rafmacalaba/datause-encoder-data huggingface dataset:
Hazaribagh 10% 6.1% Six participating farmers and a control group of four nonparticipating farmers were randomly selected for the survey. The control group consisted of farmers with land parcels in close proximity to the local JCLIS command area but not irrigated by it and were presumed to be directly comparable to their participating neighbors by all other parameters affecting agricultural productivity. Five additional nonparticipating farmers from an adjacent village were surveyed as well to account for any spillover effects that may confound results based on comparisons of farmers in the same village. These farmers in nearby villages were counted as part of the control group.

This selection process led to a total sample consisting of 757 farmers in 95 villages across 13 blocks in Jharkhand. 297 were participants and members of their local JOHAR Water User Group. 460 nonparticipating farmers comprised the control group. 92 percent of farmers in the sample, including those in the control group, were members of the JOHAR Producer Groups (PG). Farmers in the control group may also have received some training and extension from the larger JOHAR project, but they did not use JCLISs for irrigation. In fact, less than 10 percent of the beneficiary and non-beneficiary farmers in the sample reported having received agricultural extension from JOHAR or any other institutional sources. This makes it safe to assume that the main results based on comparisons between the treatment and control groups are not confounded by some control farmers benefiting indirectly from JOHAR project interventions. (For robustness, the authors also omitted all farmers in the sample who reported having received extension support from the JOHAR project. The results, which are not included in this report, do not differ from the smaller sample analyzed here.)

# _3.2 Data collection_

Primary surveys were used to collect data from participating farmers and from JCLIS operators or managers. The farmer surveys collected data on household demography (social group, age, and education of the head of the household, main occupation, etc.); ownership of land and other durable assets; area allocated to different crops and area left fallow during the Kharif, Rabi, and Summer seasons of crop year 2020-21; area irrigated from different sources in each season and the hourly cost of irrigation; sales channels used to sell fruits and vegetables and prices realized; and challenges inhibiting increases in crop production and incomes. Farmers were also questioned 5
**Table 6: Key Enterprise Characteristics by RegionRICS-AgSS**

||**All**||**Tigray**||**Amhara**||**Oromia**||**SNNP**||
|---|---|---|---|---|---|---|---|---|---|---|
||**Mean**|**Sd**|**Mean**|**Sd**|**Mean**|**Sd**|**Mean**|**Sd**|**Mean**|**Sd**|
|Firm age|6.12|7.35|6.29|8.22|7.35|8.30|5.76|6.95|5.57|6.74|
|Workers|1.40|1.31|1.67|3.31|1.33|1.49|1.36|0.73|1.46|0.92|
|1 employee|0.73|0.45|0.71|0.45|0.77|0.42|0.73|0.45|0.69|0.46|
|2 or 3 employees|0.26|0.44|0.26|0.44|0.22|0.42|0.26|0.44|0.29|0.45|
|4-9 employees|0.01|0.11|0.03|0.16|0.00|0.05|0.01|0.09|0.02|0.14|
|10+ employees|0.00|0.03|0.00|0.06|0.00|0.04|0.00|0.03|0.00|0.00|
|**_Growth_**|||||||||||
|Expanded workforce since startup?|0.08|0.27|0.09|0.28|0.07|0.25|0.07|0.27|0.08|0.27|
|Decreased workforce since startup?|0.03|0.16|0.03|0.17|0.02|0.14|0.02|0.19|0.03|0.15|
|Growth of workforce since startup|0.08|1.00|0.17|1.03|0.06|1.70|0.09|0.54|0.07|0.56|
|**_Seasonality_**|||||||||||
|months|7.82|3.70|8.02|3.57|8.16|3.72|7.96|3.57|7.38|3.80|
|days|14.42|8.09|12.62|8.66|14.75|7.88|13.11|7.97|15.94|7.98|
|seasonality|0.44|0.50|0.39|0.49|0.41|0.49|0.49|0.50|0.42|0.49|
|**_Sales & Profits_**<sup>**(1)**</sup>|||||||||||
|Profits per day (log)|1.72|1.11|2.17|1.19|1.62|1.08|1.80|1.18|1.62|0.99|
|Monthly Sales (log)|5.01|1.39|5.10|1.45|4.76|1.33|4.99|1.38|5.20|1.39|
|Monthly Profits (log)|4.00|1.45|4.48|1.37|3.87|1.44|3.96|1.59|4.04|1.27|
|Annual Profits (log)|5.83|1.71|6.36|1.54|5.73|1.78|5.81|1.84|5.83|1.51|
|Cost share|51.53|29.29|37.29|29.00|47.12|29.03|49.03|30.57|60.06|25.68|
|**_Geography_**|||||||||||
|Distance to all weather road (log)|1.64|1.15|1.60|1.28|1.89|1.18|1.48|1.12|1.64|1.11|
|Distance tonearestmarket (log)|1.70|0.81|1.70|0.95|1.62|0.95|1.92|0.66|1.51|0.75|

