rafmacalaba/datause-encoder-data huggingface dataset:
- Deininger, Klaus, and Jin, Songqing. 2009. “Securing Property Rights in Transition: Lessons from Implementation of China’s Rural Land Contracting Law.” _Journal of Economic Behavior and Organization_ , 70(1‐2), 22‐38.

- de Meza, David, and Gould, J. R. 1992. “The Social Efficiency of Private Decisions to Enforce Property Rights.” _Journal of Political Economy_ 100(3):561‐80.

- Do, Quy‐Toan, and Iyer, Lakshmi. 2008. “Land Titling and Rural Transition in Vietnam.” Economic Development and Cultural Change 56(3): 531‐79.

- Fan, Cindy. 2008. “Migration, Hukou and the City.” in Shahid Yusuf and Karen Nabeshima, eds. China Urbanizes: Consequences, Strategies, and Policies Washington, D.C.: The World Bank.

- Feder, Gershon, and Feeny, David. 1991. “Land Tenure and Property Rights: Theory and Implications for Development Policy.” _The World Bank Economic Review_ 5(1): 135‐53.

- Field, Erica. 2005. “Property Rights and Investment in Urban Slums.” _Journal of the European Economic Association_ 3(2‐3): 279‐90.

- Field, Erica. 2007. “Entitled to Work: Urban Tenure Security and Labor Supply in Peru.” _Quarterly Journal of Economics_ 4(122): 1561‐1602.

- Finlay, Keith and Leandro Magnusson. 2009. “”Implementing Weak‐Instrument Robust Tests for a General Class of Instrumental‐Variables Models,” _Stata Journal_ , 9(3): 398‐421.

- Galiani, Sebstian and Schargrodsky, Ernesto. 2005 “Property Rights for the Poor: Effects of Land Titling.” Universidad Torcuato Di Tella, Business School Working Paper No. 06/2005.

- Giles, John. 2001. “Risk, Shock and Weak Property Rights in the Labor Allocation Decision of Rural Chinese Households,” paper presented at 2000 Meetings of the Northeast Universities Development Economics Consortium, Cornell, October 2000.

- Giles, John. 2006. “Is Life More Risky in the Open? Household Risk‐Coping and the Opening of China’s Labor Markets.” Journal of Development Economics 81(1): 25‐60.

- Goldstein, Markus, and Udry, Christopher. 2008. “The Profits of Power: Land Rights and Agricultural Investment in Ghana.” Journal of Political Economy 116(6): 981‐1022.

- Harris, J., Todaro, M., 1970. Migration, unemployment, and development: a two‐sector analysis. American Economic Review 60 (1), 126–142.

- Jacoby, Hanan, Li, Guo, and Rozelle, Scott. 2002. “Hazards of Expropriation: Tenure Insecurity and Investment in Rural China.” American Economic Review 92(5): 1420‐47.

- Katz, Barbara, and Owen, Joel. 2009. “Are Property Rights Enough? Re‐evaluating a Big‐Bang Claim.” Economics of Transition 17(1): 75‐96.

_Land Tenure Insecurity and Migration, Page 32_
For over three decades after independence, India grew at an average rate of 3.5% (infamously labeled “the Hindu rate of growth”) and then accelerated to an average of about 5.6% since the 1980s. As we have seen in Table 1-A, the annual growth rate of 7.9% during 1990-2005 is the second highest among the world’s largest economies, behind only China’s 11.8%.

In 2004, 52% of India’s GDP was generated in the services sector, while manufacturing (agriculture) produced 26% (22%) of GDP. In terms of employment, however, agriculture accounts for about two-thirds of the half a billion labor force, indicating both poor productivity and widespread underemployment. Over 90% of the labor force works in the “unorganized sector.”<sup>10</sup>

# **II.2 Law, Institutions and Business Environment**

The most striking fact about India’s legal system is the difference between protection of investors _by law_ as opposed to protection _in practice_ . Table 2 (panels A through E) compares India’s scores relative to the world, different legal-origin country groups, and other emerging markets on several different dimensions of law and institutions. As discussed above, with the English common-law system, India has strong protection of investors “on paper”: The scores on both creditor rights (Table 2-B, with a score of 4/4 in LLSV (1998), based on the Company’s Act of 1956, to 2/4 in DMS (2005), based on the Sick Industrial Companies Act of 1985) and shareholder rights (5/6 in Table 2-C) are the highest of any country in the world.

