rafmacalaba/datause-encoder-data huggingface dataset:
The detailed estimates for the probits are in the Appendix; results are given there with and without the net income variable (which enters as a quadratic in log income), given the concerns about its endogeneity. (We return to this issue.) Controlling for household income per capita, we find that DB participation is more likely for larger households, living in smaller dwellings, who do not own their dwelling, have an “old style” toilet, are still using coal for cooking, have no heating, no computer, have a female head of household, have a disabled or sick head of household, or a head with little schooling or who works in services or social security/welfare, or a head who is retired, works at home, has been laid off or is unemployed. DB households have lower financial wealth, are more likely to feel that their income is “less than they need to make ends meet,” are more likely to think that their income has improved, have a lower share of wages in income, have more unemployed or students in the household but fewer retired people. Most cities have significantly lower participation rates than Beijing, controlling for household characteristics. (Later we investigate the differences across cities.) It appears that the program is putting heavier weight on certain characteristics, such as poor dwelling attributes and lack of financial wealth, than is implicit in household income per person from the UHSS. To the extent that these effects reflect measurement errors in incomes or a broader concept of “income” that is motivating the program’s targeting at local level, it can be argued that the program is doing a better job of reaching the poor than our calculations based solely on the survey-based incomes would suggest. We return to this point in section 5.

There are also indications that the program is doing better at reaching the chronically poor than those who may be vulnerable to poverty in the future. This is suggested by the fact that people who feel that they are on a downward trajectory are less likely to get support from DB.

9
26 In Vladimir, too, high income households have more housing plans. In the top quintile, 30.5% of the households intend to get better housing, with 24.8% preferring mnarket methods. The lowest income households are, however, not far behind: they count not so much on their wages as on other sources of income. Middle-income households are not so active. This can also be explained by the distribution of incomes in the past few years: unlike Moscow, in Vladimir the pensiioners have found themselves in the middle-income category.

In Gorodets, which is a small town, 40% of the households with low incomes count mainly on getting free housing. The other **600%,** whose incomes are higher, are ready to deal in the housing market. The "depression" in the second income quintile can also be explained by a high share of pensioner households.

# _c._ **_Demographic_** _profiles of households_

Table 2.3 presents the distribution of households by the demographic categories that have been used elsewhere in this analysis, and Table 2.4 shows their near-term plans for changing their places of residence.

Table 2.3. Distribution of households by demographic categories, 1996 (% total number of households in each city).

|Demographic category<br>Moscow|Vladimir|<br>Gorodets|
|---|---|---|
|.. ... .. ... . . g-EE. i<br>.<br>W. E<br>-<br>w<br>Pesoes.......................<br>X|||
|single<br>10.3|12.8|<br>17.6|
|husband and wife<br>9.7|11.7|<br>14.3|
|Sine w<br>a<br>ppl:|||
|childless<br>5.3|3.4|<br>1.6|
|childlesswithparents<br>9.1|7.9|<br>7.5|
|with children<br>6.1|5.7|<br>5.2|
|with children + prents<br>4.4<br> <br> <br>|5.7|<br>2.9|
|..<br>Z<br>,<br>g<br>.<br>%<br>.....<br>..<br>.. .....<br>N4ISO d wo.<br>.<br>i g t<br>,g<br>p.ole<br>~...........|<br>... ...............|....<br>............................|
|childless<br>4,5|_5.1_|<br>5.2|
|with children<br>32.2|29.8|<br>30.3|
|childless with parents<br>18|1.4|<br>1.3|
|with children**+**parnts<br>91<br>~~~~~~~~~~~~~~~|7.7<br>-**-**-<br>'**:**:|<br>6.5|
|..............<br>..<br>,,<br>,.,a<br>,_<br>_, ,<br>_....._<br>i_._<br>_<br>,..................... ........<br>7|..........<br>**..**<br> <br>.<br>.||
|...............<br>.......<br>.<br>............<br>..<br>..........|||
|~~~~~~~~~~~~~~~~~~~~~~~..<br>...**.**.....<br>.. E... .... .. .... .............<br>.f. ...........<br>|g. E. EEi**i**i.<br>i2 ESi..||

Sources: The Moscow Langitudinal Household Survey, January 1996; Vladimir and Gorodets Longitudinal Household Survey, December 1996.

