rafmacalaba/datause-encoder-data huggingface dataset:
and concludes that firms experiencing fewer credit constraints are more likely to own a private generator in the areas where public power supply is unreliable.

Our original contribution is to document systematically the effects of both electricity deficiencies and credit constraints on the decision to invest in mitigating technology, i.e., a generator, and to analyze how their impact varies across firm types (in particular, size) and technological characteristics of the industrial sector they belong to. The use of a theoretical model of firms’ responses to power outages allows us to derive precise predictions that are borne out by the data. Using a dataset with a wide coverage across 87 countries and 28 twodigit (ISIC) industry classifications, we show that electricity-related constraints have nonlinear effects and that they also interact with credit constraints. Moreover, the marginal and threshold effects vary according to sectors’ degree of reliance on electricity and firms’ size. Finally, we discuss potential policy implications from our results. Credit constraints appear to be the priority in sectors very reliant on electricity to spur entry and convergence to the technological frontier, while in other sectors, firms would benefit more widely from marginal improvements in electrical supply. We discuss how the power-sharing versus pricing policies issues highlighted above may be used to address these implications.

The paper is structured as follows. Section 2 presents our dataset and provides descriptive statistics on the extent of electricity deficiencies and credit constraints, as they emerge from the enterprise surveys. In Section 3, we then develop a model of investment by firms when infrastructure and credit constraints are present. Section 4 spells out the econometric specifications to be estimated with our data. Section 5 presents the results, and Section 6 discusses the policy implications and concludes.

# 2. Data and Stylized Facts

We use data from the enterprise surveys for 104 countries covered a total of 70,192 firms over the period 2002-2006.<sup>8</sup> Of these, 87 countries have data on number of power outages, 77 countries have data on generator and 34 countries have data on cost of electricity.<sup>9</sup> Table 1 presents general summary statistics, and Table 2 break these down by generator ownership.

> 8 See https://www.enterprisesurveys.org.  Unfortunately it is not possible to use the available 2006-09 data for such an exercise since key questions about power were dropped from the questionnaire.

> 9 This results in a sample of 62 countries with data on generator and number of power outages, and 32 countries with data on generator, number of power outages and cost of electricity.
for licensing, permission and registration has been implemented. As a result 85 percent of the regulatory regimes slated for optimization were modified or eliminated. In addition, a _Law on Administrative Regulation and Administrative Control on Economic Activities_ was enacted to control the development of future licensing regimes. The law establishes the principle that new regulation can only be passed with parliamentary approval; that new regulatory regimes are limited to the initiative of line ministries and other government agencies; and requires new regulation to be accompanied by an assessment of compliance cost. Specific guidelines were issued to central and municipal bodies on the design of proposed new regulatory regimes, including the identification and measurement of costs and benefits, and the establishment of a process by which new regimes evolve appropriately from concept to implementation.

<!-- Start of picture text --> Figure 14: Regulatory and Administrative Opacity<br>a. Licenses and permit system b. Communication and simplification of rules<br>4.5 4.0 1.6 1.4<br>4.0 1.4<br>3.5 1.2<br>3.0 2.7 1.0<br>2.5 2.0 2.0 2.0 2.2 0.8 0.8<br>2.01.5 0.6 0.5 0.5 0.5 0.5 0.5<br>1.0 0.4 0.3<br>0.5 0.0 0.0 0.0 0.2 0.1<br>0.0 0.0<br>HungaryRomaniaSlovak Republic Bulgaria EU15 Poland OECD MICsCzech Rep. Romania Bulgaria MICsCzech Rep. Hungary OECD EU15 PolandSlovak Republic<br><!-- End of picture text --> _Source:_ see Figure 3. _Note_ : Other MICs are Brazil, Mexico, Turkey. Values refer to 2006 for Bulgaria and Romania, 2004 for Brazil, and 2003 for all other countries. For full data set see appendix I.

The achievements in the simplification of licenses and permits requirements are visible in the levels of regulatory and administrative opacity, where Bulgaria compares well with benchmark groups, ranking as well as or better than the EU 15 or the OECD (Figure 14a).<sup>11</sup> These achievements are also reflected in the fact that businesses in Bulgaria now perceive licenses and permits as less of a problem for doing business than in 2002 (Figure 15).

> 11 Note that, since accession, the Czech Republic, Hungary, Poland and the Slovak Republic have continued progressing with their reform agenda implying that the gap between these countries today and Bulgaria is larger than what the 2003 data suggest.  For instance, in January 2004, Poland enacted a _Law on Economic Freedom_ , reducing the number of licensing regimes from 9  to 5 and  introducing the ‘silence is consent rule’ in business registration and in various areas administrative regulation.

