rafmacalaba/datause-encoder-data huggingface dataset:
WPS4085

# * The Economics of Consanguineous Marriages

Quy-Toan Do† Sriya Iyer‡ Shareen Joshi§

### **Abstract**

The institution of consanguineous marriage—a marriage contracted between close biological relatives—has been a basic building block of many societies in different parts of the world. This paper argues that the practice of consanguinity is closely related to the practice of dowry, and that both arise in response to an agency problem between the families of a bride and a groom. When marriage contracts are incomplete, dowries transfer control rights to the party with the highest incentives to invest in a marriage. When these transactions are costly however, consanguinity can be a more appropriate response since it directly reduces the agency cost. Our model predicts that dowry transfers are less likely to be observed in consanguineous unions. We also emphasize the effect of credit constraints on the relative prevalence of dowry payment and consanguinity. An empirical analysis using data from Bangladesh delivers robust results consistent with the predictions of the model.

JEL Classification Code: J1, I1, O1 Keywords: Marriage, consanguinity, dowry, credit constraints

## World Bank Policy Research Working Paper 4085, 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_ _<u>http://econ.worldbank.org.</u>_

*We would like to thank Gary Becker, Alan Bittles, Francis Bloch, Jishnu Das, Partha Dasgupta, Timothy Guinnane, Hanan Jacoby, Biju Rao, Hamid Sabourian, Paul Schultz and Chander Velu for helpful discussions.

†Development Economics Research Group. World Bank. qdo@worldbank.org. ‡Corresponding author: Faculty of Economics. University of Cambridge. Sriya.Iyer@econ.cam.ac.uk §Economics Department. University of Chicago. shareen@uchicago.edu
PEFA assessments conducted between 2005 and 2007, De Renzio (2009) uses as a dependent variable the average of the 28 questions, and finds that the quality of public financial management systems is positively associated with country size (population), per capita income, and aid dependence (ODA/GNI). Andrews (2010, 2014) notes that countries tend to suffer “implementation gaps” on public financial management systems in general, scoring better on PEFA’s _de jure_ indicators of laws and official procedures on the books than on _de facto_ indicators of PFM practices and performance. Scores also tend to be higher in areas of PFM where reform implementation requires the engagement of only a relatively small number of “concentrated agents” (e.g. in a Ministry of Finance), in contrast to procurement and other areas where implementation requires the engagement of a larger number of “distributed agents” (e.g. across sectoral ministries or sub-national governments). Knack (2014) finds that donors’ use of recipient countries’ national procurement systems (defined as the share of aid to government that is “not subject to additional or special requirements by donors for procurement of works, goods and services”) is positively and significantly related to the quality of procurement systems, as measured by PEFA question 19. Each 1-unit increment in the 7-point PEFA-19 scale is associated with a 3 percentage point increase in use of country procurement systems.

Numerous countries have multiple Enterprise Surveys, but only a small number have multiple PEFA assessments with the second version of the procurement question. It is therefore not practicable to construct a time-series cross-sectional panel. In constructing a cross-sectional database, we maximized the number of countries where PEFA assessments precede the relevant Enterprise Survey in time. In the relatively few cases where more than one PEFA assessment using the second version of the procurement question was available, this meant selecting the first one that was conducted. We then selected the first Enterprise Survey conducted subsequent to that PEFA assessment. In many cases, however, there was not one, and we then selected the most recently completed Enterprise Survey, as long as it was from 2009 or later. These decision rules produced a sample of 88 countries. For the median country, the Enterprise Survey actually precedes the PEFA assessment by two years. We therefore rely rather heavily on the assumption that the quality of procurement systems is fairly persistent, i.e. that practices in effect in year t are very highly correlated across countries with those in effect in year t-2. However, our key results on kickbacks (but not on bidding) reported below are highly robust to dropping all countries for which the Enterprise Survey precedes the PEFA assessment in time, despite the drastic decline in sample size.

9
Policy Research Working Paper 5666

# **Abstract**

Using a new tax database for 28 countries and a variety of econometric methods, this paper contributes to the debate on the effects of fiscal policy on economic activity in a number of ways. The analysis finds that tax cuts have a stimulative effect on economic growth in developing countries. Lowering the personal income tax rate by 1 percentage point, or cutting revenues by 1 gross domestic product of gross domestic product increases gross domestic product by 0.3–0.4 percent on impact and 0.8 percent in the long run. The author finds that cuts in personal income taxes are more effective in stimulating growth than cuts in corporate or valued added tax rates. The author incorporates debt dynamics into a fiscal vector autoregression model for a number of developing countries. Existing estimates of the effects of fiscal policy on growth use linear time-series methods, which may assess the effects of fiscal policy along a debtpath that is unsustainable. Incorporating the non-linear relationship between government expenditure, taxes, and debt alters estimates of the impact of fiscal policy on gross domestic product in several countries. In Brazil, for example, conventional time-series methods may overstate the effects of fiscal policy on gross domestic product, by ignoring the detrimental effects of debt accumulation.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. 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 author may be contacted at e.ilzetzki@lse.ac.uk.

