rafmacalaba/datause-encoder-data huggingface dataset:
Table A19: Impact on secondary outcomes—with false discovery rate adjusted q-values

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Marriag|e and pr|egnancy|Viole|nce (15|yo+)|Decisio|n-makin|g (indiv)|Decisio|n-making|(couples)||Finance||N|utritio|n|Me|ntal heal|th|
|5|Beta|pval|qval|Beta|pval|qval|Beta|pval|qval|Beta|pval|qval|Beta|pval|qval|Beta|pval|qval|Beta|pval|qval|
|T1 (safe spaces)<br>1|-0.004|0.960|0.805|0.051|0.567|0.661|0.068|0.563|0.661|0.115|0.426|0.567|0.028|0.826|0.805|-0.400|0.015|0.071|0.277|0.004|0.047|
|T1 + Leaders|-0.006|0.939|0.805|-0.028|0.808|0.805|0.250|0.064|0.152|0.408|0.005|0.049|0.030|0.762|0.786|-0.235|0.105|0.209|-0.003|0.975|0.805|
|T2 (safe spaces + IGA)|0.005|0.931|0.805|-0.014|0.861|0.805|0.158|0.214|0.342|0.168|0.237|0.354|0.055|0.566|0.661|-0.354|0.024|0.086|0.267|0.011|0.060|
|T2 + Leaders|-0.054|0.339|0.458|0.085|0.250|0.354|0.002|0.989|0.805|0.205|0.175|0.293|0.148|0.109|0.209|-0.153|0.321|0.434|-0.002|0.984|0.805|
|T3 (safe spaces + husbands’ clubs)|-0.035|0.597|0.678|0.141|0.117|0.217|0.200|0.051|0.127|0.294|0.021|0.082|-0.046|0.698|0.768|-0.682|0.001|0.028|0.109|0.417|0.567|
|T3 + Leaders|0.055|0.351|0.461|0.019|0.834|0.805|0.396|0.002|0.047|0.406|0.016|0.073|0.186|0.108|0.209|-0.051|0.710|0.768|0.269|0.019|0.081|
|T4 (Control Spillover)|0.007|0.908|0.805|0.049|0.574|0.661|0.081|0.443|0.567|0.223|0.067|0.155|-0.125|0.089|0.181|-0.247|0.051|0.127|0.039|0.673|0.751|

_Note:_ The coefficients and the p-values in this table (beta and pval) are obtained from the regressions in table 6 and they denote the impact of our treatments. The q-values (qval) are False Discovery Rate adjusted p-values and are estimated using the procedure described in Anderson 2008.
# Table of Contents

|1 The African currency unions .........<br>....................|2|
|---|---|
|2 Convergence of inflation ..... .....................................<br>2.1 Principal Component Analysis of the CFA zone ..................<br>2.2 Dynamic impact of core prices on periphery inflation.<sup>.... ...</sup>|<sup>4</sup><br> <sup>5</sup><br><sup>..</sup><br>8|
|3 Credit expansion and monetary equilibrium ..........................|<sup>10</sup>|
|4 Interest rates ....................................................<br>4.1 The CFA zone ..................................<br>4.2 The rand zone: effectiveness of interest rate policy in Lesotho<br>................................................................|. 13<br>14<br>...<br>14|
|5 Concluding remarks ................................................|.<sup>17</sup>|

Thanks are due to P. Berlin, G. Caprio, M. Dailami, A. Gelb, M.<sup>Long, A.</sup> Maruping, S. Moleko, R. Tenconi and D. Vittas for helpful discussions. The sections on the rand zone draw on material prepared jointly with I. Mas,<sup>to whom</sup> I am indebted.
in formal markets and covers a large sample. Furthermore, we provide evidence on the heterogeneous impact by gender, and the consequent welfare losses, both of which are not addressed in many of the literature to date.

Establishing causality between the incidence of violent shocks and changes in labor outcomes is difficult because the incidence of violent shocks is not exogenous. The presence of armed groups and attacks on the civilian population are linked with a historic tradition of conflict in a region, isolation from urban centers, and a poor quality of land, which reduces the costs of exercising terror. Since Colombia has faced a longstanding conflict for several decades, finding an instrumental variable related to the causes of conflict, yet which does not directly affect labor outcomes and consumption is difficult. Because we have a rich data-set consisting of municipal and rural district ( _vereda_ )<sup>1</sup> characteristics capturing the several dimensions that reduce the costs of exercising terror, we are able to exploit this variation in order to establish causality. The causal relation between conflict shocks on the one hand, and labor outcomes and consumption on the other, constitutes the third contribution of this paper.

