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

|**Title**|**Author**|Date|Contact<br>**for paper**|
|---|---|---|---|
|WPS1952Enterprise Isolation Programs in<br>Transition Economies|Simeon Djankov|August 1998|R. Vo<br>33722|
|WPS1953TradePolicies and Incentives in<br>indian Aariculture: Methodology,<br>Background Statistics and Protection<br>and Incentive Indicators, 1965-95--<br>Backgrounc Paper 1, Sugar and<br>Sugarcane|Garry Pursell<br>,|August 1998|L. Tabada<br>36896|
|WPS1954 Politicians and Firms in Seven<br>Central and Eastern European<br>Countries|Stijn Claessens<br>Simeon Djankov|August 1998|R. Vo<br>33722|
|WPS1955 Appraising 'Norkfare Programs|Martin Ravallion|August 1998|P. Sader<br>33902|
|WPS1 956 Benefit Incidence and the Timing<br>of Program Capture|Peter Lanjouw<br>Martin Ravallion|August 1998|P. Sader<br>33902|
|WPS1957 Bidding for Concessions|Michael Klein|August 1998|J. Dytang<br>37161|
economies; and an indicator for whether a state’s per capita GDP is above the median per capita GDP across all states. All characteristics are measured for the initial year 1981 and thus are time invariant.

# GSDP Growth�� �β� �β�Year� �β�Characterstic� x Year� �ε��      �equation 3�

The coefficient β� measures the differential growth acceleration between states with and without a given characteristic. Table 2 below highlights that India’s growth acceleration is reflected in the growth of an average state. On average, state level growth accelerated by 0.09 percentage points per year between 1981 and 2016, column 1. Our analysis does not detect differences in growth acceleration across agricultural and non-agricultural states, and across income levels.

We further examine whether the growth stabilization documented at the national level is visible at the state-level, by estimating regressions similar to above, but replacing the outcome variable with a 10-year rolling coefficient of variation for each state.<sup>13</sup> The last two columns in table 3 confirm that the variability in state-level growth has consistently declined over time, and that there is no statistically significant difference in the pace of increased growth stabilization between richer and poorer states.

**Table 2: Trend in the pace of long term growth at the state level**

|VARIABLES|(1)<br>Growth<br>Rate|(2)<br>Growth<br>Rate|(3)<br>Growth<br>Rate|(4)<br>Coefficie<br>nt of<br>Variation|(5)<br>Coefficie<br>nt of<br>Variation|
|---|---|---|---|---|---|
|Trend|0.089***|0.099**|0.089***|-2.96***|-3.56***|
||(9.27)|(2.47)|(5.99)|(4.53)|(3.22)|
|Ag. Share (1981) X Trend||-0.0002<br>(0.251)||||
|GDP per capita > Median x||||||
|Trend|||-0.0012<br>(0.064)||1.28<br>(1.03)|
|State Fixed Effects|Yes|Yes|Yes|Yes|Yes|
|Observations|734|734|734|567|567|
|R-squared|0.062|0.063|0.063|0.628|0.636|

Note: In column 3 we allow the linear trend coefficient to vary between richer and poorer states, with richer states defined by an indicator variable that takes the value one if a state has above median per capita GSDP in the initial year 1981.

> 13 The coefficient of variation is calculated as the standard deviation of GSDP growth over rolling 10-year periods (the year of observation and the nine years preceding it), divided by the sample average of GSDP growth over the same period.

9
population was not able to be fully integrated / re-integrated into the society. The poverty rate stayed around 11%.<sup>3</sup> There exist large disparities across region / counties and individual groups. Other things being equal, the individuals living in the Central and Eastern regions suffered disproportionately. In 2003, the GDP per capita of the richest county, City of Zagreb, was about three times that of the poorest county, county of Slavonski Brod-Posavina.<sup>4</sup> The employment rate varies among the 21 counties, from less than 43% (County of VukovarSirmium) to almost 70% (County of Krapina-Zagorje). Within many counties, the labor market performance differs at a more disaggregate level – for example, the county of Lika-Senj, the problem of unemployment is much more serious in the inland area than in the coastal area. Youth, the less educated, and women face more difficulties in the labor market. The unemployment rate of youth (15-25 years old) was three times that of the other working-age groups. The earnings level of the individuals with tertiary education is at least twice that of the individuals with basic education or less. Women face harder time in securing a job and in having a good salary compared with man...