_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 3. Common Trends: Schoolingand Income<br>School enrollment<sup>a</sup>|
|---|
|Year = 2002<br>0.0343**<br>(0.005)|
|Absence * Year = 2002<br>0.0238|
|(0.0143)|
|N<sup>1</sup><br>11,679|
|Per capita income<sup>b</sup>|
|Year = 2000<br>0.498**|
|(0.111)|
|Year = 2001<br>1.1019**|
|(0.1432)|
|Absence * Year = 2000<br>−0.0845|
|(0.4641)|
|Absence * Year = 2001<br>0.6144|
|(0.6028)|
|N<sup>2</sup><br>5,066|

_Source: Familias en Accion_ Evaluation Data.

_<mark>Note:</mark>_ N<sup>1</sup> is the number of children in the sample at the baseline with nonmissing school enrollment data. N<sup>2</sup> is the number of households in the sample at baseline that report income retrospectively for 1999, 2000, and 2001. Standard errors, clustered at the municipality level, in parentheses.

a Dependent variable is school enrollment. Estimates are from a household fixed effects model, controlled for quadratic in child age and gender (female = 1). Reference year is 2001.

b Dependent variable is per capita household labor income. Estimates are from a household fixed-effects model. Reference year is 1999.

34
impact the educational market, and/or if the private sector responds to rank conditions or factors associated with it in a similar fashion. We believe this to be unlikely, since the historical record shows that PCs are used only as revenue collection units, while political representation, and with it the delivery of public services, is centered at the Union-Council level, an alternate and non-overlapping classication.

Three empirical tests, in the spirit of Altonji et al. (2005), provide further support for the exclusion restriction. First, village socio-demographic characteristics are uncorrelated with the eligibility status of the village: Eligible and non-eligible villages do not dier along any observable dimensions other than those (population and area) on which the instrument is based. Second, the instrument does not predict the construction of any other type of public school nor any of a range of other public investments. Third, only local population rank corresponding to the ten-kilometer guideline matters. An analogous but more expanded local rank measure (top rank at the next higher administrative level which has a radius three times that of a PC's) does not predict girls secondary school construction. If entrepreneurs are more likely to pick locally top-ranked villages, we would expect this result to hold for the slightly more expanded top-rank measure as well. Furthermore, in a falsication exercise, we conrm that our instrument has no impact on private school location decisions in administrative units where there was no GSS construction. Thus, local population rank on its own does not aect private sector location decisions.

One natural question is whether this increase in the supply of teachers has led to an increase in educational provision or a sectoral shift from public to private schools. There are several reasons to think that the growth of private schools has had a positive impact on educational outcomes, both in terms of enrollment and learning outcomes. In a representative sample of households in the country (the Pakistan Integrated Household Survey 1998), overall enrollment is higher for villages with private schools (61 percent versus 46 percent), as is female enrollment (56 percent versus 35 percent). Moreover, Kim et al. (1999) provide strong causal evidence that private schools increase enrollment by showing that a randomly 4
# **Infrastructure and Economic Growth in East Asia**

Stéphane Straub,<sup>†</sup> Charles Vellutini,<sup>‡</sup> and Michael Warlters<sup>♫</sup>

> † University of Edinburgh. Email: stephane.Straub@ed.ac.uk

> ‡ Toulouse School of Economics (Arqade) and ECOPA. Email: charles.vellutini@ecopa.fr ♫ World Bank
# **Appraisal Econometrics for Proposed Transport Corridors: Optimal Placement, Intervention Design, and Wider Economic Benefits**<sup>**_*_**</sup>

Martin Melecky World Bank

**Keywords:** Transport Infrastructure, Wider Economic Benefits, Economic Corridor, Impact Appraisal, Methodology

**JEL Classification** : F15, R40, H40

> * I thank Mark Roberts, Simon Alder, Jean Francois Arvis, Stephane Hallegatte, Martin Rama, Georgi Panterov, Michele Ruta, and Harris Selod for useful debates and feedback, and Yan Xu for valuable research assistance. This is a background paper for the report ADB, DFID, JICA, WBG (Forthcoming): “The WEB of Transport Corridors in South Asia.” The World Bank, Washington, D.C.
differential estimates suggests that Mining and Agriculture are subject to more significant NTMs as compared to Manufacturing, where the difference between the tariffs and the border differentials that we estimate is the smallest.

