rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5402

# **Abstract**

In the past decade, the World Bank has promoted improving business environments as a key strategy for development, which has resulted in a significant amount of investment in collecting firm-level investment climate surveys across countries. What lessons have emerged from the papers using these new data? The key finding is that the effects of business environments are heterogeneous and depend crucially on industry, initial conditions, and complementary institutions. Some elements of the business environment, such as labor flexibility, low entry and exit barriers, and a reasonable protection from the “grabbing hands” of the government, seem to matter a great deal for most economies. Other elements, such as infrastructure and contracting institutions (courts and access to finance), hinge on their initial status and the size of the market.

This paper—a product of the Finance and Private Sector Development Team, Development Research Group—is part of a larger effort in the department to understand the role of business environments in development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at lxu1@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
We estimate our basic model (without the institutional variables) with the NBRM using STATA 8.2.

|Negative binomial regressi<br> <br> <br>Log likelihood = -122.8858|on <br> <br> <br>4|Number of obs = 56<br>LR chi2(5) = 62.83<br>Prob > chi2 = 0.0000<br>Pseudo R2 = 0.2036|
|---|---|---|
|--------------------------<br>|-------------<br>|---------------------------------------<br>|
|PSPSIGNED | Coef.<br>|Std. Err.<br>|z P>|z| [95% Conf. Interval]<br>|
|-------------+------------<br>LOGPOPMEAN | .5880215|------------<br>.1261757|--------------------------------------<br>4.66 0.000 .3407216 .8353213|
|<br>LOGGDPCAPM~N | .9950203|<br>.2148994|<br>4.63 0.000 .5738251 1.416215|
|LOGICRGECO | 3.25384|1.808789|1.80 0.072 -.2913212 6.799002|
|<br>COVERAGEGAP | .0000794<br>LOGBDEBTGNI | .5546001<br>_cons | -28.54979<br>FIRSTPPP | (exposure)<br>-------------+------------|<br>.0000312<br>.3261312<br>6.39603<br>-------------|<br>2.54 0.011 .0000181 .0001406<br>1.70 0.089 -.0846054 1.193806<br>-4.46 0.000 -41.08578 -16.01381<br>---------------------------------------|
|<br>/lnalpha | -.8124094<br>-------------------------|<br>.3793467<br>------------|<br>-1.555915 -.0689036<br>--------------------------------------|
|+<br>alpha | .4437875<br>|<br>.1683493<br>|<br>.2109962 .9334167<br>|
|--------------------------<br>Likelihood-ratio test of a|------------<br>lpha=0: chib|---------------------------------------<br>ar2(01) = 36.43 Prob>=chibar2 = 0.000|
|Measures of Fit for nbreg|of PSPSIGNED||
|Log-Lik Intercept Only:<br>D(49):<br>|-154.302<br>245.772<br>|Log-Lik Full Model: -122.886<br>LR(5): 62.833<br>|
|||Prob > LR: 0.000|
|<br>McFadden's R2:|<br>0.204|<br>McFadden's Adj R2: 0.158|
|<br>Maximum Likelihood R2:|<br>0.674|<br>Cragg & Uhler's R2: 0.677|
|<br>AIC:|<br>4.639|<br>AIC*n: 259.772|
|BIC:|48.529|BIC': -42.706|

**Figure 6: Observed and Predicted Frequencies of PSP Counts**

<!-- Start of picture text --> 0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>0 5 10 15 20 25 30<br>Observed Frequencies NBREG Predictions<br><!-- End of picture text --> The LR test for H0: alpha = 0 is highly significant and we can reject the null hypothesis and conclude that there is significant evidence of over-dispersion. From this and the Bayesian Information Criterion (BIC) we can see that the NBRM is to be preferred to the PRM.

Figure 6 plots the observed and predicted frequencies using the NBRM-fitted model. We can see that the model fits the data correctly even though it tends to slightly over estimate the number of zeros<sup>24</sup> . Having determined that the NBRM fits our data best, we can now test the different versions of the model.

> 24 The PRM fitted with the same data tends to underestimate zero counts.

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rect visibility of the restaurants during the experiment and ensured that participants were not visible to enumerators when visiting the restaurants and ordering a dish. The placement also guaranteed that each subject had convenient access to at least one restaurant.

