rafmacalaba/datause-encoder-data huggingface dataset:
confirming that margins that are too small are ineffectual. This result mirrors those obtained by Estevadeordal et al. (2008) for Latin America.

The last three columns show the results for regression (4) for each of the three thresholds for _BIGM_ . Again, we find tariff complementarity and that the presence of a large margin of preference by itself leads to reductions in the external tariff. We also observe that a higher intra-bloc share of imports is associated with reductions in the external tariffs. However, this effect is statistically significant only when the margin of preference is at least 7.5 percentage points. This result is in the same spirit of those of Estevadeordal et al. (2008) for Latin America,<sup>16</sup> but is quantitatively different, as there _WGHT_ proved to play a role in pushing external tariffs down even for margins that were just above 2.5 percentage points. One possible explanation for this difference is the level of aggregation. While our data for ASEAN is at the product level, Estevadeordal et al.’s data is much more aggregated, at the industry level (about 100 of them). Thus, the finding in the Latin America study that higher shares of intrabloc imports drive MFN tariffs down even for relatively small preferential margins may be driven simply by its high level of aggregation.

Overall, our results in tables 4 and 5 provide extensive evidence that ASEAN countries dropped their MFN tariffs following reductions in preferential rates. Furthermore, our findings that this effect is stronger for goods where the margins and the (pre-agreement) share of intra-bloc imports are higher provide considerable support for a causal relationship. Still, if future changes in MFN tariffs are anticipated by the governments, their previous changes in preferential rates may simply reflect those anticipated lower MFN rates—i.e., our regressions may be capturing reverse causality. Relatedly, omitted variables that affect changes in both external and (with a lag) intra-bloc tariffs may be affecting our results as well.

To address these issues, we adopt an instrumental variable approach, where we instrument actual changes in preferential tariffs with the _planned_ ones by the original six members in 1992, as outlined in their original schedule. Moreover, we

> 16 This result is related also to Manchin and Pelkmans-Balaoing’s (2008) finding that preferences affect intra-ASEAN trade only if the preferential margin is very high—in their case above 25 percentage points.

21
from, most of which provide public goods, and all of which are intended to benefit the poor. In this paper, we exploit the fact that the menu offered by the Ecuadorian Social Fund – which we describe in more detail in the next section – included basically two types of projects: local public goods (whose valuation may vary across individuals, but which are accessible to all) and excludable (private) goods. By far the most important private goods (in project numbers) provided were latrines, which were built in land plots belonging to community members with no previous access to toilet facilities. These individuals tended to be highly concentrated among the poorest, and were almost never among the richest members of the community, who might be identified with the elite.

We propose a simple model of project choice between public and private goods, under the assumption that political power is positively correlated with socio-economic status (proxied by per capita household expenditures). The model predicts that, controlling for inequality, poorer communities would select latrine projects (the excludable projects mostly needed by the poor) more often than better-off ones. It also predicts that, controlling for poverty, more unequal communities would choose latrine projects less often, as a result of a concentration of power in the hands of richer people, who do not need new latrines. To the extent that such a choice reflects differences in power, rather than need, it constitutes capture of the program.

Econometric analysis of project choice between 1993 and 1996 reveals a pattern that is consistent with the predictions of our model. Controlling for poverty, various measures of access to services, and a number of geographic and demographic variables, communities with _greater_ inequality chose pro-poor excludable (latrine) projects significantly _less_ often. Consistent both with the notion of “elite capture” and with the fact that complete access to toilet facilities is only observed at the top of the income distribution, these results are strongest for inequality measured as the expenditure share of the top 1%, 3% or 5% of households. This effect becomes smaller and less significant as our definition of the elite is expanded and completely disappears if the expenditure share of the rich is replaced by the Gini index. The results are also remarkably similar under two different econometric specifications.

