rafmacalaba/datause-encoder-data huggingface dataset:
tion, lower infant mortality, and more wealth, represented by the plotted circles. Therefore, the pooled regressions are essentially flat, potentially misleadingly showing no association between open defecation and infant mortality or a count if household assets included in the – DHS consistently with Deaton’s original result.

# **4.3 Do short mothers cause short children?**

One candidate explanation for widespread stunting among Indian children that Deaton and – Dr`eze (2009) highlight is that Indian mothers are small. What if there were a direct effect – independent of genetics and of the rest of the environment of a too-small mother, causing restricted intrauterine growth, leading to lasting stunting? If so, then in principle Indian children could be short, on average, despite healthy modern environments and adequate nutrition, merely because their mothers were stunted by depravations of the past.

Both the possible existence of this mechanism and any plausible magnitude are debated in the medical and epidemiological literature. Discussion dates to Ounsted et al.’s (1986) classic paper, which matched data on the birth weight of 1,092 children born in Oxford, UK with self-reported birth weights of family members to show that very low or very high birth weight babies are likely to have parents that are low or high birth weight, respectively, with a greater correlation with mothers than fathers. From this they hypothesize that mothers’ own _in utero_ deprivations can restrict fetal growth of the children they have as adults. Although some later scholarship has criticized the conclusion that the constraint dates back to a mother’s _fetal_ development,<sup>24</sup> it is much more widely accepted that a mother’s size and net nutrition before and during pregnancy influence her child’s birth weight and subsequent growth (Martorell and Zongrone, 2012; Chiolero, 2010). For example, Ceesay et al. (1997)

> 24In 2008 the paper was republished for commentary in the _International Journal of Epidemiology_ ; discussion noted that clean causal identification is rare in this debate. Leon (2008) summarizes that “[Ounsted et al.’s (1986)] ideas about the prepotency of maternal constraint are of less value and the hypothesis that in humans the degree of constraint a mother exerts on her offspring’s fetal growth is set by her own _in utero_ experience is not supported by [their] own data or that published subsequently” (258); Magnus (2008) and Cnattingius (2008) essentially agree, although also see Hanson and Godfrey (2008) and Horta et al. (2009)

23
difference between _pT_ and _pF_ – and higher traded volumes), as per-ton profits decrease by 13.1 percent rather than by 15.3 percent. When the complementary policy aims the trader segment, the results revert (traders’ per-ton profits fall more while exporters’ fall less than those with FUNPI contribution).

Instead, when the complementary policy aims both segments of the supply chain the net effect is a more competitive environment, which benefits farmers (see Table 8). Nevertheless, a 10 percent cost reduction is insufficient to recover farm-gate prices to pre-FUNPI tax levels, which fall short by 15.6 percent.

A full recovery in the farm-gate price could be achieved with a reduction in intermediation costs of 47 percent (as shown in scenario VIII in Table 8). Comparing ex ante and ex post equilibria, the channels through which the cost reducing policy brings the farm-gate price to ex ante level are (i) a recovery in intermediate price (it ends up 5.4 percent below the ex-ante level) and a tightening of exporters and traders margins (27 percent and 6 percent, respectively). In short, given a structural situation in the cashew sector, the complementary policy should strongly aim for cost reductions, which pass through to farm-gate prices throughout (albeit imperfect) competition.

**Table 8. Base Scenario – FUNPI contribution and complementary policies on costs (2)**

|**Policy**|**(I) Base**<br>**Scenario -**<br>**no FUNPI**|**(II) = (I) +**<br>**FUNPI**|% change<br>(vs I)|**(VII) = (I) + FUNPI**<br>**+ 10% reduction**<br>**in exporters and**<br>**traders costs**|% change<br>(vs I)<br>|**(VIII) = (I) + FUNPI**<br>**+ 47% reduction**<br>**in exporters and**<br>**traders costs**|% change<br>(vs I)|
|---|---|---|---|---|---|---|---|
|_International Price (USD/ton)_|799.88|799.88||799.88||799.88||
|_Trade taxes (USD/ton)_|93.50|199.88||199.88||199.88||
|_International Price net of taxes (USD/ton)_|706.38|600.00|-15.1%|600.00|-15.1%|600.00|-15.1%|
|_Intermediate Price (USD/ton)_|519.54|426.66|-17.9%|439.58|-15.4%|491.33|-5.4%|
|_Farm-gate Price (USD/ton)_|435.95|351.00|-19.5%|368.07|-15.6%|435.97|0.0%|
|_Exporters + cashew processors profit (USD/ton)_|73.80|67.20|-8.9%|63.81|-13.5%|48.99|-33.6%|
|_of which, exporters (USD/ton)_|70.23|61.78|-12.0%|58.66|-16.5%|44.73|-36.3%|
|_of which, cashew processors (USD/ton)_|348.26|441.14|26.7%|428.22|23.0%|376.47|8.1%|
|_HHI exporters and cashew processors_|487|510||489||397||
|_Traders profit (USD/ton)_|40.94|34.66|-15.3%|34.31|-16.2%|32.66|-20.2%|
|_HHI traders_|256|256||256||256||
|_Quantity (tons)_|156024|140000|-10.3%|143363|-8.1%|156029|0.0%|