Our results show non-agricultural labor markets are used as an alternative means for coping with covariate conflict shocks. Time spent on-farm decreases, while the supply of labor in off-farm (non-agricultural) labor markets expands. Men absorb the bulk of the adjustment in the use of time. Drops in agricultural production push men to provide more hours of work in off-farm employment and formal labor markets. As contractions in on-farm time are not fully absorbed by labor markets, men also spend more time on leisure activities and household chores. Women do not substitute for men in on-farm work, but rather try to find employment in off-farm work, apparently with little success. Given the large impact of conflict, additional off-farm supply is not covering fully drops in consumption.

The evidence in this paper indicates that when labor markets do not break down as a consequence of conflict, a changing labor supply can become an effective strategy for compensating for income lost due to conflict. These findings are important because most policies in conflict-ridden countries concentrate on short-term relief programs, which are designed as temporary measures to ensure the subsistence of the civilian population until productive activities can be resumed. Nonetheless, people in regions

> 1 Rural districts in Colombia are smaller administrative divisions within municipalities.

4
<!-- Start of picture text --> 2 Public primary deficit 10 Carbon emissions 7 Renewable energy production<br>6<br>1.5 0<br>5<br>1 -10<br>4<br>0.5 -20 3<br>2<br>0 -30<br>1<br>-0.5 -40<br>0<br>-1 -50 -1<br>0 10 20 30 40 0 10 20 30 40 0 10 20 30 40<br>Quarters after policy shock Quarters after policy shock Quarters after policy shock<br>Carbon tax Renewable energy subsidy Public investment in RE generation Pub. inv. in RE infrastructure (below saturation) Pub. inv. in RE infrastructure (above saturation)<br>1 Fossil energy production 6 Gross domestic product 4 Private investment in renewable energy<br>0 5 3<br>-1 4<br>2<br>-2 3<br>1<br>-3 2<br>0<br>-4 1<br>-5 0 -1<br>-6 -1 -2<br>0 10 20 30 40 0 10 20 30 40 0 10 20 30 40<br>Quarters after policy shock Quarters after policy shock Quarters after policy shock<br>Carbon tax Renewable energy subsidy Public investment in RE generation Pub. inv. in RE infrastructure (below saturation) Pub. inv. in RE infrastructure (above saturation)<br>0.2 Private investment in fossil energy 0.1 Exports 1.5 Non-oil imports<br>0 0<br>-0.2 -0.1 1<br>-0.4 -0.2<br>-0.6 -0.3 0.5<br>-0.8 -0.4<br>-1 -0.5 0<br>-1.2 -0.6<br>-1.4 -0.7 -0.5<br>0 10 20 30 40 0 10 20 30 40 0 10 20 30 40<br>Quarters after policy shock Quarters after policy shock Quarters after policy shock<br>Carbon tax Renewable energy subsidy Public investment in RE generation Pub. inv. in RE infrastructure (below saturation) Pub. inv. in RE infrastructure (above saturation)<br>0.5 Oil imports 0.2 Headline inflation 2 Energy price inflation<br>0 0<br>1<br>-0.5 -0.2<br>-1 -0.4 0<br>-1.5 -0.6 -1<br>-2 -0.8<br>-2<br>-2.5 -1<br>-3 -1.2 -3<br>0 10 20 30 40 0 10 20 30 40 0 10 20 30 40<br>Quarters after policy shock Quarters after policy shock Quarters after policy shock<br>Carbon tax Renewable energy subsidy Public investment in RE generation Pub. inv. in RE infrastructure (below saturation) Pub. inv. in RE infrastructure (above saturation)<br>Billions of 2015 USD<br>Millions of tonnes of CO2 emissions Millions of tonnes of oil equivalent<br>Billions of 2015 USD Billions of 2015 USD<br>Millions of tonnes of oil equivalent<br>Billions of 2015 USD Billions of 2015 USD Billions of 2015 USD<br>Billions of 2015 USD Percentage points Percentage points<br><!-- End of picture text --> Figure 3: The macroeconomic effects of selected permanent climate-fiscal policy interventions. Each policy shock is normalized to move 1 billion of constant 2015 USD before any macroeconomic adjustment.

