rafmacalaba/datause-encoder-data huggingface dataset:
more _ad hoc_ and short-sighted. Even if a scientific estimation is made by the Cotton Advisory Board, the export quota can be changed by politicians. Under the ongoing quota allocation scheme, the central government's decision to allocate export quota has been influenced by whether the central government and the cotton producing state government is headed by the same party or not. Also, the political background of the incumbent prime minister and his cabinet members - whether they are pro-farmer or pro-handloom - matters significantly for quota allocation.

Looking at the past trends, one may well wonder why so much cotton was exported under the quota in the year 1986/87, when cotton production had dropped sharply from the year before **(Table 10).** It is also difficult to explain why so little cotton export was allowed in 1991/92 compared to the year before, despite the increase in cotton production. The more carefully one analyzes the trends of cotton exports from the table, the more one is convinced that the export quota is decided erratically and inconsistently, with little scientific basis.

**Table 10:** **_Trends of Cotton Exports_**

(thousand of 170 kg bales)

|Cotton Year|Production<br>(as per DCD)|Production<br>(as per CAB)|Consumption by<br>Mills|Import|Export|
|---|---|---|---|---|---|
|1980/81|7,010|7,800|7,678|--|697|
|1981/82|7,884|8,400|7,123|50|400|
|1982/83|7,534|7,717|7,561|nil|665|
|1983/84|6,386|7,516|8,485|nil|353|
|1984/85|8,506|10,150|9,150|50|179|
|1985/86|**8,727**|**10,700**|**9,167**|**nil**|**450**|
|1986/87|**6,905**|**9,500**|**9,520**|**nil**|**1,367**|
|1987/88|6,432|9,000|9,491|300|44|
|1988/89|8,744|10,600|9,666|225|77|
|1989/90|11,422|13,575|10,236|nil|1,371|
|1990/91|**9,759**|**11,700**|**10,757**|**-**|**1,190**|
|1991/92|10,276|11,900|10,309|300|77|
|1992/93|12,067|13,500|11,281|115|1,376|
|1993/94|11,855|12,150|11,405|300|390|
|1994/95|11,756|13,850|11,937|589|108|

Source: Office of the Textile Commissioner, GOI (1995)

# **_Fluctuation_** _of_ **_Cotton Prices and Speculation_**

Domestic political instability, the _ad hoc_ decision for cotton exports by the government, and the fluctuation 23
The data both affirm and complicate a statement made on the CGAP website: “For-profit MFIs ... don’t generally charge their clients more than non-profit MFIs.”<sup>11</sup> Tables 3 and 4 show that for-profits charge slightly more on average, but the distribution of real portfolio yields is largely overlapping. That picture, though, is given nuance in Figure 4. The figure shows that when the data are segmented by customer scale (as given by normalized average loan size), banks charge less because they cluster at larger loan sizes. NGOs charge relatively less when attention is limited to smaller loan sizes. The general picture shows that the CGAP response is based on an apples to oranges comparison. Once the scale of loans is considered, the for-profit providers are seen to charge higher rates in the markets where NGOs tend to cluster.

_Regression analysis._ The pattern of interest rates holds after controlling for other variables. Table 5 presents regressions that show a quadratic relationship between real portfolio yield and average loan size, controlling for other factors. We estimate the following equation describing variation in yields:

- (1) Yi = α + β1Avg Loan Sizei + β2Avg Loan Sizei<sup>2</sup> + β3Regioni + β4Agei + β5Assetsi + β6Ownershipi + β7Ownership*Loan Sizei + β8Ownership*Loan Sizei<sup>2</sup> + εi

Where Yi is the real portfolio yield of microfinance institution _i._ Controls include regional dummy variables; the age and size of each microfinance institution (measured by total assets); and ownership type using the same categories as in the tables presented thus far -- bank (forprofit), credit union/cooperative (not-for-profit), NGO (not-for-profit), NBFI (for-profit), NBFI (not-for-profit), and rural bank. We interact the ownership type indicator variables with average loan size (divided by the per capita income at the 20<sup>th</sup> percentile of the population) to allow the

> 11 CGAP is the Consultative Group to Assist the Poor, the main microfinance donor consortium, based in Washington, DC as part of the World Bank Group. The quote is from “frequently asked questions,” available at http://www.cgap.org/about/faq. Accessed 4/10/14.

