rafmacalaba/datause-encoder-data huggingface dataset:
rebounds once the punishment institution is removed in both countries. This suggests that crackdowns and policies using moral arguments and values may not be effective in combating corruption. That is, culture and norms may not play a role in repeated bribing behavior even after a crackdown designed to signal a new norm. Finally, Li et al. (2011) conduct a variant of the repeated bribery game in Germany and China to identify the impact of gender, and individual vs. group-based decision-making in a two-person bribery game (with an externality affecting payment to a charity). Their individual treatments replicate the no-difference in behavior result of the no punishment treatment of Banuri and Eckel (2012a, 2012b).<sup>18</sup> They find that group decisions lead to a higher level of corruption in both countries, and that all male groups are the most corrupt in Germany, while mixed groups are the most corrupt in China.

In our attempt to reconcile findings between the cross-cultural bribery studies, we find a few consistent patterns. First, norms do make a difference in the lab and can be studied as to how they impact corruption. Second, repeated games reduce the impact of norms on individual behavior, by reducing uncertainty among players in such interactive settings. Third, bribery games should also follow the trust literature and elicit expectations of corrupt behavior within the game. Fourth, since subjects become socialized to their new environment over time, policy interventions should be tested within the country itself. That is, policy recommendations (such as the one for gender) do not seem to be generalizable across cultures. Secondly, differences in externality designs need to be tested. It is entirely plausible that behavior in one culture differs when the externality is for a charity, while in another the externality affects peers. What is clear is that we are just beginning to scratch the surface of experiments in bribery.

Yet another aspect of corruption that is garnering interest in the lab is nepotism. This form of corruption refers to favoritism shown toward one‟s family or group, and is most often used with respect to favoritism in the public sector. A number of studies have found a relationship between the cultural prevalence of in-group favoritism and corruption (Hooper 1995; Banfield 1958; LaPalombara 1994; Gambetta 1993). Although Husted (1999) finds little empirical support for this relationship, he states that this is likely due the high correlation between individualism and GNP per capita (Hofstede 1997). While experimental research on nepotism has not directly addressed public service corruption in the same way as research on

> 18 The percentage of individuals offering a bribe is the same in Germany and China, though the bribe levels (conditional on offering a bribe) are higher in China.  Since the US and Pakistan experiment was discrete choice, this result is replicated for China and Germany.

18
28 gets under way. Once real interest rates are<sup>positive, further increases of</sup> real interest rates on deposits and special tax<sup>treatments for savings are</sup> unlikely to cause any large increase in saving rates.

Refinements. The findings of the second report<sup>confirm and extend some of the</sup> conclusions of the first report. The sequencing<sup>and packaging of reform</sup> measures is crucial. In countries that start with<sup>high inflation and a large</sup> current account deficit, the first step is to implement<sup>stabilization</sup> measures. Structural reforms aimed at maintaining macroeconomic balance should be emphasized next. Sectoral reforms should<sup>be sequenced in a way that</sup> takes account of the linkages among sectors. For instance, trade liberalization is likely to improve efficiency more if accompanied by measures that improve the functioning of domestic markets for goods<sup>and credit, provide</sup> needed infrastructure, and reduce controls on domestic<sup>investment and</sup> impediments to labor mobility. In some low-income countries,<sup>barriers to the</sup> integration of the domestic economy -- poor roads, inappropriate<sup>domestic</sup> transport infrastructure, lack of domestic financial markets,<sup>and so on--</sup> are major impediments to economic growth, which should be<sup>tackled early.</sup> The data set on conditionality and implementation, with<sup>a much expanded</sup> sample of loans approved in FY79-88, showed most of the<sup>same patterns found in</sup> the first Report, and revealed some new ones. Countries<sup>begin implementing</sup> their structural adjustment program before the adjustment<sup>loans become</sup> effective and frequently continue implementation<sup>after disbursement ends.</sup> Progress in implementation is measured by the share of conditions<sup>in the loan</sup> agreements that have been implemented by the time of final<sup>tranche release.</sup> Of all conditions in the loan agreements in the sample, 84 percent had<sup>been</sup> implemented at least substantially -- better than found in the first Report --
year-olds in 1988, 1998 and 2006. The completion rates at preparatory level increased steadily from 43 to 69 percent and at secondary from 38 to 65 percent over the same period. Access to higher education increased substantially with completion rates increasing from 7 to 17 percent. Nevertheless, tracking into technical-vocational and general secondary schools remained high over the period. As a result, the rate of graduation from the vocational secondary schools increased faster (from 24 to 42 percent) than from the general secondary schools (from 19 to 27 percent).