# **1.1 Limited change in labor market performance at the national level**

The Croatian labor market performance is lagging behind many EU countries. The employment rate in Croatia<sup>5</sup> , which is about 55%, is 20% below the Lisbon target, which is 70% (graph 1). The average employment rate of the EU 25 countries in 2002-2004 is about 63%, and that of the EU 15 countries is 64.5%.<sup>6</sup>

**Graph 1: Total employment rate of EU countries (2002-2004)**

<!-- Start of picture text --> 90<br>80<br>70 Lisbon Agenda<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2002 2003 2004<br>IcelandNorwayDenmarkNetherlandsSwedenUnited StatesUnited KingdomPortugalAustriaCyprusJapanFinlandIrelandGermanyCzech RepublicEU (15 countries)SloveniaLuxembourgEU (25 countries)FranceEuro-zone (12 countries)Euro-zone EstoniaLatviaLithuaniaBelgiumSpainRomaniaGreeceSlovakiaHungary ItalyMaltaCroatiaPolandBulgariaTurkey<br><!-- End of picture text -->

> 3 The poverty rate is subject to the poverty line of yearly consumption of 22145 kuna per equivalent adult. See Nestic (2006) for further discussion.

> 4 Data source: Lovrincevic and Mikulic (2006)

> 5 Based on the LFS questionnaires, employment is defined as the working-age population who worked for at least an hour in the reference week or had a job to return to if they did not work in the reference week. 6 Data source: Eurostat statistics. Website:

http://epp.eurostat.cec.eu.int/portal/page?_pageid=1996,39140985&_dad=portal&_schema=PORTAL&scr een=detailref&language=en&product=sdi_ed&root=sdi_ed/sdi_ed/sdi_ed_emp/sdi_ed1400 3
lender would provide the money to the borrower only after the borrower identified the asset and retailer they planned to buy it from.

# _Experimental Details_

In collaboration with Tamweelcom, the research team hired and trained new marketing specialists to market the two loan products to households and individuals in the six largest cities in Jordan: Amman, Irbid, Zarqa, Aqaba, Madaba, and Al-Salt. The marketers used a tablet programmed with SurveyCTO software which would randomly select and display one of eight different marketing and pricing pitches (exact scripts can be found in the Appendix). The marketers were trained to use the tablet, and were provided with a detailed orientation session about the eight different pitches. They would then go to different markets and residential areas and ask individuals if they were interested in hearing about a loan product<sup>12</sup> . If individuals said “no”, marketers moved on and the individual was not included in our sample. If the individual said “yes”, marketers asked a few basic demographic questions (e.g. age, education, marital status, etc.) and read the randomly assigned marketing pitch. This sampling strategy could lead us to underestimate the true demand for sharia-compliant loans if there is a group of “sharia-compliant only” borrowers who say they are not interested in hearing about the loan because they assume that we will offer them a conventional loan (since sharia-compliant loans are rare in this context).

The eight different marketing and pricing pitches constitute the different treatment arms in our experiment. In the first pitch, which serves as the control group, individuals were offered a conventional loan to finance household asset purchases from “the Jordan Microfinance Company” the legal name of Tamweelcom, and a known brand in the market. This conventional loan is an actual product that was being offered by Tamweelcom at the time and was closest in practice to the sharia-compliant loans that Tamweelcom’s new subsidiary was going to be providing. They were told that the loan carried a monthly interest rate of 1.9 percent, a maximum loan term of 20 months and could range from 300-1500 JDs (423 – 2,115 USD). It was an individual liability loan with no grace period. They were then asked if they were willing to fill out a preliminary application for the loan with the marketer. If they said “no” they were asked why not; if they said “yes” they were then asked several more questions that are part of Tamweelcom’s standard

> 12 Both conventional loans and sharia compliant loans are referred to in Arabic as “qard”, allowing this questions to

> be sufficiently generic for our purposes.