The paper is organized as follows. In the next section, we briefly review the literature on Social Funds, and describe Ecuador’s Fondo de Inversión Social de 4
# 4. **Empirical evidence**

The existing empirical evidence can be divided into two broad groups of studies, which are distinguished by their proxies for democracy. One strand of the literature associates democracy with voting franchise. Papers in this group typically conduct analyses of historical episodes of expansion of the voting franchise and the subsequent trend in income inequality. Another strand of the literature conceives of democracy in terms of civil liberties and political rights (rights of expression, rights to organize, free press etc.) and are based on the above described indices to measure democracy. Most of the studies belonging to this group perform cross section analysis focusing on the link between these rights and inequality measures.

# 4.1. _Votingfranchise_

Studies on the effects of the expansion of the voting franchise tend to focus on the documentation of historical episodes. While this methodology in itself introduces a selection bias and precludes any sweeping generalizations, it represents the most direct effort to test economic theories of redistribution described above.

Pelzman, 1980, is probably one of the first papers addressing the issue. Reviewing the evolution of government spending in a small sample of rich countries, it does not discern any systematic effect of the expansion of voting franchise.

In contrast, Lindert, 1994, investigates a bigger panel of 21 rich countries in the period 1880-1930, coming to the conclusion that expansion of franchise, in particular, women suffrage, is associated with an increase in redistributive spending.

Justman and Gradstein, 1 999a, following the seminal work of Dicey, 1914/1962, document the gradual expansion of voting franchise in the 19gh century 15
**TABLE  3**

**Truncated MIMIC Model with Latent Variable**

|N = 231|LnL|-125.40|-121.94|-124.42|-121.17|
|---|---|---|---|---|---|
|||(1)|(2)|(3)|(4)|
|**Panel A:**|_Construction_|0.286|0.328*|0.231|0.279|
|||(0.181)|(0.178)|(0.187)<br>|(0.184)|
||_Service_|<br>0.269|<br>0.254|<br> <br>0.241|<br>0.234|
|||(0.235)|(0.231)|(0.234)|(0.230)|
||_Tourism_|0.840**<br>|0.929**<br>|0.925**<br>|0.998**<br>|
|||(0.335)|(0.342)|(0.371)|(0.373)|
||_Medium_|0.584**|0.518**|0.577**|0.519**|
|||(0.228)|(0.227)|(0.226)<br>|(0.224)|
||_Large_|<br>1.343**|<br>1.245**|<br> <br>1.342**|<br>1.254**|
|||(0.391)|(0.390)|(0.392)|(0.391)|
||_Capacity_|<br>0.271**|<br>0.240**|<br>0.266**|<br>0.237**|
|||(0.118)|(0.116)|(0.118)<br>|(0.116)|
||_Capital_|0.330**|0.323**|0.332**|0.325**|
|||(0.038)|(0.037)|(0.038)|(0.037)|
||_Credit_||-0.344**||-0.321**|
||||(0.155)||(0.158)|
||_Corruption_|||0.207|0.176|
|||||(0.172)<br>|(0.167)|
||_Constant_|4.520**|4.768**|4.619**|4.845**|
|||(0.725)|(0.725)|(0.727)|(0.727)|
|**Panel B:** **ln(** **)**||1|1|1|1|
||_Construction_|0.921**|0.917**|1.046**|1.023**|
|||(0.381)|(0.369)|(0.407)|(0.389)|
||_Service_|0.205|0.238|0.265|0.280|
|||(0.414)|(0.403)|(0.48)|(0.403)|
||_Tourism_|-0.548|-0.557|-0.738|-0.705|
|||(0.619)|(0.626)|(0.711)|(0.699)|
||_Medium_|<br>0. 800*|<br>0. 801**|<br>0.806*|<br>0.794**|
|||(0.426)|(0.414)|(0.423)|(0.407)|
||_Large_|1.513**|1.519**|1.486*|1.480**|
|||(0.788)|(0.768)|(0.790)|(0.765)|
||_Darkan_|-0.997**|-0.994**|-1.044**|-1.032**|
|||(0.366)|(0.355)|(0.369)|(0.355)|
||_Erdenet_|-1.102**|-1.062**|-1.131**|-1.071**|
|||(0.416)|(0.396)|(0.421)|(0.397)|
||_Hovd_|<br>-0.955**|<br>-0.853*|<br>-1.005**|<br>-0.896**|
|||(0.486)|(0.461)|(0.487)|(0.461)|
||_Skill_|0.750**|0.729**|0.726**|0.706**|
|||(0.243)|(0.233)|(0.241)|(0.231)|
||_Experience_|-0.270**|-0.262**|-0.280**|-0.269**|
|||(0.112)|(0.107)|(0.112)|(0.107)|
||_Credit_||-0.020||-0.058|
||||(0.271)||(0.277)|
||_Corruption_|||-0.359|<br>-0.315|
|||||(0.317)|(0.298)|
|**Panel C: ln(** **)**||0.665**|0.649**|0.680**|0.663**|
|||(0.066)|(0.067)|(0.066)|(0.067)|
||_Constant_|1.666**|1.809**|1.646**|1.798**|
|||(0.612)|(0.624)|(0.592)|(0.608)|
|**_Panel D:_**_Multivariate_|_Chi-square test (4)_|5.242|5.085|4.979|5.033|
|_Normality test_|_p-value_|0.263|0.279|0.289|0.284|
|_Asymptotic_<br>|_Chi-square test (4)_<br>|5.025<br>|4.982<br>|4.477<br>|4.940<br>|
|_Multivariate_|_p-value_|0.285|0.289|0.345|0.294|
|_Normality test_<br>||||||
|Note: dependent variables ar|e ,**ln(** **)**&**ln(** **)**, s|tandard errors|in the brackets ,|* and** denot|e significant|
|<br>at 10%and 5%,; _Source: sam_|<br>_e as Table 2_|||||
Table A9: Which growing seasons are included in shock-measure for different cohorts?

