rafmacalaba/datause-encoder-data huggingface dataset:
INSTITUTIONAL ANALYSIS **OF** CREDIT COOPERATMV-0 by

# Avishay Braverman and J. Luis Guasch

Perhaps the most common form of government intervention in the rural sector has been massive lending at subsidized inte-Ost rates. The standard .'ustification has been that credit programs are easier to implement than other policies such as land reform or infrastructure development and are beneficial to agriculture. Without subsidized interest rates, adoption of technical ilnnovation would be delayed aad there would be underusage of costly inputs, like fertilizer, goes the reasoning. Both effects slow the growth of output and the development of the agricultural sector. It has also been argued that since rural credit markets are notoriously imperfect, access to credit is severely lirited for farmers, particularly small farmers; without government intervention a high price of capital would prevail, further screening out the small farmers from the credit markets. Furthermore, because of distorted exchange rates, food price controls, imports of cheap food and inefficient markets, farmers receive low prices for their products, hampering their borrowing abilities. Credit programs generally aim to reach small farmers. However, despite the remarkable expansion of credit throughout the rural areas of developing countries over the last three decades, few farmers in low income countries seem to have received or benefited from such credit. An estimated 5 percent of farms in Africa and about 15 percent in Asia and Latin America have had access to formal credit. Moreover, there seems to be a high correlation between credit recipients and size of land holdings, (see Lipton (1981) and Braverman and Guasch (1986)). The imposition of government interest rate restrictions (credit subsidies) has induced banks
26

# 2. The Impact of Generic Competition<sup>on Drug Prices</sup>

Disentangling the role of patents<sup>on drug prices is a difficult task,</sup> particularly when other regulations--including<sup>price controls--are imposed.</sup> Furthermore, although patents<sup>provide a monopoly, prices will also reflect the</sup> extent of competition among<sup>different patented drugs in given therapeutic</sup> classes. In spite of the difficulties<sup>of analyzing the impact of patents on</sup> drug prices, two patterns tend<sup>to appear quite neatly.</sup> First, generic drug prices are well below brand name<sup>prices; also, an important share of the market</sup> is taken by generic drugs. Second, the price of drugs<sup>appears to be</sup> systematically lower in countries<sup>that do not provide patent protection for</sup> pharmaceuticals.

# 2.1. The impact of generics<sup>on drug prices</sup>

It has often been noticed that<sup>generic drugs enter the market at high</sup> discounts vis a vis the brand<sup>names. The extent of such price differentials is</sup> best studied in markets with<sup>free prices. In this regard, the U.S. is generally</sup> believed to have one of the<sup>freest pharmaceutical drug market in the world.17</sup> For example, Scherer (1980,<sup>p. 390) reports that for many years Pfizer sold the</sup> antibiotic Tetracycline at US$30.60<sup>per 100 capsules. When Pfizer's patent was</sup> challenged, competing firms<sup>sold the generic product at US$2.50 per bottle.</sup> Furthermore, Scherer asserts that<sup>many</sup> " ..similar cases of price cost-margins in the order of 90% for patented<sup>drug products have been identified..."</sup> 17 This does not mean that free<sup>pricing is always allowed. See, for</sup> example, a discussion of the<sup>debate on this issue published in The</sup> Washington Post of June 11, 1989<sup>(p. Hl) and the recent control on</sup> the AIDS drug AZT, by which the<sup>government brought down the annual</sup> cost of treatment from US$8,000<sup>to US$6,000.</sup>
<!-- Start of picture text --> foe) f<br>/<br>/<br>/<br>/<br>©/<br>/<br>/<br>/<br>+/<br>/<br>LO TTS Saas<br>ss<br>Co<br>="<br>Po) I<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>year<br>————_ Rubber and plastics —-—-—-—- Motor vehicles<br>——-——- All other industries<br><!-- End of picture text -->
Policy Research Working Paper 10589