The categories of households with the most pronounced intention to iimpro,ve their living conditions are married couples with or without children who have strong reasons to want to move to larger apartments, as well as households consisting of several generations who desire to split the household or who need more space. The mobility of these households is 1.5-2 times higher than the average due to the urgency of their housing problem and their comparative prosperity. For instance, among maried couples with children (about 30%/ of all urban households), 25.9% in Moscow, 40.4% in Vladimir and 25.8% in Gorodets are planning to move to improve their housing.
<!-- Start of picture text --> 2016 School Year 2016-17 2017 School Year 2017-18<br>Training Implementation of Test math and<br>Randomization IBAM-UNAMSep5 TeacherSep 12-Oct7 training-CBT Oct CBT2016 — sessions Apr 2017 socioemotionalskills<br>| 5-9 June<br>/ |<br>\|\}<br>| | |] | J |<br>Feb - Mar June 25, 26 July 29 Oct 16 Oct 17-NoviS Jan- Apr, 2017 August, 2017<br>Registration Application of COMIPEMS Handbooks Teacher Implementation of measure<br>to COMIPEMS COMIPEMS results deliveryto _—_training-Math Math tutoring enrollment in<br>schools 3' semester<br><!-- End of picture text -->
In the second part of the paper we propose a dynamic overlapping generations model with non-altruistic parents. After describing the environment we impose a social norm that requires intergenerational transfers and show that its sustainability is incentivecompatible across generations. Those transfers give the dynamic link between investment in schooling and future consumption for the parents, who otherwise would have no incentive to invest in their children’s schooling. This dynamic model also allows us to characterize the steady-state level of human capital in the economy. The dynamic effects of the existence of child labor are then discussed. After discussing the relevant issues for child labor in Latin America, we move on to analyze the effect of the conditional cash transfer program, PROGRESA (now OPORTUNIDADES), on child labor among indigenous and non-indigenous children in Mexico, using data from the evaluation database.

There have been several attempts to evaluate the impact of PROGRESA in several dimensions, though close to nothing has been done in order to test the differential impact of the program on indigenous versus non-indigenous people (for a summary, see IFPRI 2000). Perhaps the only attempt in this respect is Parker _et al_ . (2003), which provides an empirical estimation of the impact of language barriers in determining school achievement and the potential ameliorating role of bilingual education. By using large household data sets from PROGRESA, the authors find that, controlling for family resources and school quality, language represents an important barrier for indigenous schoolchildren. In particular, it is shown that there is a large gap in the educational performance of monolingual indigenous children relative to bilingual indigenous children and that bilingual primary schools narrow this gap. The results in the latter paper are rather consistent with our findings below.

# **2. Child Labor and Indigenous Peoples in Latin America**

The indigenous peoples of Latin America live in extreme poverty. There are over 40 million indigenous peoples in Latin America, or about 8 percent of the region’s population. Mexico has the largest indigenous population in the Americas, at over 12 million, or about 13 percent of total population. Access to formal education in Mexico 2
30

**38. The ongoing risks associated with European banks and sovereigns, as well as the long-term adjustment need in bank balance sheets, therefore, imply that adverse credit conditions may continue for some time, for Europe as well as EMDEs** . The risk of substantial deleveraging therefore has an important bearing on the level of economic activity and recovery in a given region. A review of country experiences with crises and recoveries suggests that economic recovery without credit growth is possible; in fact, ―creditless recoveries‖ are not all that uncommon (box 4). Experience also suggest, however, that creditless recoveries tend to be weaker and take longer to return to trend growth, particularly when they are preceded with a credit boom and banking crises—as in the current crisis.