||Born lastyear|Born 2years ago|Born 3years ago|
|---|---|---|---|
|Shock year t-1|X|||
|Shock year t-2||X||
|Shockyear t-3|||X|

# Annex 2:  Description of NDVI data

The images used in this analysis are so-called monthly maximum value composites. Since all atmospheric influence lowers NDVI, NASA stores only the highest greenness-value for each pixel over the period, where most pixels are recorded daily. This way cloud cover is filtered out in almost all cases. It is not straightforward to use NDVI as a proxy for drought, however. Year-on-year variation in greenness might be caused by factors other than climatic changes. As an example, deforestation quickly reduces the greenness of an area without being associated with drought. On the contrary, deforestation is often a sign of increased economic activity in a region. Broadly speaking, all factors that are non-climatic but affect the greenness of the planet will create noise in the picture of NDVI anomalies as a drought indicator. Most of these factors would be anthropogenic and, apart from deforestation, include changes in cultivation, irrigation and urban expansion. We use predicted NDVI, which takes greenness into consideration, but importantly leaves out all anthropogenic causes of change in “greenness”. The details of the construction of the index can be found in Fisker (2014).

30
Another promising dimension of the production-nutrition relationship which could be investigated in future work is the role of intrahousehold production on household dietary diversity. While it is expected that increase in agricultural income can lead to improved nutrition in the household, the literature on intrahousheold allocation (see Berhman 1997) indicates that the extent could depend on the source and the recipient of the income. Using data on pastoralists in eastern Africa, Villa et al. (2011) estimate income elasticities of dietary diversity for demographic cohorts allowing asymmetric behaviors within the household. They find that household heads disproportionately bear the nutritional burden when household income is below mean, while other cohorts disproportionately enjoy the nutritional gains when it is above mean. The authors also find that adult daughters are better off than other household members in their dietary diversity, sons as worse off, and little difference between male heads and their wives. In future work, we hope to explore intrahousehold dimensions of the production-nutrition relationship.

