rafmacalaba/datause-encoder-data huggingface dataset:
Let xj be a vector of characteristics that are commonly observed between the two surveys, where j indicates the type of survey that can either be the same household expenditure survey or another survey.<sup>9</sup> Subject to data availability, these characteristics can include household variables such as the household head’s age, sex, education, ethnicity, religion, language, occupation, household assets or incomes, and other community or regional variables. Occupation-related characteristics can generally include whether household heads work, the share of household members that work, the type of work that household members participate in, as well as context-specific variables such as the share of female household members that participate in the labor force. Regional characteristics related to macroeconomic trends such as (un)employment rates or commodity prices can also be included if such data are available. As discussed below, these variables would play a critical role in capturing the changes in estimated poverty rates.

Household consumption (or income) data exist in one survey but are missing in the other survey, thus without loss of generality, let survey 1 and survey 2 respectively represent the survey with and without household consumption data, and y1 represent household consumption in survey 1. More generally, these two surveys can be either in the same period or in different periods. We focus in this section on the latter case, before discussing the more complicated cases of combining surveys of different designs in the same period and in different periods in the next section.<sup>10</sup>

> 9 More generally, j can indicate any type of relevant surveys that collect household data sufficiently relevant for imputation purposes such as labor force surveys, demographic and health or youth surveys. To make notation less cluttered, we suppress the subscript for each household in the following equations.

> 10 Theoretically, it is trivial to consider the change in poverty estimates when we impute from one survey to another in the same time period; this change is zero by construction. But practically, this imputation exercise is relevant for validation purposes when imputation is done using two surveys with different design. We will come back to discuss this later.

12
24

**Table 3: Effect of Treatments on Average Post-Treatment Water Consumption**

|**Outcome Variable: Average of August and September 2014**<br>**Water Consumption, cubic meters, m**<sup>**3**</sup>|**I**|**II**|**III**|**IV**|**V**|**VI**|**VII**|**VIII**|
|---|---|---|---|---|---|---|---|---|
|**Treatment 1: Neighborhood Comparison, m**<sup>**3**</sup>|-1.27*|-1.28**|-0.98**|-1.47**|-1.27*|-1.29**|-0.99**|-1.47**|
|**(SE)**|(0.67)|(0.60)|(0.50)|(0.58)|(0.67)|(0.60)|(0.49)|(0.58)|
|**Treatment 2: Municipality Comparison, m**<sup>**3**</sup>|-0.81|-0.81|-0.77|-0.92|-0.82|-0.83|-0.77|-0.92|
|**(SE)**|(0.65)|(0.58)|(0.50)|(0.56)|(0.65)|(0.58)|(0.49)|(0.56)|
|**Treatment 3: Planning Postcard, m**<sup>**3**</sup>|-1.11*|-1.23**|-0.90**|-1.46***|-1.13*|-1.26**|-0.93**|-1.49***|
|**(SE)**|(0.64)|(0.59)|(0.46)|(0.57)|(0.64)|(0.59)|(0.46)|(0.57)|
|**Constant**|11.40***|7.12***|2.96***|6.09***|8.77***|5.41***|1.42|4.97***|
|**(SE)**|(2.13)|(1.89)|(1.12)|(1.71)|(1.97)|(1.75)|(1.25)|(1.59)|
|**August and September 2013 Total Consumption**|X||||X||||
|**Rainy Season 2013 Total Consumption (May 2013-**<br>**November 2013), m**<sup>**3**</sup>||X|X|||X|X||
|**May - June 2014 Total Consumption, m**<sup>**3**</sup>|||X||||X||
|**Average Annual Consumption (December 2012 - November**<br>**2013), m**<sup>**3**</sup>||||X||||X|
|**Fixed Effects for Billing Date**|||||X|X|X|X|
|**Observations**|5126|5061|5061|5061|5126|5061|5061|5061|
|**R-squared**|0.41|0.52|0.68|0.56|0.41|0.53|0.69|0.56|
|_Notes:_<br>_1. Numbers in parentheses are standard errors_<br>_2. Stars indicate statistical significance: *=p<0.10, **=p<0.05, *_|_**=p<0.01_||||||||
the household does not receive the CCT, and the budget constraint reverts to the line _AF_ , along the initial budget constraint. For non-marginal children, the household’s utility is maximized at point _b_ under both the UCT and CCT programs, indicating that both interventions have the same effects on education. However, for marginal children, point _b'_ is unattainable under a CCT. The household chooses point _c'_ , satisfies the education condition _E_ , and receives the transfer. Point _c'_ is preferred to point _a'_ , where the education condition is not satisfied, but the indifference curve at point _b'_ under the UCT would have been preferred to the outcome under the CCT.