17
9 Four stylized facts emerge in the income elasticity estimates shown in table 2:

- First, income elasticities from time series data are typically smaller than those from cross-section data. This is because cross-section analyses produce long-run elasticities, and long-run behavior is generally more responsive to income changes than short-run behavior. Roughly speaking, long-run income elasticities of motor vehicle (especially car) ownership are greater than 1, while short-run income elasticities are iess than 1.

- Second, income elasticities from urban-level data are similar to or smaller than those from country-level data largely because there are more competing modes of transportation and greater congestion in urban areas, both of which reduce the attraction of motor vehicles. In fact, the long-run elasticities from urban-level data are closer to those from country-level data than are the short-run elasticities. Using the data sets summarized in table 1, figure 1 indicates that motor vehicle ownership increases somewhat more rapidly with income at the national level than at the urban level.

- Third, income elasticities are generally larger for automobiles than for commercial vehicles, supporting the economic hypothesis that the share of passenger cars in the motor vehicle fleet increases with income.

- Finally, income elasticities of motor vehicle use are less than unity, smaller than longrun income elasticities of motor vehicle ownership, and smaller for households

**Figure 1. Per Capita Income and Motor Vehicle Ownership in 50 Countries and 35 Cities**

<!-- Start of picture text --> 1,000 Denver A * USA<br>0 * * Switzerland<br>.~~ S ~ / ~ ~~~~~ ~ sakcai ~<br>8 100 Bandung Manila Bangkok tiago<br>XvIi Jakat HK -Nai<br>- /urabaya a _8* ~~~~~Tbailand<br>i z < g ~~~~~~~a Guangzhoul<br>- Cin Cameroon * Country actual<br>+~~~~~~~~~~~~ * & City actual<br>-Malawi -National equatiorn<br>Rwanda - Urban equation<br>1 - I I I I: I , I I . ,I I I I I .<br>100 1,000 10,000 100,000<br>Per capita GNP (in 1987 US$)<br><!-- End of picture text --> Note: Both Axes are in logarithms, and the slope of a line on such a diagram is the elasticity. A straight line indicates a constant elasticity.

_Source:_ Ingram and Liu, "Vehicles, Roads, and Road Use."
land expansion of China, Journal of Urban Economics, 63: 96–115.

Desmet, K. and M. Fafchamps. 2006. Employment Concentration across US counties. Regional Science and Urban Economics 36: 482–509 Desmet, K. and E. Rossi‐Hansberg. 2009. Spatial Growth and Industry Age. Journal of Economic Theory, 144: 2477‐2502 Desmet K. and E. Rossi‐Hansberg. 2014. Spatial Development. American Economic Review, American Economic Association,104: 1211‐43.

Desmet, K., E. Ghani, S. O'Connell and E. Rossi‐Hansberg. 2015. The Spatial Development of India. Journal of Regional Science, 55: 10‐30 Dixit, A., 1973. The optimum factory town. Journal of Economics and Management 4: 637–651.

Donaldson, D., 2014. Railroads of the Raj: Estimating the Impact of Transportation Infrastructure, American Economic review, forthcoming Downs, A. 1992. Stuck in Traffic. Washington, DC: The Brookings Institution.

Duarte, C.M., C.A. NuÃ±ez, E.C. Mejia, C.P. Prieto and J.M. Tresserra. 2011. Polycentrism in the Spanish metropolitan system: an analysis for 7 metro areas. European Regional Science Association in its series ERSA conference papers with number ersa11p119.

Dumais, G., Ellison, G., Glaeser, E. 2002. Geographic concentration as a dynamic process. Review of Economics and Statistics 84 (2): 193–204.

Duranton, G. and D. Puga. 2001. Nursery cities: Urban diversity, process innovation, and the life cycle of products, Amer. Econ. Rev. 91: 1454–1477.

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30
medical literature, the type and severity of pesticide poisoning depends on: (a) the toxicity of the pesticide, (b) the amount of pesticide involved in the exposure, (c) the route of exposure, and (d) the duration of exposure (Extension Toxicology Network, 2004). The health effects specification accounted for these factors, and following Antle and Pingali (1994), we also included age, nutritional status, income, the number of protections taken while handling pesticides and location.<sup>9</sup> A farmers’ perception of pesticides can be shaped by a variety of factors including the potential health implications, formal education and pesticide training (Warburton, Palis and Pingali, 1995). Thus in the second equation, farmers’ misperception was specified as a function of the health effect, the degree of highly toxic use, age, education, training and location. In the third equation, overuse was specified as a function of farmer misperception, toxic use, age, income, education, farm size, ownership, training, practicing IPM and location. As crops differ substantially with respect to their vulnerability to pests, crop area proportions were also added to the overuse equation (see Appendix 1 for variable details).

