rafmacalaba/datause-encoder-data huggingface dataset:
firm is introduced, the information costs in regulated firms diminish more than the social costs induced by excessive prices and entry prevailing in private Cournot oligopolies. The advantage of private unregulated structures disappears for large, profitable markets. For instance the experience in industrialized countries shows that regulation of access pricing to bottleneck facilities (e.g. fixed-line network) is a key component of successful market liberalization. As illustrated by the empirical evidence, price and entry regulation is a problem in developing countries which usually lack the human resources and the institutions to enforce effective regulation. Since privatization reforms cannot be successful without effective regulation of entry and prices, developing countries are better off at keeping their profitable utilities public as long as they cannot establish credible regulatory bodies.<sup>11</sup>

# **1.1 Relationship with the literature**

It is well-known that public ownership generates inefficiencies because it encourages governments to bail out or subsidize money-losing firms. Such inefficiencies were first coined by Kornai (1980) as the ‘soft budget constraint’ problem:<sup>12</sup> _”The softening of the budget constraint appears when the strict relationship between expenditure and earnings of an economic unit (firm, household, etc) has been relaxed, because expenditure will be paid by some other institution, typically the paternalistic state.”_ (Kornai 1980). The author shows that soft-budget constraints explain many inefficiencies occurring in socialist economies such as shortages or low price responsiveness. Since less efficient firms are allowed to rely on government funding, they lack the financial discipline required for efficient management. For instance, under contract incompleteness, soft-budget constraints affect the level of investments made by public managers. The transfer of public to private ownership is therefore often advocated as a remedy for the poor economic performance of public 11A major concern with privatization reforms in developing countries is the lack of governments’ ability to commit to the concession contracts. For instance, according to a World Bank database on Latin America, the concessions that were granted to private operators following the divestiture of public firms have been renegotiated after an average 2.1 years only (see Laffont 2001).

> 12Interesting surveys are available in Kornai (2000) and Kornai, Maskin and Roland (2002).

6
<!-- Start of picture text --> presents per-capita market income plus pensions as a proportion of the per-capita monetary expenditure for each<br>decile. For decile 1, this proportion is 24 percent. That is to say, households in decile 1 must take on debt to<br>maintain their consumption patterns. However, the proportion in decile 10 is 185 percent, which represents that<br>households spend approximately half of their income. At the time of rescaling, VAT payment is implicitly being<br>imputed of approximately double of what would be paid by the households in the 10th decile according to their<br>reported consumption patterns. Likewise, the households from decile 1 see their VAT payment reduced to a<br>quarter of what was reported in the survey.<br>Table 1: Monetary expenditure and market income + average per-capita pensions per<br>decile of income<br>Average monetary Market income +<br>Income as a proportion<br>Decile expenditure pensions<br>of monetary expenditure<br>(per-capita) (per-capita)<br>1 157,142.20 38,086.95 0.24<br>2 177,522.20 108,903.50 0.61<br>3 212,845.50 171,886.50 0.81<br>4 257,530.70 235,362.50 0.91<br>5 298,831.50 306,337.70 1.03<br>6 349,712.80 392,755.80 1.12<br>7 422,133.40 506,056.90 1.20<br>8 503,352.80 664,992.10 1.32<br>9 663,107.70 952,224.20 1.44<br>10 1,441,124.00 2,661,722.00 1.85<br>Source: World Bank Group and staff estimates based on the 2017 ENPH<br>Note: The deciles were built using market income + per-capita pensions<br>Finally, Figure 1 presents the results of progressivity in relative (Panel A) and in absolute (Panel B) terms applying<br>the rescaling and without doing so. On the one hand, the empirical evidence shows that the results for the case<br>of Colombia do not seem to make much sense when performing this adjustment, the system goes from being<br>regressive to progressive, on the other hand, as the implementation of said rescaling is still under discussion, the<br>team decided not to include this adjustment in this version of the CEQ.<br>FIGURE 1: PROGRESSIVITY IN ABSOLUTE AND RELATIVE TERMS WITH AND WITHOUT RESCALING<br>Panel A: Progressivity in Relative Terms Panel B: Progressivity in Absolute Terms<br>60%<br>15%<br>50%<br>10% 40%<br>30%<br>5% 20%<br>10%<br>0% 0%<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>W/o rescaling With rescaling W/o rescaling With rescaling<br>Source: World Bank Group staff estimates based on the 2017 ENPH<br>Note: The deciles were built using per-capita market income + pensions<br>*The results presented in this paper are due to the short-term distributional effects of taxes and spending in Colombia.<br>Along these lines, for example, in the case of tobacco taxes, only the short-term effects on household disposable income<br>are considered, but not the possible redistributive effects that the measure can have on improvements in long-term health<br>% market income + pensions % of tax paid per decile<br><!-- End of picture text --> 27
describes some instruments governments use for industrial policy, while still operating within the legal constraints of the international trading system; for example, governments can provide targeted industries or sectors with improved access to new customers or input suppliers, training, physical and regulatory infrastructure, or product quality certification. Some interventions along these lines have been evaluated in recent literature on firms, trade, and development (for discussion, see the surveys by Verhoogen 2023; McKenzie 2024). Potentially important institutional details of export promotion agencies are highlighted, drawing on the case of Costa Rica, a small country that has grown rapidly through exports and foreign investment, while reducing poverty and protecting the environment. Though the discussion focuses on export promotion, I expect these institutional details have external validity for industrial policy targeting production for the domestic market, especially in countries with a large middle class.

