rafmacalaba/datause-encoder-data huggingface dataset:
# **Abbreviations**

|**Abbreviation in**<br>**English **|**Explanation in English **|**Abbreviation**<br>**in Polish **|<br>**Explanation in Polish **|
|---|---|---|---|
|BWM|Bureau of Water<br>Management|BGW<br>|Biuro Gospodarki Wodnej|
|CIEP|Chief Inspectorate of<br>Environmental Protection|GIOŚ|Główny Inspektorat Ochrony<br>Środowiska|
|DDWM|District Directorate of Water<br>Management|ODGW|Okręgowa Dyrekcja Gospodarki<br>Wodnej|
|IMWM|Institute of Meteorology and<br>Water Management|IMGW|Instytut Meteorologii i<br>GospodarkiWodnej|
|Mm<sup>3</sup>|Millioncubicmeters|||
|NBWM|National Board of Water<br>Management|KZGW|Krajowy Zarząd Gospodarki<br>Wodnej|
|NCWM|National Council of Water<br>Management|KRGW|Krajowa Rada Gospodarki<br>Wodnej|
|NFEPWM|National Fund for<br>Environmental Protection &<br>Water Management|NFOŚGW|Narodowy Fundusz Ochrony<br>Środowiska i Gospodarki<br>Wodnej|
|RBWM|Regional Board of Water<br>Management|RZGW|Regionalny Zarzad Gospodarki<br>Wodnej (stary)|
|RWMA|Regional Water Management<br>Authority|<br>RZGW|Regionalny Zarzad Gospodarki<br>Wodnej (nowy)|
|RCWM|Regional Council of Water<br>Management|RGWRW|Rada Gospodarki Wodnej<br>Regionu Wodnego|
|VFEPWM|Voivodeship Fund for<br>Environmental Protection &<br>Water Management|WFOŚGW|Wojewódzki Fundusz Ochrony<br>Środowiska i Gospodarki<br>Wodnej|
|VIEP|Voivodeship Inspectorate of<br>Environmental Protection|WIOŚ|Wojewódzki Inspektorat<br>Ochrony Środowiska|

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activity, since there is no information on gross domestic product at the district level.<sup>2</sup> From SUNAT, we retrieved information on the total number of firms from each region and the location of their local offices for all the regions covered by EMYPE. The location of local SUNAT offices allows constructing the distance from the EMYPE enumeration area to the nearest SUNAT office, which we use as a proxy variable for business formality enforcement.

From the Ministry of Labor, we retrieved regional-level information on the number of firms registered in the Electronic Payroll Registry. The registry records information on formal firms and their workers from the standpoint of labor regulations. All formal employers with more than two workers are required to submit payroll information to the Ministry of Labor. Registered employment represents the most formal employment in the country, since employers who report to the Electronic Payroll Registry comply with all the labor benefits and entitlements established by Peruvian labor laws. We combined the information on firms in the Electronic Payroll Registry with information on firms in the SUNAT registry to compute the fraction of firms that comply with the labor regulations by region. We use this variable as a proxy for labor formality enforcement.

Table 1 presents summary statistics for the main variables used in the analysis. In fiscal year 2009, when the sample was drawn, the average SME in the data set had 11 years of business operations, and 25 percent of the firms had been in operation for five or fewer years. In 2012, the average firm in the data generated monthly product per worker of about US$2,670 and had eight workers, and 56 percent of the firms were incorporated businesses. Around 31 percent of the firms are in the apparel industry; 27 percent, metal-mechanics industry; 17 percent, leather and related industries; 14 percent, furniture industry; and the rest, wood (7 percent), food processing (2 percent), and jewelry and related industries (1 percent). Geographically, the SMEs are from eight regions (states) of the country, with the capital city (Lima and Callao), Arequipa, and La Libertad contributing to approximately 76 percent of the sample (31, 23, and 22 percent, respectively) and Junin, Lambayeque, Loreto, Piura, and Callao the remaining 24 percent (9, 7, 4, 3, and 1 percent, respectively).

> 2 We thank Nikita Cespedes who provided us with these data. He uses electricity consumption as a proxy for economic activity at the district level in a study assessing the elasticity of labor formality to economic activity (Cespedes 2015).

