rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8831

# **Abstract**

In patriarchal societies, sticky norms affect married women’s social circles, their autonomy, and the outcomes of intra-household bargaining. This paper uses primary data on women’s social networks in Uttarakhand, India; the modal woman has only three friends, and over 80 percent do not have any friends of another caste. This paper examines the effect of a shock to friends’ empowerment on a woman’s autonomy, specifically physical mobility, access to social safety nets, and employment outside the household; perceived social norms; and an outcome of household bargaining: investments in her children. The analysis instruments for endogenous network formation using a woman’s age and her caste network in the village. The key peer effect is the impact of having a friend who received an empowerment shock on a woman who did not receive that shock. The results show significant peer effects on only a few of the examined measures of women’s autonomy. In contrast, peer effects exist on all considered outcomes of a daughters’ diet and time spent on chores. The findings suggest a large decay rate between effects on own empowerment and peer effects. Interventions targeting child welfare through women’s empowerment may generate second-order effects on intra-household decision-making, albeit with substantial decay rates, and thus benefit from targeted rather than randomized rollout. In contract, interventions on gender roles and women’s autonomy may be limited by the stickiness of social norms.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ekandpal@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
|Log GDP per capita at decade of birth<br>(cohort weighted)|0.0202<br>(0.0454)|0.0328<br>(0.0449)|-0.0007<br>(0.0480)|-0.0093<br>(0.0387)|
|---|---|---|---|---|
|Population at decade of birth (cohort|0.0220<sup>**</sup>|0.0226<sup>**</sup>|0.0049|0.0122|
|weighted)|(0.0103)|(0.0093)|(0.0117)|(0.0082)|
|Population at decade of birth squared|-0.0001<sup>**</sup>|-0.0001<sup>**</sup>|-0.0000|-0.0000|
|(cohort weighted)|(0.0000)|(0.0000)|(0.0000)|(0.0000)|
|Infant mortality rate at decade of birth|0.0008|0.0007|-0.0022|0.0048<sup>**</sup>|
|(cohort weighted)|(0.0029)|(0.0027)|(0.0031)|(0.0020)|
|_R_<sup>2</sup>|0.988|0.988|0.988|0.990|
|Number of observations|1015|1015|1015|1015|
|Number of countries|68|68|68|68|

_<mark>Source:</mark>_ <mark>All specifications include country and year fixed effects. Standard errors (in parentheses) are clustered at the country level.</mark>

*** significant at the 1% level, ** significant at the 5% level, * significant at the 10% level.

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insurance in Mexico.

Note that a similar logic might apply to other information besides information on losses. In particular, if the disaster insurance scheme involves some kind of index - see Section 5.4 - then committing not to manipulate the index may be more credible if the private sector is involved.

# **5.4 Disaster indices**

Many of the recent financial products which have been promoted to help increase recipients’ protection against disasters involve the use of indices. These are basically statistics, often based on relatively objective data such as satellite data on wind speed or rainfall, or monitoring station readouts of ground acceleration, that are correlated with recipients losses from disasters such as tropical cyclones, drought or earthquakes. Basing financial products on indices, rather than losses directly, holds a number of attractions. It is typically less expensive to collect and less vulnerable to manipulation than loss information, and since it does not depend on recipients’ losses, basing insurance on it avoids problems of moral hazard or adverse selection.

In our model, we can introduce a basic index _L_<sup>ˆ</sup> , which takes the value 1 or 0 and is correlated with whether a recipient suffers a loss. In particular, we can specify that, if a recipient suffers a loss, then _L_<sup>ˆ</sup> = 1 with probability 1 _− μ_ , and if a recipient does not suffer a loss then _L_<sup>ˆ</sup> = 1 with probability . The parameter _μ ≤_ 1 _− π_ here represents the amount of basis risk in the index, such that a perfect index would therefore have _μ_ = 0, and deviations from this represent ‘basis risk’. Note that our parameter _μ_ is comparable to _η_ and _γ_ in Section 4.2, and hence to a certain extent we can directly compare the risks of misallocation to basis risk.

