rafmacalaba/datause-encoder-data huggingface dataset:
# **LEGISLATIVE MALAPPORTIONMENT AND INSTITUTIONAL PERSISTENCE**<sup></sup>

Miriam Bruhn<sup>1</sup> Francisco Gallego<sup>2</sup> Massimiliano Onorato<sup>3</sup>

**Keywords:** democracy, dictatorship, institutions, Latin America, persistence, political economy.

**JEL Classification:** H1, N46, N10, P16, P48.

1 Miriam Bruhn, The World Bank, Washington DC, USA. email: mbruhn@worldbank.org 2Francisco Gallego, Pontificia Universidad Católica de Chile, Department of Economics and Economic History and Cliometrics Laboratory, Santiago, Chile. email: <u>fgallego@uc.cl</u> 3Massimiliano Onorato, Post-Doctoral Associate, Leitner Program in International and Comparative Political Economy, Yale University, New Haven CT, USA. email: <u>massimiliano.onorato@yale.edu</u> We thank Daron Acemoglu, Pedro Dal Bó, Leopoldo Fergusson, Claudio Ferraz, John Londregan, Matthias Messner, Nathan Nunn, Pablo Querubin, James Robinson, Pablo Selaya, Guido Tabellini, and participants in seminars at Bocconi, MIT, Brown, Yale, the Catholic University of Chile, the 13<sup>th</sup> Annual Conference of the International Society for New Institutional Economics, the 2009 North American Summer Meetings of the Econometric Society, the 2009 SECHI meetings, the 2009 UNU WIDER Conference on “The Role of Elite in Economic Development”, the 2008 LACEA meetings and IMT Lucca for comments. We also thank Felipe González, Felipe Joannon, Piero Montebruno, Francisco Muñoz, and Diego Verdugo for excellent research assistance; David Samuels and Richard Snyder for sharing with us their data on malapportionment in Latin America; Jasmin Chakeri, Christian Gonzalez, Delia Lanz, Julio Loayza, Lars Moller, Rossana Polastri, Rashmi Shankar, Emily Sinnott, Rajeev Swami, and Manuel Vargas for their help with obtaining sub-national data on transfers from the central government for various countries; and Claudio Ferraz for providing us with electoral data for Brazil during dictatorships. We are also grateful to the Millennium Nuclei Research in Social Sciences, Planning Ministry (MIDEPLAN), Republic of Chile and Fondecyt (Grant # 11070181), for financial support. A previous version of the paper circulated with the title “Legislative Malapportionment and Economic Outcomes: A First Assessment”. The usual disclaimer applies.
Policy Research Working Paper 7397

# **Abstract**

Self-help groups (SHGs) are the most common form of microfinance in India. The authors provide evidence that SHGs, composed of women only, undertake collective actions for the provision of public goods within village communities. Using a theoretical model, this paper shows that an elected official, whose aim is to maximize reelection chances, exerts higher effort in providing public goods when private citizens undertake collective action and coordinate their voluntary contributions towards the same goods. This effect occurs although government and private contributions are assumed to be substitutes in the technology of providing public goods. Using first-hand data on SHGs in India, the paper tests the prediction of the model and shows that, in response to collective action by SHGs, local authorities tackle a larger variety of public issues, and are more likely to tackle issues of interest to SHGs. The findings highlight how the social behavior of SHGs can influence the governance of rural Indian communities.

This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. 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://econ.worldbank.org. The authors may be contacted at lore.vandewalle@graduateinstitute.ch.