11
# **5 Model**

Our deconstruction of the missing middle led us to interpret the evidence as suggestive of distortions that encourage informality and discourage formal firm growth. Entry barriers and productivitydependent idiosyncratic distortions have the potential of generating such outcomes. While the formal sector in the low-income countries under study exhibited a notable dynamism when compared with its informal counterpart, it still fell short of the dynamism of firms in advanced economies. This leaves a role to be played by productivity-dependent distortions, which discourage firm growth and misallocate resources across firms. Entry barriers, in turn, limit the incentives of firms to enter the formal sector and magnify the effect of idiosyncratic distortions.

We investigate this hypothesis formally in this section by feeding entry barriers and idiosyncratic distortions into a model of firm dynamics with endogenous informality. Our goal is to assess the extent to which these distortions can account for the prevalence of informality and the patterns of the size distribution we observed in the data and to quantify their aggregate implications. The model features a rich formal sector, with endogenous entry and exit of firms and endogenous innovation decisions, all responsive to the incentives created by the distortions. The informal sector also features endogenous entry but we assume that firm growth and the exit rate are exogenous so that the size distribution is governed by the rate of productivity growth over the firms’ life cycle vis-a-vis the exit rate. We assume that formal and informal firms produce differentiated varieties that aggregate into differentiated baskets of formal and informal goods. Consumers face a constant elasticity of substitution across varieties within a type of good and across formal and informal goods. We calibrate the model to the U.S. and estimate entry barriers and idiosyncratic distortions in Ghana, the only country in our sample for which we also count with a companion formal sector census of

## **5.1 Demand Side**

Consumers derive utility from baskets of formal and informal goods, which they combine through a Constant Elasticity of Substitution demand system. Formally: where _Q_ is the final good and _Q_ 1 and _Q_ 2 are formal and informal good baskets that go into the production of the final good.<sup>16</sup> In our calibration, parameters will be set so that the equilibrium expenditure and employment share in the informal sector are low, targeting values of the informal sector share in the U.S. economy. When distortions are identified and fed into the model, the elasticity of substitution _λ_ will be a key determinant of the strength of the response of the informal sector to the distortions.

Formal and informal good baskets, in turn, are produced according to a Constant Elasticity of

> 16 One could have directly defined _Q_ to be aggregate consumption, where the aggregation is done at the level of the household. This is isomorphic to our specification of _Q_ as a good produced by a final good aggregator.

28
# IVi INTRODUCTION

Table 13 Population and Percentage Distribution by World Bank RegiQn and Country Department, 1985-2100