Over eight days in November 2023, enumerators at each table encouraged passersby to participate in a survey, offering a coupon to their restaurant of choice as an incentive. Participation was limited to adults (18 years or older) who are active wild meat consumers, defined as subjects who reported eating bushmeat at least once in the past month. Subjects were required to use their coupon within two weeks. Enumerators followed a strict protocol, attending to each participant individually to prevent interaction between participants that could bias their choices. Subjects were randomly assigned to watch one of two videos on a tablet with headphones. The treatment group watched a publicly available 90-second clip from the 2019 multimedia campaign “Let’s eat less bushmeat in the city” (“Mangeons moins de viande de brousse en ville”), led by the Government of neighboring Republic of the Congo with the aim of reducing wild meat consumption in the city of Pointe Noire. In the video, two childhood friends reunite, and when one suggests cooking wild meat, they discuss the environmental consequences of urban wild meat consumption on the depletion of forest wildlife (Minist`ere de l’Economie Foresti`ere - Congo, 2019). They decide to avoid such consumption in the future and instead embrace Congolese dishes that do not contain wild meat (Appendix B). Rather than address all drivers of wild meat consumption simultaneously, the video we used focused on messages that target pride in Congolese forests and wildlife, whilst also supporting traditional Congolese cuisine. These could be considered as targeting drivers primarily linked to culture and tradition. The control group viewed a 90-second clip from a Congolese soap opera unrelated to wild meat or the environment.

After watching the video, subjects chose one of four restaurants for a $5 coupon (2,500 Congolese Francs). We provided subjects with the name, location, and a sample menu from 8
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-3H “it “Bd hd 50) 8 46<br>2] Guana SURINAME, + + \w + +2<br>4 ee,<br>3 + Se, + +f 0<br>a + [Rte BR OO Oa Et Od / +} -4<br>(Se<br>{ ay t i. > ee arlctaal pre A<br>4 Vie yeh oh Cele poo ens (4 TOCANTINS<br>tar : als<br>MATO GROSSO ? —<br>-58 “56 ad 52 <5) dd 46<br><!-- End of picture text -->
- Giesecke, J., & Schilling, C. (2010). The economic impact of the New Zealand fiscal stimulus package. _New Zealand Economic Papers_ , 44(3), 231–257.

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- Henbest, S. (2020). The First Phase of the Transition Is about Electricity, Not Primary Energy. BloombergNEF. https://about.bnef.com/blog/the-first-phase-of-the-transition-is-about-electricity- <u>not-primary-energy/ (last accessed February 10, 2021).</u>

- Hepburn, C., O’Callaghan, B., Stern, N., Stiglitz, J., and Zenghelis, D., (2020). “Will COVID-19 fiscal recovery packages accelerate or retard progress on climate change?” Smith School Working Paper 20–02.

- Horvat, B. (1958). The Depreciation Multiplier and a Generalized Theory of Fixed Capital Costs. _Manchester School_ , Volume 26, Issue 2 Pages 136-159

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electricity service reliability (reduced blackouts) and increased household electricity consumption in higher saturation transformers.

We implement this experiment in the Kyrgyz Republic, a lower-middle-income developing country that suffers from frequent electricity outages and therefore offers suitable conditions for research on electricity reliability. Although CFLs were available in some stores at the project onset, they were not commonly used outside of Bishkek, the capital city. Implemented in a district adjacent to Bishkek, the randomized saturation design employs an electricity utility’s data on its residential customers and the infrastructure through which these consumers are served.

Treatment status was randomly assigned in two stages. First, we randomize electricity transformers to different treatment saturations (i.e. different proportions of households are assigned to treatment within a transformer). Second, we randomize households individually to treatment and control groups, according to the transformer saturation intensity assigned in the first stage. After completing a baseline survey, the treated households receive up to four CFLs at a highly subsidized price. We follow household monthly electricity consumption via the electricity utility’s records for 18 months post-intervention to estimate the impacts on electricity consumption. To measure CFL use and spillovers in adoption, we return for a follow-up survey one year after CFL distribution.

We measure the impacts of CFLs on residential electricity consumption, both with and without controls for any potential spillovers in CFL take-up. The randomized saturation design in the initial CFL distribution allows us to test for such spillovers. In our sample, control households close to treated households are more likely to have CFLs at follow-up than pure control households in located control transformers. Not accounting for spillovers in take-up among the control group leads to estimates of electricity consumption reductions that are downward biased (in other words, they understate the savings from energy efficiency). When accounting for these spillovers, we find that CFLs lead to a significant reduction in monthly electricity consumption that falls within the expected range for the technology based on engineering performance estimates.

