rafmacalaba/datause-encoder-data huggingface dataset:
household member living in household _i_ at time of death. Thus, these deaths occur sometime between period _0_ (October 1991- January 1994) and period _1_ (2004).

Estimated using OLS, equation (1) is equivalent to a difference-in-difference specification with a set of controls, and the parameter γ offers the average treatment effect. There are important econometric difficulties with estimating equation (1). First, while the difference-indifference specification controls for unobserved (time-invariant) heterogeneity affecting _levels_ of consumption, and _Xi0_ captures observed heterogeneity affecting _changes_ in consumption, there could be unobserved heterogeneity affecting consumption changes, correlated with adult death. For example, deaths may occur with higher frequency in poor and vulnerable households with limited growth potential. The controls at baseline may not limit this problem sufficiently. Also, death could be endogenous to consumption since consumption expenditures can influence health and mortality.<sup>4</sup> Our (drastic) solution is to estimate a model with initial household fixed effects (IHHFE) whereby individuals are compared to others who were living with them in the same household in 1991.<sup>5</sup> Having data which follows individuals who have moved from the original household allows us to exploit the following variation: some baseline household members may be living with the deceased individual at the time of the death (between 1991 and 2004), while others will have moved into different households prior to the death (see Appendix 1 for further discussion). Defining the initial household for panel household _i_ as household _j_ , we estimate

> 4 Grimm (2006), for example, addresses this by instrumenting deaths. In our context, some of this concern is mitigated because the deaths are primarily caused by a disease which is almost always undiagnosed and fatal. Moreover, treatment is extremely limited; ARV treatment has only recently been available for a small number of HIV-infected persons through the Kagera’s Regional Hospital in Bukoba.

> 5 Witoelar (2005) uses a similar approach which he calls extended-family fixed effects.  He studies consumption smoothing and income pooling among split-off households using data from the Indonesia Family Life Survey panel. He finds evidence against income pooling within the set of split-off households, although he does conclude that to some degree households within the extended family pool resources.

13
regression was re-estimated in order to reduce the size of the location effect. These regression models and the relevant diagnostics for the urban and rural strata are summarized in Table 3 and 4 in the Appendix C respectively. Examining those tables, we find that the regression models quite successfully explain the variation in monthly adult equivalent consumption. The R-Square ranges from 0.29 to 0.56 in urban areas to 0.24 to 0.52 in rural areas.

Using the parameters estimates derived in the first stage modelling, we then proceed with the census imputation<sup>12</sup> , as described in the methodology section above (see table 3 and 4 in the Appendix C for results of OLS regression for stage 1). A relative poverty line of 40 per cent lowest quintile was used which corresponds to 215,235.2 Manat per month.

# **Results**

Table 1 below presents the results for average adult equivalent consumption, the headcount index (FGT(0)) taking the value of the 40th percentile as the poverty line for both the HBS and the Census for each strata. Comparison of results from the Census and HBS shows that the prediction model seems to perform relatively well at this level, with exception of the rural area of Karabah and urban area of Mugan and Shirvan where the census imputation of the adult equivalent consumption is above the one estimated by the HBS and where the headcount measure imputed in the census is below the headcount estimated by the HBS.

# **Table: 1 Average adult equivalent, poverty and inequality in Azerbaijan, by regions**

# **(strata)**

||Mean Adu<br>|lt<br>|Poverty line 40%<br>|lowest<br>|
|---|---|---|---|---|
||_Census_|_HBS_|_Census_|HBS|
|**Rural**|||||
|Nakhchivan|219146|218726|0.52|0.51|
||(6191)|(4575)|(0.038)|(0.038)|
|Absheron-Guba|239589|247547|0.40|0.36|
||(3930)|(5685)|(0.024)|(0.034)|
|Mugan-Salyan|245288|243113|0.37|0.37|
||(3045)|(3927)|(0.016)|(0.027)|
|Ganja-Gazakh|264507|266946|0.36|0.30|

> 12 For the census imputation we use the application Povmap Version 1.1a developed by Qingua Zhao, from the Development Research Group of The World Bank.