**Table 1: Completion rates of 21-24 year-olds by education level**

||**1988**|**1998**|**2006**|
|---|---|---|---|
|Preparatory|0.434|0.527|0.692|
|Secondary|0.379|0.480|0.645|
|General secondary|0.194|0.167|0.269|
|College|0.0668|0.111|0.172|

_Source:_ 1988 LFSS; 1998 ELMS; and 2006 ELMPS. Sample: 21-24 year-olds

**Table** 2 presents probit model estimates of the relationship between educational attainment and circumstances beyond the control of an individual. The effects of circumstance variables on completion of preparatory, general secondary and college education are substantial. Other things being equal, girls are less likely to complete basic education over the entire period (with differentials of 2 in 1988 and 1998 and 4 points in 2006). However, gender gaps have decreased at general secondary and college levels. The disadvantage of girls has disappeared over the period and even been reversed at secondary levels with girls 3 percent more likely than boys to complete general secondary education in 2006. Similarly, the disadvantage of girls at higher education levels appears to have reversed with a 2 percent higher college completion rate in 2006.

The differentials between youth of different family backgrounds also tend to decrease over time. For instance, the differential associated with the agricultural (or elementary) occupation of a father (13 percentage points in 2006) declined by more than twice, as did the differential associated with parental education. Similarly, although youth born in rural Lower and Upper Egypt are much less likely to complete basic education (respectively 23 and 15 percentage points less in 2006, 5
On the Quantity and Quality of Girls: Fertility, Parental Investments, and Mortality<sup>_∗_</sup> S Anukriti Sonia Bhalotra Eddy HF Tam The World Bank<sup>_†_</sup> University of Essex<sup>_‡_</sup> University of University of Oxford<sup>§</sup> _Note: The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._

# _JEL Codes_ : I15, J13, J16

_Keywords_ : abortion, child mortality, fertility, gender, health, India, missing girls, parental investments, prenatal sex detection, sex-selection, ultrasound

> _∗_ We thank Prashant Bharadwaj, Abhishek Chakravarty, Ritam Chaurey, Diana Greene Foster, Liberated Gonzalez, Rachel Heath, Mahesh Karra, Melanie Khamis, Adriana Kugler, Annemie Maertens, Claudia Olivetti, Rohini Pande, Laura Schechter, Alessandro Tarozzi, Ushma Upadhyay, and Shing-Yi Wang, and participants at various seminars and conferences for their helpful comments and suggestions. We are grateful to Luojia Hu and Analia Schlosser for discussion of our related papers.

- Development Research Group and IZA. `sanukriti@worldbank.org` .

- Department of Economics and ISER. `srbhal@essex.ac.uk.`

- §Centre for Business Taxation, Said Business School. `eddy.tam@sbs.ox.ac.uk.`
**Figure 8 - Decomposition of the change in GHG emissions (tn.) per worker by sector in LAC, 2005-2018**

<!-- Start of picture text --> 2000<br>1500<br>1000<br>500<br>0<br>-500<br>-1000<br>-1500<br>-2000<br>-2500<br>Non-green sectors Green sectors<br>Within Between (structural change) Total<br>Education All sectors<br>defence<br>and fishing communications<br>GHG emissions (tonnes) per worker Financial intermediation Mining and quarrying Transport, storage and as employers Health and social work Hotels and restaurants Real estate, renting and business activities<br>Industrial processes personal service activities Public administration and Wholesale and retail trade<br>Agriculture, hunting, forestry Electricity, gas and water supply Manufacturing, Construction & Other community, social and Activities of private households<br><!-- End of picture text --> Source: Own elaboration based on Climate watch data (GHG emissions in tons) and SEDLAC (Number of workers). Notes: (1) LAC region is composed of the 7 countries with emissions data: Argentina, Brazil, Chile, Colombia, Costa Rica, Mexico, and Peru. (2) In Mexico, data from 2006 were used instead of 2005 due to the lack of estimates of the number of workers. For the same reason, data from years 2006 and 2017 were used in Chile instead of 2005 and 2018. This decomposition follows the traditional shift-share approach from structural transformation studies, where changes in labor productivity are decomposed into productivity growth within the sector (“within” component) and productivity growth driven by workers moving from lower to higher productivity sectors (“between” or “structural change” component).