13
After SEI and FEI variables are combined in the additive model, resulting values are normalized and rescaled to generate SEI and FEI scores between 0 and 100 for each project. The rescaled score z� can be expressed as z� � �z� �Z���� / �Z��� �Z��� � � 100, where Z��� is the minimum value for variable z and Z��� is the maximum value. These rescaled scores are used as the SEI and FEI scores for plotting in Step 4.

# Step 4. Creating the Visual Interface: The Investment Prioritization Matrix

To create a visual comparison, projects are plotted on a two-dimensional Cartesian plane, with axes representing the SEI and FEI. The budget limit for the sector is also imposed along each axis (intercepting the axis where funds are exhausted). First, however, the budget must be hypothetically allocated separately to the SEI- and FEI-ranked project lists to determine the fundable sets in each. In other words, the budget limit is hypothetically allocated to the top-ranked projects on each list (as if selection were based only on SEI or only on FEI) until resources are exhausted. The resulting fundable sets are compared simultaneously on the investment matrix.

A ‘good’ project in terms of financial and economic performance may nevertheless be undesirable from a social and environmental perspective, and vice versa. As such, decision-makers must consider projects along both dimensions. Projects can be compared by their respective SEI and FEI scores on a visual interface called the Infrastructure Prioritization Matrix (Figure 3). Once projects are plotted, the budget limit is imposed onto the plane – perpendicular to each axis – at the point where the budget would be exhausted if funding were determined solely by each index. The plane is intersected by the dually-imposed budget limit, creating four quadrants.

Figure 3. Example Investment Prioritization Matrix, Panama Water and Sanitation Pilot, 2015 <!-- Start of picture text --> 50<br>45 P15<br>P3<br>40 P23<br>P10<br>P1<br>P24<br>35<br>30<br>P27<br>25 P17 P29 P4 P16<br>P12<br>P22 P28<br>20 P7<br>P35<br>P19<br>P21 15 P18 P13<br>P26 P25 P31 P5<br>P11 P34<br>10 P32 P6<br>P20<br>P30 P33P14<br>5<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>FEI<br>SEI<br><!-- End of picture text --> Source: Prioritizing Infrastructure Investments in Panama: Pilot Application of the World Bank Infrastructure Prioritization Framework (April 2016) 20
fastgini<sup>15</sup> in STATA). We do this for both the cluster and district level. None of the effects of mining are statistically significant, but they point to increased asset wealth.

**Table 9 OLS estimates for wealth and inequality in the DHS individual-level analysis**

||<br>|Wealt<br>|h Gini<br>|
|---|---|---|---|
||wealth<br>index|cluster<br>level|district<br>level|
|active*mine|7,290|-0.004|0.004|
||(12,849)|(0.013)|(0.018)|
|mine|9,922|0.011|0.006|
||(8,676)|(0.013)|(0.016)|
|active|7,854|-0.006|0.034**|
||(9,016)|(0.010)|(0.017)|
|Observations|4,909|4,909|4,909|
|R-squared|0.613|0.227|0.548|

_Note:_ Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and district fixed effects, urban dummy, age, and years of education. Active is active status of mine in the survey year. *** p<0.01, **p<0.05, *p<0.1. OLS = ordinary least squares.

# **6.4 Bottom 40% of the population**

To understand the welfare effects of the bottom 40 percent of the population in the income scale, we split the sample according to the wealth score provided by DHS. Given the data structure, which is repeated cross-section, we cannot follow a particular household that was identified as belonging to the bottom 40 percent in the initial time period. Instead, we identify the bottom 40 percent in four groups: far away, before mine or during mine, and close to mine, before mine or during mine. The summary statistics for selected main outcomes are presented in table 10. As the table shows, the bottom 40 percent in mining communities are more likely engaging in agriculture than the bottom 40 percent elsewhere. This could illustrate that agricultural workers are overrepresented among the less well-off in mining communities. However, women in this group still more often work in services than women did before in the same communities.