(1) The cost share is the share of operating costs as a percentage of total sales. It is the answer to the question “During the most recent year, what percent of total monthly sales were total monthly operating costs?” Profits are computed as sales*(1-share of operating cost as a percentage of total sales)

-27-
<!-- Start of picture text --> PI Benefits (Yuan) PI Replacement Rate (%)<br>2000 2002 2004 2006 2008 2010 2000 2002 2004 2006 2008 2010<br>25000 100<br>20000<br>80<br>15000<br>60<br>10000<br>40<br>5000<br><!-- End of picture text -->

**Figure A.4. Trends in Pension Benefits and Replacement Rates by Province**

Note: PI-Pension Insurance.

Source: NBS, China Labor Statistical Yearbook (2001-2010), China Statistics Press, Beijing.

<!-- Start of picture text --> MI Benefits (Yuan) MI Proportion of Average Wage (%)<br>2000 2002 2004 2006 2008 2010 2000 2002 2004 2006 2008 2010<br>8<br>2500<br>2000<br>6<br>1500<br>4<br>1000<br>2<br>500<br>0 0<br><!-- End of picture text -->

**Figure A.5. Trends in Medical Insurance Benefits by Province: Benefit Levels and Benefits as a Share of Wages** Note: MI-Medical insurance. Source: NBS, China Labor Statistical Yearbook (2001-2010), China Statistics Press, Beijing.

49
computer into a treatment group, which was invited to financial literacy training, and a control group, which was not.

Table 1 displays the baseline characteristics of each of the three samples by treatment status. For each sample an F-test cannot reject joint orthogonality, confirming that we did not get an unlucky draw and that randomization succeeded in achieving balance on baseline characteristics.

We see the three migrant groups differ from one another in a number of important ways. The Pacific Island migrants are younger and less educated than the other groups, with almost half aged under 35 and only 9 percent having a university degree. 39% are male, and just under half have a parent or child in a Pacific Island home country. They are relatively frequent remitters, with 59% remitting at least once every 3 months, with an mean remittance amount of NZ$299 (US$244) and median of NZ$200. The East Asian migrants are older, with only one-third under 35, and more settled, with only one-quarter having immigrated in the past 5 years. 43% are male and 57% have university degrees. They are infrequent remitters, with only 6.5% remitting within the past 3 months, despite 66% having a parent or child remaining in the home country. The few remittances which do occur are for relatively large amounts, with a mean (median) of NZ$4235 (NZ$1000). The Sri Lankan migrants differ in being majority male (73%), and have the highest education and employment rates, with 59% having university education. They are also relatively frequent remitters, with 55% remitting at least every 3 months, and a mean (median) remittance of NZ$1525 (NZ$675), with 75% having a parent or child remaining in Sri Lanka. These differences across groups likely in part reflect the different immigration categories migrants entered through: Pacific Islanders tend to immigrate to New Zealand through family reunification and special concessionary migration quotas (McKenzie et al, 2010), whereas the Asian migrants to New Zealand typically entered through points systems that reward skills and wealth. The Sri Lankan migrants are typically individuals who entered Australia either as students or as a result of civil conflict in Sri Lanka, qualifying under Australia’s points-based migration system.