Does the simple act of building an index help with any of the commitment problems previously identified? In terms of the Samaritan’s dilemma, building an index does not help if true losses are freely observed by benefactors - they will simply ignore the index, as they have better information on recipient’s utility. However, if observing losses is relatively expensive compared to observing the index (or the cost of collecting the index has already been committed to), then ex-post a benefactor may choose not to pay to learn the true loss, and instead rely on the index when it comes to allocating relief. In this case, she avoids the Samaritan’s dilemma problem, because she does not observe net losses, and hence recipients’ self-protection decisions do not impact the relief they receive.

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# **Table S1.3: Robustness: Including respondent’s characteristics as controls**

||(1)<br>DP|(2)<br>FDC|(3)<br>NRM|(4)<br>UPC|
|---|---|---|---|---|
|Enumerator gender (male)|-0.566***|-0.416**|-0.101***|-0.517***|
||(0.150)|(0.158)|(0.034)|(0.115)|
|Enumerator age|-0.021|-0.006|0.002|-0.033**|
||(0.021)|(0.020)|(0.004)|(0.016)|
|Enumerator education|0.116|0.134|-0.073*|0.066|
||(0.110)|(0.121)|(0.036)|(0.110)|
|Enumerator from urban locality|0.459**|0.575***|-0.002|0.433***|
||(0.184)|(0.200)|(0.039)|(0.148)|
|Respondent gender (male)|-0.000|0.049|-0.021|0.007|
||(0.032)|(0.039)|(0.022)|(0.036)|
|Respondent age|-0.001|-0.000|0.000|0.001|
||(0.001)|(0.002)|(0.001)|(0.001)|
|Respondent marital status (single)|0.084|0.120**|-0.032|0.066|
||(0.059)|(0.057)|(0.029)|(0.060)|
|Respondent education level|0.036|0.098***|-0.047***|0.057*|
||(0.031)|(0.030)|(0.016)|(0.029)|
|Observations|6,245|6,332|6,552|6,319|
|R-squared|0.486|0.461|0.236|0.479|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

Note: This table presents OLS regression results for equation (1). For each column, the outcome variable is (an increasing) categorical variable (1-4) which measures the openness to vote for a political party, namely, the Democratic Party (DP), the Forum for Democratic Change (FDC), the National Resistance Movement (NRM), and the Uganda People’s Congress (UPC). Each regression includes community fixed effects and a constant. For all regression, the sample includes enumerators who have completed at least 24 interviews. Robust standard errors in parenthesis are clustered at the enumerator level. *** p<0.01, ** p<0.05, * p<0.10.

36
index by district matches the WHO estimate rankings for Ebola cases. In our sample, Port Loko has the highest disruption measure, Pujehun has the lowest. Second, a variance decomposition of the disruption index reveals that within district variation accounts for the majority (70%) of the overall variation. Using the dummy measure to refer to high and low disruption, we see that within all districts, there are both high and low disruption villages, with the greatest incidence of high disruption being in Port Loko.

As a third and final step, we provide evidence that the disruption measure picks up localized variation in the intensity of the aggregate shock, rather than capturing more permanent features of local infrastructure, economy or state capacity. Panel A of Figure A3 shows within district partial correlations between the disruption index and the village level covariates related to the location of the village, distance to key infrastructures, political alignment, poverty, size and exposure to NGOs (from Panel A in Table 1): we see insignificant correlations with all covariates although a standard deviation increase in village poverty increases the chance of being high disruption by 4 _._ 8pp. All these village covariates are conditioned on in throughout our analysis. Panel B of Figure A3 then shows within district partial correlations between the disruption index and historic measures of health services or knowledge, as measured at the Chiefdom level using DHS2013 data or the 2007 National Public Survey. Reassuringly, the dummy index of disruption is uncorrelated to multiple past health related measures, including actual health behaviors, health outcomes, access to health facilities, and state capacity related to the health sector.

Of course, the quantitative results are sensitive to the exact construction of the disruption dummy, _Evd_ . We later show our core results are qualitatively robust to three alternative constructions: (i) using the continuous disruption index; (ii) defining disruption to be within-district, so that a quarter of villages in all districts, by construction, are high-disruption; (iii) additionally using information on the disruption of daily and periodic markets to construct _Evd_ .