The approach to export-led industrial policy described here—choosing sectors to target based on trade data and choosing instruments that do not violate existing trade rules—is similar in spirit to what countries around the world have already adopted. As one example, it is somewhat like the Enterprise Map Project of John Sutton, which detailed the capabilities and ambitions of leading firms in several African economies, yielding messages about what government inputs could help them grow. Of course, if any industrial policy is to succeed, it requires a high-bandwidth government agency capable of doing the analysis and putting the public good ahead of rent-seeking behavior by large firms.

# **The What: Growth, Competition, and Productivity Potential as Guides for Industrial Policy**

Within the broad category of exports, there is no shortage of theories that certain sectors are more beneficial for development than others. Manufacturing has long received focus, though that has given way to an alternative focus on tradable services. Other theories emphasize products exported by countries with a high GDP per capita (Hausmann, Hwang, and Rodrik 2007), products exported by countries with a similar GDP per capita (Lin 2011), complex products (Hidalgo and Hausmann 2009), or upstream products (Liu 2019). Some products are promoted because they are green, in the sense that they have an end-use related to decarbonization, or are produced without carbon emissions.

4
Policy Research Working Paper 10119

# **Abstract**

This paper considers the political influence of private firms. While such influence is frequently discussed, there is limited analysis of how firms combine political interactions, and under what conditions, to gain influence. The exception is the large literature on firms with political connections, with findings generally showing large gains to firms with those direct relationships. This paper extends the discussion of influence beyond political connections alone and uses a rich firm-level data set from 41 economies, which includes information on several interactions with political actors. Using a Bayesian item response theory (IRT) measurement model, an index of Political Influence is estimated, with the prior assumption that political connections yield more influence. Membership in a business association is found to enhance influence, while such influence is offset by bribes, state ownership, firm size, and a reliance on collective lobbying. Political Influence is found to be broadly higher in economies with poorer governance but more dispersed in those with better governance. Within economies, higher influence is associated with a higher likelihood of reporting a small number of competitors, higher sales, and lower labor inputs relative to sales. These findings are robust across several models that incorporate high-dimensional fixed effects, incorporating measurement error in the index, and varying these relationships over several governance measures.