The baseline survey asked three questions to measure remittance-specific financial literacy, as well as two questions on broader financial literacy related to credit (see Appendix 1).

9
<mark>Prices belonging to the annual agriculture group are defined as follows:</mark>

1. **Cotton** (Cotlook “A” index): Middling 1-3/32 inch, traded in Far East, C/F.

2. **Maize** (U.S.): No. 2, yellow, f.o.b. U.S. Gulf ports.

3. **Rice** (Thailand): 5 percent broken, white rice (WR), milled, indicative price based on weekly surveys of export transactions, government standard, f.o.b. Bangkok.

4. **Soybean meal** : Brazilian pellets 48 percent protein, cif Rotterdam.

5. **Soybean oil** : Dutch crude, degummed, f.o.b. NW Europe.

6. **Wheat** (U.S.): No. 1, hard red winter (HRW), ordinary protein, export price delivered at the U.S. Gulf port for prompt or 30 days shipment.

# <mark>A.6. Perennial agriculture (5 series)</mark>

<mark>This group contains the following five commodities: Cocoa, coffee Arabica, coffee Robusta, natural rubber, and tea. These commodities are produced by trees, often termed tree crops, and therefore cannot be substituted on an annual basis. Thus, from a production perspective they resemble extractive commodities, such as metals. Coffee, consumed in a form of a beverage, comes in two main varieties, Arabica and Robusta—it is the only commodity in this study that includes more than one price indicator. Arabica, grown at high altitudes in Latin America (including Brazil) and northeastern Africa, accounts for two-thirds of total world output. It has a strong aroma and low level of caffeine. Robusta, with a much stronger taste than arabica, is grown in humid areas at low altitudes in Asia, western and southern Africa, and Brazil. Cocoa, a confectionary product mostly going to chocolate making is produced mostly in Africa—Côte d’Ivoire and Ghana account for nearly two-thirds of global supplies. Cocoa is traded at the Intercontinental Exchange (ICE) and on the London International Financial Futures and Options Exchange (LIFFE). Tea, like coffee, is consumed mostly as a beverage. More than 60 percent of global production comes from China and India, but East Africa tea producing countries, especially Kenya, play an important role in the export market (hence, the choice of Mombasa as the world price barometer). Natural Rubber, two thirds of which is produced by Thailand, Indonesia, and Vietnam is mostly consumed by the tire industry. Natural rubber futures contracts are is traded at Singapore Commodity Exchange (SICOM), Tokyo Commodity Exchange (TOCOM), and the Shanghai Commodity Exchange.</mark>

- <mark>Prices belonging to perennial agriculture group are defined as follows:</mark>

1. **Cocoa** (ICCO): International Cocoa Organisation daily price, average of the first three positions on the terminal markets of New York and London, nearest three future trading months.

2. **Coffee Arabica** (ICO): International Coffee Organization indicator price, other mild Arabicas, average New York and Bremen/Hamburg markets, ex-dock.

3. **Coffee Robusta** (ICO): International Coffee Organization indicator price, Robustas, average New York and Le Havre/Marseilles markets, ex-dock.