# **Abstract**

The mobility restrictions and health measures imposed during the COVID-19 pandemic have had highly adverse impacts on small-scale cross-border trade. One coping strategy that traders have pursued is to engage in group trade, that is to combine their loads and cross the border using a larger cart or vehicle. This paper uses a cross-sectional data set derived from a survey of traders at the borders between the Democratic Republic of Congo and Burundi and Rwanda to assess the determinants of participation in group trade. The findings from the econometric analysis point to association membership, business registration, and motorized transport as being important factors for traders’ participation in new cooperative trade arrangements. Moreover, successful group traders have been in a position to increase their incomes by reaching new clients and obtaining higher prices. These results suggest that policy efforts to promote group trade could usefully focus on enhancing the integration of small-scale traders into regional supply chains. However, group trade has mainly benefitted the better-off segments of the trader population, so that any assistance projects to enhance group trade risk further increasing the income gap in border communities.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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 at jkeyser@worldbank.org, ckunaka@worldbank.org, and pwalkenhorst@aup.edu.

_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
Policy ReseaRch WoRking PaPeR 4880

# **Abstract**

Household surveys have long been used to estimate poverty and inequality trends, as well as trends in education and health indicators, but they have not been used to the same extent to assess trends in the access to or coverage of modern infrastructure services. In this paper, we use Demographic and Health Surveys from a larger sample of sub-Saharan African countries in order to collect comparable information across countries on coverage of piped water, flush toilets, electricity, and landline telephones over time. The results suggest that coverage rates for electricity, flush toilets have improved slightly over the last decade. Coverage of piped water has declined, at the same time as coverage of landline (as well as cellular) telephone has increased rapidly. The decline has been primarily in the urban areas while the infrastructure coverage has either increased or remained stable in rural Africa. For all four services, among the poorest households coverage remains virtually inexistent. If business as usual continues, it would take a very long time to reach universal or widely shared coverage even in countries where coverage has improved. These results point to the need to increase efforts by governments and international community to progressively increase access to modern infrastructure services in Africa.

This paper—a product of the Development Dialogue on Values and Ethics in the Human Development Network and of the Sustainable Development Department in the Africa Region Vice Presidency—is part of a larger effort in the Network and Region to document the access to, and affordability of basic infrastructure services. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The corresponding author may be contacted at sbanerjee@ 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
# **III. The Chile Data**

The data used in this study come from two sources – the 2004 Chile Investment Climate Survey (ICS) and the _Encuesta Nacional Industrial Annual_ (Annual Industrial Survey) or ENIA, both fielded by the national statistical office INE:

1. The 2004 Chile ICS was commissioned by the World Bank and contains about 1,000 enterprises in five regions and nine sectors, six of which are in the manufacturing sector. In common with other World Bank investment climate surveys, the Chile ICS elicited firms’ perceptions about a wide range of business environment constraints, as well as detailed quantitative information on firm attributes, technology, training, workforce characteristics, wages, and production or sales over the past three years. In addition, it contained a small module of questions on their familiarity with and participation in different government-sponsored SME support programs, and critically the dates of participation in each of the programs used.

2. The ENIA contains information typical of industrial surveys, including firm characteristics (ownership, geographic location, and sector) and quantitative variables such as production inputs and outputs, exports, sub-contracting, fixed assets, employment, wages and other financial data. A panel covering the years 1992-2002 years was provided to the World Bank by INE and was updated to include the years between 2003 and 2006.<sup>4</sup> These panel data allow us to track changes over time in different indicators of firm performance and to estimate the effects of program participation on long-term outcomes such as sales, employment and productivity growth.

Both data sets were linked using a cross-walk variable provided by INE to the World Bank. Responses to the ICS program module were used to identify the treatment and control groups and, from dates of program participation, the pre- and post-program periods in the linked ICS-ENIA panel.