Columns IX to X in Table 9 simulate the joint effect of the FUNPI contribution and a complementary policy that levels the playing field at the export link, by reducing costs of a given number of inefficient exporters. An unambitious policy (say, improving the competitive condition of 2 out of 47 inefficient exporters) helps with the recovery of the farm-gate price, which would fall 15.7 percent (rather than 19.5 percent caused by FUNPI).

20
savings for investment (garment sector, construction and tourism). Of these four key drivers, agriculture had a relatively limited exposure to the crisis (Guimbert 2010). The next figure shows that after many years of high growth, the economy was strongly hit by the global financial crisis in 2008 with growth declining from 6.7% in 2008 to 0.1% in 2009. However, the negative shock was temporary as growth resumed quickly to 6.0% by 2010.

**Figure 1: Cambodia GDP growth (%) – total economy and key sectors**

<!-- Start of picture text --> 100.0<br>80.0<br>Agriculture<br>60.0<br>Garments and footwear<br>40.0 Construction and real<br>estate<br>20.0 Tourist arrivals<br>total GDP<br>0.0<br>-20.0<br>1995 1997 1999 2001 2003 2005 2007 2009 2011<br><!-- End of picture text --> Note: growth rates for GDP in 2000 constant prices, except ‘tourist arrivals’ (annual number of international visitors). Source: National Institute of Statistics, authors’ calculation.

In this paper, we look at the responses of individual firms to the large negative shock between 2008 and 2009. For this, we use two firm surveys that were implemented respectively in August 2007 - March 2008 (Investment Climate Survey, ICS 2007/2008) and in July-November 2009 (the Cambodia Rapid Business Survey, CRBS 2009). Both surveys randomly sampled firms in Phnom Penh and in four other main cities in Cambodia (Battambang, Kampong Cham, Siem Reap and Sihanouk Ville). The sample was limited to firms in the formal sector that were registered with the Ministry of Commerce and which had at least five employees. Because no full census of establishments was available at the time, a sampling frame was constructed from the registry of firms maintained by the Ministry of Commerce, the registry of firms maintained by the Garment Manufacturers Association in Cambodia (GMAC), a list of tourism firms 4
schooling (nonfarm). This reflects the fact that the sons born into nonfarm households gain more from the higher schooling of a father, although they start from a lower intercept.

The picture for rural China is different (please see figure 4). The evidence in Table 2 shows that there is no statistically significant difference across the farm and nonfarm households in the intercepts of the intergenerational persistence regressions (p-values are 0.279 (IGRC intercept) and 0.43 (IRC intercept)). When combined with the evidence on IGRC and IRC in Table 1, this implies that the schooling attainment of the sons in rural China converges to virtually the same steady state (8.53 years of schooling) irrespective of whether the father is a farmer or is engaged in a nonfarm occupation.<sup>32</sup>

# **(5.3) Structural Change and Cross-Sectional Schooling Inequality**

To understand the implications of the higher variance in the nonfarm households in India for the cross-sectional variance in rural schooling, it is important to consider the structure of the rural economy (i.e., proportion of fathers employed in the nonfarm sector) and both the within group and between groups variances. Denote the proportion of nonfarm households by _ω_ , then we can write the long-term variance as: where _μn_ and _μf_ are the long-term means (the steady state) of father’s education in farm and nonfarm households, respectively. The effects of a marginal increase in the proportion of nonfarm sector is given by: As discussed in sections (5.1) and (5.2) above, there are no significant differences in the long-term means or long-term variances across the farm and nonfarm households in rural China. This implies that both terms in equation (18) are zero, indicating that structural change in favor of the non-farm sector during the decades of 1970s-1990s is unlikely to contribute to the cross-sectional variance of schooling. As noted earlier, this, however, does not imply that the nonfarm sector did not play any role in the increasing income inequality in rural China during this period, only that the nonfarm sector’s effect is not mediated through intergenerational educational persistence.