12
Despite larger improvements in toilet construction and open defecation practices among BPL households or those with no toilet at baseline, we did not observe consistently larger improvements in child health in those subgroups. Instead, we observed larger reductions in diarrhea and _Giardia_ among households who had a toilet at baseline, yet in this subgroup the intervention did not increase toilet construction, toilet use, or reduce open defecation. This suggests that the differences in child health could be due to chance given the small size of the subgroup (<100 individuals per arm) and the large number of subgroup comparisons made.

# **Discussion**

## **Summary of Main Results**

This 80-village study conducted in rural Madhya Pradesh represents the one of the first largescale, randomized evaluations of India’s Total Sanitation Campaign (TSC) to include child health measurement. Based on the latest reported CLTS triggering activity in each village, the program was not implemented to its full extent at the time of the follow-up survey (only 14 of 40 intervention villages had > 6 months of exposure). However, the information from other sources and especially the exposure to TSC related activities measured at the village level shows clear evidence of more social mobilization and behavior change activities as well as toilet construction in intervention villages compared to control villages, We observed as well some evidence for contamination in 10 of 40 control villages close to our follow-up survey. The incomplete program delivery and slight contamination of the control group would in theory bias our findings towards an underestimate of program impact.

Despite the absence of full exposure to the program at the time of our follow-up measurement, we found that the TSC increased toilet coverage by 19 percentage points in intervention villages compared to control (41% v. 22%). Improvements in sanitation-related behavior were more modest: reported open defecation among adults decreased by 10 percentage points (e.g., 73% v. 83% among women) and correct child feces disposal increased by 9 percentage points (27% v. 18%). Among households in the intervention arm with a JMP improved toilet, 41% reported that adults still practiced daily OD. We found that program impacts on toilet coverage and sanitation-related behavior were concentrated almost entirely among households without a toilet at baseline and households that were below the poverty line, which is consistent with the program design that provided higher amounts of subsidies to such households. Reduced levels of _E. coli_ in intervention household drinking water and lower _Giardia_ prevalence in intervention children are consistent with the possibility that the TSC program reduced waterborne transmission of this pathogen in intervention villages. However, we found no evidence for reductions in helminth infection or diarrheal disease, and no evidence for more distal impacts on anemia or growth during the study period.

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the quantities of interest here. Household and area characteristics are also available, the conditioning on which is left implicit in what follows.

# **2.2 Diary Interviews**

Diaries are a self-administered form of data collection in which respondents must record their market purchases and the estimated market values of acquisitions from non-market sources. In our case study, the quantity _Y_<sup>_d_</sup> is defined as acquisitions captured over the one week diary.

The theoretical case for differences between consumption _Y_<sup>_∗_</sup> and acquisition _Y_<sup>_d_</sup> can be made with the aid of a model of frequency of purchase and consumer demand (see Meghir and Robin, 1992, and Coibion et al., 2020, for examples). Using the notation above, households decide simultaneously on the desired consumption over _τ_ weeks (e.g., one month) and a purchasing strategy. Assume, for simplicity, that acquisition is only conducted through cash outlays. Let _N_<sup>_∗_</sup> be the desired number of purchases in one week implied by utility maximization. An implication of this choice is that _Y_<sup>_∗_</sup> _/N_<sup>_∗_</sup> must be the expected amount of each purchase. If _N_ denotes the number of purchases recorded in the diary week, the assumption of purchases of a constant amount yields:<sup>6</sup> The main take-away message from this model is that household acquisition in the diary week is a random variable centered at the quantity of interest _Y_<sup>_∗_</sup> . Specifically, in a randomly chosen diary week, we have _E_ [ _N |N_<sup>_∗_</sup> = _n_<sup>_∗_</sup> _, Y_<sup>_∗_</sup> = _y_<sup>_∗_</sup> ] = _n_<sup>_∗_</sup> and:

The model also implies that distributional indicators obtained from diary acquisitions may be liable to provide incorrect policy conclusions. For example, calculations reported in the Online Appendix show that if acquisitions occur independently at the same rate _N_<sup>_∗_</sup> across households, we have: where _CV_ ( _Y_<sup>_∗_</sup> ) is the coefficient of variation of consumption. The last expression implies that for items usually acquired once a week ( _N_<sup>_∗_</sup> = 1), the variance of acquisition must be at least twice as large as the variance of consumption. When the coefficient of variation is equal to one, the variance of acquisition becomes three times the variance of consumption.