# **A. SME Program Participation**

The Chile ICS contains a sample of 948 enterprises randomly sampled from five regions and nine sectors. Excluding respondents from the information technology (IT) services, biotechnology and aquaculture sectors<sup>5</sup> resulted in a sample size of 603 establishments from six sectors – food and beverages, chemicals, metal products (excluding machinery), machinery and equipment, wood products and paper products.

The 2004 Chile ICS included a SME program module of questions in which firm respondents were asked about seven different CORFO matching grants and credit

> 4 The original 1992-2004 ENIA was updated using a recently available public-use ENIA panel data set covering the period between 1998 and 2006. Establishments in the two panels were matched on the basis of sector, year and production values in the overlapping 1998-2002 period.

> 5 These sectors cannot be linked to the ENIA which only covers the manufacturing sector.

9
**Table 1 Maximum coverage on expenditure with a matching outcome, 1990–2002**

||Generalgo|vernment|Centralgo|vernment|
|---|---|---|---|---|
||Number of<br>observations|Number of<br>countries|Number of<br>observations|Number of<br>countries|
|Transport|50|21|606|84|
|Energy andfuel|35|21|511|71|
|Health|18|3|147|61|
|Education|9|9|142|45|
||Memoitems:||||
|Energy+ Transport = Infrastructure|24|12|75|13|
|Health + Education =Social|2|2|49|36|
|Social + infrastructure|0|0|5|5|

Source: IMF Government Finance Statistics database.

Looking at table 1, we reached the initial conclusion that we would be better off assessing the partial performance of governments in each subsector for as many of the years for which we have information.<sup>15</sup> For instance, given the outcome data we have, efforts to cover energy and transport in the same country for the same years would limit our sample size to 13 countries. For health and education, the sample size would be limited to 36 countries. Looking at sectors one by one allows us to work with at least 45 countries (education) and up to 84 countries (transport). The specific countries covered for each sector are reported in the appendix. With this approach, there is enough data to generate the “big picture” country groupings (see table 2).

**Table 2 Number of observations available on central government expenditure per sector and per country grouping for the 1990–2002 period**

|Country income<br>category|Transport|Energy and fuel|Health|Education|
|---|---|---|---|---|
|High-income|225|216|244|244|
|Low-income|179|159|142|142|
|Lower-middle-income|198|199|214|214|
|Upper-middle-income|167|162|208|208|
|Total|769|736|808|808|

The problems with the data have by now been well established. Still, we should report additional issues that emerged during our data-cleaning exercise. First, on the outcome side,

> 15 Another way to increase the sample size would be to look at blocks of years or to work with moving averages, recognizing that data are not available for every year. Another option could be to do some more sophisticated smoothing, involving Kalman filters or kernel estimators, but this would be a major project.

7
<!-- Start of picture text --> oo<br>=<br>fa)<br>io}xo)<br>=<br>Pana<br>c<br>a2Bba<br>—TM<br>~<br>z<br>2<br>ao<br>y<br>a,3<br>+<br>t-1 t t+ +2 t+3 t4 t+5 +6 +7 t8 t+9<br>Horizon, h (years)<br><!-- End of picture text --> <!-- Start of picture text --> fa<br>fa)<br>xio}o)<br>es wn<br>Prann<br>c<br>a2pTa<br>E<br>~<br>goin<br>2<br>a<br>eo?<br>a,3<br>"<br>t-1 t t+1 +2 t+3 te t+5 +6 +7 t8 t+9<br>Horizon, h (years)<br><!-- End of picture text -->
# **Abstract**

From its inception, the Penn World Tables (PWT), building on the International Comparisons Program (ICP) of the United Nations, has sought to compare the _standard of living_ of individuals in different countries. That is, the term “real GDP per capita” as reported in the PWT is intended to represent the ability to purchase goods and services by a representative agent in the economy. The same is true of benchmark comparisons as published by the United Nations, Eurostat or OECD. But this expenditure side interpretation of real GDP is quite different from the uses to which benchmark ICP and PWT data are frequently applied, such as in growth regressions where “real GDP” is intended to reflect the production side of the economy.