Table 2-D compares law enforcement and the quality of institutions in India and LLSV countries, while Table 2-E provides similar comparisons among major emerging economies. We did not use the measures from LLSV (1998) because they are dated and do not accurately capture the current protection of investors in India. We employ four sets of up-to-date and widely used measures for our purpose. First, the legal formalism (DLLS 2003) index, based on extensive surveys

> 10 According to the official definition, the unorganized sector is comprised of: 1) all the enterprises except units registered under Section 2m(i) and 2m(ii) of the Factories Act, 1948, and Bidi and Cigar Workers (condition of employment) Act, 1966; and 2) all enterprises except those run by the government (central, state and local bodies) or Public Sector Enterprises.

9
vector<sup>_Y_</sup> _k_<sup>is the collection of covariates expected to influence remittance sent by the migrant—</sup> (log) annualized income as reported by the migrant, an indicator variable if another family member is working abroad, household income in India, the total amount the household spent over the past 12 months on medical expenses expressed as a share of the migrant’s income and (log) median monthly income of overseas members reported by households in Kerala.

Figure 2. Income Shares of Remittances Reported by Migrants and Wives _Source_ : Authors’ analysis based on data from QSKF In particular, having another family member working abroad is likely to reduce the amount of remittance sent as the responsibility of supporting the household is distributed between the members who are employed abroad. A higher share of income spent on medical expenses over the year may reflect an unexpected event that warranted additional remittances.

The median income of migrants from households in origin districts may directly influence remittance norms. Bootstrapped standard errors are clustered at the district level where the 26
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Power

## **Achievements**

Côte d’Ivoire has a relatively well-developed power sector (table 12). The national power grid relies on a balanced portfolio of hydropower and gas-fired plant based on domestic hydrocarbon resources, and 71 percent of the population lives in electrified areas. Yet access to power appears to be relatively low; according to the national utility only around 20 percent of households are connected to the electricity grid, although household surveys suggest that the real value may be significantly higher. Initial connection charges remain a barrier to access Basic indicators of installed capacity and power consumption compare favorably with the peer group for fragile states but are less than a tenth of the benchmarks for middleincome countries.

Côte d’Ivoire has been a pioneer in private sector participation in the power sector in the region. In 1990 the country awarded a concession contract for the national power utility Compagnie Ivoirienne d’Electricite, or CIE. The concession helped to improve performance and attract private investment in power generation (IPPs), and by 1999 distribution losses had been reduced to 14 percent and average outage duration to 13 hours compared to 32 hours in 1990. In 1994, the country awarded the first Independent Power Project (IPP) in Africa to CIPREL, and in 1999 it awarded the then largest IPP to Azito. Both contracts have withstood the crisis and continue to supply power effectively. Côte d’Ivoire has become a significant power exporter in the region, supplying neighboring Benin, Burkina Faso, Ghana, Mali, and Togo. That export contracts were honored during the recent period of crisis, earning the country a reputation as a reliable supplier of power. Côte d’Ivoire’s future role in regional power trade will depend on developments in neighboring Guinea and Ghana. If fully developed, Guinean hydropower would be more competitive for regional trade than Ivoirian gas. Yet due to the political and economic situation in Guinea, these hydro resources may not be developed for some time to come. Gas was recently discovered in Ghana, which could change that country from a net importer to a net exporter of power, but the relative competitiveness of Ghanaian and Ivoirian gas is not yet clear. In any case, Côte d’Ivoire has a key role to play in wheeling regional electricity exchange by taking advantage of its central position, its transmission network, and its reputation as a power exporter.

21
# **Annex 7: Quality of service data**

## **Table A7: Quality of service data**

||**Data related to duration**|**Data related to frequency**|
|---|---|---|
|Burkina Faso|Average outage time of 82 hours|2405 incidents related to load-shedding,works and incidents.|
|Cameroon|SAIDI: 105 hours|SAIFI: 25|
|Cape Verde|Average outage time of one hour|418 blackouts in 2012|
|Côte d’Ivoire|Average outage time of 40 hours||
|Gabon|Average outage time of 126 hours||
|Gambia|Total load-sheddingtime of 6031 hours|124 MV outages,3985 LV outages|
|Ghana|150 hours of outages per year for urban customers, 174 hours of<br>outages for rural customers|83 outages per year for urban customers, 92 per year for rural<br>customers|
|Guinea|Average outage time of 21 hours|1962 outages due to breakdownsperyear|
|Liberia|SAIDI: 35 hours|SAIFI: 212|
|Mali|Average 1 hour duration for HV outages, average 1.5 hours<br>duration for MV outages|70 HV outages, 224 MV outages|
|Mauritania|Average 1.2 hours duration for MV outages|116 outages at MV level|
|Mozambique|SAIDI Transmission system: 59 hours<br>SAIDI Distribution system: 1.25 hours|SAIFI Transmission system: 52<br>SAIFI Distribution system: 1.25|
|Senegal|29,891 system interruptionsperyear||
|Sierra Leone|Average duration of 10 hoursper interruption at the MV level|Average interruption frequencyof 183peryear at the MV level|
|South Africa|SAIDI: 36|SAIFI: 20|
|Zimbabwe|Average duration of 3 hours per outage in the transmission<br>system|185 interruptions in the transmission system per year|

_Sources_ : Utility annual reports and other documents.