38
# **6.    Directions for New Research**

- For the purposes of this research the countries in the sample were divided into different categories based on the specific threshold levels of system size and income. The selected thresholds are in line with the previous research (Besant-Jones, 2006). The alternative way of categorizing countries would be to apply a cluster analysis. This technique allows assigning a set of observations into subsets, such that observations in the same subset are similar in some parameters (for example, GDP per capita and power system size), but that the subsets differ between each other. The application of the cluster analysis allows obtaining the optimal number of subsets as well as the threshold levels based on the underlying data.

- Different definitions of the aggregate index of reforms can be used to examine the robustness of the results to the different specifications. More sophisticated dynamic error correction models and the  instrumental variable approach to allow for potential possible endogeneity biases can be used in future research.

- Finally, additional performance indicators, including indicators of quality (such as SAIDI and SAIFI), operational efficiency (including capacity utilization, load factor), financial efficiency (such as cost recovery index), and long run environmentally sustainability indicators (such as the share renewables) and energy endowment (such as reserve capacity, and self-sufficiency) can be studied in more detail with the help of the econometric models.

# **7. Bibliography**

Arizu, Beatriz, Defne Gencer and Luiz Maurer (2005) ―Centralized Purchasing Arrangements: International Practices and Lessons Learned on Variations to the Single Buyer Model‖ Energy and Mining Sector Board Discussion Paper Series, Paper No.10. Washington, D.C.: World Bank Arocena, Pablo, Tim Coelli and David Saal (2010) ―Measuring Economies of Integration in the US Power Industry: How Costly is Vertical Unbundling?‖, mimeo.

Besant-Jones, John E. (2006) " _Reforming Power Markets in Developing Countries: What Have We Learned?_ ", World Bank Discussion Paper No.19.

Chao, Hung-po, Shmuel Oren and Robert Wilson (2008), ― _Re-evaluation of vertical integration and unbundling_ ‖, in Sioshansi, F.P. (ed.) (2008), Competitive Electricity Markets: Design, Implementation, Performance, Oxford: Elsevier.

Chao, Hung-po, Shmuel Oren and Robert Wilson (2005) ― _Restructured Electricity Markets: Reevaluation of Vertical Integration and Unbundling_ ", mimeo.

Dupuy, M. (2006) ―Electricity Generation: Competition, Market Power and Investment‖, Policy Perspectives Paper 06/04, New Zealand Treasury.

ESMAP (2007) ―Technical and economic assessment of off-grid, mini-grid and grid electrification technologies‖ ESMAP Technical Paper 121/07. World Bank, Washington D.C.

Jara-Diaz, S., Ramos-Real, F. Martinez-Budria (2004) ―Economies of integration in the Spanish electricity industry‖, Energy Economics, 26, 6, 995-1013.

27
# **Table 2. Effect of Drought Relief Compensation on Share of Votes for the Incumbent, Main Results**

||Dependent<br>|variable: Share of<br>|votes for incumb<br>|ent in 2006<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Below Cutoff|10.395|8.211|8.332|7.69|
||(1.421)***|(1.040)***|(1.219)***|(1.000)***|
|Rain Deviation|-0.078|0.057|0.045|0.03|
||(0.012)***|(0.015)***|(0.018)**|(0.018)*|
|Constant|45.188|14.38|-13.398|7.255|
||(0.845)***|(3.574)***|(10.258)|(9.071)|
|Observations|1038|1038|1038|1038|
|R-squared|0.12|0.78|0.79|0.82|
|State controls|No|Yes|Yes|Yes|
|Controls at electoral section|||||
|level|No|No|Yes|Yes|
|Controls at municipal level|No|No|No|Yes|
|Mean of dependent variable|45.37|45.37|45.37|45.37|

Robust standard errors in parentheses State controls are dummy variables for each state. Controls at the electoral sections include altitude, distance from the weather station, distance to the nearest river and distance to the "cabecera". Controls at the municipal level include municipal income per capita for the year 2000.

* significant at 10%; ** significant at 5%; *** significant at 1%

Page **24** of **29**
participation rates for all cohorts and that the increase has been rather consistent across cohorts. The best performing cohorts are middle-aged cohorts as compared to young or older cohorts, which is what determines the hump shape. The overall improvement worldwide is around 7-8 percentage points in terms of participation rates.