|Region<br>and||Po|pulati|on<br>(mill|ions)||Pe|rcent|age<br>of w|orLd<br>p|<br>opuL|ation|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|country<br>department|1985|1990|2000|<br>2025|2050|<br>2100|1985|1990|2000|2025|2050|2100|
|World|4,844|5,285|6,204|8,415|10,035|11,330|100.0|100.0|100.0|100.0|100.0|100.0|
|Borrowers and non-borrowers|||||||||||||
|Sub-Saharan Africa|457|<br>534|<br>720|1,359|1,986|2,634|9.4|10.1|11.6|16.2|19.8|23.2|
|Asia and Oceania|2,574|2,808|3,270|4,186|4,715|5,001|53.1|53.1|52.7|49.7|47.0|44.1|
|Europe,<br>Middle<br>East,|||||||||||||
|and North<br>Africa|1,147|1,223|1,38|8 1,831|2,187|2,502|23.7|23.1|22.4|21.8|21.8|22.1|
|America|667|<br>721|<br>826|1,040|1,146|1,192|13.8|13.6|13.3|12.4|11.4|10.5|
|Borrowers<br>only|||||||||||||
|Africa<br>Region|415|<br>486|<br>660|1,263|1,858|2,480|<br>8.6|9.2|10.6|15.0|18.5|21.9|
|1 Occidental<br>and Central|<br>40|<br>47|<br>63|<br>123|<br>185|<br>250|<br>0.8|0.9|1.0|1.5|1.8|2.2|
|2 Eastern<br>|106|<br>124|<br>169|<br>328|<br>486|<br>654|<br>2.2|2.3|2.7|3.9|4.8|5.8|
|3South-Central and|||||||||||||
|Indian<br>Ocean|52|<br>61|<br>84|<br>163|<br>245|<br>333|1.1|1.2|1.3|1.9|2.4|2.9|
|4 Western|119|<br>139|<br>187|341|<br>472|<br>592|<br>2.5|2.6|3.0|4.0|4.7|5.2|
|5 Sahetian|36|<br>42|<br>56|<br>111|<br>175|<br>252|<br>0.7|0.8|0.9|1.3|1.7|2.2|
|6Southern|62|<br>73|<br>101|<br>197|<br>295|<br>400|<br>1.3|1.4|1.6|2.3|2.9|3.5|
|Asia Region|2,384|2,608|3,055|3,952|4,483|4,771|49.2|49.4|49.2|47.0|44.7|42.1|
|1 Other South|134|<br>152|<br>190|<br>282|<br>351|<br>401|<br>2.8|2.9|3.1|3.3|3.5|3.5|
|2 Southeast|274|<br>304|<br>364|<br>488|<br>568|<br>609|<br>5.7|5.8|5.9|5.8|5.7|5.4|
|3 China|1,040|1,117|1,269|1,530|1,626|1,672|21.5|21.1|20.5|18.2|16.2|14.8|
|4 India|765|<br>848|1,011|1,361|1,602|1,732|15.8|16.0|16.3|16.2|16.0|15.3|
|5 Indonesia<br>and PaCific|170|<br>187|<br>221|<br>291|<br>335|<br>356|<br>3.5|3.5|3.6|3.5|3.3|3.1|
|EMN Region|474|<br>536|<br>678|1,090|1,451|1,766|<br>9.8|10.1|10.9|13.0|14.5|15.6|
|1 Pakistan<br>and Turkey|*146|170|<br>223|<br>375|<br>502|<br>607|<br>3.0|3.2|3.6|4.5|5.0|5.4|
|2 North<br>Africa|55|<br>63|<br>83|<br>131|<br>170|<br>197|<br>1.1|1.2|1.3|1.6|1.7|1.7|
|3 Middle<br>East|168|<br>195|<br>260|<br>464|<br>657|<br>839|<br>3.5|3.7|4.2|5.5|6.5|7.4|
|4 Europe|105|<br>107|<br>111|<br>119|<br>121|<br>123|<br>2.2|2.0|1.8|1.4|1.2|1.1|
|LAC Region|387|<br>429|<br>512|<br>689|<br>800|<br>850|<br>8.0|8.1|8.3|8.2|8.0|7.5|
|1 Brazil|136|<br>150|<br>178|<br>233|<br>265|<br>278|<br>2.8|2.8|2.9|2.8|2.6|2.4|
|2 Central<br>America|104|<br>116|<br>141|<br>199|<br>237|<br>254|<br>2.1|2.2|2.3|2.4|2.4|2.2|
|3 Caribbean<br>and Other|70|<br>78|<br>94|<br>128|<br>150|<br>162|<br>1.5|1.5|1.5|1.5|1.5|1.4|
|4 Temperate<br>and Tropical|<br>78|<br>85|<br>99|<br>129|<br>148|<br>156|<br>1.6|1.6|1.6|1.5|1.5|1.4|