- _SICKNESS = f_ ( _PESTAMT_ , _PWHOIab_ , _PWHOII_ , _PWHOIII_ , _NUTRTON_ , _AGE_ , _INCOME, NPROTECT, District Dummies_ )

- _MISPERCEPTION_ = _f_ ( _SICKNESS_ , _AGE, EDUCATION, TRAIN_ , _PWHOIab_ , _PWHOII_ , _PWHOIII_ , _District Dummies_ )

- _OVERUSE_ = _f_ ( _MISPERCEPTION, AGE, FARMSIZE, OWNER, INCOME_ , _EDUCATION, TRAIN_ , _IPM Dummy_ , _PWHOIab_ , _PWHOII_ , _PWHOIII, Crop proportions, District Dummies_ )

# _Estimation results_

We estimated the three probit equations in (4) simultaneously, with the results presented in Table 2. The correlation coefficient between the error terms of the health effects and misperception equations is significantly different from zero at 1% level of significance, indicating that the unobserved variables influencing health effects are positively correlated with the unobserved characteristics affecting the misperception of pesticide risk. The other two correlation coefficients are not significant, suggesting that health and misperception are not endogenous in the overuse equation.

> 9 Location controls were also included in the regression to take into account any possible pervasive contamination from neighboring pesticide use.

11
The average income ratio between whites and blacks and mulattos reveals that, in the top 20 percent of the income distribution, white household heads earn an average 50 percent more than black and mulatto household heads (Figure 5.3).

**Figure 5.3:   Average Income Ratio of Whites and Blacks** **<u>& Mulattos, Bahia 2001</u>**

<!-- Start of picture text -->
1.6<br>1.5<br>1.4<br>1.3<br>1.2<br>1.1<br>1<br>0.9<br>0.8<br>q1 q2 q3 q4 q5<br>Source:  Calculations based on PNAD 2001.<br><!-- End of picture text -->

_Education levels are strongly related to poverty_ . That is, being able to read and write is important in determining the likelihood of being in poverty. In Bahia, the P0 is 37 percent for household heads that are literate, and 50 percent for those that are not. These headcounts are high compared to the national averages of 19 and 39 percent, respectively. However, while a negative relationship between years of education completed and poverty is typical, it is less marked in Bahia and the Northeast.

There appears to be relatively little difference in P0 between household heads with no education (48 percent) and household heads with completed primary education (42 percent). Nevertheless, household heads that have completed secondary education are much better off (22 percent are poor) than those with only primary education. Of the household heads with more than 12 years of schooling only 2 percent were extremely poor in 2001, down from 7 percent in 1993. These findings indicate that education is a very important key to poverty reduction in Bahia.

The gap in P0 between the educated and less educated is widening: the more educated are experiencing less poverty, while the less educated are getting left behind. For each level of education (lower primary only, upper primary only, secondary only, and tertiary) the likelihood of being poor is estimated for Bahia. Figure 5.4 shows that there are very large differences in poverty levels by education, and that they have increased over time: since 1995, the P0 for people with primary and secondary education appears to have increased, while the P0 for people with some university education stabilized at its already very low level. In Bahia, as elsewhere, there is a great deal of debate about the causes of these changes: skill-biased technological change, changes in the relative supply of and 22
**Table 12** : Poverty in the states by social groups at the Tendulkar Line (percent)