# **4.6**

We use a potential outcomes framework to make precise the identifying assumptions under which the factorial design yields causal estimates of ELA, exposure to high disruption, and their interaction. For expositional ease we ignore covariates ( _yi_ 0 _, Xivd, Xvd, λd_ ). Let _Yi_<sup>_TE_</sup> be the potential outcome for individual _i_ , receiving ELA treatment _T ∈{_ 0 _,_ 1 _}_ and low/high Ebola-related disruption _E ∈{_ 0 _,_ 1 _}_ as defined above. One of the following potential outcomes is then observed, 21
We call the first version (Equation 9) the linear model of conflict risk and the second (Equation 10) the heterogeneous model. We simulate a version of this model where f(i,t) = (αt – βt<sup>2</sup> )(δi – γi<sup>2</sup> ) where α, β, δ, and γ are fixed parameters, t=1...32 are years and i=1...126 represent an index identifying countries. Figures A14 and A15 show how simulated conflict risk varies by year according to the function f(i,t) in Equation 10. In all countries, conflict risk peaks in the middle of the period, consistent with the observed facts, but the intensity of the trend varies by country. Similarly, conflict risk varies across countries in a nonlinear way.

Figures A14 and A15 show how the linear and heterogeneous models produce different conflict risk. In both models, however, conflict risk is highest in the middle period, because ENSO surface temperatures are highest in this period, but the heterogeneous model (Equation 10, shown in Figure A16) differs in two regards. First, the tendency of conflict risk to peak in the middle of the period is more pronounced. Second, for any given year, conflict risk varies by country, so that country 126 is always at the highest risk of conflict and country 1 is always at the lowest, though risk of conflict in country 60 is higher in 1985 than for country 1 in 1970.

To demonstrate the importance of this kind heterogeneous response to conflict risk, we simulate 100 draws of the system where (11) Conflictit = 1 if θit> θ̄ where θit = riskit + εit and εit ̴ U(0, 0.01) (12) Aidit = α * max(riskit - εit,0) where α is a constant The aid allocation again captures three stylized facts about modern food aid allocations. First, additional aid is given to countries chronically at higher risk for conflict, as is clearly true in the data. Second, surprise realizations of conflict risk may be associated with lower allocations in the immediate year in which they occur because aid is more difficult to mobilize and deliver. Third, aid allocations cannot be negative. We use the actual data for realizations of wheat production and ENSO sea surface temperatures to simulate this system. Figure A18 shows the distribution of coefficients of food aid and conflict using NQ’s IV strategy and their OLS strategy for the heterogeneous model and including year and country fixed effects (not ENSO) as controls. Clearly such a model is capable of 67
The use of self-reported data about the food price shock may lead to biased results. Households might report a severe price shock in order to justify reducing their educational investment in children or increasing their labor. In order to avoid this endogeneity problem of the self-reported food price shock, the change in district-level food prices between 2008 and 2010 was used to measure the food price shock. Specifically, the district of PSLM data was mapped to the nearest urban center in which the retail price data were collected. Then the corresponding retail prices of wheat were matched to the sample households based on their residential district and interview month.

Since 1991, the FBS in Pakistan has collected monthly retail price data for 374 goods and services covering ten consumption categories from 71 markets in 35 cities. In this study, the district-level retail price of wheat as a proxy for food prices was used. This measure was chosen because wheat is the single most important source of household calories in Pakistan. According to PSLM 2005–06 and 2007–08, 50 percent or more of the calories consumed in an average Pakistani household come from wheat. Retail prices in rural areas are matched to prices in urban centers, so the implicit assumption is that the wheat price in the rural areas is similar to that in the nearest urban centers. The households in the Balochistan region were excluded from the analysis because there is only one urban center (Quetta) in this region covering a fairly large area (44% of the total geographic area of Pakistan) and the price match with this one urban center with the rest of the region tends not to be reliable. Furthermore, although only 5 percent of the population lives in Balochistan, 18% of the sample households were located in Balochistan due to oversampling in the region.

# **3.2. Empirical Strategy**

This study investigates the impact of the food price increase on child schooling and labor in Pakistan. Three binary outcome measures, attending school, working, and being idle were used to capture children’s activities. Children engaged in both work and school were not separately analyzed since only 1 percent of children aged 10–14 fall into this category. Two food price shock measures were used, the self-reported food price shock based on recall data and districtlevel wheat price changes between two waves. The latter is exogenous because it is based on geographic and temporal (two waves) variations.