# Profile of green job holders in LAC

Overall, green occupations are more prevalent among male and urban workers (see Figure 9).<sup>18</sup> Granata and Posadas use a different methodology to define green occupations in Indonesia, and also find that their share is higher among males than among females. However, total employment (including green and non-green occupations) in green sectors is higher for females and more educated people. A more granular picture of the gender patterns of green occupations can be grasped in Figure 10. Men are more likely to have green occupations than women (as measured by the gender gap, that is share of green occupations held by males minus share held by females), across all types of green occupations and in all LAC countries

> 18 Some empirical articles find that such policies would generally benefit workers with higher levels of education and specialized in non-manual tasks (Vona, Marin, Consoli, & Popp, 2016; Marin & Vona, 2019).

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poor children. Children below 5 years old whose parents were exposed to intense handwashing, hygiene and sanitation information campaigns improved their development and socio-emotional scores, when they were 5–7 years old. These findings suggest that handwashing and hygiene promotion could be an important strategy for improving children’s long-term welfare (Oberhelman et at., 1998; Bowen et al., 2012; Lancet, 2011). Similarly, other economic studies have shown that 40 percent of early deaths are caused by behavioral patterns, so targeting prevention through information is efficient and productive to reach these outcomes. Adequate health practices and caregiving time early on in children, from birth to age 5, are forms of preventive health and economic investments that drive cognitive improvements and future economic returns (McGinnis et al., 2002; Conti and Heckman, 2010).

<mark>For the purpose of this study, we explore the variation in exposure to sanitation information campaigns coupled with certain communication assets present in the household. Specifically, the differences of assets’ ownership across households are used as proxy means to spread information within the household. Assets such as television, radio, and VCR/PC are considered. These assets may interact with the presence of hygiene and handwashing information campaigns which ultimately may have effects on parents’ behavior and children’s socio-emotional outcomes. Campaigns delivered through various interventions were captured in survey data, with the following characterization: 1) the exposure to a hygiene and sanitation information campaign with different levels of intensity</mark><sup>2</sup> <mark>, 2) ownership of communication assets in the household may be related to households’ wealth but also to investment decisions made by household members, and 3) communication assets in the household affect directly the</mark> <mark>communication and socio-emotional skills of patents and, thus, their children. Under this profile of intervention, other similar interventions took place in treatment and control communities which equalized the exposure of basic information between groups, but the variation lies on the intensity of exposure to information campaigns of treated localities.</mark>

> 2 Exposure to all information campaigns available provides exogenous variation to socio‐emotional outcomes. Interaction between the exogenous variation of exposure and the presence of certain communication assets in the household may provide the attributed effect of the campaigns, with intrinsic sources of bias.

4
the program for an extra one (or two) years displayed lower malnutrition rates than the communities that had shorter length of exposure. The reduction in malnutrition rates for different age groups takes time to materialize, and it takes up to two years for all of the three age groups (0-6 months, 7-12 months, 13-36 months) to achieve a 7- 9 percentage points reduction in malnutrition rates. With an initial malnutrition rate of around 46 percentage points, this effect on malnutrition rates amounts to 15 to 20 percentage points over a two year period. When looking at the time profile of these effects, we find that the differential returns to exposure decrease over time although they do not converge to zero as time and duration increase.

Furthermore, when examining how the effects of differential length of exposure to the program vary according to some key social and environmental conditions, we find that the differential returns are higher for poorer communities, and communities more vulnerable to cyclones and to a longer lean season.