The picture changes as we move to MENA countries. On average, FPR in MENA countries have increased for all cohorts between 1990 and 2010. Improvements have been more significant in MENA countries as compared to the world with increases in the FPR around ten percentage points. However, the hump shape is no longer visible and this is replaced by a negative slope indicating that older cohorts perform always worse than younger cohorts. Essentially, the decline starts around the age of 25, which largely coincides with the marriage age in the MENA countries. It is also visible that the improvement experienced by older cohorts is smaller as compared to improvements of younger cohorts.<sup>6</sup> The picture changes once again as we move to the figure for Morocco. In this case, we see a clear improvement in FPR between 1990 and 2000. This improvement applies to all cohorts although the change is smaller as the one observed for the world or MENA countries. We can also recognize the negative slope, similarly to the one observed for MENA countries as a whole, although the curve is clearly less steep. More importantly and contrary to world and MENA countries, we can see that FPRs declined for all cohorts between 2000 and 2010. It is important to note here that the change between 1990 and 2000 may be explained by changes in data collection as the Moroccan LFS was limited to urban areas until 1998 and extended to rural areas in 1999. However, if this change affected the statistics of the Morocco graph, this would apply only to the period 1990-2000 where we observed a growth in FPR while the negative performance between 1999 and 2010 is actually validated by the fact that the Moroccan LFS has been a very consistent survey since 1999. Hence, Morocco has performed worse than the average as compared to the world and also as compared to MENA countries in terms of FPR for all age cohorts. This is at odds with the good macroeconomic performance of the country relatively to both the world and the MENA region and confirms previous findings on Morocco (Verme et al. 2014).

> 6 The MENA curve could also be explained by improvements in education. In countries where female education is increasing fast, younger cohorts will always be more educated than older cohorts and this may explain higher participation rates.

10
# Exploring Mechanisms

So far, we have been working with a general definition of business enterprises i.e., households engagement in any form of non-agricultural business or startup over the period. This abstracts from multiple aspects, which we shall explore to explain the mechanisms underlying our baseline results. Engagement in business enterprises takes several forms including households’ ownership of a business service or center (e.g., a Mobile-Money center); ownership of a commercial transport business; ownership of a restaurant or bar; ownership of other small income generating activities; the ownership of a construction establishment; involvement in the making of clothing; involvement in the transformation of any agricultural products; and having any non-farm profession. Our panel surveys provide information about these aspects.

|**Table 8: 2SLS:**<br>|**Effect of m**<br>|**obile activ**<br>|**ity on aspec**<br>|**ts of busin**<br>|**ess enterp**<br>|**rise**<br>|||
|---|---|---|---|---|---|---|---|---|
|DV|(1)<br>Transform<br>Agr.<br>product|(2) Make<br>clothing|(3) Own<br>construction|(4) Any<br>profession<br>for own|(5) Own<br>bus<br>service|(6) Own<br>commercial<br>transport|(7) Own<br>restaurant<br>or bar|(8) Own<br>small<br>activity|
|MOBILE|-0.178*|-0.0932*|-0.105**|0.00611|-0.125**|-0.0434|-0.00434|-0.147*|
||(0.0954)|(0.0550)|(0.0501)|(0.0431)|(0.0633)|(0.0440)|(0.0409)|(0.0763)|
|Constant|0.437***|0.286***|0.0605**|0.374***|0.0550**|0.0160|-0.00378|0.0668**|
||(0.0378)|(0.0218)|(0.0279)|(0.0175)|(0.0255)|(0.0175)|(0.0164)|(0.0325)|
|# of OBS|1,337|1,337|1,337|1,337|1,337|1,337|1,337|1,337|
|R-squared (%)|25.20|18.50|18.50|19.00|19.80|18.90|21.70|31.90|
|Controls|No|No|No|No|No|No|No|No|
|Cluster FEs|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FESs|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|1<sup>st</sup>Stage Estimates<br>[-0.052,-0.125]**<br>n X R-squared ≥ 51.6<br>√|||||||||
|No evidence of_“weak_<br>_instruments”_|||||||||