> Note: World Bank regions and country departments are defined in Exhibit r.
# AbbrevlatPons and Acrotms

|AF2PH|East Africa Department, Population and Human Resources Operations<br>Division<br>|
|---|---|
|AFIPN<br>|Africa lechnical Department, Population, Health and Nutrition Division<br>|
|AFISP<br>|Africa lIchnical Department, Poverty and Social Policy Division<br>|
|AIDS<br>|Acquired Immune Deficiency Syndrome<br>|
|ARIS<br>|Annual Review of Implementation and Supervision<br>|
|ASR|Annual Sector Review<br>|
|COD|Country Operations Division<br>|
|EDI|Economic Development Institute<br>|
|EM3|Europe, Middle East and Nonh Africa Country Department III<br>|
|EMENA|Europe, Middle East and North Africa Region<br>|
|EXTIE<br>|International Economic Relations Division<br>|
|IBRD<br>|International Bank for Reconstruction and Development<br>|
|**IDA**|International Development Association<br>|
|IEC|Information, Education and Communication<br>|
|LAC|Latin America and the Caribbean Region<br>|
|MIS<br>|Management Information System<br>|
|NGO<br>|Non-governmental Organization<br>|
|OED|Operations Evaluation Department<br>|
|PCR|Project Completion Report<br>|
|PER|Public Expenditure Review<br>|
|PHN<br>|Population, Health and Nutrition<br>|
|PHR|Populalion and Human Resources Department<br>|
|PHRHN|Population, Health and Nutrition Division of the Population and Human<br>|
||Resources Department<br>|
|**PIR**|Public Investment Review<br>|
|PIU|Prcject Implementation Unit<br>|
|PRE|Policy, Research and External Affairs<br>|
|PRS|Sector Policy and Research<br>|
|PRSVP|Office of the Vice-President for Sector Policy and Research|
|SAR|Staff Appraisal Report|
|SOD|Sector Operations Division|
|TD|Tbchnical Division<br>|
|WDR|World Development Report<br>|
|WID|Women in Development|
**Figure 3.7: Predicted returns to fertilizer use under bad weather conditions**

<!-- Start of picture text --> 150<br>100<br>50<br>0<br>5 25 50 75 95<br>‐50<br>Price percentile<br>‐100<br>Maize Beans Matooke Coffee<br>%<br><!-- End of picture text --> Source: Staff calculations using yield responsiveness and cost of inputs estimated in the OFRA Response Function Database (2017) from the University of Nebraska and in Bold et al. (2017) and wholesale prices from UBOS, 2000 ‐ 2012.

# _3.3. Quality of input use_

The quality of inputs in markets frequented by farmers is commonly low in Uganda. A 2014 Deloitte study finds that low quality inputs in Uganda are most common in the maize herbicide market, followed by maize seeds and fertilizer. In the country, low quality has been associated with bulk breaking – that is, when smallholder farmers demand quantities of fertilizer or seed that are smaller than the contents of the package. For example, fertilizer is sold in 50 kg bags, but farmers will often demand fertilizer in 1 kg, 2kg or 5kg bags. Similarly, seed companies package seed in 2kg bags while farmers often demand smaller quantities than this. When selling these smaller quantities, the input can either be diluted and/or the label is counterfeited. This problem is particularly worrisome, as a large majority of farmers report buying their agricultural inputs from the market. According to the 2015 National Service Delivery Survey (NSDS), a little more than 90 percent of the households engaged in agriculture obtain their herbicides, fungicides, pesticides and fertilizer from the market, shops or local vendor. In the case of hybrid seeds, the share drops to 64 percent (see Figure 3.8.1).

Recent economic studies have carefully quantified the magnitude of the counterfeiting problem in these markets. Ashour et al. (2017) collected data on 483 samples of herbicide across 120 markets in Uganda and find that 30 percent contain less than 75 percent of the active ingredient advertised. Similarly, in Bold et al. (2017), 369 samples of fertilizer purchased from 129 retail shops by a mystery shopper, _<u>no samples</u>_ were found to contain the required nitrogen content. The average nitrogen content was 30 percent lower than what it should be (Figure 3.8.2); a larger shortfall than the 10 percent shortfall found in neighboring Tanzania (Fairbarn et al. 2017). The Bold et al. (2017) study also finds that the average quality of hybrid seeds purchased at 30 retail shops was also below standard, and estimated to be equivalent to a mix of 50 percent hybrid seeds purchased directly from the seed dealer and 50 percent traditional seeds purchased from farmers.