This paper is a product of the Global Indicators 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 atdfrancis@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
# **Appendix for online publication**

## **A Baseline sample**

### **A.1 Full summary statistics and balance tests**

Table A.1 expands the balance table in the main paper for the full set of baseline covariates available and used in the treatment effects regressions.<sup>1</sup> Column 1 reports the sample mean for each covariate, and Columns 2 to 7 report the coefficients and _p_ values on treatment indicators from ordinary least squares (OLS) regressions of each baseline covariate on three treatment indicators (one for assignment to each treatment arm) controlling for block fixed effects. Column 8 reports the _p_ value from a joint test of significance of the three coefficients. Finally, at the base of the table we report the _p_ value from a test of joint significance of all covariates from an OLS regression of each treatment indicator on all covariates (including that treatment group and the control group alone).

Of 171 coefficients (57 covariates and 3 treatment arms), 10 (5.8%) have a _p < ._ 05, and 26 (15.2%) have a _p < ._ 1. Within treatment arms the covariates are not jointly significant, as seen from the joint test reported at the base of the table. Furthermore, 9 (15.8%) of the tests of joint significance have a _p < ._ 1.

Table A.2 repeats the same balance analysis for the 947 subjects interviewed at endline. Of 171 coefficients (57 covariates and 3 treatment arms), 13 (7.6%) have a _p < ._ 05, and 20 (11.6%) have a _p < ._ 1. Within treatment arms the covariates are not jointly significant, as seen from the joint test reported at the base of the table. Furthermore, 12 (21.1%) of the tests of joint significance have a _p < ._ 1.

Overall, therefore, there is minor imbalance. We control for all baseline covariates in all treatment effects regressions in the paper to account for this.

### **A.2 Neighborhoods and recruitment**

Table A.3 describes each of the study neighborhoods where we recruited, along with population estimates. We report the estimates of the number of all adult males, as well as our low-end estimates of the number of target males in each neighborhoods—men 18 to 35 in the bottom decile of income.

### **A.3 Tracking and attrition**

We achieved tracking rates of roughly 93% over a year.<sup>2</sup> Given that this was such a transient population, we took special measures to minimize attrition.

> 1We maintained the Phase 1 baseline survey for all Phases for the sake of consistency and completeness.

> 2Rates of 80, 90 or even 95 percent are not uncommon in developing country field experiments and panel surveys. For example, the Indonesia Family Life Survey reached 94% of households and 91% of target individuals after four years. The Kenyan Life Panel Survey made contact with 84 percent of target respondents over a seven-year period. Similarly, in the US, researchers were able to reach 98% of the Perry Pre-school children at age 19 and 95% at age 27. One reason is that a small sample is easier to track intensively. Another reason is that enumerator wages are lower in Liberia in the U.S. and this means that intensive sleuthing and tracking is affordable.

i
additional percentage point of forced displacement as a share of the municipal population of 1990.

To gauge the aggregate magnitude of these effects, note that a municipality which lost 14 percent of its 1990 population to Colombia (the average implied by UNHCR figures) would experience a decline of 39.3 percentage points in electoral turnout (= 2.81 _×_ 14) and 21.1 percentage points in opposition support (= 1.51 _×_ 14). These effects are large enough to alter electoral outcomes decisively.

Table 2: Forced Displacement and Electoral Outcomes

||Tur|nout|Oppo|sition|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|**Panel A: OLS**|||||
|Migration Outfows|-0.425***|-0.325**|-0.197***|-0.136**|
||(0.145)|(0.132)|(0.054)|(0.055)|
|**Panel B: 2SLS**|||||
|_Second Stage_|||||
|Migration Outfows|-2.806***<br>(0.924)|-2.815***<br>(0.923)|-1.505***<br>(0.451)|-1.515***<br>(0.450)|
|_First Stage_|||||
|Imputed Outfows|0.206***|0.207***|0.206***|0.207***|
||(0.054)|(0.053)|(0.054)|(0.053)|
|F Statistic (excl. instrument)|14.38|15.11|14.38|15.11|
|**Panel C: Reduced Form**|||||
|Imputed Outfows|-0.576***<br>(0.123)|-0.583***<br>(0.124)|-0.309***<br>(0.052)|-0.314***<br>(0.051)|
|Observations|1,648|1,648|1,648|1,648|
|Dep. Var. Mean 2006|31.359|31.359|10.188|10.188|
|Municipality & Year FE|✓|✓|✓|✓|
|Controls _×_ Time Trends||✓||✓|