> 15 Fastgini is a user-written command in STATA that helps calculate the gini coefficient.

27
where Ci*Yt are country-year-fixed effects. We thus compare Islamic and conventional banks within a country and a specific year. Below we also use additional specifications, including interacting the Islamic bank dummy with the market share of Islamic banks.

We control for an array of time-variant bank characteristics that might confound the relationship between bank type, on the one hand, and business orientation, asset quality, efficiency and stability, on the other hand. Specifically, we control for bank size, using the log of total assets. Larger banks might be more efficient due to scale economies, while the theoretical and empirical literature on the relationship between size and stability is ambiguous (Beck, Demirguc-Kunt and Levine, 2006; Beck, 2008). They might also be more likely to engage in fee-based business and have easier access to wholesale markets. We include the ratio of fixed assets to total assets to control for the opportunity costs that arise from having non-earning assets on the balance sheet, as well as the share of non-interest earning assets to control for non-lending business of banks, which previous research has shown to affect both efficiency and stability of banks (Demirguc-Kunt, Laeven and Levine, 2004; Demirguc-Kunt and Huizinga, 2008). In our large sample, total assets vary from 57 million USD to 22 billion USD, with an average of 948 million USD. The share of fixed in total assets varies from close to zero to 5.4%, with an average of 1.9%. The share of non-loan earning assets in total assets ranges from 14% to 85%, with an average of 44%. In our smaller sample, Islamic banks are significantly smaller than conventional banks, have higher fixed assets, but lower non-interest earning assets.

We would also like to control for the ownership structure of banks, but face the problem that the ownership dummy is not well populated in Bankscope and only reflects the current ownership structure. We therefore control for the ownership structure on the country level, using data from Barth, Caprio, and Levine (2008). The foreign bank share ranges from zero to 100%, while the government ownership share ranges from zero to 98%. On average, the foreign (government) ownership share is 35% (17%).

In some of our regressions, we also include several country-level indicators. Specifically, we include GDP per capita as indicator of economic development, GDP growth, Private Credit to GDP as measure of financial development and the three-bank concentration ratio. A large literature has related bank concentration to both the efficiency and stability of financial systems 13
discretionary power of the crown as the origin of the common law legal system (Glaeser and Shleifer 2002). Furthermore, they suggest that the actions of the independent judiciary system in countries that adopted the British Common Law system are more conducive to lower levels of corruption (see for example, La Porta et al. 1999, Pellegrini and Gerlagh 2008). Empirically, Treisman (2000) found that countries with common law had lower levels of corruption than the rest. Similar findings are reported by Goel and Nelson (2010). These findings suggest that if the degree of ethnic fractionalization and type of legal system also vary with the size of the country our main results could be spuriously affected. Thus, we control for the degree of ethnic fractionalization and the legal origin of the country. For ethnic fractionalization, we use the estimates provided by Alesina et al. (2003). For legal origin, we use dummy variables indicating if the country’s legal system is based on Common Law or Civil Law. The omitted category here is the Socialist legal system. The data source for the variable is La Porta et al. (1999).

It is often argued that civil participation in the form of democracy can combat corruption. Democracy offers a mechanism to monitor the behavior of government officials more closely and to remove corrupt politicians via the electoral process. However, the opposite is also possible. That is, greater democracy could increase corruption as it provides more people with access to public funds and positions in the public sector.<sup>8</sup> Both these effects could play out simultaneously and their net impact will determine how corruption and democracy are related. Existing empirical evidence on the corruption and democracy link is mixed. That is, while some studies find corruption is significantly lower under democracy than autocracy (Treisman 2000, Bhattacharyya and Hodler 2015, and Iwasaki and Suzuki 2012), others find no significant relationship between the two (Fisman and Gatti 2002, Ades and Di Tella 1999) or that the direction of the relationship

> 8 Theoretical models that show greater rent seeking by public officials and politicians and therefore greater corruption under democracy than in autocracy include for example, Corchon (2008) and Mohtadi and Roe (2003).