_— 36 —_
# **Appendix A. Variable definitions and data sources**

|Variable|Description|Sources|
|---|---|---|
|Feeincome|Share of non-interestincomeintotaloperatingincome|Bankscope|
|Feeincome sq|Square of feeincome share|Bankscope|
|Non-deposit funding|Share of non-deposit short-term funding in total deposits & short-term<br>funding.|Bankscope|
|Non-depositfunding sq|Square of non-depositfunding|Bankscope|
|Tradingincome|Share oftradingincomeintotaloperatingincome|Bankscope|
|Non-trading,non-interestincome|Share of non-interest,non-tradingincomeintotaloperatingincome|Bankscope|
|Returnonassets|Pre-taxprofits divided by assets|Bankscope|
|Z-score|Index of bank solvency constructed as<br>_SROA_<br>_CAR_<br>_ROA_+<br>where ROA is<br>return on assets, CAR represents capital assets ratio and SROA stands<br>forstandard deviationof returnonassets.|Bankscope|
|Sharpe ratio|Risk-adjusted rate of return constructed as<br>_SROE_<br>_ROE_where ROE is return<br>onequity and SROE represents standard deviationof returnonequity.|Bankscope|
|Stock return variability|Annualized standard deviation of weekly dividend-inclusive bank stock<br>returns|Bankscope|
|Assets|Log ofassetsin millions ofUS dollars|Bankscope|
|Equity|Lag of ratio ofequity to assets|Bankscope|
|Assets growth|Real growth rate of bank assets computed using CPI|Bankscope and World<br>DevelopmentIndicators (WDI)|
|Overhead|Overhead expenses divided by assets|Bankscope|
|Inflation|Consumerpriceinflation rate|WDI|
|GDPgrowth|Rate of realpercapita GDPgrowth|WDI|
|GDPpercapita|GDPpercapitainthousands of 2000 constant U.S. dollars|WDI|
|Restrict|Indexof regulatoryrestrictions onbankactivities|Barthet al. (2004)|
||Index of legal protection of shareholders, ranging from 0 to 5 with||
|Rights|highervaluesindicatingmore protectionofshareholders|LaPorta et al. (1998)|

55
dummy variable for DTAs with investment provisions ( _INVij,t_ ) with corresponding indicator variables for each of the other seventeen types of provisions from DCRTA. For brevity, in Table 3 we just summarize our main ndings with respect to the DTA provisions.

Table 3: Estimates of the Eects of Alternative DTA Provisions on FDI

|(1)|(2)|(3)|
|---|---|---|
|Provision Description|Estimate|Standard Error|
|Investment|0.295|0.108|
|Labor Market Regulations|0.298|0.103|
|Export Taxes|0.273|0.164|
|Public Procurement|0.542|0.135|
|State-Owned Enterprises|0.364|0.134|
|Movement of Capital|0.182|0.117|
|Environmental Laws|-0.221|0.239|
|Intellectual Property Rights|-0.054|0.124|
|Visa and Asylum|0.121|0.113|
|Rules of Origin|0.116|0.147|
|Services|0.084|0.102|
|Technical Barriers to Trade|0.034|0.109|
|Subsidies|-0.278|0.235|
|Sanitary and Phytosanitary|-0.063|0.122|
|Trade Facilitation and Customs|-0.246|0.187|
|Anti-dumping Duties|.|.|
|Countervailing Duties<br>Competition Policy|.<br>.|.<br>.|

Notes: This table reports estimates of the eects of a series of DTA provisions on FDI over the period 1990-2011. Each row corresponds to a separate econometric model based on the specication from column (3) of Table 2 after replacing the dummy variable for DTAs with investment provisions ( _INVij,t_ ) with corresponding indicator variables for each of the other seventeen types of provisions from DCRTA. The dependent variable is the value of FDI stock. The estimator is PPML. All estimates are obtained with three-year interval data and three-way xed eects, including origin-time, destination-time, directional pair xed eects, and all other control variables from column (3) of Table 2. The estimates of all xed eects and controls are omitted for brevity. The standard errors in all specications are clustered by country pair. See text for further details.

The following results stand out from our analysis. First, due to (near) perfect collinearity with the DTAs dummy, we could not identify the eects of agreements with provisions covering `antidumping duties', `countervailing duties', and `competition policy'. Our indicator for agreements including `competition policy' provisions is perfectly collinear with the DTA dummy, while the correlations between the indicators for agreements with provisions 28
20.8% as mildly unhelpful, 28.6% as neutral, 26% as mildly helpful and 9.6% as very helpful.