|State|1993-<br>94|ST<br>2004-<br>05|2009-<br>10|1993-<br>94|SC<br>2004-<br>05|2009-<br>10|1993-<br>94|NS<br>2004-<br>05|2009-<br>10|
|---|---|---|---|---|---|---|---|---|---|
|Uttar Pradesh|45.7|41.7|40.1|68.1|55.2|52.4|42.8|36.7|32.9|
|Maharashtra|71.5|68.1|48.5|65.0|52.9|34.7|41.9|32.3|19.8|
|Bihar|72.1|59.1|62.0|75.4|77.0|67.7|56.0|48.2|49.2|
|Andhra Pradesh|56.7|59.3|37.6|61.7|40.3|24.5|39.8|24.7|19.4|
|West Bengal|64.2|54.0|31.6|48.5|37.9|32.6|33.5|31.9|24.5|
|Tamil Nadu|47.4|41.9|14.1|64.0|48.6|28.8|39.4|25.5|14.7|
|Madhya Pradesh|68.3|77.4|61.0|55.6|62.0|41.9|33.0|35.9|27.9|
|Rajasthan|62.1|57.9|35.4|54.0|49.0|37.1|29.6|25.2|18.7|
|Gujarat|51.2|54.7|47.6|54.1|40.1|21.8|32.6|27.1|17.6|
|Karnataka|68.6|51.2|24.2|69.1|53.8|34.4|43.6|27.6|21.2|
|Orissa|80.4|82.8|62.7|60.6|67.4|47.1|50.6|44.8|24.0|
|Kerala|35.2|54.4|21.2|50.3|31.2|27.4|29.4|17.8|10.4|
|Assam|54.1|28.8|31.9|57.8|44.3|36.6|51.3|35.2|40.2|
|Jharkhand|71.2|59.8|50.9|72.5|59.7|43.5|53.3|38.9|31.5|
|Haryana|65.7|6.7|57.4|59.1|47.4|37.8|27.4|16.3|12.1|
|Punjab|36.8|18.7|15.5|37.7|37.9|29.2|13.9|11.5|7.3|
|Chhattisgarh|64.0|62.9|65.0|52.6|48.0|60.1|42.1|44.5|39.6|
|Total(India)|63.5|62.4|45.6|60.2|51.0|40.6|39.3|31.5|24.9|

The negative relationship between poverty ratios and per-capita expenditures we depicted in Figure 3 can also be observed for the social groups taken separately. Using the rural poverty estimates by social groups from the appendix, we show this relationship between SC poverty and per capita rural expenditures in the left panel and that between the ST poverty and per capita rural expenditures in the right panel of in Figure 4. Figure 4 closely resembles Figure 3. The fit in the right panel is poorer than that in the left panel as well as those in Figure 3. This is partially because the ST are often outside the mainstream of the economy and less responsive to rising per-capita incomes. This factor is presumably exacerbated by the fact that the number of observations in the case of the ST has been reduced to 11 due to the number of ST households in the sample dropping below 100 in six of the 17 states.

25
5 The paper is organized as follows. Section 2 briefly summarizes our general approach to the measurement of inequality of opportunity, which is developed more fully in a companion paper (Ferreira and Gignoux, 2008). Section 3 describes the datasets and presents the two alternative indicators of economic advantage that we construct: the “wealth index” and an imputed measure of household per capita consumption expenditure. Section 4 adapts the measure of inequality of opportunity from Section 2 to these alternative indicators, and discusses alternative parametric and non-parametric estimation methods. Section 5 presents the results of the analysis for Turkey. Section 6 introduces the concept of opportunity profiles, and presents our estimates for Turkey. Section 7 concludes.

# **2. The measurement of inequality of opportunity**<sup>**6**</sup>

Most empirical studies have followed Roemer (1998) in associating inequality of opportunity with that part of inequality which is due to morally irrelevant, pre-determined circumstances, over which individuals have no control, and for which they can therefore not take personal responsibility. Specifically, Roemer proposed that “leveling the playing field means guaranteeing that those who apply equal _degrees_ of effort end up with equal achievement, regardless of their circumstances. The centile of the effort distribution of one’s type provides a meaningful intertype comparison of the degree of effort expended in the sense that the _level_ of effort does not” (1998, p.12, emphasis added).

To see what such a definition implies formally, consider a finite population of agents indexed by _i_ ∈{ 1,... _N_ } , where each individual _i_ is characterized exclusively by a set of attributes { _yi_ , _Ci_ , _ei_ } , with _y_ denoting an advantage, **_C_** denoting a vector of circumstance characteristics, and _e_ denoting an effort level. Let us also follow Roemer (1998) in treating effort as a continuous variable, while the vector **_Ci_** consists of _J_

> respondents to the Life in Transition Survey of 2006 felt that the “gap between the rich and the poor was too high” in Turkey, and when asked what was the “main reason why there are some people in need in our country today?” 63% choose “injustice in society” as their answer. 6 This section is based on and summarizes the more comprehensive discussion in Ferreira and Gignoux (2008).