_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
|dishonor to families|SNE|-0.11<br>(0.09)|-0.15<br>(0.10)|-0.06<br>(0.09)|
|---|---|---|---|---|
||SEE|-0.04<br>(0.10)|-0.06<br>(0.09)|-0.06<br>(0.13)|
|Women going outside|PNB|-0.13*|-0.22***|-0.03|
|exposes them to harassment||(0.07)|(0.07)|(0.08)|
||SNE|-0.12|-0.12|-0.12|
|||(0.10)|(0.10)|(0.10)|
||SEE|-0.08<br>(0.09)|-0.06<br>(0.09)|-0.09<br>(0.11)|
|Women should not/do not|PNB|-0.09|-0.11|-0.06|
|work ingender-mixed||(0.07)|(0.07)|(0.10)|
|environment|SNE|-0.11<br>(0.07)|-0.14*<br>(0.07)|-0.07<br>(0.08)|
||SEE|-0.18***|-0.22***|-0.14*|
|||(0.05)|(0.04)|(0.07)|
||Sanc.|-0.02|-0.0370|0.01|
|||(0.03)|(0.03)|(0.03)|

_Notes_ : (1) Negative correlation means that more conservative beliefs are associated with lower probability of women working for pay outside the house. (2) All items on attitudes/beliefs are scaled from 0 to 1, with 0 being the most liberal and 1 being the most conservative. Outcome variable is binary with 1 if the female reported a "yes" to "I work outside the house for pay." and if a male reported "yes" to "My wife works outside the house for pay." (3) No controls and no regional fixed effects are included. (4) SEs clustered at the ward level. (5) Robust standard errors in parentheses. (6) *** p<0.01, ** p<0.05, * p<0.1.

Table A.10. Individual-level regressions of sanctions index predicting women’s work outcomes

||Full s|ample|Females|only|Males|only|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||Paid work|Paid work|Paid work|Paid work|Paid work|Paid work|
|Sanc. Index|-0.01|-0.01*|-0.01*|-0.01**|-0.01|-0.01|
||(0.01)|(0.01)|(0.00)|(0.00)|(0.01)|(0.01)|
|Constant|-0.00|0.47*** (0.09)|0.02|0.50***|-0.06|0.44***|
||(0.30)||(0.30)|(0.08)|(0.30)|(0.12)|
|Obs.|2,000|2,000|1,150|1,150|850|850|
|Adjusted R2|0.02|0.01|0.03|0.01|0.01|0.00|
|Controls|YES|NO|YES|NO|YES|NO|
|Regional FE|YES|NO|YES|NO|YES|NO|

_Notes_ : (1) Negative correlation means that more conservative beliefs and social expectations are associated with lower probability of women working for pay outside the house. (2) Higher the sanctions index, higher the perceived social sanctions. Outcome variable “Paid work” is binary with 1 if the female reported a “yes” to “I work outside the house for pay.” And if a male reported “yes” to “My wife works outside the house for pay.” (3) Standard errors clustered at the ward level. (4) Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

42
international or regional regulations, and strengthening the integration of new technologies into the transport and communications infrastructure.

Meeting these “domestic” international trade facilitation challenges places enormous importance on the need for capacity building. As countries—and the international donor community—decide on how best to deploy resources for such capacity-building a critical policy question arises: What is the relative impact of improvements in trade facilitation in contrast to gains from lowering traditional trade barriers, such as tariffs and quotas? Trade facilitation was added to the policy dialogue on trade issues at the Singapore Ministerial of the WTO in 1996.

In August 2004, the WTO decided to focus part of the negotiations currently underway in the Doha Round on trade facilitation issues. As a result, today, trade facilitation discussions are at the center of the Doha Development Agenda. The European Union, among others, has been a leading advocate of such negotiations. At the same time, regulatory reform, modernization of customs regimes, and infrastructure investment related to lowering trade logistics costs are key components of development strategies for the new members of the EU and those in line for accession. Clearly, then, earmarking sufficient resources—both financial and human—to build the requisite institutional capacity in order to implement trade facilitation reforms is increasingly an important policy issue for governments.

There are a number of important challenges in carrying out empirical research on the impact of trade facilitation initiatives to help inform policy decisions and capacity-building priorities. They include selecting the most appropriate measures to gauge the extent of trade facilitation, identifying a suitable modeling methodology to estimate the impact of trade facilitation on exports and imports, and establishing an analytical framework to estimate the effect of improved trade facilitation on trade flows.