16
opening/privatization which would affect incentives to invest. Whether a firm facing competition is likely to invest more or less than a monopoly is an empirical question. In several cases Latin American state-owned infrastructure firms were privatized as monopolies or granted "exclusivity periods" of varying lengths, as in the telecommunications sector. As shown by Wallsten (2001b) in his study of telecommunications privatization in developing countries, granting a monopoly concession seriously reduces investment by the privatized firm relative to firms that face competition. Unfortunately such data were not available for most of the sectors and countries studied.<sup>8</sup> The estimation includes previous period capital stock since the coefficient on it is highly significant, although it is a physical measure that varies by sector and is not easy to interpret. The coefficient on capital stock is positive, which is contrary to what theory would suggest. However, this appears to be an artifact of aggregation since it is uniformly negative in the regressions disaggregated by sector.<sup>9</sup> Finally, the primary regressions show that after controlling for other factors, the water sector received significantly less private investment than the other sectors, while private investment in power was relatively higher than in the other sectors.

# _Characteristics of the regulatory system_

Given that a regulatory body exists, what aspects of the regulatory structure are critical to attracting private investment in infrastructure? Model II in Table 2 presents the results of a fixed effects regression restricted to the years after a regulatory body had been established. The

> 8 In a study of Latin American countnes, Martin et al (2001) show that infrastructure privatization has an impact on GDP per capita, with the effect both, being dependent on whether privatization occurs in the transport or the utilities sectors and _varying by the form of privatization:_ whether **it** is through greenfield investment, divestitures or concessions. Since our investment data are not disaggregated by the form of private participation we have used the date of the passage of legislation opening up the sector in the analysis.

> 9 Dropping this variable made no difference to the sign or significance of other variables.

18
# **Table 14:  Cross-country covariates associated with changes in demographic penetration of branches**

The results of OLS regressions of the changes in natural logarithm of number of commercial bank branches per 100,000 adults on different country characteristics.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)|-0.018**<br>(0.008)|-0.018**<br>(0.008)<br>|-0.020***<br>(0.007)|-0.025**<br>(0.012)|-0.023**<br>(0.009)||||||
|Population density (log)||-0.001<br>(0.006)|||||||||
|Expected real economic growth|||-0.092<br>(0.079)||||||||
|Branches er adult in 2008 (lo)||||0.014|0.008|-0.017*|-0.002|-0.015|-0.016|-0.018|
|p g||||(0.015)|(0.015)|(0.010)|(0.012)|(0.010)|(0.011)|(0.012)|
|% h i b f bk|||||0.336*|0.384**|0.353**|0.392**|0.411**|0.267|
|cange n numer o ans|||||(0.170)|(0.163)|(0.158)|(0.166)|(0.168)|(0.194)|
|East Asia & Pacific|||||||-0.069*<br>(0.036)||||
|Europe & Central Asia|||||||-0.061<br>(0.038)||||
|High income|||||||-0.071**<br>(0.031)||||
|Latin America & Caribbean|||||||-0.056**<br>(0.026)||||
|Middle East & North Africa|||||||-0.041<br>(0.025)||||
|South Asia|||||||0.036<br>(0.059)||||
|Offshore financial center||||||||-0.074**<br>(0.028)|||
|Concentration|||||||||-0.034<br>(0.054)||
|Foreign bank share||||||||||0.013<br>(0.040)|
|Government bank share||||||||||-0.034<br>(0.062)|
|Ctt|0.190***|0.201***|0.289***|0.376*|0.294|-0.132|0.054|-0.107|-0.098|<br>-0.140|
|onsan|(0.069)|(0.072)|(0.093)|(0.216)|(0.207)|(0.092)|(0.120)|(0.093)|(0.084)|(0.105)|
|N|104|103|103|104|87|87|87|87|84|55|
|Adjusted R<sup>2</sup>|0.0535|0.0475|0.0924|0.0511|0.214|0.164|0.188|0.183|0.150|0.0477|
|Robust standard errors in parentheses<br>***p<0.01,**p<0.05,*p<0.1|||||||||||

40