**Figure 23. Longer-Term Deleveraging Impact on EMDE Credit across Simulation Scenarios**

<!-- Start of picture text --> Result of Euro Area Deleveraging to a Level of 12 Times Leverage (€ billion)<br>€1,200 1200.0<br>LAC Middle East<br>Asia CIS<br>€1,000 978 1000.0<br>CEE Africa<br>€800 1<br>800.0 304.0<br>676<br>€600 3<br>2 525 600.0 200.6<br>€400<br>4 400.0 201.1 154.8<br>€200<br>151.5<br>100.3<br>105.0<br>€0 200.0 210.0<br>133.4 75.7<br>10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 106.0 66.7<br>Necessary asset deleveraging via loan book (%) 0.0<br>Equity increase: 20%<br>Equity increase: 30% Scenario 1 Scenario 2 Scenario 3 Scenario 4<br>Scenario 1 Scenario 2 Scenario 3 Scenario 4<br>Fraction of deleveraging via loans (percent) 100 50 100 50<br>Fraction of current equity increase (percent) 0 0 20 20<br>Source: Authors‘ calculations.<br>Credit reduction in EMs (bn)<br><!-- End of picture text -->

**VI. CONCLUSIONS AND POLICY IMPLICATIONS**

**39. The true extent of European bank deleveraging and its potential implications for the rest of the world have continued to occupy the minds of policymakers and financial market participants.** European banks tightened their lending terms and conditions, reduced cross-border lending to EMDEs, put assets on sale across various business lines and geographies, and slowed down their credit extension to the private sector since the last two quarters of 2011, with the lending cuts focused primarily on dollar-denominated loans and riskier assets. Tighter lending conditions have spilled over to EMDEs as a consequence of tight funding conditions and credit standards, European bank retrenchment, and increasing borrowing costs. While many observers acknowledge that deleveraging is inevitable and even desirable as financial institutions clean up and restore the health of their balance sheets, concerns have centered on the possibility of simultaneous and disorderly balance sheet adjustments that could result in massive deleveraging at a global scale and undermine the already weak prospects of a global economic recovery.
coefficients on the 11 dummies for the months of birth, most of which are significant<sup>8</sup> . The seasonal patterns in child health are similar to patterns revealed by OLS estimations: children born in fall-winter months are healthier compared to children born in summer. The month-of-birth coefficients from the FE model are larger than the coefficients estimated by OLS. The difference can be explained by the fact that the FE regression is estimated on a relatively small sample of households—each with two children below age 3, while the sample for OLS regression includes all households<sup>9</sup> .

The average WAZ and HAZ of children born during the monsoon season is about 0.5 standard deviations (SD) lower than the average WAZ and HAZ of children born in the fall-winter months (Figure 4). After controlling for the characteristics of the child, the mother and the household they live in, the ‘month-of-birth’ effects ranged from 0.2 to 0.4 SD for both measures (Table 2). The magnitude of this effect is similar to the differences in Z-scores between children of illiterate mothers and mothers with incomplete secondary education, as observed in our data. Alderman, Hoogeveen and Rossi (2005) report that the anthropometric measures of children born during the ‘lean’ season in Tanzania are 0.2 to 0.4 SD lower than the measures of children born in other months of the year. These effects are comparable with the effects of nutrition programs and the estimated elasticities of the changes in households’ and mothers’ characteristics. For example, in Bangladesh, the average WAZ and HAZ of children with illiterate mothers is about 0.4 SD lower than the WAZ and HAZ of children whose mothers hold university degrees (Moestue and Huttly, 2008). Similar effects of maternal education on children’s anthropometrics are found by Alderman and Garcia (1994) for Pakistan, and Kassouf and Senauer (1996) for Brazil. The provision of micronutrient supplements resulted in a 0.2 SD increase in HAZ and 0.1 SD in WAZ for children in Vietnam and Mexico (Thu _et al_ ., 1999; Rivera _et al_ ., 2001). Vaccinations against upper respiratory illness improve WAZ by about 0.5 SD and HAZ by more than 1 SD, according to Alderman and Garcia (1994).

> **8** The F-tests on the FE, in all regressions in Table 2c, reject the pooled-OLS specification in favor of the FE specification.

> **9** Several studies point to problems with the quality of age data in NFHS (for example, Bhat 1995; Narasimhan et al., 1997; Mishra, Lahiri and Luther 1999). This measurement error in reported age would attenuate our results towards zero, so that the estimated effect of the months of birth on children’s health would be the lower bound (in absolute terms) of the true effects.