We conclude that acquisition diaries may yield a distorted idea of consumption _Y_<sup>_∗_</sup> even if all entries are recorded correctly. However, entries may not be exempt from errors. As the effects of fatigue on accuracy have long been known to researchers (Silberstein and Scott, 2004), it is common practice to assist households through frequent visits by enumerators during the recording process. It is generally presumed that this design yields the most accurate data (see Brzozowski et al., 2017, for discussion).

> 6The model can be easily generalized to allow for random spending around _Y ∗/N ∗_ .

6
# **Identifying and Spurring High-Growth Entrepreneurship:**

# **Experimental Evidence from a Business Plan Competition**<sup>*****</sup>

David McKenzie, _World Bank_ _Keywords:_ Business plan competition; entrepreneurship; high-growth _JEL codes:_ M13, O12, L26, C93.

* I thank DFiD, the World Bank, the WLSME trust fund, and the John Templeton Foundation for funding this study, Halima Zarma and Yuvraj Pathak for research assistance, Michael Wong and Anthea Dickie for their work and advice on the operational side of this project, and Muyi Aina and Olasupo Olusi for their advice on how the program operated in practice. Comments from Lorenzo Casaburi, Giacomo de Giorgi, Antoinette Schoar and seminar participants at CGD, Columbia, HBS, HECER, the IPA-SME conference, the NBER, Stanford, Wharton and the World Bank are greatly appreciated. A pre-analysis plan for this work was first registered in the J-PAL hypothesis registry on November 14, 2012, and subsequently in the AEA trial registry (AEA-RCTR-0000078) on September 24, 2013, and in the Registry for International Development Impact Evaluations (RIDIE) on October 10, 2013 (RIDIE-STUDY-ID52559be5a72eb). All views expressed are those of the author, and do not necessarily reflect those of the World Bank.
diesel oil, heavy fuel oil, coal, kerosene and electricity. The modes of transportation considered are domestic aviation, road, rail and domestic navigation<sup>9</sup> . We have excluded energy consumption in oil and gas pipeline transport.

Emission coefficients are based on the carbon contents of fuels and are obtained from Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories (IPCC, 2006) for all fuels except electricity. Emission coefficients for electricity are derived using IEA data on electricity output and CO2 emissions from electricity production (IEA, 2007a, 2007d, 2007e). While CO2 emission factors for other transportation fuels (e.g., gasoline, diesel, etc.) remain the same throughout the study period, CO2 emission factors for electricity vary with time. This is because, the carbon content of a fossil fuel is not expected to change over time, but CO2 emission coefficients of an electricity grid change over time as the electricity generation mix and thereby the input fuel mix for electricity generation changes every year. Moreover, the CO2 emission coefficient of a particular fossil fuel is the same for all countries, whereas the coefficient for electricity varies across the countries depending on their electricity generation mix. CO2 emissions are estimated by type of fuel and mode, using the corresponding fuel consumption and associated emission factors. Data on gross domestic product (GDP), expressed in 2000 constant dollar measured at purchasing power parity, and population are also taken from the IEA (IEA, 2007f, 2007g).

> 9 In energy statistics, energy consumption by international aviation and maritime transportation are not considered part of national energy consumption. These are treated separately under their international conventions (i.e., International Civil Aviation Organization and International Maritime Organization).