25
limited role in the different aspects of partial credit guarantee schemes. Third, while governments do have an important role in funding – over a third of schemes rely at least partially on government funding – they have a much more limited role in management, risk assessment and recovery. Finally, while the private sector shares in funding with governments, it is dominant in management, risk assessment and recovery, i.e. the banks that are generating the loans being guaranteed are mostly responsible for credit risk assessment and recovery of defaulting loans.

**<u>Table 3:  Correlations of responsibilities</u>**

||**Funding_G**|**Manage_G**|**CrRisk_G**|**Recovery_G**|**Funding_P**|**Manage_P**|**CrRisk_P**|
|---|---|---|---|---|---|---|---|
|**Manage_G**|0.23**|||||||
|**CrRisk_G**|0.05|0.46***||||||
|**Recovery_G**|0.12|0.47***|0.51***|||||
|**Funding_P**|-0.02|-0.15|-0.10|-0.21*||||
|**Manage_P**|-0.04|-0.18|-0.16|-0.11|0.49***|||
|**CrRisk_P**|-0.05|0.19|0.06|0.18|0.00|0.31***||
|**Recovery_P**|-0.03|0.25**|0.19*|0.10|0.10|0.18|0.69***|

Note: Asterisks *, **, and *** indicate significance at 1%, 5%, and 10%, respectively. P indicates private, while G indicates government or government-related.

Table 3 shows the correlation among responsibilities. We find, for example, that PCGs with government or government related funding (including government agencies, banking supervisors and central banks) are significantly more likely to also have government or government related management. Similar results hold for private funding and management. Interestingly, even funds with government management and credit risk assessment responsibilities are significantly more likely to use private parties to recover loan losses.

We also include two indices to measure government and private sector ‘responsibility’. The first index measures the government’s role in the PCG, _Responsibilities-Government_ ; a higher value indicates a smaller role for the private sector. The index is calculated as the sum of four sub-indices for Credit Risk, Funding, Management, and Recovery. Each sub index is equal to three if government agency; two if government related (central bank or bank supervisor); one if NGO or bilateral organization; and zero otherwise (financial institution or private company; i.e. the minimum is zero (if all four categories are private) and the maximum is 12 (if all four 14
# Figure 3. The Expansion of Bank Deposits in Thailand

<!-- Start of picture text --> 0.7 .<br>0.6 -<br>0.5<br>[ j<br>0.4 Z-_- 0 o K-MIZ<br>0. 3 VW* M2Z<br>1 *- ~~~~~~~~~~~~~~~~~~~~~~INF<br>0.2<br><!-- End of picture text --> <!-- Start of picture text -->
*o.1 9-0-t  -2-3-4-5-B-7-6-9-0-1-2-3-4-5-6-7<br><!-- End of picture text -->

MIZ %II/GDP .M'2Z M2GD; INF lnflaton rate (CPl) Source [FS
Santos, J. A. C., and A. Winton. 2019. “Bank Capital, Borrower Power, and Loan Rates.” _Review of Financial Studies_ 32 (11): 4501–41.

Schnabl, P. 2012. “The International Transmission of Bank Liquidity Shocks: Evidence from an Emerging Market.” _Journal of Finance_ 67 (3): 897–932.

Tabak, B. M., D. M. Fazio, and D. O. Cajueiro. 2011. “The Effects of Loan Portfolio Concentration on Brazilian Banks’ Return and Risk.” _Journal of Banking & Finance_ 35 (11): 3065–76.

Westernhagen, N., E. Harada, T. Nagata, B. Vale, J. Ayuso, J. Saurina, S. Daltung, S. Ziegler, E. Kent, J. Reidhill, and S. Peristiani. 2004. “Bank Failures in Mature Economies.” Working Paper 13, Basel Committee on Banking Supervision, Basel, Switzerland.

Winton, A. 1999. “Don’t Put All Your Eggs in One Basket? Diversification and Specialization in Lending.” Working Paper 16, Wharton School Center for Financial Institutions, University of Pennsylvania, Philadelphia, PA.