Using the transformer-level randomization of treatment intensity, we then test for externality effects resulting from the energy efficiency, in the form of improved electricity reliability.

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# Table of Contents

||Tit|le|**Psi.**|
|---|---|---|---|
|Intro|duct|ion........<br>.<br>.<br>.<br>.<br>.|1|
|I.|The<br>1.<br>2.|Current State of Stabilization.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>..<br>.<br>The Nominal System<br>Real Effects of Disinflation|4|
|II.|The|Change of Regimes. ................|9|
||1.<br>2.<br>3.<br>**_4._**<br>S.<br>6.<br>7.<br>8.<br>9.|The Inflationary<br>Regime<br>The Change in the Rules of the Game<br>The Focus on the external Position<br> The Effect of Reducing Inflation on Reserves<br>The Role of U.S. Aid<br>Real Aspects of fiscal Policies<br>Financial Aspects of the Fiscal Adjustment<br>Domestic Investment and Monetary Policy<br>Summing Up the Influences on theExternal<br>Position||
|III.|Evo|lution of Policies in the Course of Stability|26|
||1.<br>2.<br>3.|Fiscal Policy<br>Monetary Policy<br>Exchange Rate Policy||
|IV.|Pol|icy Dilemmas..<br>.<br>.<br>.<br>.<br>..<br>.<br>.<br>.|29|
||1.<br>2.|The Low Inflation Step<br>The Issue of Real Appreciation||
|Footn|otes|and References..<br>..<br>.<br>....<br>. .<br>.<br>.<br>.<br>.<br>. ..<br>..|36-38|
|Table|s..|3..<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>..<br>.<br>.<br>.<br>.<br>.<br>.<br>.|39-47|
|Figur|es.|.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.|48-50|
15 payments on a cash basis are ore-third higher.<sup>7</sup> By aggregating debt for all developing countries, the statement seriously obscured the progress in the major Latin American debtor countries that had been and remain the central core of the debt crisis as a systemic problem. Thus, it may be seen in table 1 that the increase in debt in the 17 Highly Indebted Countries (HICs), or the iriginal Baker 15 plus Costa Rica and Jamaica, was far smaller than in other developing countries (34.8 percent versus 63.2 percent). Moreover, of the large volume debt increases in other developing countries, the bulk occurred in countries well capable of carrying debt; indeed, had they not been, the debt would not have increased as it did. The largest absolute and percentage increase in debt occurred in China. Other large absolute increases occurred in Greece, Hungary, India, Indonesia, Malaysia, Thailand, and Turkey, all countries that have remained outside the locus of the debt crisis (and indeed, Turkey overcame its earlier crisis to achieve new access to credit).8 In short, for the major Latin American debtors at the center of the debt crisis, there has been much more improvement in the key debt indicators than is generally recognized. Large increases in debt of such countries as China, India, Greece and Portugal 7 World Bank Tables 1988, Vol. I, p. xi.

B The World Bank data, which only extend through 1987, do not capture the reduction in Latin American debt that occurred in 1988, as discussed above. Note also that the World Bank data show larger 1987 debt than does the Economic Commission for Latin America (table 1).
might be correlated with foreign bank penetration (e.g., if supervisory authorities use restrictions on entry to restrict the presence of foreign banks) and with reduced access to credit (e.g., if entry restrictions reduce domestic competition). Similarly, the quality of supervision might also affect both foreign banks' entry decisions and sector performance. To control for this in the empirical analysis, two indices presented in Barth _et al._ (2001a) are included in some model specifications.'<sup>6</sup> Finally, the estimations include controls for the macroeconomic environment in which firms operate. In particular, we control for countries' GDP per capita, level of financial development (as measured by the ratio of M2 to GDP), inflation rate, and average growth rate. All of these variables come from the World Bank's World Development Indicators database. Other things being equal, access to credit could be better and foreign penetration could be higher in countries that are more financially developed and have better growth opportunities. We include the macro variables to be able to disentangle the independent effect of foreign penetration on access to credit.