Figure 1’s simple framework can motivate a clear empirically testable proposition: when considering only human capital investments, relative to UCTs, CCTs increase investment in human capital of marginal children and no non-marginal child is made worse off. The empirical implications are the following. First, CCTs increase education for marginal children more than UCTs. Second, UCTs and CCTs have similar educational impacts for non-marginal children.

# **3. Context and Experimental Design**

# _3.1 Context_

Burkina Faso offers an important setting for exploring the effects of cash transfers on rural children’s education. Even by African standards, education outcomes in Burkina Faso are poor. In 2010, the net attendance ratio for primary school in rural Burkina Faso was 44.4 (45.5 for boys and 43.1 for girls) and the gross attendance ratio was 64.9 (66.2 for boys and 63.5 for girls) (Institut National de la Statistique et de la Démographie and ICF International, 2012).<sup>10</sup> The cash transfer program was run in Nahouri province in southern Burkina Faso, 100 miles from the capital, Ouagadougou. Households in the region are mainly subsistence farmers

> 10 The primary school net attendance ratio is the percentage of children attending primary school who are of the official school age. The primary school gross attendance ratio is the number of primary school students, irrespective of age, as a percentage of the official primary-school-age population. If there are a significant number of underage or overage students in primary school, the gross attendance ratio is higher than the net attendance ratio.

8
Across the world, agricultural and food policies distort incentives for farmers and food consumers. Historically, governments in wealthy countries have subsidized farmers, whereas governments in poor countries have taxed farmers and subsidized food consumers (Anderson and Hayami 1986; Krueger et al. 1988; Anderson 1995). These observations have puzzled economists and other social scientists and triggered a series of studies in the 1980s and 1990s on “the political economy of agricultural policies” (see de Gorter and Swinnen 2002; Swinnen 2010 for reviews).

A recent global study on policy distortions in agriculture concludes that although policy distortions remain important, since the 1980s, the antiagricultural policy bias in developing countries and the proagricultural bias in high income countries have declined substantially (Anderson 2009). Interestingly, this was also the period during which important political reforms occurred in many countries. For example, the fall of the Berlin wall triggered democratic transitions across Eastern Europe in the early 1990s.

Furthermore, several developing and emerging countries have become more democratic in recent decades. In Eastern Europe, political reforms induced radical economic liberalizations in the food system. In contrast, in the absence of major political reforms in East Asia, gradual economic liberalization was introduced, including the reduced taxation of farmers (Swinnen and Rozelle 2006).

Thus, the question arises whether and to what degree political reforms have affected agricultural and food policies worldwide. Political economy studies have demonstrated the importance of a variety of factors, such as economic structural factors and resource endowments, but the role of political institutions and reforms has received less attention. A few studies have attempted to analyze this issue, but the evidence on the impact of political reforms on agricultural and food policies is not clear because of problems with the data (see section II for references and details).

2
upward-sloping part of the supply curve, representing congestion, shifts rightward and downward. At the new equilibrium E1, both increase in the port tariff/charges and decrease in congestion costs take place. Only when the latter surpasses the former, this framework can consistently explain the negative coefficients of the port congestion.