In this paper we propose a new approach to international comparisons of real GDP measured from the output side. We modify the traditional Gary-Khamis system, which measures real GDP from the expenditure side using real domestic expenditure, to include differences in the terms of trade between countries. The analysis shows that this system has a strictly positive solution under mild assumptions. On the basis of a set of domestic final output, import, and export prices and values for 151 countries in 1996, differences between real GDP measured from the expenditure and output side can be substantial, especially for small open economies.

2
|**Australia**|11.4|11.5|10.8|**Chile**||–|21.8|18.4|
|---|---|---|---|---|---|---|---|---|
|**Denmark**|6.2|11.0|10.8|**Spain**||15.9|24.3|19.1|
|**Austria**|12.0|10.3|11.1|**Mexico**||23.2|24.0|20.9|
|**Latvia**|14.4|19.6|11.2|**Greece**||16.7|21.6|21.5|
|**Portugal**|12.4|15.3|11.6|**Colombia**||–|20.1|22.7|
|**Ireland**|10.4|21.9|11.7|**Costa Rica**||–|20.1|23.1|
|**Canada**|12.0|13.4|11.9|**Italy**||20.1|23.2|23.9|
|**Finland**|10.4|11.8|11.9|**Brazil**||–|19.3|24.9|
|**United Kingdom**|15.1|15.5|12.6|**Turkey**||42.6|34.6|26.5|
|_Source:_<br>OECD<br>Family<br>http://www.oecd.org/els/fami<br>_Notes:_For 2011, data for Swi<br>data for Japan refer to 2014, a|Database,<br>ly/database.h<br>tzerland refe<br>nd for Chile|Indicator<br>tm.<br>r to 2009, fo<br>to 2017. The|CO3.5<br> <br>r Lithuania t<br>OECD ave|“Young<br>people<br>o 2010, and for Co<br>rage excludes Chil|not<br>lombia<br>e and|in<br>education<br>and Costa Rica to<br>Korea.|or<br>empl<br>2013. For|oyment”,<br>2018,|

As shown in Figure 1, NEET rates are generally higher for young people in their 20s than for those in their teens. In the OECD countries, on average about 18 percent of those aged between 25 and 29 years old, compared to less than 6 percent of 15-19 year-olds, are NEET. This difference may be the result of the expansion of upper secondary education in many OECD countries. Furthermore, as the OECD reports, NEETs are less likely to live with their parents than non-NEETs. About 50 percent of NEETs, compared to about 75 percent of non-NEETs, live with their parents.

Caring for children and/or living with a partner can also make a substantial difference. About 26 percent of NEETs, but only 9 percent of non-NEETs, live with a partner and one or more children— an almost 3:1 ratio. The reason is likely that parenthood compels young people to devote their time and energy to childcare rather than education or working outside the home. Parenthood _with no partner_ makes even more of a difference. Single (non-partnered) young women account for just 1 percent of non-NEETs, compared to 5 percent of NEETs—a 5:1 ratio versus 3:1. Caring for children, especially all by oneself, often forces a young person to stay at home instead of attending school or working outside (OECD, 2016).