9
Policy ReseaRch WoRking PaPeR 4700

# **Abstract**

Income inequality in China has risen rapidly in the past decades across regions, between rural and urban sectors, and within provinces. The dynamics of divergence across these sub-national areas have taken the form of a “race to the top”—meaning that _all_ segments of the population, including the poor with low education in lagging inland rural areas, have experienced gains in average income. The largest gains have been registered by those with higher income and education in leading coastal urban areas. Using the China Economic, Population, Nutrition and Health Survey data of 1989 and 2004, we show that the most important factors explaining overall inequality are differential returns to schooling and sector of employment. A decomposition analysis based on household income determination shows that the increase in returns to education explains two-thirds of income changes in urban areas and one-sixth in rural areas. The widening income gaps are the consequence of higher growth in leading urban and coastal areas and that the skilled population has benefited more from the economic reforms carried out during the last 25 years. The authors argue that rising income inequality can be part of a normal process of development at a certain stage, and that the dynamics of spatial income divergence in the form of "a race to the top" can be desirable to some extent as it unleashes competitive pressure and creates incentives for investment in skills. Continuing to improve market efficiency and investing in people, in particular improving education service in lagging areas to poor people, are important for sustainable growth and equitable distribution in the long run.

This paper is a product of the Poverty Reduction and Economic Management Department, East Asia and Pacific Region. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at xluo@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
well-being most likely varies across individuals, countries, and time (Deaton et al., 2009; Radcliff, 2001). While for some individuals or groups in a society a lack of civil liberties is a key source of grievances, others might put more weight on poverty-related issues. Hence, because grievances are broadly-defined and essentially value- and preference-based, subjective measures might be more relevant and valid than objective indicators for predicting uprisings.

Second, objective indicators of growth also tend to raise expectations and aspirations (Tocqueville, 1856), which, if unmet, can decrease well-being. The ‘tunnel effect’ describes this phenomenon using the parable of a traffic congestion in a tunnel (Hirschman, 1973). If one of the lanes starts moving while the other lanes are still jammed, those who are stuck initially experience hope as the end of the traffic jam seems to be in sight, but unless they start moving forward, their hope will give way to envy and frustration. The tunnel effect ties in with the modernization theory (Inglehart & Welzel, 2005; Lipset, 1959), which posits that income growth leads to changes in values and fosters expectations of political and civil freedoms. When these expectations are unmet, this can lead to feelings of great disappointment and resentment, which can in turn mobilize citizens to demand changes within the political system (Gurr, 1970). In these cases, objective indicators reveal signs of optimism, whereas subjective indicators could indicate that anti-government sentiments are on the rise. This mechanism was at work during the Arab Spring, which was preceded by a period of ‘unhappy development’ in many Arab countries, especially the ones where the uprisings were most intense (Arampatzi et al., 2018).

This paper tests whether subjective well-being data can explain variation in nonviolent conflict going beyond what is captured by the typical objective measures. We propose a hybrid model, which captures the main features of the grievance, resource-mobilization, and politicalopportunity theories of conflict, and in which we include additional subjective well-being indicators of grievances. We also use additional specifications of this model to explore the role 5
<!-- Start of picture text --> NO2: Data<br>Financial Crisis Pandemic<br>08 09 10 11 12 13 14 15 16 17 18 19 20 21<br>NO2: Cyclical Component<br>Financial Crisis Pandemic<br>08 09 10 11 12 13 14 15 16 17 18 19 20 21<br>1.5 1.5<br>1.4 1.4<br>1.3 1.3<br>1.2 1.2<br>NO2: % Difference from Trend<br>1.1 1.1<br>10 10<br>5 5<br>0 0<br>-5 -5<br>NO2: % Difference from Trend<br>-10 -10<br><!-- End of picture text --> Figure S1: NO2: United States. _Notes._ Data is 365 day rolling average. Trend component calculated using Hamilton (2018) filter.