> 32The estimate of the steady state is based on the combined farm and nonfarm sample. Although they are not statistically different, the point estimates differ numerically across farm and nonfarm subsamples.

20
use in many contexts and be able to generalize the findings more than with data from smaller or more isolated sample populations common in the current condom literature.

Previous studies on condom use have often used non-representative samples either because they follow a specific cohort in a particular location (for example, Biraro et al. 2009; Chimbiri 2007; Gregson et al. 2006; Kirungi et al. 2006, Taha et al. 1996), examine one group in a particular region in depth (for example, Maharaj and Cleland 2004; Mnyika et al. 1997) or compare samples from different environments (for example, Fylkesnes et al. 2001). Others use data from national surveys but are limited to one country (for example, Bunnell et al. 2008; Hendriksen et al. 2007). Cleland et al. (2006) also use data DHS from countries across Africa but they compare only single and married women, not men. Pullum and others (2005) use nationally representative data from the WHO for many African countries but focus more on attitudes towards contraception. This is one of the few studies to use nationally representative data to compare a wide range of data on condom use for both genders.

It has been established in a number of previous studies that males report using condoms more often than females (Ahmed et al. 2001; Biraro et al. 2009; Chimbiri 2007; Gregson et al. 2006; Mnyika 1997; Pullum 2005). This study confirms that men consistently report using condoms more often at last intercourse than women both inside and outside of marriage for married and unmarried individuals in all countries with differences significant to the 1 percent confidence level, except for Rwanda. In Rwanda, there is not a significant difference between condom use between males and females at last intercourse with a spouse, however the differences are still significant to the 1 percent confidence level for the last intercourse with any partner and specifically with 19
Policy Research Working Paper 6963

# **Abstract**

The paper assesses the impact of overall inequality, as well as inequality among the poor and among the rich, on the growth rates along various percentiles of the income distribution. The analysis uses micro-census data from U.S. states covering the period from 1960 to 2010. The paper finds evidence that high levels of inequality reduce the income growth of the poor and, if anything, help the growth of the rich. When inequality is deconstructed into bottom and top inequality, the analysis finds that it is mostly top inequality that is holding back growth at the bottom.

This paper is a product of the Poverty and Inequality 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 authors may be contacted at rvanderweide@worldbank.org.

_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
_top of its negative collateral consequences on health, the aerial spraying program is not a costeffective strategy in reducing cocaine supply. Thus, the Colombian government decision is unlikely to cause a large surge in cocaine supply._

# **8 References**

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- Subsidized Training on the Quantiles of Trainee Earnings. _Econometrica_ , 70 (1): 91 117.

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- Abadie, A., J. Angrist, and G. Imbens. 2010. “On the failure of Bootstrap for Matching Estimators.” _Econometrica_ , 76 (6): 1537–57.

- Andreoni, J., B. Erard, and J. Feinstein. 1998. “Tax Compliance.” _Journal of Economic Literature_ 36 (2): 818–60.

- Angrist, J. 1998. “Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants.” _Econometrica_ 66 (2): 249–88.

- Angrist, J., and J.-S. Pischke. 2009. _Mostly Harmless Econometrics,_ Princeton University Press, New Jersey.

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- Becker, G. S. 1968. “Crime and Punishment: An Economic Approach.” _Journal of Political Economy_ 76 (2): 169–217.

- Benabou, R., and J. Tirole. 2003. “Intrinsic and Extrinsic Motivation.” _The Review of Economic Studies_ 70 (3): 489–520.

- ———. 2006. “Incentives and Prosocial Behavior.” _The American Economic Review_ , 96 (5): 1652–78.

- Beron, K., H. Tauchen, and A. Witte. (1992). “The Effect of Audits and Socioeconomic Variables on Compliance.” In J. Slemrod, ed., _Why People Pay Taxes_ . Ann Arbor: Univ. of Michigan Press.

- Bogliacino, F., and A. Naranjo. 2012. “Coca Leaves Production and Eradication: A General Equilibrium Analysis.” _Economics Bulletin_ 32 (1): 382–97.