The results do not address the question of not whether the program should be pursued or not, but rather how it should expanded once it a small scale (internally valid) evaluation has shown promising and significant effects of the program and a country has decided to pursue such an approach on a larger scale. How should the program be phased-in geographically and where are the highest marginal returns from program expansion? Our study shows that the use of monitoring data creates an opportunity for applications in the context of large-scale programs, where the socio-economic characteristics and the potential returns to the program vary greatly across different intervention areas. In this respect, the current analysis can be complemented by the evaluations that are based on household survey data. Indeed, our future research will utilize household survey data to quantify other parameters of interest and highlight how the estimated return to exposure quantified in this study is related to the estimated parameters of interest in our future studies that use the household survey data.

24
Table 5: Relationship between the green goods trade and earning across gender and skill levels (with industry fixed effects)

||All|Male|Female|High skilled|Low skilled|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||**A**|**nnual Earn**|**ings**|||
|Green Goods Export Share|0.000644|0.00007|0.00128|-0.00014|0.00154|
||(0.44)|(0.06)|(0.69)|(-0.16)|(0.73)|
|Green Goods Import Share|-0.00141|-0.0003|-0.003|0.00019|-0.00259|
||(-0.49)|(-0.19)|(-0.64)|(0.10)|(-0.70)|
|Industry FE|Yes|Yes|Yes|Yes|Yes|
|Workers control|Yes|Yes|Yes|Yes|Yes|
|N|77544|52868|24676|15708|61836|
|Adj. R2|0.441|0.444|0.456|0.267|0.255|

_Note: Results are based on repeated cross-sectional labor force survey data that are merged with industry level trade data. Industry fixed effects are at two-digit ISIC Rev 4 level. Worker’s control includes age, age squared, gender, marital status and 8 groups of educations in column (1). In column (2) and (3) gender is excluded while in column (4) and (5) education is not included as these regression are gender and skill based, respectively. Standard errors clustered at industry level. t-statistics in parentheses. p < .10, ** p < .05, *** p < .01_

# **5 Conclusion**

This paper examines the green goods trade landscape in the Philippines to understand its current level, its evolution over time, its situation compared to peer countries, and the predominant green industry sectors. Second, we estimate the ad valorem equivalent (AVE) of non-tariff measures (NTMs) on green goods to assess the cost of regulations on green goods imports and identify the most burdensome NTMs and candidates for regulatory climatefriendly trade policy reform. Finally, since reforms on NTMs will increase imports and 30
data set is representative in exactly the same way as the original LSMS data set, and can be used to make reasonable simulations about changes in expenditures and poverty at the national level. .

# **4. Results**

What is the effect of geographic targeting with alternative indicators in Peru? As a first step towards answering this question, I present estimates of poverty and allocations of funds by region. About two-thirds of the population of Peru lives in urban areas, and well over a third of these urban residents live in the capital city, Lima. Table 1 decomposes poverty measures and allocations by alternative geographic targeting indicators into three categories: Lima, other urban, and rural. The values in each cell correspond to the proportion of total poverty or the proportion of total allocations by region, so that every row sums to 100%. Here, and for all of the results presented in the paper, individuals in the household surveys are weighted with the appropriate expansion factors.

Since the no targeting scenario makes an equal transfer to every Peruvian, regional allocations when there is no targeting correspond exactly to the fraction of the population living in each region. A comparison of these allocations with the various poverty measures shows that poverty in urban areas is below average: about 29% of the population lives in Lima, but only 14% to 22% of total poverty was found there in 1994, and 13% to 20% in 1997. Other urban areas account for about 36% of the population, and about one-third of poverty. Poverty in rural areas, by contrast, is well above average: just over one-third of the population lives in rural areas, but between 44% and 55% of poverty was found there in 1994, and as much as 47% to 57% in 1997.