This paper builds on Wilson, Mann, Otsuki (2005) [hereafter WMO (2005)] and uses four separate indicators to estimate trade gains due to trade facilitation progress in (i) port efficiency, (ii) customs regimes, (iii) regulatory policy and (iv) information technology 3
expectations based on the view that the weaker standards of specifying the quality of goods and supervision in poor countries had entailed that, particularly for the poorest countries, the 1993 ICP was using a lower quality of goods than would have been found in the US market.

These observations are at best suggestive. The data problems are unlikely to be confined to the 1993 PPPs; errors in the 2005 PPPs and the inflation rates are no doubt also present. But these results are at least consistent with the interpretation that less rigorous specification and monitoring of quality standards in the 1993 ICP entailed that lower quality goods had been priced in poor countries, leading to an underestimation of the PPP for many of the poorest countries, or (equivalently) that the true cost-of-living had been underestimated.

Note that these findings do not do not cast doubt on the so-called “Penn effect;”<sup>34</sup> the ratio of the PPP to the market exchange rate tends to be lower in poorer countries (see World Bank, 2008a, Figure 5). What they suggest, however, is that the extent of this effect has been somewhat overstated, such that the PPPs of poor countries are higher than has been thought.

**_Relative poverty lines:_** It is also of interest to compare our new set of relative poverty lines with those of Chen and Ravallion (2001, 2004), who used a line of the form * _Z_ = max( _Z_ , _kCi_ ) where 0 < _k_ ≤ 1. Following Atkinson and Bourguignon (2001), this can be rationalized by arguing that a non-poor person must have <u>both</u> an absolute minimum income and not be relatively deprived, which is assumed to require an income above some proportion _k_ of * the mean. Chen and Ravallion chose _Z_ = $.108 a day and _k_ =1/3, which gave a good fit with the RDV poverty lines at 1993 PPP.

The constant of proportionality is the same, but our new poverty lines require that a fixed increment of $0.60 a day is required above one third of mean consumption (equation 3) to not be relatively deprived. Two points are notable about this property. First, the elasticity of our relative poverty line to mean consumption never reaches unity (the elasticity is zero below $1.95 a day and 1/(1 + .180 / _Ci_ ) above that value, and only reaches unity in the limit, as _C_ goes to infinity). Thus our relative poverty lines avoid the anomalous feature of poverty lines that are set at a constant proportion of the mean (including those described above, when consumption is above

> 34 This is the empirical observations that the PPP exchange rate is less than the market exchange rate in developing countries. This is usually attributed to the Balassa-Samuelson effect, namely that the market rate only brings the prices of internationally traded goods into parity, while non-traded goods are an important component of consumption in developing countries and these tend to be cheaper than in developed countries.

19
- **Morck, Randall, Bernard Yeung, and Wayne Yu** , “The information content of stock markets: Why do emerging markets have synchronous stock price movements?,” _Journal of Financial Economics_ , 2000, _58_ (1), 215–260.

- **Peress, J.** , “Product market competition, insider trading, and stock market efficiency,” _Journal of Finance_ , 2010, _85._

**Roll, R.** , “R2,” _Journal of Finance_ , 1988, _43._

- **Shleifer, A. and R.W. Vishny** , “The limits of arbitrage,” _Journal of Finance_ , 1997, _51._

- **Subrahmanyam, A. and S. Titman** , “The Going-Public Decision and the Development of Financial

- Markets,” _Journal of Finance_ , 1999, _54._

25
both the forest and plot levels, but especially at the plot level when plots are matched based on plot and forest characteristics. We also find, though, that the Nepal Community Forestry Programme does not provide a unique collective action path to forest health or carbon sequestration. Indeed, in several models CFs do no better than non-community forests (NCFs), while broader measures of collective action show consistent effects, particularly on carbon.