95
79

# TABLE 20: Comparison of average productivity and related values for Private and Ejido farms of three sizes in a **sample** from Irrigated lands in Mexico, 1987

||Small**F**|**arms**_t/_|Medium|Farms o/|Large F|arms_p/_|
|---|---|---|---|---|---|---|
||Private|Ejido|Private|Ejido|Private|Ejido|
|No. of Farm Units|41|122|34|43|78|6|
|Land Productivity|||||||
|Cultivated land(hectaros)|7.3|6.0<br>|19.0<br>|13.6<br>|129.8<br>|47.6<br>|
|<br>Product per h-ctsre<br>(pesos)<br>Gross income b/ hectare (pesos)|2400<br>673|2500<br>1267|3200<br>610|4800<br>1462|7734<br>28U6|8069<br>3813|
|Capital Productivity|||||||
|Capital<br>per**farm**(1000 pesos)|22|12|111|27<br>|1040<br>|160<br>|
|<br> <br>Capital<br>per hectare<br>(1000 pesos)|3.4|2.1<br>|656|2.4|8.3|3.7<br>|
|<br> <br> <br>Product/capital|.68|.93|.47|1.24|.72|1.16|
|Labor Productivity|||||||
|Man-days per farmS/|e88|468|1284|989|8169<br>|3021<br>|
|Man-days per hectarec/|99|82<br>|70<br>|87|66<br>|69<br>|
|<br>Product per man-day (pesos)E/|24|30|46|65|119|116|
|Total Factor Productivity|||||||
|Product per farm (1000 pesos)|16|14|61|64<br>|969<br>|**362**<br>|
|<br>Cross income b/ per farm (1000 pesos)|4.2|7.6|11.6|19.7|369|172|
|<br>Product per total<br>inputsJ|.89|.98<br>|.80<br>|1.10<br>|1.17<br>|1.44<br>|
|Product per total<br>Inputs<br>_JI_<br> <br>|.96<br>|1.40<br>|.82<br>|1.27<br>|1.18<br>|1.61<br>|
|Value-added per total<br>inputsI/|.82|1.05|.46|.80|.89|1.06|

*/ Sizo groups based on gross value<sup>of output in pesos:</sup> small = 6,000 - 25,000; medium = 26,000 - 100,000; large 100,000 and over.

b/ Value of product minus purchased Inputs. c/ Hired labor plus labor of owner/ojidatarios.

a, Inputs include those purchased plus inputed<sup>valuesfor</sup> land and owner/ejidatario labor.

*/ Same as d., but excluding imputed value of owner/ojidatario labor from the denominator.
<!-- Start of picture text --> DDS Per Capita Consumption<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95 .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile Quintile<br>95% Confidence Interval OLS 95% Confidence Interval OLS<br>RIF-OLS RIF-OLS<br>Food Share in Total Expenditure<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile<br>95% Confidence Interval OLS<br>RIF-OLS<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>-.2 -.2<br>.2<br>0<br>-.2<br>-.4<br><!-- End of picture text --> 72
<!-- Start of picture text --> ~°~ CIF/FOB Ratio -*- Median DI<br>Pit tee P Saal,<br>1 RADET A ED .reseT | > Ne.~ we a7 ¢ = =<, -= a ~ hw «<br>i} -—- OS aap re ee Sr ig<br>0.5 7<br>&<br>0 ns ae pd A pikeitat cs i<br>: eoeegesstmecomee”<br>-0.5 + wa<br>+,*, amen ie "28<br>ta rl<br>1990 1995 2000 2005 2010 2015 2020<br>SM +++» As reported by partners<br>— As reported by SEN<br>4M “F<br>O 3M R # ws<br>o3.<br>=<br>-vo<br>obsctassessstees<br>1990 1995 2000 2005 2010 2015 2020<br>Year<br><!-- End of picture text -->
**Table 6: The Impact of Migration on the Education of Females Aged 12 to 15**

