging Role of HEs in the Labor Market**

## **2.1 Growth and Poverty Reduction in Tanzania**

10. Tanzania has achieved very modest gains in poverty reduction amidst sustained economic growth.<sup>5</sup> Over the last five years, the economy has grown at a rate of at least 5 percent per year (Table 2.1). However, the most recent data shows a weak impact on consumption of key commodities; the percentage of the population in poverty fell from 35.6 in 2001 to 33.6 in 2007 (Table 2.2).

**Table ‎2.1:  Selected Economic Indicators (percent*)**

||**2004 **|**2005**|**2006**|**2007 **|**2008**|**2009**<sup>**.**</sup>|
|---|---|---|---|---|---|---|
|GDP growth|7.8|7.4|6.7|7.1|7.4|5.0|
|Annual Inflation (CPI, period avg.)|4.7|5.0|7.2|7.0|10.2|11.9|
|Private credit to GDP|8.5|9.7|12.1|14.9|16.2|19.5|
|Current account balance to GDP|-2.9|-6.1|-8.0|-9.3|-12.3|-11.1|
|Exchange rate (TSh per USD)|1043|1165|1266|1132|1259|1319|
|Interest rate (T-Bond)|9.3|8.3|9.3|17.1|7.8|-|
|Domestic Revenue to GDP|11.9|12.4|14.1|16.0|15.9|16.1|
|Overall budget deficit to GDP (after grants)|-4.9|-4.9|-4.8|-1.6|-4.5|-6.1|
|Domestic borrowingto GDP|1.0|1.5|1.2|-1.5|0.8|1.6|

_Note: *unless otherwise specified Source: Ministry of Finance and Economic Affairs, (various years); World Bank, 2010_

**Table ‎2.2:  Poverty Incidence (percent)**

||**1991**|**2001**|**2007**|
|---|---|---|---|
|Dar es Salaam|28.1|17.6|16.4|
|Other urban|28.7|25.8|24.1|
|Rural|40.8|38.7|37.6|
|Tanzania Mainland|38.6|35.6|33.6|

_Source: World Bank, 2008b_ 11. Close to 850,000 new job-seekers enter the labor market every year. Recognizing this trend, job creation has become a flagship policy of the government. In the search for workable solutions, an analysis of the labor market is critical. With its growing share in the non-agriculture labor market, the household enterprise sector plays an essential role in job and income creation, and can provide a route out of poverty.

> 5 See Utz (2008).

5
attracted to areas that already have firms established in their lines of business – _localization economies_ . Why? Because new firms can learn from existing ones about business processes, new technologies and informal regulations, as well as benefit from a pool of trained workers. Finally, firms may value the overall economic diversity of an area. These are often referred to as _urbanization economies,_ and are associated with good access to a broad range of producer and consumer goods that typically increase with size of the agglomeration.

A recent paper surveying industrial location decisions in developing countries identifies the following factors as being important (Deichmann et. al 2008). These are:

- Factor prices.

- Quality and cost of complementary utility services, including electricity, water and telecommunication.

- Market access as a function of the size of the region that can be reached given existing transport infrastructure.

- Agglomeration economies as measured by the presence of firms in own industry and of firms in related—e.g., buying or supplying—industries.

- Labor and other regulations.

In many of the papers covered in the survey, benefits of agglomeration economies (both own industry and overall diversity), market access and infrastructure endowments outweigh the costs imposed by congestion, increasing wages and land prices (Deichmann et. al 2008). Using firm survey data from India, Lall and Mengistae (2005) find that localization economies, as measured by own industry concentration, have significant bearing on firm location decisions across cities. This effect is the highest for technology intensive sectors. Deichmann et al. (2005) find similar evidence for manufacturing firms in Indonesia. Localization effects are more important for high-technology (e.g., office computing) and natural resource-based industries (such as wood or rubber and plastic).

Empirical work on urbanization economies is mixed. Empirical studies for the United States by Bostic (1997), Garcia-Mila and McGuire (1993), and Glaeser et. al (1992) show that diversity in economic activity has positive impacts on regional economic growth. On the other hand, also using data for the United States, Mirachy

-  9
# TABLE 1 EGYPT 1975-80 THE DIRECT IMPACT OF WEANING ON CHILD MORTALITY (before age 60 months)

% per cent weaned (ceased breastfeeding prior to child death) RR relative risk of child death (weaned / not weaned) AR attributable risk of child death

-------------FERTILITY CONTROL--------------ALL (N-7375 children) NO (N-4714 children) YES (N-2661 children) EST. (95 % CI) EST. (95 % CI) EST. (95 % CI) WEANED at any age % 51.19 (50.05 - 52.33) 44.63 (43.21 - 46.05) 62.80 (60.96 - 64.63) RR 2.06 ( 1.43 - 2.98) 2.21 ( 1.34 - 3.65) * AR 35.22 (16.63 - 49.67) 35.10 (10.51 - 52.94) * WEANED before 12 months % 12.54 (11.79 - 13.30) 9.61 ( 8.77 - 10.45) 17.74 (16.29 - 19.19) RR 4.29 ( 2.91 - 6.33) 5.13 ( .3.02 - 8.71) 3.35 ( 1.78 - 6.30) AR 29.21 (17.84 - 39.01) 28.39 (13.52 - 40.71) 29.41 ( 7.91 - 45.89) WEANED at 12 months or later % 57.25 (55.88 - 58.63) 57.39 (55.58 - 59.19) 57.07 (54.95 - 59.18) RR 1.95 ( 1.23 - 3.09) 2.60 ( 1.44 - 4.68) * AR 35.17 ( 9.44 - 53.58) 47.81 (17.48 - 66.99) *

based on Weibull proportional hazards model controlling for: next pregnancy of mother, parity, age of mother, preceding birth interval length, sex, region, urban/rural residence, fertility control, education of mother, education of father based on data from the Egyptian Fertility Survey 1980 restrictions: children born within 60 months of survey to married women last two children only no multiple births no marital disruption prior to child death Muslim only note: some children still breastfeeding at survey (not weaned) some children breastfed until died (not weaned)

*-not statistically distinguishable

from 0.0 for per cent (%) and attributable risk (AR) and from 1.0 for relative risk (RR) 33
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