6
weight-for-weight z-score (WHZ) and height-for-age z-score (HAZ).<sup>78</sup> Comparing across the two groups of households, we see that children in coffee-producing households have slightly better anthropometric outcomes than those in non-coffee-producing households.

It is worth discussing the distribution of some key variables in detail. First, table 2 provides summary statistics of the share of farmland allocated to coffee for different sub-samples of households. The first row presents the distribution of the share of land allocated to coffee across _all_ farmers in the ESS data (rural sample). An average farmer allocates about 5% of its farmland to coffee. However, because the majority of farmers are not coffee producers (the 75th percentile farmer allocates zero fraction of its farmland to coffee), the distribution of the share of land allocated to coffee is highly positively skewed. The second row provides the summary statistics of the share of land allocated to coffee among farmers who allocate at least 5% of their farmland to coffee. This sample accounts for nearly 15% of the total sample. Among these farmers, the average farmer allocates about one-third of its farmland to coffee, while the median and the 75th percentile farmers, respectively, allocate a quarter and half of their farm land to coffee. The third row reports similar summary statistics for sub-sample of farmers who allocate at least 10% of their farmland to coffee. These farmers account for slightly above 10% of the total sample, and they allocate about 40% of their farmland to coffee, on average. Rows 4-6 report similar summary statistics for sub-samples of framers who allocate at least a quarter, one third and half of their farmland to coffee production, respectively. This summary statistics for sub-samples of the households is useful later when we interpret the magnitude of the estimated regression coefficients.

The second important variable is household consumption expenditure. The average adult equivalent consumption in 2011 per day is 14.5 Birr (about 2.75 in current PPP $). The median adult equivalent consumption per day is about 11 ETB (about 2 in current PPP $). The national poverty line for Ethiopia during the same year is 10.35 Birr (World Bank Group, 2015). The average (median) adult equivalent consumption decreases slightly to 1.94 in current PPP $ (1.68 in current PPP $) in 2015. Figure 4 presents the distribution of log total adult equivalent consumption spending.<sup>9</sup> The left panel in this figure shows the distribution of nominal

> 7WAZ, WHZ and HAZ values are computed using the stata program, which is based on 2006 WHO child growth standards.

> 8Ethiopia performs worse than Sub-Saharan African (SSA) countries’ average in the proportion of under-5 children stunted, underweight, and wasted. Ethiopia has the highest percent (76%) of children who have not received any of the eight EPI immunizations in SSA countries. See Kanamori and Pullum (2013) for a detailed comparison of child health outcomes across 30 SSA countries using DHS data.

> 9For details about construction of the consumption variable from survey data, see `https://microdata.`

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34 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

# **6. Conclusions**

This paper proposes a more integral way to measure urban form and its relationship with economic performance, measured as the density of radiance within the urban extent, _dr_ntl_2010_ , computed as the sum of the NTL DN values in 2010 divided by area, in square kilometers, of the urban extent in 2010. Instead of using population density as a proxy for urban form, we use seven variables that cover the three dimensions of urban form: shape of the urban extent, structure of the urban texture and land use pattern. For the empirical evidence we use two strategies for dealing with the endogeneity between urban form and productivity: First, using lagged explanatory variables as in Fallah et al (2010); and second, using instrumental variables as in Harari (2015).

Using information from 919 urban extents in Latin America, the results show that the shape of the urban extent, the inner-city connectedness, the level of urbanization and the population level have a statistically significant influence over the productivity level of the city. We also found evidence of the presence of a congestion effect on productivity since the results for _population_<sup>_2_</sup> and _fullness_<sup>_2_</sup> are both significant and negatively associated with productivity. According to these results, a compact, dense and well-connected city meets important conditions to be highly productive.

An important consequence of decomposing the concept of urban form into three dimensions is that a non-compact city can reach high levels of productivity by guaranteeing a high level of inner-city connectedness; or, it may happen that a compact city that is poorly connected can show low levels of productivity. Another feasible situation using our way to characterize urban form is that compact cities that are poorly urbanized can have low productivity. In summary, our empirical evidence shows that each city can find its way towards a higher productivity level by analyzing the status of each dimension of its urban form and implementing the corresponding strategies to improve its current conditions.