17
In Sections V and VI, we provide examples of further analyses that can be conducted once our estimates of governance structures are obtained. The observations are at the firm level and the variables of interest are the probabilities that the firm has chosen each of the four governance structures. Notably, in Section V, we find that regional variation in the effectiveness of different governance structures is more important than cross-country variation. This is somewhat puzzling given that institutional rules relevant to transactions are set at the national level in all the countries we analyze. It suggests that institutional implementation is at least as important as the quality of formal rules. In Section VI, we venture into analysis of determinants of governance structures and provide evidence that the uncertainty that firms face through their suppliers influences the firms' choices around reliance on third-party enforcement. This finding shows that our data can provide a wholly new way of testing existing hypotheses that have been the subject of much theoretical discussion but without any consensus arising in the empirical literature, as is the case with the effect of uncertainty on governance.

While explorations of the variations in and determinants of governance structures, as in Sections V and VI, have not been this paper's prime objective, these exercises show the validity of the methodology we have developed and demonstrate the potential of the data sets that we have generated.<sup>78</sup> Specifically, our methodology allows readers to test other hypotheses, using firms' governance structures as dependent variables as we have done, or as independent variables helping to explain variations in firms' behavior and performance. Moreover, given the information we provide, readers could add different countries, or cities, or sectors to those we have studied here. If the question that we lay out in Section II were to be implemented in a survey of any size, even

> 78 The data is posted at www.enterprisesurveys.org/portal. Users need an account (available free of charge) to gain access to the data. After signing in, go to the "Combined Data" tab. There, download the zip file "Landscape of Transactions Data and Estimates".

52
- **Selvaraju, R.** , “Impact of El Ni ̃no-southern Oscillation on Indian Foodgrain Production,” _International Journal of Climatology_ , feb 2003, _23_ (2), 187–206.

- **Shamdasani, Yogita** , “Rural Road Infrastructure & Agricultural Production: Evidence from India,” 2016. Working paper.

- **Shrestha, Slesh A.** , “Roads, Participation in Markets, and Benefits to Agricultural Households: Evidence from the Topography-based Highway Network in Nepal,” 2017. Working paper.

- **Sotelo, Sebastian** , “Domestic Trade Frictions and Agriculture,” 2016. Working paper.

- **Spears, Dean** , “Effects of Sanitation on Early-life Health: Evidence From a Governance Incentive in Rural India,” 2015. Working paper.

- **Storeygard, Adam** , “Farther on Down the Road: Transport Costs, Trade and Urban Growth in Sub-saharan Africa,” _Review of Economic Studies_ , 2016, _83_ (3).

- **Wantchekon, Leonard, Marko Klaˇsnja, and Natalija Novta** , “Education and Human Capital Externalities: Evidence from Benin,” _The Quarterly Journal of Economics_ , 2015, _130_ (2), 703–757.

- **World Bank Group** , “Measuring Rural Access Using new technologies,” 2016, p. 108.

- **Zhang, Xiaobo and Shenggen Fan** , “How Productive is Infrastructure? A New Approach and Evidence from Rural India,” _American Journal of Agricultural Economics_ , 2004, _86_ (2), 492–501.

31
|NPV<br>OMRG|Net Present Value<br>Mauritanian Agency for Geological Research (Office Mauritanien des Recherches<br>Géologiques)|
|---|---|
|OMVS|Senegal River Basin Development Authority (Organisation pour la Mise en Valeur du<br>Fleuve Sénégal)|
|ONS|National Statistics Agency of Mauritania (Office National de la Statistique)|
|PK|Produced Capital|
|PMD|Particulate Matter Damages|
|RR|Resource Rent|
|SDR|Social Discount Rate|
|SEEA|System of Economic-Environmental Accounting|
|SMCP|Mauritanian Company for Fish Exports (Société Mauritanienne de Commercialisation<br>de Poissons)|
|SMH|Hydrocarbon Agency of Mauritania (Société Mauritanienne des Hydrocarbures)|
|SNIM|Mauritanian Mining and Industrial National Agency (Société Nationale Industrielle et<br>Minière de Mauritanie)|
|SSA|Sub-Saharan Region|
|Tcf|Trillion cubic feet|
|TW|Total Wealth|
|UK|United Kingdom|
|UN|United Nations|
|USD|United States dollar|
|WAVES|Wealth Accounting and Valuation of Ecosystem Services|
|WBG|World Bank Group|
|WDI|World Development Indicators|
|WITS|World Integrated Trade Solutions|