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consumption data can lead to improvements in the measurement of outcomes and in the effectiveness of targeting.

This paper has also ignored aspects of political economy, administration and other outcomes that may be equally important for beneficiaries. Governments have often a bias for geographical targeting covering areas relevant for selected constituencies rather than focusing on the poor. Administrative constraints such as complex logistics or lack of administrative budgets can make reaching the poor impossible in some cases making geographical or other forms of targeting more appealing. Outcomes different from poverty reduction such as nutrition levels may be as important or more important than poverty reduction in a humanitarian and emergency context. These are all elements to factor in when considering the method proposed in this paper.

# **References**

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complexity are also needed. All these recommendations, which are reasonably within reach, are needed before more conclusive findings can be drawn as to the relationship between specific forms of citizen participation and specific categories of political trust.

# References

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Arizti, P., Brumby, J., Manning, N., Senderowitsch, R. & Thomas T. (2010). Results, Performance Budgeting, and Trust in Government. Washington, DC: World Bank Bächtiger, A., Dryzek, J., Mansbridge, J., & Warren, M. (2018). _The Oxford Handbook of Deliberative Democracy_ . Oxford University Press.

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# **_7_ The Impact of the FSP on Household Poverty**

In order to examine the impact of the FSP on poverty, the data samples (for the years 1989 to 1991) were divided into household groups according to the age of children present in the household. This categorization was chosen because the eligibility criteria of the FSP is based on household composition that is closely tied to the presence of young children within the household. Households were also differentiated according to whether they moved into and out of poverty (transient poor) or whether they remained in poverty (structural poor) throughout the period 1989-1991. This categorization of structural versus transient poverty was used to examine the effect of the FSP on the dynamic aspects of poverty. The following three questions were examined regarding the impact of the FSP on the poverty headcount and gap:

- i) How effective was the FSP in reducing poverty within each year (1989-1991)?

# ii) Was the FSP more effective in some years relative to others?

iii) Was the FSP more effective in reducing poverty measures for certain vulnerable groups compared to others?

_How Effective were Food Stamps in Reducing Poverty, 1989-1991?_ The results of first and second order dominance tests are summarized in Table 9. First order dominance tests show that in 1989 pre-FSP and post-FSP expenditure cannot be ranked for any type of household. In 1990 however, post-FSP dominates pre-FSP for all households except those households with children aged 0 to 5, and households that have elderly members.

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# Appendix F. Classification of ICT and Non-ICT Assets

Table F.7: ICT Assets

||Share o|f Aggregate Ca|pital(%)|Averag|e Growth in Sh|are(%)|
|---|---|---|---|---|---|---|
|ICT Assets|1960-1980|1980-2000|2000-2013|1960-1980|1980-2000|2000-2013|
|EP20: Communications|2.73|3.91|3.39|2.87|1.78|-2.63|
|ENS3: Own account software|0.24|0.75|1.56|27.26|6.68|2.58|
|ENS2: Custom software|0.11|0.61|1.40|34.82|8.49|2.06|
|EP34: Nonelectro medical instruments|0.35|0.76|1.08|4.87|2.97|2.30|
|EP36: Nonmedical instruments|0.51|0.92|0.92|0.62|2.41|-1.08|
|ENS1: Prepackaged software|0.02|0.33|0.83|32.28|14.63|-1.04|
|EP35: Electro medical instruments|0.11|0.36|0.66|7.25|3.43|4.28|
|EP1B: PCs|0.00|0.31|0.45||12.12|0.96|
|RD23: Semiconductor and other component manufacturing|0.05|0.23|0.43|6.58|8.21|2.75|
|RD22: Communications equipment manufacturing|0.26|0.21|0.27|3.27|0.89|0.24|
|EP31: Photocopy and related equipment|0.53|0.75|0.26|6.75|-2.11|-7.70|
|EP1A: Mainframes|0.19|0.36|0.24|24.00|1.91|-4.97|
|EP1H: System integrators|0.00|0.03|0.23||42.85|3.45|
|RD24: Navigational and other instruments manufacturing|0.05|0.19|0.22|3.20|5.78|-1.59|
|EP1D: Printers|0.07|0.22|0.19|20.75|7.20|-9.76|
|EP1E: Terminals|0.02|0.14|0.16|71.14|5.48|-4.62|
|EP1G: Storage devices|0.00|0.17|0.12||7.55|-9.55|
|EP12: Offce and accounting equipment|0.48|0.32|0.12|-3.09|-5.00|-6.13|
|RD40: Software publishers|0.00|0.05|0.09||16.91|-1.13|
|RD21: Computers and peripheral equipment manufacturing|0.16|0.09|0.07|3.68|-3.07|-0.60|
|RD25: Other computer and electronic manufacturing, n.e.c.|0.01|0.01|0.02|0.91|3.24|-0.34|
|EP1C: DASDs|0.09|0.13|0.00|30.38|-36.26|-78.36|
|EP1F: Tape drives|0.06|0.03|0.00|22.77|-40.33|-186.06|