_Notes_ : Table reports estimates from regressions of the various aspects of engagement in non-agricultural business enterprises on mobile use and activity. Columns differ based on the combination of the dependent variable, model type and fixed effects included. All standard errors are clustered at the region level and <u>shown in parentheses. Significance levels are indicated by *** 1%, ** 5%, *10%.</u> _<u>Unit of Analysis</u>_ <u>:</u> **<u>cluster-level.</u>** Tables 6-9 replicate previous estimations, but separately for each aspect of household business enterprises. All dependent variables are binary, and the columns reflect specified models. The OLS estimates are reported in Tables 6 and 7, while those from our preferred 2SLS are presented ‐ 17 ‐
Table 2: Rice Yields, Irrigation and Rainfall

||Log(Ric<br>(1)|e Yield)<br>(2)|% of Area<br>Irrigated<br>(3)|
|---|---|---|---|
|Log(Rainfall)|0.376***|0.493***|24.17**|
||(6.382)|(9.098)|(2.118)|
|Travel time to nearest large city||0.0005***|0.0199***|
|||(10.80)|(3.672)|
|Proportion of households with electricity||0.00718|8.296|
|||(0.269)|(1.616)|
|Share of urban population||-0.130*|4.502|
|||(-1.854)|(0.420)|
|Log(Population)||0.00754|0.237|
|||(0.368)|(0.0796)|
|Proportion with secondary or above<br>education||1.291**|-212.4**|
|||(1.970)|(-2.492)|
|Log(population91)*trend||-0.0108|1.551|
|||(-0.728)|(0.710)|
|Floodplain*trend||0.00360|-3.066|
|||(0.256)|(-1.296)|
|Year and Upazila Fixed Effects|Yes|Yes|Yes|
|R-squared|0.509|0.623|0.084|
|Number of Upazilas|355|355|355|

Robust t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1
Policy Research Working Paper 9168

# **Abstract**

Intimate partner violence is the most common form of violence against women in conflict and non-conflict settings, but in conflict settings it often receives less attention than other forms of gender-based violence, such as conflict-related sexual violence. Using data from the 2008 and 2013 Domestic Violence module of the Nigerian Demographic and Health Survey spatially linked to the Boko Haram actor file of the Armed Conflict Location and Events Database, this paper employs a kernel-based difference-in-difference model to examine the effect of the Boko Haram insurgency on women’s experience of physical and sexual intimate partner violence. It also examines the effect of the Boko Haram insurgency on women’s experience of controlling behavior from a husband or partner, women’s autonomy in household decision making, and their control over their own earnings. The paper finds that the Boko Haram insurgency is associated with slower progress toward preventing and eliminating women’s experiences of physical and sexual intimate partner violence. Controlling behaviors from husbands/partners and reductions in women’s autonomy in household decision making are heightened in locations that are impacted by the Boko Haram insurgency, indicating that the Boko Haram insurgency adversely affects women’s agency and exacerbates behaviors that are often precursors to intimate partner violence.

This paper is a product of the Gender Global Theme. 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 uekhator@worldbank. org, lhanmer@worldbank.org, erubiano@worldbank.org and darango@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
mobility and support for redistribution (for example, if people who are more likely to be unemployed are also more likely to support redistribution), our OLS estimates will be too big. The Altonji, Elder, and Taber’s (2005) statistic, however, indicates these problems probably are modest, if there are any. Besides, downward mobility caused by a global crisis or structural reforms is, to some extent, beyond the control of any individual in our sample. And people may have systematic biases when they evaluate their relative income, but we address this concern by analyzing downward mobility using sub-samples of people whose biases are small. Downward mobility, which we define using retrospective information, may be unreliable—a concern that we have no answer to. But, perhaps, recalling the years when we experienced economic shocks is less likely to be unreliable, especially if the shocks are severe.

22
distance from the managerial frontier.<sup>6</sup> Firms increasingly do not seem to know what they do not know about the frontier technology of even running their firm -- they are, in fact, only “more or less competent” as Lundvall put it- and they are certainly not using full information to locate it.