iii
# **KENYA: DEFICIT PROJECTIONS**

<!-- Start of picture text --> 1988 1991<br><!-- End of picture text --> <!-- Start of picture text --> 2 -<br>IB Required Adjust.<br>-<br>1 ETarget<br>Sustainable 4<br>0<br>a U 1<br>a..<br>-2-<br>-3<br>-4-<br>1988 1989 1990 1991<br>Fiscal Years<br><!-- End of picture text -->
# **4. Corruption and Growth across Mexican States**

In this section we build a growth regression based on cross-state data in order to figure out how corruption influences the path of economic development. By controlling for many other variables, we can identify which ones were of importance to promote growth in the analyzed period from 2001 to 2005.

# **a. Description of the Data**

# **a.1.   Perception versus behavior of corruption**

The index we use to proxy corruption in the 32 Mexican states is drawn by _Transparencia Mexicana_ (TM) a non-governmental organization based in Mexico City. They started to build a National Index of Corruption and Good Governance (ICBG) in 2001, which has been published every second year since then. The focus is on households, not on companies and the barriers of doing business. It includes corruption in 35 public services offered by the three levels of government and individuals, using a scale with values between 0 and 100, a small number associated with less corruption. The index is based on experiences of almost 15,000 households with the same structure like the one used for the national census of the Mexican Statistical Office. A challenge for TM was to select the appropriate services, since they vary by State. The formula is easy and understandable by everybody involved or interested to replicate it: ���� ��� ��� ��������<sup>Number of times a service was obtained by paying a bribe</sup> �100 Total number of times a single service was used The general ICBG we use in this paper contains all 35 services and looks as follows: ���� �<sup>Number of times a bribe was given in 35 services</sup> �100 Total number of times the 35 services were used This method is different than the one based on perception, as used by Transparency International, the global civil society organization leading the fight against corruption. Their annual Corruption Perception Index (CPI), which was first released in 1995 and ranks 180 countries, is calculated by the perceived level of corruption, as determined by expert assessments and opinion surveys. A perception index has the risk that the answers of the participants in the survey may be very sensitive to public policy changes. This potential shortcoming is thus avoided using the ICBG.

# **a.2. Correlation of corruption and growth**

We wanted to explore the correlation between initial corruption levels with the (per capita) growth rate of the following couple of years. Due to the volatility of the results of the corruption index we preferred to take the average of the years 2001, 2003 and 2005 and analyze how it influences the per capita growth rate from 2001 to 2005.

The scatter plots in Figure 7 show the correlation between average corruption and average growth on the left and average per capita growth on the right. According to these figures there is a 13
Policy Research Working Paper 8356

# **Abstract**

Internal labor migration rates in India have been largely static and low in recent times compared with those in other countries. This is a cause for concern because internal migration for economic reasons can promote the agglomeration of economic activity in more productive locations and directly contribute to reducing poverty through remittances. New evidence based on the India Human Development Survey, which provides a more recent source of data compared with the Census and other household surveys, shows that labor mobility is higher than previously estimated—the stock of labor migrants increased from 16 million in 2004–05 to 60 million in 2011–12.

The absolute number of circular migrants, at more than 200 million in 2011–12, is also much higher than previously documented estimates. Tracking the same households between 2004–05 and 2011–12, empirical analysis based on the India Human Development Survey highlights several socioeconomic factors associated with the migration decision: household income, the availability of information, as well as community networks in source and destination areas. There is also a possible administrative dimension to interstate migration barriers, owing to domicile provisions for work and study, lack of portability of social benefits, and legal and other entitlements upon relocation.

This paper is a product of the Finance, Competitiveness and Innovation 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://econ.worldbank.org. The authors may be contacted at gnayyar@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