To continue our examination, in Section 2 we provide a very brief discussion of the literature at the intersection of carbon sequestration and collective action. We present the Nepal community forestry experience in Section 3. In Section 4 we discuss our sample frame and data. Our empirical approach is presented in Section 5 and Section 6 discusses our results. Section 7 draws key conclusions, policy implications and highlights areas for further research.

# **2. Key Literature on Carbon Sequestration and Collective Action**

Forests play a critical role in climate change, because they are a source of greenhouse gas emissions and offer sequestration opportunities (Chaturvedi et al., 2008). Deforestation and forest degradation in tropical countries constitute about 17.4% of global anthropogenic emissions (IPCC 2007), but carbon sequestration in forests may also be particularly cost-effective climate investments (McKinsey & Company 2010; Kindermann et al., 2008).

An estimated 15.5% of global forest is under the control of communities, providing key subsistence products and the trend toward community control is increasing (RRI, 2014). Using worldwide, but fairly coarse, forest data and highly aggregated forest collective action elements, Chhatre and Agrawal (2009) demonstrate there are possibilities for both tradeoffs and synergies between carbon sequestration and community livelihoods. They conclude by suggesting the need for detailed studies to better understand the implications when forests are controlled by communities. Similarly, in the Amazon, Bottazoi et al. (2014) recommend focusing on the

**5 |** P a g e
**Table 3. Impact of EPAG on Participation in Income Generating Activities**

||Baseline||**ITT Estimates**||**ATT Estimates**|
|---|---|---|---|---|---|
||Mean|OLS|OLS|OLS|UsingIV|
|Any IGA|0.381|0.181***|0.181***|0.181***|0.190***|
||[0.486]|(0.026)|(0.026)|(0.037)|(0.026)|
|Observations|1601|3200|3200|3200|3200|
|Controls||No|Yes|No|Yes|
|Using individual fixed||||||
|effects||No|No|Yes|No|

For all tables: Standard deviation in brackets. Standard error in parentheses, clustered by classroom. ITT: Intent to treat estimator. ATT: Average treatment effect on the treated. 46 individuals were offered a space but declined to join the first round of training. We use treatment as an instrumental variable for participation in the first round of training. Notes: Two observations are missing for participation in income generating activities at midline. Controls used are type of training, household size, sex of household head, asset based quintiles, community, age of adolescent, educational status of adolescent, marital status of respondent, parental status of respondent (indicator variables for adolescent having a child and being pregnant), orphan status of respondent (one parent or both parents being deceased), and month of interview at midline; all controls are from baseline data except month of midline interview.

**Table 3a. Impact of EPAG on Participation in income generating activities, by training track**

|||**Job Skills Traine**<br>**(n= 608)**<sup>**+**</sup>|**es**||**BDS Trainees**<br>**(n=993)**<sup>**+**</sup>||
|---|---|---|---|---|---|---|
||Baseline-<br>Mean|OLS<br>(w controls)|<br>Observations|Baseline-<br>Mean|OLS<br> (w controls)|Observations|
|Any IGA|0.309|0.101**|3200|0.425|0.226***|3200|
||[0.463]|(0.041)||[0.495]|(0.031)||
|Wage-employment|0.051|0.145***|3200|0.055|0.017|3200|
||[0.220]|(0.039)||[0.229]|(0.018)||
|Self-employment|0.258|-0.053|3200|0.370|0.204***|3200|
||[0.438]|(0.042)||[0.483]|(0.033)||

+ Table reports the sum of coefficients and joint p-values of the interaction terms “post x treat” and “post x treat x type” where type is “job skills” or “BDS”. Controls used are household size, sex of household head, asset based quintiles, community, age of adolescent, educational status of adolescent, marital status of respondent, parental status of respondent (indicator variables for adolescent having a child and being pregnant), orphan status of respondent (one parent or both parents being deceased), and month of interview at midline; all controls are from baseline data except for month of midline interview.