# _Optimality and the role of the market_

It is not obvious that these deviations from rationality dictate discarding the market and the neoclassical framework that simplistically attempts to model it. It is important to stress that “more or less competent” does not imply “incompetent,” that “bounded rationality” does not imply “irrationality.” Interviews with firms suggest that however badly they score on the World Management Survey, they do broadly adopt practices that are consistent with profit maximization, that is, they weigh the costs of inputs, think of how best to combine complementary factors to produce output, etc., and they respond to incentives set by the market. Even while questioning what can be said about the overall “optimality” of the resulting market outcome- a concern shared by many neoclassical economists schooled in the theory of the second best- it is not obvious that government functionaries (or international bureaucrats) have so much better information or capability to replace it. There are writers in the NIS literature who appear to broadly accept this view. Despite the scale and ubiquity of the market failures, many of which Stiglitz details in his Nobel lecture, Cimoli, Dosi, Nelson and Stiglitz (2009) make a plea for consensus based on a “pragmatic view of markets.” Chaminade and Edquist (2010) working firmly in the NIS tradition, argue that “Innovation policy – or other kinds of public intervention – should be complementary to the market, not replace or duplicate it.” Nonetheless, important voices in the NIS literature, including Nelson in other writings, are skeptical about the value of mainstream analytical tools and thus form part of a long standing and much broader debate about where psychological and behavioral insights will lead economics. Rabin (2013) and Harstad and Selten (2013) in a recent and balanced discussion in the _Journal of Economic Literature_ explore both the limitations of new bounded rationality models, and the incipient progress integrating bounded rationality in mainstream optimization models. What emerges is that key pioneers in introducing psychological insights into economics and exploring bounded rationality, such as Rabin, still see the core of microeconomic theory and empirics as having been a success and hence see

> 6 The subjective scale ranges from 1-10 and the objective from 1-5.  We have simply divided the former by two and this may lead to a misalignment of means.  However, the downward slope would remain however we center the data.

6
<!-- Start of picture text --> – -<br>Figure 4 contd.: BICs and Korea distance of largest industries to the Korean Technological Frontier (1980 2005)<br>V.A. per Worker (Korea=100)- product 351 V.A. per Worker (Current '000 US$/worker) - product 351 V.A. per Worker (Korea=100)- product 371 V.A. per Worker (Current '000 US$/worker) - product 371<br>1980 1985 1990 1995 2000 2005 1980 1985 1990 1995 2000 2005 1980 1985 1990 1995 2000 2005 1980 1985 1990 1995 2000 2005<br>KOR BRA KOR BRA KOR BRA KOR BRA<br>IND CHN IND CHN IND CHN IND CHN<br>Product: Industrial chemicals Product: Iron and steel<br>Source: UNIDO Industrial Statistics Database, Rev. 2 and 3 Source: UNIDO Industrial Statistics Database, Rev. 2 and 3<br>V.A. per Worker (Korea=100)- product 322 V.A. per Worker (Current '000 US$/worker) - product 322 V.A. per Worker (Korea=100)- product 384 V.A. per Worker (Current '000 US$/worker) - product 384<br>1980 1985 1990 1995 2000 2005 1980 1985 1990 1995 2000 2005 1980 1985 1990 1995 2000 2005 1980 1985 1990 1995 2000 2005<br>KOR BRA KOR BRA KOR BRA KOR BRA<br>IND CHN IND CHN IND CHN IND CHN<br>Product: Wearing apparel, except footwear Product: Transport equipment<br>Source: UNIDO Industrial Statistics Database, Rev. 2 and 3 Source: UNIDO Industrial Statistics Database, Rev. 2 and 3<br>Source: Authors’ calculations<br>100 150 100 200<br>80 80<br>150<br>100<br>60 60<br>100<br>40 40<br>50<br>50<br>20 20<br>0 0 0 0<br>400 40 80<br>100<br>80<br>300 30 60<br>60<br>200 20 40<br>40<br>100 10 20<br>20<br>0 0 0 0<br><!-- End of picture text --> 10