# **7. References**

Abrahams, A; Lozano-Gracia, N; Oram, C (2016). Deblurring DSMP Nighttime Lights. Working paper. The World Bank, Washington, DC.
Firm-level regressions (cont.)

||(Appr 1)<br>Growth|(Appr 2)<br>Growth|(Appr 1)<br>Growth|(Appr 2)<br>Growth|(Appr 1)<br>Growth|(Appr 2)<br>Growth|
|---|---|---|---|---|---|---|
||Turnover|Turnover|Labor|Labor|TFP|TFP|
|Connected|-5.666***|-16.246***|-13.020***|-28.481***|-4.612***|-10.116***|
|(1 if yes)|(0.466)|(1.133)|(2.571)|(6.215)|(0.450)|(1.055)|
|Log Turnover|5.770***|5.779***|||3.081***|3.082***|
||(0.030)|(0.030)|||(0.031)|(0.031)|
|Log Labor|||18.507***|18.494***|||
||||(0.265)|(0.265)|||
|Log TFP|||||||
|Lag Growth Turnover|0.000<br>(0.000)|0.000<br>(0.000)|||||
|Lag Growth Labor|||-0.031***|-0.031***|||
||||(0.001)|(0.001)|||
|Growth Labor|0.039***|0.039***|||||
||(0.000)|(0.000)|||||
|Lag Growth TFP|||||-0.000***|-0.000***|
||||||(0.000)|(0.000)|
|Age|-0.489***|-0.480***|-1.895***|-1.881***|-0.079***|-0.073***|
||(0.013)|(0.013)|(0.074)|(0.074)|(0.013)|(0.013)|
|Age^2|0.001***|0.001***|0.006***|0.006***|-0.000|-0.000|
||(0.000)|(0.000)|(0.001)|(0.001)|(0.000)|(0.000)|
|Share of turnover in industry, %|-21.330***|-19.636***|-38.531*|-35.953|-14.056***|-13.241***|
||(3.388)|(3.393)|(19.447)|(19.474)|(3.194)|(3.198)|
|Herfindahl-Hirschman Index|-0.022***|-0.021***|-0.296***|-0.295***|-0.043***|-0.042***|
||(0.004)|(0.004)|(0.024)|(0.024)|(0.005)|(0.005)|
|Intercept|-10.083***|-10.421***|9.179***|8.582***|4.969***|4.704***|
||(0.379)|(0.380)|(2.006)|(2.007)|(0.411)|(0.412)|
|Year effects|YES|YES|YES|YES|YES|YES|
|Industry effects|YES|YES|YES|YES|YES|YES|
|N|1366038|1366038|1492182|1492182|1106080|1106080|
|R2|0.052|0.052|0.002|0.002|0.028|0.028|

_Standard errors in parentheses * p<0.05 ** p<0.01 *** p<0.001_ Logit Regression on Probability of Audit for VAT Refund

|Parameter|Coefficient|z(p-value)|
|---|---|---|
|conn_firm|-0.48|-3.5 (0.000)|
|log-turnover|0.15|10.58 (0.000)|
|_cons|-1.08|-9.52(0.000)|

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of 52.6 and an initial headcount poverty rate of 63.8% (World Bank 2012; Backiny-Yetna _et al_ . no date, p. 8). By 2012, the poverty rate is simulated to have declined to 60.2%). We report on the sensitivity of poverty outcomes to a decline in the Gini coefficient to a gradual decline such that, by 2030, it has fallen to the current world-wide average of 40.9.<sup>21</sup>

> 21 It is widely accepted that a log-normal distribution provides a good approximation for within-country income and consumption distributions (Bourguignon 2003; Easterly 2009). Inter alia, as noted by Easterly (2009, pp. 2829), (i) empirical cross-country analysis indicates that the higher the initial poverty rate, the lower the poverty elasticity of growth, and (ii) the absolute value of the simulated poverty-elasticity of growth with a log-normal distribution is inversely related to the initial poverty rate and positively related to per-capita income. The average of the most recent Gini coefficient for all 124 countries with data for the period 2000-2008 was 40.9 (World Bank 2012).

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