_Notes:_ The data are drawn from the BEA’s detailed fixed asset accounts. ICT assets are defined as BEA asset codes starting with EP, EN, RD2, or RD4. Notice that the EP category incorporates two assets that are strictly speaking likely not ICT: EP34 and EP36. Our results do not critically hinge on these two assets and we therefore stick with the more standard BEA aggregation of EP. Assets are ranked by their average share in aggregate capital during 2000-2013. The share of aggregate captial is the value of each individual asset, as estimated by the BEA at current cost, as a fraction of the value of all assets in Tables F.7 and F.8. Panel A reports averages of these shares for three time periods. Panel B reports the average annual growth in these shares over same three time periods.

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# **4.4 Industry-Level TFP Changes and Policy Reforms**

To what extent can the increase in productivity be attributed to the trade and other policy reforms that occurred during the 1990’s? To answer this question, we exploit the variation in those policies across industries to examine whether changes in the individual components of productivity were systematically related to specific reforms.

In order to use the policy variation across industries, we re-create our aggregate, average, and reallocation TFP measures at the state-industry level. We use the state-industry level because this level of disaggregation allows us to consider variations in policies and other characteristics across both industries and states, and because the ASI survey is designed to be representative at this level. Figure 7 and Table 5 present the average results across all groups, weighting each group by the total number of firms that appear in that group across all years. Doing so ensures that the results are more comparable to the all-India results, since larger state-industry groups are given more weight.

This weighting scheme ensures that average productivity is nearly the same at the state-industry and all-India levels. However, the reallocation component is lower across most years at the stateindustry level. The reason is that at this level, we can only measure reallocation _within_ stateindustry groups. For example, suppose that the steel industry is more productive than the chemical industry, and that all firms in the steel industry increase output by 10%, while all firms in the chemical industry reduce output by 10%. The all-India reallocation measure will increase, but the state-industry reallocation measure will not. While it would be ideal to capture between-industry as well as within-industry market-share reallocations, our identification strategy (described below) does not allow us to use an all-India measure of productivity. Nonetheless, despite some differences, the reallocation component at the state-industry level follows the same basic pattern as the all-India measure.

We exploit the fact that the trade, licensing, and FDI reforms occurred differentially across industries to isolate the impacts of each policy on each productivity measure. Consider the relationship between our outcomes of interest and the reforms: 20
-2 -

this discussion. These include particularly the problems of providing investment incentives in economies in which a good deal of investment takes place in risky activities, in firms which are in a loss position, and in which foreign capital is important. We illustrate some of these problems with a case study using the Malaysian tax system.

Finally, we provide a summary of the recent empirical work that has been done to estimate the effectveness of investment incentives in developing countries.

**H.** Investment Incentives in Developing Countries: Types of Instruments and Frequency of Use A. Some Issues of General Relevance

As outlined below, there are a wide variety of types of investment incentives used in developing countries and they might be expected to have differing effects. Yet, there are a number of common issues which affect many of these incentives and which we repeatedly refer to in the analysis to follow. Ihe purpose of this section is to highlight up front some of the more important of these issues and to discuss their relevance for the evaluation of investment incentives.

The first of these concerns whether or not the incentives are _discredonary_ or _azuomadc_ policy instruments. Discretionary investment incentives are those which are Implemented on a case-by-case basis by administrative decision. There may, of course, be general rules that the administrators follow. However, the decision as to whether to award an incentive is contingent on administrative approval. Automatic incentives, on the other hand, are those which are available to any firms meeting certain stated objective criteria. Examples include type and size of investment, location of firm, ownership of firm, profitability of firm, etc.

Economists stress the advantages of using automatic policy instrument whenever possible. Such istruments reduce the uncertainty attached to incentives, reduce the planning time for investments, and reduce the possibility that non-economic considerations or favoritism will enter the decision. Presumably they also reduce the costs of administering the incentives. On the other hand, it could be argued that discretion allows the administrators to be more selective in awarding grants and thereby increase the cost-effectiveness of them by screening out infra-marginal projects.