_Notes:_ The table illustrates the estimates of specifications (1), (3), and (2). Panel A presents the OLS estimates of equation (2). Panel B represent the 2SLS estimates of equations (3) and (2). Panel C presents the reduced-form estimates of the outcomes in the instrumental variable. Odd-numbered columns exclude baseline controls and even-numbered columns include baseline municipal characteristics interacted with year fixed effects. The F-statistic is the Kleibergen-Paap Wald F-statistic. Controls in the baseline are interacted with time trends and include urban coverage, water bodies, and tree-cover loss for 2001; night-light density for 1992; and political repression records for 2004. Robust standard errors clustered at the municipality level are in parentheses. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1.

34
up in 2005, with the third round completed in 2008. During the 2005 follow-up survey, 74 households could not be retraced, leaving 1,186 households from the first round to make up the panel. Moreover, 68 more households were discarded from panel analysis during the data cleaning process, leaving eventually 1,120 households from 41 communes.

Since sample attrition can be an issue for any panel data analysis, we would like to examine first how it may affect our analysis. There is a large body of literature that demonstrates that even a high attrition rate is a non-issue as long as it is random (Alderman and others 2000; Fitzgerald, Gottschalk and Moffitt 1998; Thomas, Frankenberg and Smith 2001; Ziliak and Kniesner 1998). For example, Fitzgerald, Gottschalk and Moffitt (1998) found from Michigan Panel Study of Income Dynamics (PSID) that households with lower earnings, lower educational levels and lower marriage propensities are more prone to attrition. Their dataset had a high 50 percent attrition rate, still they found that there is no relationship between attrition rate and magnitude of attrition bias and that even large attrition causes unbiased estimation if attrition is random.

This study has been quite successful in keeping attrition of the sample to a moderately low level. Altogether 142 households from 2002 were not available for panel analysis, implying an attrition rate of 11.3 percent. We can formally test if attrition biases our estimates, that is, if reduced sample size because of attrition matters in our estimation. We investigate the issue of attrition in two ways. The first includes investigating the determinants of household attrition and the second is to analyze to what extent attrition biases the estimates. To estimate the determinants of attrition, a probit regression is run with attrition dummy (1 for households that were in first round but not the follow-up survey, and 0 otherwise) as dependent variable and household- and commune-level characteristics observed in 2002 data as explanatory variables (Appendix Table A1). As is evident 8
sorting in amplifying inequality in household expenditure per capita, particularly at both ends of the distribution.

Table 5. Variance decomposition by four tiers of location

||**Bottom**<br>**locations**|**Average**<br>**locations**|**Catchment**<br>**areas**|**Top**<br>**locations**|**All**|
|---|---|---|---|---|---|
|**_Value_**||||||
|Observed expenditure|0.060|0.206|0.043|0.079|0.388|
|Predicted expenditure|0.046|0.122|0.024|0.049|0.241|
|Within|0.020|0.120|0.014|0.017|0.172|
|Household|0.018|0.101|0.014|0.016|0.148|
|Location|0.001|0.009|0.000|0.001|0.012|
|Interaction|0.000|0.011|0.000|0.001|0.012|
|Between|0.026|0.001|0.010|0.032|0.070|
|Household|0.001|0.000|0.001|0.003|0.005|
|Location|0.016|0.000|0.005|0.016|0.038|
|Interaction|0.008|0.001|0.004|0.013|0.026|
|Cross|0.000|0.000|0.000|0.000|0.000|
|**_Percentage_**||||||
|Observed expenditure|15.4|53.1|11.2|20.3|100.0|
|Predicted expenditure|11.8|31.4|6.2|12.8|62.2|
|Within|5.1|31.1|3.6|4.5|44.2|
|Household|4.7|25.9|3.6|4.0|38.3|
|Location|0.3|2.3|0.0|0.3|3.0|
|Interaction|0.0|2.8|0.0|0.2|3.0|
|Between|6.7|0.4|2.7|8.3|17.9|
|Household|0.3|0.1|0.2|0.7|1.3|
|Location|4.2|0.1|1.4|4.1|9.9|
|Interaction|2.2|0.2|1.0|3.4|6.8|
|Cross|0.0|0.0|0.0|0.0|0.0|