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2

**TabIle1**

|**Social Protection Expenditur**|**es**<br>**as%**|**of GDP**||||
|---|---|---|---|---|---|
||1992|1993|1994|1995|1996<br>(est|
|Total|16.2|13.1|13.7|13.6|14.5|
|Financed out of payroll taxes|13.2|10.6|11.4|11.1|11.4|
|Pension Fund|8.8|8.3|8.0|7.9|8.1|
|Social Insurance Fund|1.2|0.8|1.1|1.0|1.0|
|Employment Fund|0.2|0.2|0.3|0.3|0.3|
|Chernobyl Fund|3.0|1.3|2.0|1.9|2.0|
|Financed out of general budget revenues||||||
|Allowance for elderly&children|3.0|2.5|2.3|2.5|3.1|
|Other social spending||||||
|Health, Education,&Culture|11.2|9.6|11.3|11.7|10.5|
|Consumer subsidies|5.6|10.3|6.7|3.2|2.5|
|Memo: GDP (trillions of Kbv)|5.033|148.3|1138|5293|8686|

Source: Data Provided by the Ukrainian authorities and World Bank estimates.

The following section provides a brief description of the structure and features of the pension system in Ukraine. Section 3 analyzes the impact of recent macroeconomic trends and transformation of the labor market on the balance of the pension fund. It also documents the changes introduced in the system since the country's independence. Section 4 describes the model and contains an analysis of the medium-term sustainability prospects. The analysis is made assuming that Ukraine will pursue its reform efforts and recover a growth path. The first question examined in the paper is whether sustained growth and macroeconomic stability over a period of about 30 years would make possible the restoration of benefit levels and features prevailing in the past. It will be shown that this option needs to be ruled out even in the context of a fairly optimistic growth scenario. Section 4 then turns to a second question: whether it would be viable to maintain the system "as it is" currently, that is, with the less generous features introduced recently. The answer will be again negative based on expected behavioral changes.

As reforms appear unavoidable, Section 5 analyzes several alternatives. The first part of the section deals with reforms of limited scope which focus on reducing coverage and expenditures without changing the nature of the system. Particular attention is paid to a possible increase in the retirement age. An attempt is made to assess whether these reforms would be
of different segments of the long-finance segment, including banking, capital markets and institutional investors and the regulatory framework and business environment underpinning the intermediation process.<sup>19</sup>

# **4.2 A Shifting Global Financial Cycle and Geopolitical Tensions**

As discussed above, Africa’s banking systems have gained in resilience over the past decades; however, they are not immune against global financial cycles and sovereign debt fragility. The rise in interest rates since late 2021 across major advanced economies has increased debt burdens across the globe. The increase in foreign debt over the past decade, partly underpinned by rising commnodity prices and not all of it made transparent at time of origination, and the economic losses due to the pandemic have brought many developing countries close to unsustainable debt levels and some are already beyond that threshold.

This has also affected countries across the regions, with several countries defaulting on their government debt. And while sovereign debt restructuring has been traditionally limited to foreign currency bonds, recent restructuring events, such as in Ghana have also affected domestic bonds, which are typically treated as safe assets by local banks and subsidiaries. Ultimately, this can have negative repercussions for banks through several channels: losses incurred on their government bond holdings, losses related to exchange rate devaluation, and credit losses related to companies either exposed to governments (through ownership or contract relationships) or affected by recession.

> 19 For more information: https://altf.afdb.org/en/home-page.

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this may lead to a slight underestimation of seasonality in non-food consumption, the core interest here is in exploring the intra-annual variation in food consumption. These fluctuations are the ones most detrimental to welfare.

Second, a substantial part of food consumption in developing countries typically comes from own production. Auto-consumption was valued at the median unit value for that item obtained from the same enumeration area at the same month. If insufficient observations were available to obtain a unit value for that food item for that month within the same enumeration area, median values were obtained from higher levels (district, regions, national). All values of food and non-food subaggregate components have subsequently been spatially and intra-annually deflated (i.e. expressed in the prices of one region-month). Expenditures are thus expressed in real terms, and intra-annual fluctuations can be interpreted as changes in (aggregate) quantities.

Turning to the survey design, even though the samples were drawn to be nationally representative per period, slight deviations may have been introduced during survey implementation for practical purposes, to accommodate the survey teams. To control for differences in the composition of the samples across periods that may have been introduced in this way, seasonally invariant household and location characteristics ( **Xiy** ) were added in equation (6). These include the education, sex, and age of the household head, plus agro-economic zone and regional controls.<sup>18</sup> Robustness of the seasonal gap estimates was further checked against specifications without regional dummies and/or without accounting for sampling weights and survey strata.