5
**Table 2 ‐ COMPARISON GROUP**

|||GDPPCGR|Pub<br>spending<br>/GDP|Fiscal<br>balance/<br>GDP|Pro/TEXP|Soc/TEXP|Eco/TEXP|Edu/TEXP|Hea/TEXP|TRA/TEXP|CORE/TEXP|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Chile|1970‐79|0.86|33.20|‐1.85|58.77|53.82|15.15|14.24|7.41|4.75|46.66|
|Chile|1980‐89|2.72|28.29|‐0.51|50.04|62.77|9.21|13.41|7.23|3.29|38.35|
|Chile|1990‐99|4.66|19.79|1.21||65.68||14.62|11.47|||
|Chile|2000‐05|3.09|14.73|0.02||70.94||18.02|12.54|||
|Costa Rica|1970‐79|3.70|20.18|‐2.75|67.87|62.75|19.89|28.00|9.32|14.51|64.97|
|Costa Rica|1980‐89|‐0.46|20.01|‐2.02|70.40|64.19|15.99|20.14|25.26|9.67|68.09|
|Costa Rica|1990‐99|2.90|21.37|‐1.62|54.62|63.12|10.25|19.18|23.20|4.84|54.96|
|Costa Rica|2000‐05|1.69|22.91|‐1.26|53.15|67.04|10.69|20.98|22.03|6.10|53.15|
|Mexico|1970‐79|3.33|14.53|‐2.48|41.47|45.88|33.44|17.92|4.28|9.50|40.39|
|Mexico|1980‐89|0.12|23.21|‐7.85|31.65|28.36|22.48|12.97|1.58|5.03|32.48|
|Mexico|1990‐99|1.66|15.44|‐0.69|43.54|50.38|14.55|23.37|3.25|4.23|41.06|
|Mexico|2000‐05|1.49|15.93|‐1.17|48.53|57.28|8.11|24.73|4.95|2.32|46.87|
|Philippines|1970‐79|2.90|13.86|0.20|70.39|24.07|43.28|14.53|4.42|17.15|56.13|
|Philippines|1980‐89|‐0.45|13.70|‐0.42|73.91|27.06|45.55|17.23|5.01|21.18|67.05|
|Philippines|1990‐99|0.53|18.98|‐0.91|50.69|25.36|22.85|17.57|3.17|11.08|43.89|
|Philippines|2000‐05|2.57|19.35|‐3.92|38.94|25.23|14.86|17.95|2.10|8.54|34.28|
|Turkey|1970‐79|2.28|18.64|‐2.02|66.64|27.14|43.23|19.50|2.98|15.46|62.65|
|Turkey|1980‐89|1.70|17.44|‐3.35|71.98|19.67|26.07|13.10|2.25|8.20|65.39|
|Turkey|1990‐99|2.03|18.35|‐4.59|59.12|24.91|14.94|14.49|3.28|5.11|52.87|
|Turkey|2000‐05|3.60|33.14|‐11.18|26.38|21.53|7.98|8.78|3.24|2.32|20.50|
|Uruguay|1970‐79|2.33|23.18|‐1.73|45.95|61.96|10.18|10.64|4.19|5.20|37.30|
|Uruguay|1980‐89|0.07|25.23|‐2.46|38.62|62.79|9.08|7.34|4.14|6.29|27.93|
|Uruguay|1990‐99|2.60|28.89|‐0.93|28.51|72.45|7.42|6.91|5.49|4.45|22.84|
|Uruguay|2000‐05|0.71|31.41|‐4.05|26.06|74.73|5.56|7.33|6.17|2.89|22.68|
|Venezuela|1970‐79|0.45|19.44|4.26|54.77|36.55|28.94|16.47|9.99|9.75|51.24|
|Venezuela|1980‐89|‐2.89|21.93|1.94|49.30|39.61|21.47|18.55|8.62|6.19|45.92|
|Venezuela|1990‐99|0.30|20.21|0.11|41.52|41.80|4.72|17.03|7.05|2.38|35.95|
|Venezuela|2000‐05|1.35|24.07|‐2.62|49.34|46.74|6.98|21.22|6.65|2.69|44.43|
|**AVERAGE**||**1.64**|**21.34**|**‐1.88**|**50.47**|**47.28**|**18.19**|**16.29**|**7.55**|**7.43**|**45.31**|

Note: GDPPCGR is the growth rate of GDP per capita in real terms. Pub spending/GDP is the ratio of total public spending in %. Fiscal balance is the ratio of fiscal balance to GDP in %. Pro/TEXP is public productive expenditure in total public expenditure in %. Soc/TEXP is public social expenditure in total public expenditure in %. Eco/TEXP is public economic expenditure in total public expenditure in %. Edu/TEXP is public education expenditure in total public expenditure in %. Hea/TEXP is public health expenditure in total public expenditure in %. Tra/TEXP is public transportation expenditure in total public expenditure in %. CORE/EXP is the core spending in percent of total expenditure.