# 8. Correlates of location effects

An obvious question, in light of the wide spatial disparities in location effects and household expenditure per capita, is what makes for good places. In particular, some clusters and top locations are characterized by high labor productivity and not necessarily by higher inequality. From a policy making perspective, it would be important to understand which location characteristics support such superior performance. This understanding would help policy makers maximize the payoffs from the rural-urban transformation.

30
important information on the severity of malnutrition that is not always represented by headcount measures.

Table 1 Changes in headcount and gap for child stunting, underweight and overweight

||Changes fr<br>20<br>|om 1993 to<br>05|Changes f<br>20<br>|rom 2000-<br>09|Changes fr<br>20<br>|om 2005 to<br>14|
|---|---|---|---|---|---|---|
||Number||Number||Number||
||<br>of<br>countries|% of<br>countries|<br>of<br>countries|% of<br>countries|<br>of<br>countries|% of<br>countries|
|_Stunting_|||||||
|<br>Headcount and gap<br>moving in same direction|24|71%|29|83%|49|83%|
|<br>Headcount and gap<br>moving in different<br>direction|10|29%|6|17%|10|17%|
|_Underweight_<br>Headcount and gap<br>moving in same direction|27|79%|29|83%|49|83%|
|<br>Headcount and gap<br>moving in different<br>direction|7|21%|6|17%|10|17%|
|_Overweight_|||||||
|<br>Headcount and gap<br>moving in same direction|32|91%|31|89%|56|95%|
|<br>Headcount and gap<br>moving in different<br>direction|2|6%|4|11%|3|5%|

_Source: Authors’ calculations using DHS and MICS._

# _4.2: Comparison of Severity for Countries with Similar Headcounts_

In addition to understanding changes in malnutrition over time, it would also be useful to examine how the malnutrition gap and gap-squared vary for countries with similar headcount rates. If the malnutrition gap and gap-squared measures are substantially different for countries that have similar headcount rates, it would indicate that the gap measures are capturing information regarding severity that is not captured in the headcount measures. This is why we compare the gap and gap-squared measures for countries that have similar headcount rates, i.e. headcount rates within one percentage point of another country.

12
> <sup>21</sup>

_UtilizationMPLADS d s_   1 * _PartyStronghold_ * ( _CandidateSwitched_ ) _ds_  2 * _PartyStronghold_ * ( _NotSwitched_ ) _ds_  3 * _Reserved ds_  4 * _MarginVictory ds_  5 * _1993_  _99Spending ds_  _d_   _s_   _ds_ The first interaction term equals 1 for those party stronghold constituencies where the party switched its candidate, and the second those constituencies where the party retained the same candidate on its ticket. We test for the equality of coefficients<sup></sup> 1<sup>and</sup>  2 to ensure that the effect of party strongholds is driven by voter attachment to parties rather than candidates.

Specifications (1) and (2) are obviously susceptible to reverse causality. Incumbents who spent more of their allocation before 1999 to maintain their dominance would have relatively smaller accumulated entitlements by the time they were re-elected in 1999. It would therefore require less effort for them to disburse a given fraction of their remaining allocation than it would have for incumbents whose predecessors had left them a larger legacy of unspent MPLADS funds. However, this reverse causality argument predicts exactly the _opposite_ sign (positive instead of negative) for the coefficients of _PartyStronghold_ and _CandidateStronghold_ than the one predicted by our argument that strong parties reduce legislator incentives to spend. We control for previous spending under MPLADS from 1993 to 1999 by including the variable _1993_  _99Spending_ which measures the total MPLADS spending undertaken in constituency _d_ since the inception of the program until the 1999 elected cohort took office. This variable also captures other constituency-specific characteristics related to administrative effectiveness or opportunities for rent-seeking using MPLADS.