World Bank. 2018. _Global Economic Prospects_ . World Bank, Washington, DC, WorldBank.org.

23
DOES ENERGY CONSUMPTION RESPOND TO PRICE SHOCKS? EVIDENCE FROM A REGRESSION-DISCONTINUITY DESIGN<sup>*</sup> LUCIO CASTRO<sup>‡</sup> JULIAN CRISTIA<sup>§</sup> CARLOS SCARTASCINI<sup>**</sup> JEL classification: L95, D12, L51.

Keywords: Energy consumption, elasticity of demand, regulation of public utilities, regression discontinuity design.

Sector Board: EPOL

> * We would like to thank three anonymous referees for very helpful comments and suggestions. We are also grateful to seminar participants at the Inter-American Development Bank and the Northeast Workshop on Energy Policy and Environmental Economics, and in particular to Matías Busso, Omar Chisari, Sebastian Galiani and Howard Chong for thoughtful discussions. Mauricio Cordiviola (tariff manager), Hernan Maurette and Jorge Montanari from the Public Affairs Department, and their teams at MetroGAS S.A., provided valuable cooperation and guidance in the extraction and processing of the company's customer data, as well as in distilling the information contained in the data and in official documents regarding the tariff changes. Gastón Astesiano and Ramón Espinasa provided insightful comments on the overall project. María Antonella Mancino provided superb assistance to this project, and Margherita Calderone and Melisa Iorianni collaborated at different stages of the process. Results have been screened to ensure that no confidential customer data are revealed or could be retrieved. The opinions expressed herein are those of the authors and do not necessarily reflect those of the institutions they are affiliated with.

> † Development Research Group, World Bank, 1818 H Street NW, Washington DC, 20433, United States.

_e-mail: pbastos@worldbank.org_

> ‡ Centro de Implementación de Políticas Públicas para la Equidad y el Crecimiento (CIPPEC), Av. Callao 25, 1B - C1022AAA Buenos Aires, Argentina.

_e-mail: lcastro@cippec.org_

> § Corresponding author. Research Department, Inter-American Development Bank, 1300 New York Avenue NW, Stop E1007, Washington DC, 20577, United States.

_e-mail: jcristia@iadb.org_

> ** Research Department, Inter-American Development Bank, 1300 New York Avenue NW, Washington DC, 20577, United States.

_e-mail: carlossc@iadb.org_ 1
debate.' Research has suggested that investment typically pauses in the wake of adjustment lending and this pause has been attributed to the overall uncertainty that accompanies adjustment lending, which in turn has a negative effect on investment.28 Therefore, over and above the various factors highlighted by the alternative theories of investment that was discussed earlier, it is necessary to consider uncertainty as an additional factor in the context of developing countries undertaking economic reforms. This issue has also become an important for transitional economies.<sup>2</sup> ' A proper appreciation of the underlying factors behind capital expenditure decisions at the firm-level in the case of developing countries has also become important in the context of the recent world-wide interest in emerging markets. Increasingly, capital flows to developing countries have become rapid, though transient at times, in the wake of the constant search for unexploited arbitrage opportunities by global investors, especially institutional investors, on a world-wide basis. From the perspective of portfolio investment as well as foreign direct investment, a proper understanding of the dynamics of investment and economic growth in these emerging markets is essential for sustaining these flows for the long-term.

The results of the study also has some interesting methodological and data-related issues for developing countries. As discussed earlier, this study has shown that the firm rather than 7 See Corbo et al. (1992) for a discussion of issues related to structural adjustment and adjustment lending. Pritchett and Summers (1993) also provide a recent overview of the issues.

I See Serven and Solimano (1994) for a recent survey of evidence regarding the negative effect of uncertainty on investment. See Dixit and Pindyck (1994) for a more general treatment of issues relating to uncertainty and investment. See also, Chhibber et al. (1992), Bleaney and Greenaway (1993), and Branson and Jayarajah (1995) for discussions of the impact of adjustment on investment.