33
# **Table 4: Prediction models of the number of deposit accounts in commercial banks**

This table shows the estimates of OLS regressions of the number of deposit accounts in commercial banks per thousand adults on several covariates. Each column represents a different regression. The dependent variable in each regression is ln(Number of accounts in commercial banks per 1,000 adults).

|**2008**|Model<br>(1)|Model<br>(2)|Model<br>(3)|Model<br>(4)|Model<br>(5)|Model<br>(6)|
|---|---|---|---|---|---|---|
|ln(GDP/capita)|0.540***<br>(0.117)|0.696***<br>(0.101)|0.530***<br>(0.114)|0.687***<br>(0.101)|0.864***<br>(0.085)||
|High Income dummy|-1.035***<br>(0.319)|-1.167***<br>(0.326)|-1.026***<br>(0.323)|-1.153***<br>(0.330)|-1.170***<br>(0.328)|1.225***<br>(0.220)|
|ln(Polation Densit)|0.095|0.110|0.117|0.131|0.212**|0.110|
|pu y|(0.081)|(0.101)|(0.074)|(0.093)|(0.099)|(0.112)|
|ln(Private Cradit/GDP)|0.262<br>(0.172)|0.367**<br>(0.165)|0.327**<br>(0.142)|0.428***<br>(0.152)|||
|ln(Branches/100,000 Adults)|0.324**<br>(0.145)||0.333**<br>(0.143)||||
|ln(Value Deposits/GDP)|0.149<br>(0.159)|0.134<br>(0.157)|||||
|Constant|1.330<br>(1.034)|0.841<br>(1.224)|1.248<br>(1.006)|0.795<br>(1.189)|-1.415<br>(0.951)|5.572***<br>(0.525)|
|Observations|77|78|78|79|83|85|
|R-squared|0.729|0.695|0.725|0.689|0.672|0.172|
|AdjustedR-squared|0.706|0.674|0.706|0.672|0.659|0.152|
|Robust standard errors in parentheses<br>***p<0.01,**p<0.05,*p<0.1|||||||

|**2009**|Model<br>(1)|Model<br>(2)|Model<br>(3)|Model<br>(4)|Model<br>(5)|Model<br>(6)|
|---|---|---|---|---|---|---|
|ln(GDP/capita)|0.568***<br>(0.119)|0.668***<br>(0.099)|0.570***<br>(0.113)|0.709***<br>(0.090)|0.815***<br>(0.073)||
|High Income dummy|-0.786***<br>(0.276)|-0.878***<br>(0.256)|-0.801***<br>(0.272)|-0.977***<br>(0.255)|-0.874***<br>(0.274)|1.412***<br>(0.209)|
|ln(Poulation Densit)|0.092|0.088|0.132*|0.144|0.206**|0.095|
|p y|(0.077)|(0.092)|(0.072)|(0.088)|(0.090)|(0.101)|
|l(Pit Cdit/GDP)|0.116|0.194|0.212|0.317**|||
|nrvae ra|(0.208)|(0.185)|(0.142)|(0.140)|||
|ln(Branches/100,000 Adults)|0.225<br>(0.155)||0.259*<br>(0.150)||||
|ln(Value Deposits/GDP)|0.231<br>(0.206)|0.228<br>(0.195)|||||
|Ctt|1.268|1.055|0.872|0.366|-1.118|5.638***|
|onsan|(1.064)|(1.145)|(1.031)|(1.105)|(0.826)|(0.462)|
|Observations|83|83|89|91|98|100|
|R-squared|0.701|0.687|0.721|0.718|0.681|0.204|
|AdjustedR-squared|0.677|0.667|0.704|0.705|0.670|0.188|
|Robust standard errors in parentheses<br>***p<0.01,**p<0.05,*p<0.1|||||||

24