# _Specifications and Data of the Trade Cost Regression: The U.S. Data_

With the reference of the simple model illustrated above, we adopt the following specification for the regression model of the U.S. import charges per weight (equation (1)), which are similar to Clerk, Dollar and Micco (2004). The source of the data is _U.S. Imports of Merchandise_ , DVDs, unless mentioned otherwise. The estimation period is from 2001 to 2006, when trade rapidly increased after the Economic Crisis and the congestion in the ports materialized.

where: _TCikt_ : the amount of the import charge for the imports of the United States via vessels from country _i_ for commodity _k_ at 6-digit level, at the year _t_ .

_distit_ : bilateral distance between country _i_ and the United States. The distance is calculated as the weighted average of the port-to-port liner distances between major ports in country _i_ and Seattle, Los Angeles and New York, using the actual flows of container cargos in 1998 and 2003 as the weight (Shibasaki et. al. (2004)<sup>22</sup> ). The distance estimated for 1998 is applied to the observations for 2001 and 2002, and that for 2003 is applied to those thereafter.

_Wgtikt_ : the weight of the imports of the United States via vessels from country _i_ for commodity _k_ at 6-digit level, at the year _t_ .

_Valueikt_ : the import customs value of the United States via vessels from country _i_ for commodity _k_ at 6-digit level, at the year _t_ .

_Cntit_ : the ratio of containerization, as the import weights via containerized vessels divided by those via all the vessels from country _i_ at the year _t_ .

> 22 The authors appreciate the kind provision of the data in the electronic form from Dr. Shibasaki.

20
The second equation in the model is about the formation of perceptions of inequality. We assume that information about inequality is acquired by being exposed to a specific economic context (in concrete, unemployment, poverty, and inequality) and argue thus that the metric or the definition of inequality might differ between the researcher (who typically uses the Gini coefficient) and individuals in the society, who might relate inequalities also to economic uncertainty (unemployment) or to poverty. We also argue that perceptions of inequality might depend on the fairness of the process that has generated them and therefore we include intergenerational mobility in the set of variables describing the economic context. In concrete, we use the intergenerational elasticity of education.<sup>14</sup> Similarly, and to account for future mobility, we also postulate that individuals’ perceptions might be influenced by current government expenditures in education. A second key element of our model, as in Zeller’s, is the dependence of inequality perceptions on ideology, notably the different views on social values and norms (hard work, meritocracy, circumstances, luck) spanning from left-leaning individuals to right-leaning ones.

Finally, we assume that perceptions of inequality relate also to other personal characteristics, such as employment, age, or gender. We write the equation as: Where EC represents the economic context, ID is ideology, and X is the set of individual characteristics including income and education levels. We will discuss the functional form of this relationship in section 5.

Equation (2) differs from the model of ‘knowledge’ acquisition of Blinder and Krueger in an important respect. In their model, individuals have individual-specific exposures to information; in fact, for each individual, they have micro data about sources of information, quantity of information, and ‘desire’ to acquire information. In contrast, we assume that everyone is exposed to the same degree to the relevant economic context, but ideology plays a role in interpreting the elements of such context. That is, faced with a same context – a high unemployment rate, for instance – individuals with different ideologies may form different perceptions of equality. Similarly, given everything else constant, high earnings individuals might also perceive inequality differently.

In sum and starting from the bottom, the model says that people’s ideology, exposure to a specific economic context (inequality, poverty, unemployment and government expenditures), and their personal characteristics form their perceptions of inequality. These perceptions, in turn, influence, together again

> 14 Data on intergenerational mobility are just beginning to become available for many countries and long time periods. In order to cover all the countries/years of our sample we had to use intergenerational mobility of education. For more details about intergenerational mobility and equality of opportunity see www.equalchances.org.