34
# **Participation in Bangladesh and Ethiopia (2/2)**

Table continued from the previous page

||||**Bang**<br>Trivariate p|**ladesh**<br>robit models|||Trivariate|probit model|**Ethi**<br>s for women|**opia**<br>, univariate p|roibt models|for men|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**N**|**FE**|**Agric**|**ulture**|**W**|**age **|**NF**|**E**|**Agricul**|**ture**|**Wag **|**e**|
||**women**|**men**|**women**|**men**|**women**|**men**|**women**|**men**|**women**|**men**|**women**|**men**|
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|ln dist market|0.017|-0.074|0.061|-0.077|-0.051|-0.112|-0.358***|-0.315***|0.077|0.364***|0.014|-0.010|
||(0.119)|(0.098)|(0.123)|(0.111)|(0.237)|(0.095)|(0.073)|(0.075)|(0.053)|(0.069)|(0.068)|(0.050)|
|credit institution|<br>0.056|<br>0.392***|<br>0.056|<br>-0.028|<br>-0.246|<br>-0.268**|<br>0.084|<br>-0.098|<br>0.047|<br>0.014|<br>0.123|<br>-0.034|
||(0.176)|(0.149)|(0.212)|(0.147)|(0.279)|(0.128)|(0.095)|(0.098)|(0.068)|(0.097)|(0.101)|(0.077)|
|ln local wage|-0.452*|-0.421|-0.856***|-1.102***|-0.656|0.216|-0.169*|-0.083|-0.178*|0.058|-0.084|0.121|
||(0.265)|(0.259)|(0.330)|(0.266)|(0.459)|(0.238)|(0.100)|(0.107)|(0.091)|(0.102)|(0.097)|(0.093)|
|partner primary|0.034|-0.221|-0.147|0.084|0.565|-0.116|0.387***|-0.011|0.008|0.017|0.129|0.043|
||(0.180)|(0.217)|(0.207)|(0.186)|(0.386)|(0.216)|(0.128)|(0.170)|(0.089)|(0.178)|(0.158)|(0.154)|
|partner secondary|-0.009|-0.313|0.139|0.070|0.696*|0.145|0.274|-0.454|-0.181|0.076|0.190|0.684**|
||(0.246)|(0.199)|(0.260)|(0.190)|(0.402)|(0.194)|(0.358)|(0.401)|(0.246)|(0.319)|(0.285)|(0.338)|
|partner tertiary|0.119|-1.408**|-0.834|0.378|0.836*|0.574|||||||
||(0.432)|(0.693)|(0.522)|(0.615)|(0.498)|(0.569)|||||||
|h father NFE|<br>0.261*|<br>0.193|<br>-0.364*|<br>-0.489***|<br>0.101|<br>0.150|||||||
||(0.150)|(0.179)|(0.216)|(0.170)|(0.242)|(0.153)|||||||
|h father wage|<br>-0.071|<br>-0.159|<br>-0.592**|<br>-0.841***|<br>0.949***|<br>0.963***|||||||
||(0.169)|(0.154)|(0.231)|(0.179)|(0.257)|(0.160)|||||||
|constant|<br>2.326|<br>3.984|<br>6.487*|<br>10.703***|<br>4.747|<br>-0.860|-0.536|-1.863*|1.636**|-1.283|-2.289***|-1.372*|
||(2.666)|(2.665)|(3.321)|(2.850)|(4.810)|(2.557)|(0.905)|(0.991)|(0.823)|(0.936)|(0.844)|(0.804)|
|ρNFE-Ag|0.118|-0.047|||||-0.264***||||||
||(0.101)|(0.082)|||||(0.061)||||||
|ρNFE-Wage|<br>-0.221*|<br>-0.728***|||||<br>-0.338***||||||
||(0.123)|(0.109)|||||(0.109)||||||
|ρAg-Wage|-0.249|-0.344***|||||-0.284***||||||
||(0.153)|(0.093)|||||(0.064)||||||
|Observations|<br>3,515|<br>4,158|||||<br>2,805|2,252||2,252||2,252|
|Pseudo R2||||||||<br>0.122||<br>0.258||<br>0.098|
|Log pseudolikelihood|-2605305|-4901829|||||-2633||||||
|Wald chi2|58.36(23)|98.79(23)|||||393.87(19)||||||