Fourth, the differences between the CEFTA-EU border and the EU-EU border are the largest in Agriculture (about 20% tariff equivalent), followed by Services (about 17% tariff equivalent), Mining (about 11% tariff equivalent), and Manufacturing (about 8% tariff equivalent). We find this heterogeneity intuitive. The border between CEFTA and EU is likely to impose the most trade costs on agricultural products for two reasons: (1) most tariffs between EU and CEFTA countries have gone to zero for all industrial products, but for some agricultural products there are still import tariffs for CEFTA products into the EU and vice versa, and (2) even with low tariff barriers, the EU imposes high sanitary and phytosanitary (SPS) conditions which results in traders facing long delays at the borders crossing into EU (for e.g. from Serbia into Croatia). The large border differences in services trade can be due to various unified standards within the EU, which impose barriers to services trade with outsiders. Another possible explanation is that trade in services is highly localized consumption. Finally, a possible explanation for the relatively small borders in Mining and Manufacturing is that the barriers to trade in those sectors have been decreased.

Stimulated by the importance of the manufacturing sector for the CEFTA economies and by the potential for very heterogeneous differences in the impact of borders on trade between CEFTA and EU vs. trade within the EU within the aggregate manufacturing sector, next we obtain and present estimates for 11 disaggregated manufacturing industries. Our results appear in Table 2, where, as before, we include the PPML gravity estimates in Panel A, the differences between the CEFTA-EU and EU-EU borders in Panel B, and the tariff equivalents of the border differences are reported in Panel C of Table 2. Standard errors in panels B and C are constructed with the Delta method.

Without going into details, we note that our conclusions regarding the impact of the standard gravity variables for total manufacturing are supported by the disaggregated manufacturing estimates from Table 2. More importantly, the results in panels B and C reveal that the differences in the borders between CEFTA and EU vs. the borders within the EU are quite heterogeneous across the eleven manufacturing sectors in our sample. Overall, we confirm the finding that the CEFTA countries face larger barriers to trade with the EU as compared to the barriers faced by the EU 19
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1997. Table 1 illustrates the acceleration of manufacturing growth and GDP growth in general in India since the early 1980s.

**Table 1: Historical Sectoral Growth Rates in India**

|PLAN|Agriculture|Manufacturing|GNP|
|---|---|---|---|
|FIRST(1951-56)|2.5|5.0|3.7|
|SECOND(1956-61)|2.2|4.5|4.2|
|THIRD(1961-66)|-.30|5.3|2.8|
|ThreeAnnual Plans (1966-69)|4.5|2.7|3.9|
|FOURTH(1969-74)|1.3|1.8|3.4|
|FIFTH(1974-79)|3.6|5.9|5.0|
|SIXTH(1980-85)|3.1|4.9|5.4|
|SEVENTH(1985-90)|3.1|6.2|5.9|
|EIGHTH(1992-97)|3.4|6.9|6.8|

Source: Government of India, _Economic Survey_ , 2000-2001, Tables S-4 and S-5. Notes: GDP is estimated to be 27% industry, 25% agriculture for 2000. (World Bank India Data Profile: www.worldbank.org/data) In 1991, a new industrial policy ushered in a period of truly dramatic reforms. The number of industries reserved for the public sector was halved.<sup>6</sup> Licensing was abolished in all but a small group of industries. Perhaps more importantly from our perspective, there were complementary changes in policies relating to trade and financial markets. Foreign investment was encouraged, quantitative import restrictions were largely abandoned and tariffs were significantly reduced. A phased reduction in import tariffs has been undertaken since. The peak tariff rate was reduced from over 300 to 110 percent in 1992, 85 percent in 1993, 65 percent in 1994, 50 percent in 1995 and 25 percent in 2003.<sup>7</sup> It seems plausible that these regulatory reforms, taken together, made it much easier for firms to adopt imported technologies. We also might expect that changes in the pattern of skill demand experienced by developed countries adopting these technologies would be repeated in India in the liberalized environment of the 1990s. The ASI does display a clear pattern of skill 5
<!-- Start of picture text --> 0.1 0.25 0.05<br>0 Shock 0.2 No shock -0.050 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76<br>1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 0.15<br>-0.1<br>-0.1 0.1 -0.15<br>No shock<br>-0.2<br>0.05<br>-0.2 No shock -0.25<br>0 1 Shock6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 -0.3 Shock<br>-0.3 -0.05 -0.35<br>(a) NX Y : d (b) NX Y : em (c) Interest rate ( r )<br><!-- End of picture text --> Figure 6: The equilibrium dynamics with and without the shock: net exports and the interest rate.