2 See World Bank (1996) for an interesting review of the evidence relating to transitional economies.

29
**Table A11: Relative Mobility and Absolute Mobility Across Sons vs. Daughters in Rural Area**

|||**CHINA**|||**INDIA**||**IN**|**DONESI**|**A**<br>|
|---|---|---|---|---|---|---|---|---|---|
||RIGRC|RES|Linear<br>ES|RIGRC|RES|Linear<br>ES|RIGRC|RES|Linear<br>ES|
||||||**Rural Sons**|<br>||||
|No Sch|0.378|4.936|6.267|0.916|2.917|4.972|0.525|5.773|6.901|
||(0.021)|(0.206)|(0.166)|(0.014)|(0.084)|(0.058)|(0.019)|(0.165)|(0.137)|
|Primary|0.338|7.071|7.889|0.719|6.894|7.932|0.481|8.770|9.489|
||(0.018)|(0.105)|(0.092)|(0.009)|(0.050)|(0.043)|(0.016)|(0.100)|(0.091)|
|Junior High|0.326|8.067|8.699|0.660|10.318|10.892|0.470|10.195|10.783|
||(0.018)|(0.087)|(0.078)|(0.008)|(0.063)|(0.058)|(0.016)|(0.102)|(0.095)|
|Senior High|0.317|9.031|9.510|0.647|11.624|12.076|0.462|11.592|12.076|
||(0.017)|(0.101)|(0.092)|(0.008)|(0.074)|(0.070)|(0.016)|(0.123)|(0.116)|
|College|0.310|9.971|10.321|0.634|13.544|13.852|0.455|13.425|13.801|
||(0.017)|(0.133)|(0.126)|(0.008)|(0.093)|(0.089)|(0.015)|(0.168)|(0.160)|
|IGRC|0.270|||0.592|||0.431|||
||(0.016)|||(0.008)|||(0.015)|||
|||||**Rur**|**al Daught**|**ers**||||
|No Sch|0.580|2.299|4.281|1.073|-0.322|2.431|0.637|4.453|5.816|
||(0.028)|(0.262)|(0.179)|(0.035)|(0.119)|(0.052)|(0.023)|(0.174)|(0.134)|
|Primary|0.463|5.355|6.529|0.682|3.703|5.330|0.559|7.993|8.814|
||(0.018)|(0.155)|(0.128)|(0.013)|(0.087)|(0.065)|(0.019)|(0.097)|(0.087)|
|Junior High|0.439|6.705|7.652|0.623|6.934|8.229|0.543|9.645|10.312|
||(0.017)|(0.141)|(0.120)|(0.011)|(0.118)|(0.100)|(0.018)|(0.108)|(0.100)|
|Senior High|0.423|7.997|8.776|0.613|8.169|9.388|0.533|11.258|11.811|
||(0.016)|(0.146)|(0.127)|(0.011)|(0.133)|(0.116)|(0.018)|(0.140)|(0.132)|
|College|0.413|9.251|9.900|0.603|9.992|11.128|0.524|13.370|13.810|
||(0.016)|(0.164)|(0.147)|(0.011)|(0.158)|(0.141)|(0.018)|(0.196)|(0.187)|
|IGRC|0.375|||0.580|||0.500|||
||(0.014)|||(0.009)|||(0.017)|||

_Notes_ : (1) The data are from children who were born between 1950 and 1989 using the CFPS 2010 round for China, the IHDS 2012 round for India, and the IFLS 2014 round for Indonesia. (2) The RIGRC refers to the Risk Adjusted Intergenerational Marginal Effect. The RES refers to the Risk Adjusted Expected Schooling. The Linear ES refers to the Expected Schooling from a linear CEF mobility model. And the IGRC is the Intergenerational Regressional Coefficient from a linear CEF mobility model.

47