Note: *** p<0.01, ** p<0.05, * p<0.1. Regressions are weighted. Standard errors are heteroscedasticity robust and clustered at the household level.

37
(c) and (d) of Figure 10). This indicates that even in the presence of strong institutions, rules adopted in environments with greater political power do not tend to produce sustainable outcomes.

Building consensus supported by conducive political conditions has been central to the successful implementation of fiscal rules and fiscal adjustments. As an example, Jamaica adopted a fiscal rule in 2014 that sought to address the country’s chronic fiscal challenges. The fiscal rules were instrumental in helping Jamaica reduce its debt stock from a peak of 144 percent of GDP in 2012 to about 72 percent in 2023. This was possible in part because Jamaica forged partnerships that built and sustained consensus for fiscal adjustment, while credibly monitoring and reporting on the government’s adherence to its fiscal rules and the progress of the overall economic reform program (Arslanalp, Eichengreen, and Henry 2024).

# **6. Robustness analyses**

## **6.1 Nickell bias**

Mei, Sheng, and Shi (2023) demonstrate that the fixed effects estimator in the local projections model may suffer from the presence of the Nickell bias (Nickell 1981), even when lagged dependent variables are omitted from the model. They highlight a consistent pattern of underestimation of the shock's impact in the FE estimator.

To eliminate asymptotic bias and restore standard statistical inference, we use a splitpanel jackknife (SPJ) estimator following Dhaene and Jochmans (2015) and Chudik et al., (2018), such that: (h)fe (h)fe where β̂<sup>(h)fe</sup> , β̂a , and β̂b are the FE estimates from the full sample period, the first half ( _t_ ≤ _T_ /2), and the second half ( _t_ > _T_ /2), respectively. Panel (a) of <u>Figure 11 shows that</u> when we use the SPJ estimator, the results closely align with those from the baseline. Under the SPJ estimator, the primary balance follows a similar trajectory post-adoption of fiscal rules, converging to a 1.1 percent of GDP improvement after ten years.

27
31 1995 the impact of tranfers on transportation subsidies is negative, which is an unique situation in our analysis.29 Our interpretation for this is substantial growth in transportation subsidies that year in regions less dependent on federal transfers, such as Moscow and Saint Petersburg.

In their level of explanatory power and the role of the main explanatory factors (other than federal transfers), these regression results are much closer to those obtained in the case of housing than those for agriculture. Subsidies to transportation are also of a regressive nature and discriminate against rural households. However, in contrast to housing, transportation subsidies are less determined by federal transfers.

_Table 18. Determinants of Subsidies to Transportation, 1993-95_

||1993|1994|1995|
|---|---|---|---|
||(N=83)|(N=77)|(N=86)|
|**Pre-transfer regional revenue**|.023|.026|.022|
||(6.1)|(6.3)|(6.5)|
|**Federal transfers**||.023|-.093|
|||(6.8)|(-2.2)|
|**Household incomes**|||.002<br>(2.0)|
|**Unemployment level**||-2.18<br>(-2.6)||
|**Share of rural population, 1994**|-.477|-.488|-.514|
|**data**|(-2.4)|(-3.0)|(-3.1)|
|**Constant**|40.5|39.14|31.73|
||(4.2)|(3.7)|(3.9)|
|**AdjustedR**<sup>**2**</sup>|.45|.74|.64|

29 The same effect was observed earlier in Tables 11 and 12.