- Buonanno, P., and G. Mastrobuoni. 2012. “Police and Crime: Evidence from Dictated Delays in Centralized Police Hiring.” Institute for the Study of Labor, Discussion Paper N. 6477.
# **1. Introduction**

Trade policy has changed fundamentally since the days of structural adjustment and economywide trade reforms. Partly in reaction to the uneven results of trade policy reforms, the focus has shifted to more targeted interventions aimed at reducing trade costs and addressing market failures that inhibit exports. Significant national resources and international assistance are now devoted to trade facilitation and export promotion, and the international development community has galvanized around a new ―aid-for-trade‖ (AfT) mantra as a means of helping low-income countries integrate into the global economy.

The environment in which trade-related assistance is provided has also changed. In times of fiscal austerity, taxpayers increasingly question the justification for large aid flows and, at the very least, demand results and accountability.<sup>2</sup> The development community has struggled to respond to these demands because there is surprisingly little evidence about what works and what doesn’t in the area of trade and industrial policies.

An authoritative survey of trade and industrial policy recently acknowledged that there is hardly any microeconomic evidence to guide specific trade interventions (Harrison and RodríguezClare, 2010). There are several reasons for the disappointing pace at which such evidence has been gathered. Trade policy research has been slow to respond to changing needs. Tariffs continue to occupy center stage in policy research, in spite of their declining importance as trade barriers, simply because they are easy to measure. The aid-for-trade community has in turn been slow to build a culture of rigorous evaluation. For instance, a review of 85 recent World Bank trade-related projects conducted by the authors revealed that only five of them included rigorous evaluation components. Worse, those few evaluations relied on crude before-after comparisons, which are known to be vulnerable to confounding influences. The ―knowledge-market failures‖ identified by Ravallion (2009) have also inhibited rigorous evaluations in the trade context: demanders of knowledge about the effectiveness of trade interventions have inadequate information about the quality of any potential evaluation, especially because there are so few good examples; project managers tend to have ―monopolistic‖ control over which projects get evaluated, at what cost and how; and the benefits from the rigorous evaluation of a particular trade project accrue in large part to other future projects which do not share in the cost of evaluating the project.

Still, trade evaluation itself can benefit from the positive externalities generated by research in other areas. In fact, the tools for a serious evaluation of trade-related interventions are already

> 2 A recent poll featured by the Financial Times (Financial Times, July 12, 2010) showed that a majority of respondents in OECD countries considered defense and development aid as priority areas for spending cuts.

2
in Africa is higher in urban areas, where education levels of adults are also higher. Thus the positive correlation between education and HIV status suggested by the first rows for both countries in Table 1 is driven entirely by the fact that people in urban areas are both more likely to be HIV–positive and more educated; education may not be causally related to higher HIV infection probabilities. Consequently, how one interprets the link between education and HIV depends critically on how the correlation is modeled. As Hargreaves and Glynn (2002, p. 496) note, “Crude, unadjusted analyses can give misleading results.” A second example of the difficulty in studying the education–HIV prevalence gradient is drawn from early in the epidemic in Uganda. Data from Round 1 (1989/1990) of the General Population Cohort of the Medical Research Council in rural Uganda reveal how the education–HIV gradient depends on how other covariates are factored into the analysis. The mean of the marginal effects indicates that more-educated adults appear to be more likely to be HIV–positive than less-educated adults (column 1 of Table 2). The point estimate of the marginal effects suggests that, compared with the baseline of no education, having some primary education increases the probability of being infected by 2.9 percentage points and having some secondary education increases the risk by 5.5 percentage points. The second column of Table 2 introduces age dummies in the regression. Controlling for age, the positive relation between education and HIV infection actually disappears: the coefficients on the education category dummies lose their significance. This result reflects the fact that HIV prevalence is concentrated in age groups (20- to 40-year-olds) in which the proportion of better-educated adults is relatively high.<sup>7</sup> The correlation between HIV prevalence and the fraction of individuals with secondary education is very strong across age groups, confirming this interpretation (Figure 1).

This discussion emphasizes the importance of moving beyond bivariate and simple multivariate analyses to examine how demographic and socioeconomic status indicators relate to HIV prevalence. Of course, one needs to be cautious about over-controlling for other factors: as de Walque (2009) and Hargreaves and Glynn (2002) note, “overadjusted” analyses may mask some true associations. In their review of studies on the

> 7 In an earlier study of risk factors for HIV, Nunn and others (1997) found a positive association between education and HIV seropositivity, but the result was not robust when corrected for age.

8