In practice, the line between discretionary and automatic incentives may not be clear-cut. The criteria for eligibility may themselves require administrative decisions, the more so the more selective the incentives are intended to be. Furthermore, administrators will rarely be fully informed about whether the firms using incentives are fully entitled to use them. Enforcement and compliance will necessarily require some administrative participation. Therefore, incentives will differ only in the degree
Figure 7. Demographics A. Impact of 1 percentage point higher growth of working age population share on per capita GDP growth

# B. Working-age population

<!-- Start of picture text --> Percent Percent of total population<br>3 70<br>World<br>AEs<br>EMDEs<br>2 65<br>1<br>60<br>0<br>Aiyar and Bloom and Bloom and Bloom et Ahmed 55<br>Mody 2011 Williamson Canning al. 2000 and Cruz<br>1998 2004 2016<br>C. Working-age population D. Global potential growth and contributions<br>Percent of total population Percent<br>75 10<br>MNA EAP ECA Other factors<br>LAC SAR SSA 8 Demographic trends<br>70<br>6 Baseline<br>4<br>65<br>2<br>60 0<br>55<br>50<br>World AEs EMDEs<br>E. EMDE potential growth and contributions F. EMDE potential growth and contributions<br>Percent Baseline Percent Baseline<br>10 Demographic trends 10 Demographic trends<br>8 Other factors 8 Other factors<br>6 6<br>4 4<br>2 2<br>0 0<br>EAP ECA LAC MNA SAR SSA<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025<br>2003-07 Contribution 2013-17 2003-07 Contribution 2013-17 2003-07 Contribution 2013-17<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025<br>2003-07 Contribution 2013-17 2003-07 Contribution 2013-17 2003-07 Contribution 2013-17 2003-07 Contribution 2013-17 2003-07 Contribution 2013-17 2003-07 Contribution 2013-17<br><!-- End of picture text --> Source: United Nations World Population Prospects: The 2017 Revision. Note: EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa. A. The sample of each study differs from each other. Aiyar and Mody (2011): Indian states, 1961-2001; Bloom and Williamson (1998): 78 countries, 1965-90; Bloom and Canning (2004): Over 70 countries, 1965-95; Bloom et al. (2000): 70 countries, 1965-90; Amer and Cruz (2016): 160 countries, 1960-2010. B.C. Population weighted averages. The working-age population is defined as people aged 15-64 years. D. -F. GDP-weighted averages. Other factors include contribution from policy changes, cohort effects, changes in population growth, convergence-driven productivity growth, and investment. “Contribution” reflects percentage-point changes between the averages of 2003-07 and 2013-17.

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# FIGURE 4 Exchange rate responses to domestic shocks

# A. Monetary policy shocks

<!-- Start of picture text --> Percentage points<br>3.5 Interquartile range Median<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>All Advanced EMDEs<br>economies<br><!-- End of picture text -->

# C. Domestic demand shocks

<!-- Start of picture text --> Percentage points<br>1.2 Interquartile range Median<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>All Advanced EMDEs<br>economies<br><!-- End of picture text -->

# E. Domestic supply shocks

<!-- Start of picture text --> Percentage points<br>1.0<br>Interquartile range Median<br>0.5<br>0.0<br>-0.5<br>All Advanced EMDEs<br>economies<br><!-- End of picture text -->

# B. EMDEs:  Monetary policy shocks

<!-- Start of picture text --> Percentage points<br>4 Interquartile range Median<br>3<br>2<br>1<br>0<br>Trade Exch. Inflation Energy Current<br>openness regime target account<br>D. EMDEs: Domestic demand shocks<br>Percentage points<br>2 Interquartile range Median<br>1<br>0<br>Trade Exch. Inflation Energy Current<br>openness regime target account<br>F. EMDEs: Domestic supply shocks<br>Percentage points<br>3 Interquartile range Median<br>2<br>1<br>0<br>-1<br>Trade Exch. Inflation Energy Current<br>openness regime target account<br>Low High Peg. Non-IT IT Exporter Deficits Surpluses<br>Peg. Non-IT IT Importer Exporter Deficits Surpluses<br>Float Importer<br>Low High Float<br>Low High Float Peg. Non-IT IT Importer Exporter Deficits Surpluses<br><!-- End of picture text --> Note: One-year impulse responses of the exchange rate to domestic shocks (monetary policy, domestic demand, and domestic supply) from country-specific factor-augmented vector autoregression models estimated for 29 advanced economies and 26 EMDEs over 1998-2017. Bars show the interquartile range and markers represent the median across countries. A positive number indicates an appreciation. EMDEs = emerging market and developing economies; IMF = International Monetary Fund; IT = inflation targeting.

B.D.F. Countries with “high” trade openness are defined as those with above median trade-to-GDP ratios; all others are considered to have “low” trade openness. Exchange rate and IT regimes are based on IMF classifications (see the Appendix for details). Energy exporters are defined as in World Bank (2018); all other countries are considered energy importers. Countries with current account deficits are those with a negative average current account balance over 1998-2017.

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