Finally, heterogeneous timing in seasonality across locations may attenuate the intra-annual variation when the estimation is on the national samples. Therefore, the seasonal factors are estimated separately for regional sub-samples. Urban, Northern, Central, and Southern rural sub-

> 18 The inclusion of other common controls (such as household size, income, assets) was avoided, because they exhibited mild seasonality themselves. Note that the primary purpose at this junction is not to explain the reasons for intra-annual variation in consumption (e.g. due to seasonality in prices or income), but rather to document the degree of intra-annual variation in consumption itself. The inclusion of seasonally varying controls would lead to an underestimation of the intra-annual variation in consumption, as they may pick up some of the intra-annual variation. This was confirmed in robustness test regressions that also included household size, income, and assets (not reported, but available upon request).

13
improving the efficiency and coverage of public (infrastructure and non-infrastructure) service provision, income inequality may decline (Estache, Gomez-Lobo and Leipziger, 2000).

Finally, infrastructure reform may lead to price and/or supply responses that reduce the access and affordability of services for the poor. For example, removing subsidies may lead to higher post-reform prices, new private providers may charge higher connection fees than governmentowned providers, or they may be reluctant to reach poorer areas (Estache, Foster and Wodon, 2002). As a result, infrastructure services may become unaffordable to lower-income groups. The likelihood of this outcome depends on the overall design of the reforms. In practice, however, there are numerous episodes in which the access by the poor improved after reforms involving private participation.

# _3.2 Empirical Studies_

The empirical literature on infrastructure and inequality broadly follows two main strands. One is concerned with the effects of infrastructure stocks and/or service flows on standard inequality indicators. It includes the majority of the studies using macroeconomic data. The other examines the effects of specific infrastructure interventions, usually focusing on the income of poor households or backward geographic areas.

A few studies have directly examined the inequality impact of infrastructure at the aggregate level, by regressing Gini coefficients and similar inequality measures on indicators of infrastructure development in a cross-country panel data setting. Among them, López (2004) proxies infrastructure development by fixed telephone density, while Calderón and Chong (2004) consider the quantity and quality of different infrastructure sectors (telecommunications, energy, roads and railways) both separately and jointly, using a qualitative summary indicator in the spirit of Hulten (1996). In turn, Calderón and Servén (2004, 2010a,b) employ synthetic indices of infrastructure quantity and quality combining multiple infrastructure sectors, built through a principal components procedure. These papers find that, ceteris paribus, income inequality is negatively related to their respective measures of infrastructure development. In a similar setting, Seneviratne and Sun (2013) reach the same result for ASEAN countries, but they also find that public investment does not bear any significant relation with inequality. This again suggests that public investment data offer a poor proxy for infrastructure development, 13
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adjusted price measures for a few commodities La calculated, as well as the off -t on world price indexes, under two possible assumptions about the relation between quality and price change in the U.S. and price change in other countries' exports.

# B. Data Collection

For these studies, export and domestic price data have been collected at the most detailed level available, some from price data on computer tape or diskettes, but most keypunched from published or unpublished price compendia. These price data, which go back to 1953 for most countries included, are arranged according to the 4-digit SITC Revision 1. The countries included, some mov:e extensively than others, are the U.S., the U.K., Germany, Japan, Canada, The Netherlands, Sweden, and France. We have also begun to collect data for Korea, Hong Kong, Singapore, and Taiwan (China), but they are not included in the indexes discussed hero. The data are classified into the SITC Revision 1 at the four-digit level and then aggregated to 3-digit, 2-digit, and 1-digit levels for SITC 5-8. and to total manufactures, defined as SITC 5 through 8. The methods are explained most recently in Lipsey, Molinari, and Kravis (1991).

# Factors Affecting Measures of Exnort Prices of Manufactured Goods

One advantage of the NBER price indexes used here over the UN unit value indexes and other representations of manufactured goods prices is that the price indexes here are transparent; the ingredients and how they were put together are known. The UN unit value indexes, on the other hand, are derived from a variety of national indexes, put together by national statistical offices in many different ways, and some of which, in fact, are not even based on unit values.