Table 1 suggests that geographic targeting using any of the indicators under consideration appears to approximate the distribution of poverty reasonably well. When any of these indicators is used for geographic targeting, about one-half of all resources are transferred to rural areas, and about two-thirds of the remaining resources to urban areas outside Lima. Comparing the three indicators, the FONCODES index transfers more to Lima than the measures of infant mortality and imputed poverty, while the measure of infant mortality makes the largest transfers to rural areas.

irrespective of the district in which these households live. If a number of samples were drawn, on average, the total budget would be the same across indicators, but this does not hold for any one sample. I have corrected for this problem by normalizing the budget-in effect, summing equation (2) above only over the sample of districts in the LSMS. Note that this is not an issue with the concentration curve approach because concentration curves graph out the _proportion_ of a given budget that is allocated to each household

**10**
# **11 Tables and Figures**

Table 1. Covariate Balance between Treatment and Control Groups

||All<br>(1)|Game<br>(2)|Video<br>(3)|Control<br>(4)|Obs<br>(5)|
|---|---|---|---|---|---|
|Student [=1]|0.621|0.019|-0.001|0.614|831|
||(0.485)|(0.040)|(0.043)|(0.030)||
|Male [=1]|0.464|-0.080*|0.005|0.493|828|
||(0.499)|(0.041)|(0.044)|(0.030)||
|Age|25.255|0.328|0.496|24.985|829|
||(8.271)|(0.658)|(0.760)|(0.487)||
|Married [=1]|0.066|0.031|0.022|0.048|831|
||(0.249)|(0.020)|(0.021)|(0.013)||
|Religious [=1]|0.798|0.013|-0.004|0.794|831|
||(0.402)|(0.033)|(0.036)|(0.025)||
|Primary education completed [=1]|0.025|-0.017|-0.004|0.033|830|
||(0.157)|(0.013)|(0.015)|(0.011)||
|Secondary education completed [=1]|0.566|-0.045|-0.042|0.596|830|
||(0.496)|(0.041)|(0.044)|(0.030)||
|Higher education completed [=1]|0.408|0.062|0.046|0.371|830|
||(0.492)|(0.041)|(0.043)|(0.029)||
|Income (1 low- 10 high)|2.329|-0.090|-0.005|2.364|821|
||(1.072)|(0.092)|(0.095)|(0.070)||
|Low economic strata [=1]|0.726|0.015|-0.040|0.732|831|
||(0.446)|(0.036)|(0.040)|(0.027)||
|Children [=1]|0.143|-0.007|0.009|0.143|831|
||(0.350)|(0.029)|(0.031)|(0.021)||
|Joint F-Test (Prob_>_F)|0.567|0.227|0.963|||

_Notes:_ The table presents the mean and standard deviation (in parentheses) for the entire sample in column (1). Columns (2) through (4) display the coefficients and robust standard errors (in parentheses) derived from an OLS regression. The dependent variable in the regression is the corresponding covariate, with the treatment dummies utilized as regressors. Joint F-test (Prob _>_ F) indicates the p-value of the orthogonality joint test, which is obtained by running the treatment variable in all covariates and then testing the joint hypothesis that all coefficients are equal to zero (b1 = b2 = b3 = ... = b20 = 0). Significance levels are denoted as follows:<sup>_∗∗∗_</sup> at 1%,<sup>_∗∗_</sup> at 5%,<sup>_∗_</sup> at 10%.
with income per capita, freedom of property and (in small samples) measures of regulatory efficiency. The two rural-sector variables (population density; proportion of GDP in agriculture and forestry) are only weakly associated with the Green index (Table 9a). The fit is much better for the Brown index: degree of urbanization, population density and manufacturing share in GDP all have the expected signs and relatively high significance (Table 9b). Life expectancy as a proxy for public health priority has no independent effect.

# **6. Summary**

Using a multidimensional survey analysis of the UNCED reports, we have developed a set of comparative indices of environmental policy and performance in thirty-one countries. We find a strong positive correlation between our environmental indicators and the level of economic development. The fit is substantially better when national incomes are adjusted for purchasing power parity. The income elasticity of the indices is positive and highly significant in all environmental dimensions. The pattern of elasticities suggests that protection measures for land and living resources precede those for water; action for reducing air pollution comes later.

Some impact for institutional development is also suggested by our results, although the information base is quite limited.

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