8
# **1 Introduction**

The disruptive impacts of violent conflicts are seldom gender-neutral. Women suffer in very specific ways during times of conflict as existing law and order break down, making them more susceptible to sexual violence. While sexual violence adversely impacts women’s physical, social, and psychological well-being, the death of earning members, loss of wealth, or displacement also exposes them to economic hardships during times of conflict (Annan et al., 2011). These effects can exacerbate further in the presence of existing gender discrimination and disparities in human capital in developing countries.

Rape and sexual atrocities are often viewed as weapons of conflict to create fear and terror (World Bank Blog, 2023). While the direct consequences of violent conflicts on women include rape and other forms of abuse, its indirect consequences could involve unwanted pregnancies and changes in their marital outcomes and agency due to changes in socioeconomic and demographic characteristics of the conflict-affected regions (La Matthina, 2017; Gutierrez and Gallegos, 2016). More specifically, fear of sexual violence, changes in sexratios, and wealth effects in riot-affected regions could drive marriage market outcomes of not just women who were victims of violence during riots, but also the wider community.

While there have been several incidents of communal riots in India, the Hindu-Muslim riots of 2002 in the Indian state of Gujarat are considered one of the most extreme in terms of the systematic nature, geography and duration (Jaffrelot, 2003). During these riots, not only were several killed, injured and displaced, sexual violence against women had become its key feature (Jha and Farasat, 2016). In fact, some studies suggest that during the riots in Gujarat in 2002, women were victims of most extreme forms of sexual cruelty, including rape, gang rape, stripping, insertion of objects into their body, and molestation (Hameed et al., 2002). This paper examines how women’s marriage age and postmarital socioeconomic status evolved in Gujarat after the 2002 riots. Even though several studies have examined the impact of Hindu-Muslim riots on several social, economic and political outcomes of interest, we specifically contribute to the literature by assessing the effect of the riots on women’s marital outcomes.

To examine the effects of the Gujarat riots of 2002 on women’s marital outcomes, we use a retrospective state-year level dataset from the fourth round of the National Family Health Surveys (2015-2016) and a difference-in-differences strategy. Our empirical strategy relies on variation over time in marriages that took place before and after 2002 and the statewise variation in the occurrence of riots. We also use the event study approach to observe differential trends in women’s age at marriage between Gujarat and the other Indian states. The difference-in-differences estimates suggest that age at marriage declined for women 2
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would be more reasonable to interpret the evidence in (3) as reflecting some myopia on the part of investors."8

## IV. METHODOLOGICAL ISSUES

This section discusses the relative merits of the alternative approaches used to assess the probability of a currency crisis, and proceeds to describe in some detail a methodology that serves as the basis for the warning system proposed in this paper.

# A. **Alternative Approaches: An Evaluation**

The studies reviewed above have used essentially two alternative methodologies that could serve as the basis for an early warning system of currency crises. The most commonly used approach has been to estimate the one-step (or k-step) ahead probability of devaluation in the context of a multivariate logit or probit model. While the explanatory variables have been quite varied, the estimation technique has been quite uniform."9 The second approach has been to compare the behavior of selected variables in the period preceding crises with their behavior in a control group, and to identify those variables whose distinctive behavior could be used to help assess the likelihood of a crisis. The particular variant of this approach presented in Kaminsky and Reinhart (1996) has progressed to construct a warning system based on signals issued by those selected variables.

The methodology that estimates the one-step (or k-step) ahead probability of devaluation has the advantage that it summarizes the information about the likelihood of a crisis in one useful number, the probability of devaluation. Also, as this approach considers all the variables simultaneously, and it disregards those variables that do not contribute information that is independent from that provided by other variables already included in the analysis.

This methodology, however, also has some important limitations. First, the methodology does not provide a metric for ranking the indicators according to their ability to accurately predict crises and avoid false signals, since a variable either enters the regression significantly or it does not. While measures of statistical significance can help pinpoint which are the more reliable indicators, they provide no information on whether the relative strength of that indicator lies in accurately calling a high proportion of crises at the expense of sending numerous false alarms, or instead missing a large share of crises but seldom sending false "Jeanne (1995) takes a different approach to test for the existence of self-fulfilling crises using data on the French Franc/deutsche Mark exchange rate for the period 1992-1993, and concludes that in fact the estimated relationship has the shape needed to produce multiple equilibria and self-fulfilling crises. These findings, however, are not entirely persuasive, mainly because of the way in which the fundamentals are treated in the estimation.