# **III.** ECONOMETRIC METHODOLOGY

Three of the dependent variables used in this analysis - enterprise managers' responses to questions about whether high interest rates, access to long-term loans, and to non-bank financing represent obstacles to enterprise operations and growth - are limited dependent variables.'<sup>7</sup> These variables can take four discrete values, in ascending order, corresponding to no obstacle, minor obstacle, moderate obstacle and major obstacle. Since the responses to the questions about perceptions are ordered, but are not actual count data, we estimate this model as an ordered 10
efficiency- to reduce air transport costs.<sup>32</sup> In columns (4) to (6) we present our results using regulatory quality and/or government efficiency obtained from Kaufmann et al. (2003).<sup>33</sup> As shown, separately, both dimensions have the expected sign and both are statistically significant at conventional levels (Column 4 and 5). But, when we include both institutional variables at the same time, only regulatory quality has the expected sign and is significant at standard levels.<sup>34</sup> Not surprisingly, this last result suggests that regulatory quality, which should directly affect airport efficiency, has a significant effect on air transport costs. In our sample, a country that increases its level of regulatory quality from the level of Ecuador (p25) to the level of France (p75) can experience a reduction in its air transport costs of 14 percent.

The last two columns of Table 1 report the results when we include our Open Skies Agreement dummy variable<sup>35</sup> with and without regulatory quality. In both cases, Open Skies Agreement has the expected negative sign, but it is not statistically different from zero at conventional levels. It is possible that this result is biased to zero due to the endogeneity problem described at the beginning of this section (countries with initial high transport cost are willing to sign Open Skies Agreements).

Unreported regressions show that our results are robust to the use of other years in our sample instead of 2000.<sup>36</sup> To see if our results are robust to the addition of country-income controls, in Table 2 we include, in all specifications, a dummy variable that takes the value of one if the foreign country is a developed country (high income country), and zero otherwise. We found that all our previous results are robust to the inclusion of this dummy, except for the total volume variable, which keeps its negative sign in all specifications but is not statistically different from zero. The variables of airport infrastructure and regulatory

> 32  See Kaufmann,Kraay, and Mastruzzi (2003) for a general discussion of the role of the institutional environment in country performance.

> 33  To check the robustness of these results, we estimated the benchmark equation with Rule of Law and Control of Corruption and we found that the results obtained where very similar in magnitude of coefficients and in level of significance.

> 34  The lack of significance of government efficiency may come from the fact that both institutional variables are highly correlated in our sample.

> 35 The dummy variable that accounts for the existence of an Open Skies Agreement between partner countries was obtained from the U.S. Department of Transport. The introduction of this paper presents a table with all Open Skies Agreements and the years they were signed.

> 36 For all other years we have to use year 2000 unbalance

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**Figure 2. Effects of Different Parameters on the SDI**

<!-- Start of picture text --> An increase in... Leads to the SDI...<br>Social Opportunity Costs (m) +<br>Interest rate on concessionaryfunds (c) -<br>Pre tax profit (P) -<br>Equity (E) +<br>SDI =<br>A(m-c)+[(E x m)-P]+K<br>LP x i<br>Other subsidies such as coverage<br>of operational costs (K) +<br>Outstanding loan portfolio (LP) -<br>Yield on loan portfolio (i) -<br><!-- End of picture text --> An SDI of zero means that a DFI has achieved full self-sustainability. An SDI of 100 percent indicates technically, for simplicity,<sup>14</sup> that a doubling of the average lending interest rate is required if subsidies are to be eliminated. A negative SDI indicates not only that a DFI has fully achieved self-sustainability, but also that its annual profits—minus its capital (equity) that was charged at the approximate market interest rate—exceeded the total annual value of subsidies. Thus, subsidies could be effectively reduced or eliminated or services could be provided at lower costs for clients (as found with respect to Banrural).

The SDI-based analysis can be refined further, particularly by analyzing the drivers of the profit ( _P_ ) term in its calculation. Two drivers of particular interest in microfinance institutions and DFI are net loan losses and transactions costs. Successful institutions of this type keep annual loan losses in the range of 2–5 percent of average annual OLP. However, this success is in many cases achieved by using specialized lending technologies that lead to transaction costs that are significantly above those of regular commercial banks.

In many microfinance institutions, the value of the transaction cost measured against the OLP exceeds 30 percent and reaches a somewhat lower figure when measured against total assets.<sup>15</sup> Future work requires going beyond merely computing the SDI— to measuring the

> 14 The SDI is a rough indicator. It ignores a situation where a substantial increase, for example 100 percent, in the lending rate is also likely to result in increased loan losses. Therefore, it would require either a higher percentage interest rate hike to eliminate subsidies or no increase in the lending rate, which could lead to the elimination of the subsidies because of their adverse effect on loan collection. 15 See the MicroBanking Bulletin, July 2005.

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