S2
Landuse within 500m Buffer of Chronic Flood Spot/ 200m Buffer of Water Logging Spots

# WARD P NORTH

<!-- Start of picture text --> Cy re<br>Aven omen ae<br>ARABIAN SEA we ‘i x Bie E3 i 5 WARD T<br>MM casescre<br>ro<br>TBbe tee<br>etrte<br>— pacer WARDPS<br>HE sear er<br>res<br><!-- End of picture text --> <!-- Start of picture text --> = oe<br>— nen<br>+ — fern<br>Ceres,<br>rene TAT WARD KE<br>Teter AReenene rita<br>G R E A T E R M U M B A |<br><!-- End of picture text -->
variable, the plausibly exogenous inter-annual variation in US wheat production are likely not identifying the correlation between the IV and the dependent variable; rather the differential underlying trends are.

Appendix 2 presents a detailed analysis of these heterogeneous nonlinear trends along the (potentially endogenous) regular-irregular aid recipient dimension NQ use for identification. This is problematic because NQ construct an instrument that interacts the exogenous time series variable and the endogenous cross-sectional variable, attempting to control for endogeneity through region-year and country fixed effects, and in some specifications with linear time trends. The problem is that inclusion of region-year and country fixed effects or a linear time trend does not preclude the possibility that an observed relationship between the (instrumented) endogenous variable and the outcome variable arises from spurious correlation from dominant nonlinear trends.

In the NQ setting, wheat production and conflict both show strong trends over time that swamp the more plausibly exogenous year-to-year variation. The trend in conflict propensity is most apparent for countries that receive aid most often and weakest for countries that do not receive aid frequently, meaning that including year fixed effects as controls does not eliminate this source of endogeneity.

Figure 1 summarizes the source of the problem in the NQ identification strategy. The essence of the NQ story is that elevated wheat production in the US obliges the US government to purchase wheat in order to maintain a price target, leading to higher stocks of government owned wheat. The surplus wheat is used the following year to provide food aid to recipient nations, with the bulk of the excess directed to particularly favored recipients who frequently receive aid shipments. This excess aid then causes sustained conflict in recipient countries. The panels in Figure 1 trace through the story on the time dimension for the NQ data. Panel A shows that periods of elevated production are strongly clustered in the middle of the period, demonstrating that most of the variation arises from decadal changes in 12
<!-- Start of picture text --> Figure 4. Quality-adjusted Education Benefits (JOD million) Figure 5 Quality-adjusted Health Benefits (JOD million) by<br>by Market Income Decile Market Income Decile<br>140 700<br>120 600<br>100 500<br>80 400<br>60 300<br>40 200<br>20 100<br>0 0<br>Household per capita market income decile Household per capita market income decile<br>Net benefits Quality-adjusted benefits Net benefits Quality-adjusted benefits<br>Note: Households are grouped into per capita market Note: Households are grouped into per capita market income<br>income deciles. Net benefits are those estimated in the deciles. Net benefits are those estimated in the baseline<br>baseline scenario. Quality-adjusted benefits are adjusted scenario. Quality-adjusted benefits are adjusted according to<br>according to learning outcomes in the S-HCI by decile. under-five stunting in the S-HCI by decile.<br><!-- End of picture text -->

# Impact on measured inequality and poverty

In cash and non-cash terms, Jordan’s fiscal policy appears modestly progressive; the poorest households receive more benefits than they pay while other households pay more into the system at a rate which slowly increases as they get richer. How does this impact inequality, as commonly measured by the Gini Index, and what is the effect on poverty?

Jordan’s inequality (Gini) is measured in the HEIS at 35.1 points based on market income—before the fiscal system affects households—and at 29.3 points based on final income—after accounting for all fiscal policy (Figure 6.).<sup>50</sup> This indicates that overall fiscal policy reduces inequality in Jordan by 5.8 points. The largest fall is observed between consumable and final income, which is when in-kind transfers (health and education) are included. In purely cash terms, inequality falls modestly by 2.6 points from market to consumable income.

50 The Gini Index is the most commonly used measure of income or consumption inequality. It ranges from 0 (perfect equality) to 100 (perfect inequality where one household has all income). In international context, a Gini less than 30 is quite equal, from 30 to 50 increasingly less equal, and above 50 quite unequal.