path. These predictions are roughly consistent with the observed changes in investment around the 2008-2009 crisis. The model predicts that the investment share of output in the developed economy should drop by 6.4% in the period following the shock. In the US, the investment share dropped by 5.1% between 2007-2009. The subsequent recovery is also of a similar magnitude: the model predicts that after two (additional) periods, the investment share in the developed economy should increase by 0.96%, which is roughly consistent with the 0.8% increase in the investment share in the US between 2009 and 2011. For the emerging economy, the model predicts an increase in the investment share of 3.7%, which is somewhat smaller that the increase in the investment share in China between 2007-2009 of 6.5%.<sup>15</sup> Third, the model predicts a temporary current account reversal: in the period of the shock, the developed economy becomes a net exporter, and the emerging economy becomes a net importer. While the magnitudes in the model are large compared to the data, this result is qualitatively in line with current account movements during the crisis: the current account surplus in China dropped form 8.5% of GDP in 2006 to 4% of GDP in 2010, and the current account deficit in the US dropped from 6% of GDP in 2006 to 3.3% of GDP in 2010.<sup>16</sup>

> 15Source: World Development Indicators, Gross Capital Formation (% of GDP).

> 16Source: World Development Indicators, 2013: Current account balance (% of GDP).

34
for moving among women were school (7%), following parents/relative (29%), and marriage (49%). Among men, following parents and relatives were also frequently reported (35%), but compared to women, marriage was less so (21%).

Using our three rounds of survey data, we can also examine sequencing of moves further from the baseline community. Table 2 shows the composition of our sample with respect to residence in Round 2 (2008) and Round 3 (2009), compared to the baseline location. Among those who moved to a nearby village from 2007 to 2008, 90% continued to reside in a nearby village. That is, 10% of these young adults moved even further away from the baseline between 2008 and 2009.

Table 3 compares characteristics of migrants and non-migrants at baseline and at the end of the survey, during the final round in 2009. Several differences among the two groups are apparent. First, respondents living in a household headed by one of their parents are significantly less likely to move, compared to those who are other relatives of the head. This is this case for both men and women. Among men, 77% of non-movers were the child of the head, compared to 59% of movers. Among women, these numbers are 79% and 67%, respectively. Other relationships to the household head seem to matter for men, and not to the same degree as to women. For instance, men who are nephews or siblings of household heads are significantly more likely to move than those who were not, but the effects of being a niece or sibling are not statistically significant for women. Young men with both parents alive are more likely to be stayers compared to those with at least one parent deceased, but there is no difference in orphanhood rates by migration status for women.

Among both women and men, those in the wealthier households at baseline (the highest wealth quartile) are more likely to move between baseline and follow-up, suggesting that there is 11
specific determinants of crime. Our daily and monthly fixed effects account for variation in crimes over the course of a week (for instance, there may be more crimes on Fridays) and the year (for instance, there is less outdoor activity in the winter and generally lower crime). Our results are robust to several alternative sets of fixed effects, including a month _×_ year fixed effect. To alleviate concerns about smooth variation in temperature within months, we also show that our results are robust to including a day-of-year fixed effect (see Table A1).

_βk_ is the coefficient of interest and measures the approximate percentage change in daily crimes if the maximum temperature is in temperature bin _k_ relative to the 60-65<sup>_◦_</sup> F bin. We cluster standard errors at the county level (Abadie et al., 2017) and weight our regressions by the total population of the county in each year, as captured in the ACS.

We estimate this fixed effects Poisson model using maximum likelihood (Hausman et al., 1984; Wooldridge, 1999; Correia et al., 2019). We choose a Poisson model both because of the count and skewed nature of the outcome crime data and because of the properties of the fixed effects Poisson estimator. Since there are many county-days with no crimes, our data has many zeros. The Poisson model accounts for these zeros more easily than a linear fixed effects model with log( _Cidmy_ ). It also avoids the bias caused, when the share of zeros is non-trivial, by some common methods of transforming data to account for zeros (Nichols et al., 2010).

In our primary analysis _Cidmy_ represents the count of total crimes in a county on a given day. We conduct several alternative analyses to examine how heat impacts different types of crimes or impacts crimes in neighborhoods with different characteristics. In those analyses the specification is the same, but we change the outcome to be the count of crimes in a particular category. For example, _Cidmy_ can be the count of violent or non-violent crimes. We also conduct analyses where _Cidmy_ is the count of crimes in block groups where the median house was built prior to 1990 or after 2000 or in block groups within various income and poverty bins.

15