'<sup>9</sup> Sachs, Tornell, and Velasco (1996) use an alternative strategy, but they examine the different, although related, issues of which countries were vulnerable on the wake of the Mexican crisis and what accounted for their vulnerability.
_φ_ = 0 _._ 8 so that an infected working adult is only 80 percent effective in supplying labor. This is equivalent to assuming that 80 percent of the infected prime-age population is either asymptomatic or experiences a mild case.<sup>25</sup> We set _θ_ to 1 so that the steady state labor supply in the pre-pandemic world is normalized to 1 in all countries.

The parameter _σ_ governs the elasticity of substitution between household members. We – set it to 3, so that the loss of a non-productive household member the children or the elderly – with mass _l_<sup>_m_</sup> reduces the instantaneous utility, _u_ ( _·_ ), by a proportion _l_<sup>_m_</sup> in steady state. Appendix A.3 provides the details of the derivation.

The values of Γ<sup> ̄</sup> _t_ are calibrated based on the Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE) data set. In the dataset, 1.55 percent of GDP was spent on social assistance programs on average. We assume that Γ<sup> ̄</sup> _t_ is constant across the entire simulation, and calibrate it to be 1.55 percent of GDP in every country.

Table 1 summarizes all the parameters of the model and indicates the data sources used to calibrate them. Appendix Table B.3 lists the countries in the sample. Appendix Tables B.4 and B.5 list all the country-specific parameters for each country.

# **4 Results**

To quantitatively illustrate how the same policy might lead to different mortality outcomes across countries, we compare two scenarios. The first scenario traces economic and diseaserelated behavior without any government intervention. The second scenario involves the reference lockdown as described above, where a labor tax of 38 percent is imposed for a seven-week period once the rate of infection prevalence reaches 2.6 percent. While the reference lockdown is picked to mimic policies adopted during the early months of the pandemic, it is not designed to capture all the complexities of mobility and social gathering restrictions imposed by various countries. Rather, the results below aim at highlighting the large heterogeneity in outcomes following the adoption of the same policy rule.

> 25This corresponds to media reports on the results of an unpublished sero-prevalence study conducted in New York in April 2020 (nytimes.com/2020/04/23/nyregion/coronavirus-antibodies-test-ny.html). Li et al. (2020) and Stringhini et al. (2020) report even higher rates of asymptomatic cases in China and Switzerland.

29
# **Impact of Property Rights Reform to Support China’s Rural-Urban Integration:**

# **Village-Level Evidence from the Chengdu National Experiment**<sup>**¶**</sup>

Klaus Deininger<sup>†</sup> , Songqing Jin<sup>‡</sup> , Shouying Liu<sup>♯</sup> , Ting Shao<sup>♯</sup> , Fang Xia<sup>♦</sup> †World Bank, Washington DC ‡Michigan State University, East Lansing MI ♯Development Research Center of the State Council, Beijing ♦Central University of Finance and Economics, Beijing

## **JEL Codes O1, O43, R14, R3**

## **Keywords: Urban development, land use change, China, financial development**

> ¶ Email:kdeininger@worldbank.org; <u>jins@anr.msu.edu; liusy@drc.gov.cn; shaoting@drc.gov.cn; xia.fang.fx@gmail.com. We thank the Chengdu</u> Statistical office, especially Taixiang Zhao, Xiaoying Gu, Yuan Lin, Yufang Chen, Qiuyan Chen, and Shiming Ren for access to data and support, and Yihao Li for research assistance. Funding support from the Knowledge for Change Program is gratefully acknowledged..