Our main explanatory variable, _English_ , is a dummy variable equal to 1 if a country’s legal structure is based on the English common law (18 countries) and 0 otherwise (French civil law, 30 countries). We exclude countries that follow the German, Scandinavian or the Socialist legal tradition and also the transition countries.<sup>1</sup> La Porta et al (2007) note that omitted religious, cultural and political factors constitute the most serious threat to legal traditions. As a remedy, we follow their approach by controlling for the proportion of population in the country that is Catholic, Muslim and Protestant, an index of ethnolinguistic fractionalization ( _ELF_ ) and a dummy variable equal to 1 if the electoral rule for the lower house is a form of proportional representation and 0 otherwise ( _PR_ ). The religious variables and _ELF_ are taken from La Porta et al (1999) and _PR_ from World Bank’s Database for Political Institutions (2004). Other controls include dummy indicators for firm-size (small and large, medium-sized firms is the omitted category) and measures of overall development proxied by (log of) GDP per capita (PPP adjusted, constant 2000 USD) for the year 2000 taken from the World Bank’s World Development Indicators and regional fixed effects (Asia, Africa, Latin America and Caribbean, North America and Western Europe). The robustness section contains a number of additional variables for regulations, firm attributes, etc.

> 1 We restrict the sample to only the fully private firms leaving out partially or fully government owned firms (6.3% of the sample). Keeping these firms in the sample makes virtually no difference to our results. We also experimented with including the transition/socialist countries in the sample by clubbing them with the French legal origin countries and also as a separate category (Socialist legal origin countries). These alternations did not change our main results discussed above much. Relative to French civil law countries, English common law countries showed the government as significantly more helpful (as above) and less helpful for the Socialist countries. These specifications were run without controlling for two variables included in the regressions above: ethnolinguistic fractionalization index (missing for many Socialist countries) and continental fixed effects (most Socialist countries are from one continent, Asia).

3
Policy ReseaRch WoRking PaPeR 4509

# **Abstract**

The authors explore the relation between the establishment of a regulatory agency and the performance of the electricity sector. The authors exploit a unique dataset comprising firm-level information on a representative sample of 220 electric utilities from 51 development and transition countries for the years 1985 to 2005. Their results indicate that regulatory agencies are associated with more efficient firms and with higher social welfare.

This paper—a product of the Economics Unit of the Finance, Economics and Urban Department of the Sustainable Development Network (SDN) of the World Bank—is part of a larger effort in the department to increase the understanding of the ways in which regulatory reforms, including institutional reforms, impact the outcome in regulated infrastructure industries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at aestache@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
Pará’s population is growing much faster than Brazil’s population as a whole. During the last four decades, Pará’s population has more than quadrupled. In 1960, Pará was home to 1.5 million people. In 2000, Pará’s population has increased to 6.2 million (see Figure 2.1). After expanding at 3.5 percent per year between 1980 and 1991, Pará’s population growth fell and its population increased by 2.5 percent per year during 1991-2000. In 2000, the population growth rate in Pará was lower than the North region as a whole (3.4 percent) but still higher than the national population growth rate of 2.0 percent. The rapid reduction in the population growth rate in Pará can be contributed to lower birth rates and a net migration outflow (see below). In Pará, the population growth was not equally distributed across the regions; _Sudeste Paraense_ grew at 3.8 percent annually, while _Baixo Amazonas_ grew at 2.3 percent annually (see also Section 4 on location, population, and poverty).

**-** **<u>Figure 2.1:               Population in Pará, 1980 2002 (in 100,000)</u>**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1980 1991 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> The North of Brazil continues being a net recipient of national migrants. During 19952002, the net inflow to the North region was around 33,000 people. The Center-West and Northeast of Brazil were the two largest sending regions, 16 and 13 percent respectively. However, the picture in Pará is different.

Pará experienced less migration inflow than most other states in Brazil during 19912000. In 2000, Pará moved down in the national ranking of the largest migrant, to a 14<sup>th</sup> place, with around 4 percent of the population having lived less than 10 years in the state. This compares to a much higher figure, close to 10 percent, in 1991.

Pará has become a net-exporter of people during the 1990s. In Pará, the population growth rate is affected by the fact that more people left than arrived during 1991-2000. Pará received around 273,000 inhabitants from other states in Brazil and the vast majority (62 percent) arrived before 1998 (Table 2.3). In 1999, the number of inmigrants fell to slightly below 13,000 people annually. Out-migrants counted 370,000 people during 1991-2000 and the majority (65 percent) left before 1998 (Table 2.4). Hence, in Pará, the migration flows have been dramatically tamed in recent years and it 11