||(1)<br>OLS|(2)<br>2SLS|(3)<br>Ordered <br>Probit|(4)<br> IV-Ordered <br>Probit|(5)<br>Censored <br> Ordered<br>Probit|(6)<br> IV-Censored<br>Ordered<br>Probit|
|---|---|---|---|---|---|---|
|Child is in a Migrant Household|0.272|-0.225|0.125|-0.205|0.126|-0.307|
||(3.36)**|(0.77)|(2.78)**|(1.34)|(1.53)|(1.04)|
|Age of Child|1.613|1.671|0.748|0.787|0.475|0.477|
||(2.18)*|(2.23)*|(1.81)|(1.90)|(0.21)|(0.39)|
|Age of Child Squared|-0.036|-0.038|-0.010|-0.012|-0.016|-0.016|
||(1.31)|(1.36)|(0.66)|(0.74)|(0.19)|(0.35)|
|Mother's Years of Schooling|0.216|0.217|0.144|0.145|0.196|0.198|
||(25.93)**|(24.32)**|(23.28)**|(23.29)**|(13.30)**|(13.37)**|
|Proportion of rural households owning land in 1910|-0.094|-0.082|-0.060|-0.052|-0.031|-0.020|
||(3.98)**|(2.82)**|(3.75)**|(3.37)**|(1.13)|(0.73)|
|Female School Attendance in 1930 (6 to 10 year olds)|0.002|0.007|-0.000|0.003|-0.005|-0.001|
||(0.34)|(1.00)|(0.13)|(0.90)|(1.09)|(0.11)|
|Gini of Income in 1960|-1.143|-0.887|-0.665|-0.496|0.323|0.561|
||(1.61)|(1.06)|(1.41)|(0.98)|(0.50)|(0.94)|
|Number of Schools per 1000 population in 1930|0.066|-0.112|0.086|-0.032|0.256|0.098|
||(0.53)|(0.62)|(0.95)|(0.29)|(2.42)**|(0.67)|
|Gini of Female Years of Schooling for 15-20 year olds in 1960|-4.061|-3.969|-2.295|-2.236|-0.798|-0.690|
||(4.83)**|(5.11)**|(4.64)**|(4.67)**|(0.76)|(0.68)|
|Average Female Years of Schooling in 1960 for 15-20 year olds|-0.304|-0.305|-0.150|-0.150|0.088|0.087|
||(2.94)**|(2.64)**|(2.57)*|(2.24)*|(0.61)|(0.64)|
|Observations|6107|6107|6107|6107|3053|3053|

# Robust t-statistics in parentheses clustered at the state level. Instrument is the 1924 state-level migration rate.

Censored ordered probit regressions are carried out on a 50% random sample, and use school attendance as censoring variable. * significant at 5%; ** significant at 1% 38
independent relationship between any trade or internationa. price distortion indicator and growth. +? These three results indicate that an important part of the relationship between trade and growth is based on erhanced resource accumulation and not necessarily on the improved allocation of resources.

The major results are in Table 6. Two measures of trade performance that have received considerable attention are the export share of GDP and the growth rate of the export share. The results suggest that there is little link between che export (or import or trade) share and growth once investment is controlled for as none of the coefficients are significant. Measures of export share growth, import share growth, and total trade share growth were tried and also found to be insignificantly related to growth.

Arguing that exports have important externality effects, Feder (1983) uses export growth and export growth times the share of exports in GDP to explain Cross-country growth differentials. Table 6-A presents results showing that export growth is not robustly related to growth when government growth variables are included. Similar results hold for exports multiplied by export share or when we substitute imports or total trade for exports. Given the national accounts identity, even if we found a robust relationship, it is not clear what worthwhile inferences could be drawn.

Romer (1990a) finds a strong link between the export share and investment. We confirm this result. The extreme lower bound still indicates a significant, positive correlation between the investment and export shares. The SummersHeston data set produced similar results. Total trade was also tested and the trade-investment link was still robust. Since outliers may be especially 17 pollar’s (1990) measure of the distortion between domestic and foreign prices, however, is negatively - though not strictly robustly - correlated with long-run average growth rates.

20
# **Table A11: Identifying the $1.90 poor based on covariates of poverty**

|<br>|<br>|<br>_Pakistan_<br>|<br>_2018_<br>|<br>|<br>||
|---|---|---|---|---|---|---|
|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Years of schooling|-0.001***|-0.004***|0|-0.003***|-0.006***|0|
|Asset ownership|-0.012***|-0.028***|0|-0.036***|-0.052***|0|
|Literacy|-0.009***|-0.037***|0|-0.034***|-0.062***|0|
|Not employed in the<br>agricultural sector|-0.006***|-0.024***|0|-0.018***|-0.036***|0|
|Access to electricity|-0.022***|-0.080***|0|-0.104***|-0.162***|0|
|Piped drinking water|-0.010***|-0.009*|0.9891|-0.027***|-0.027**|0.9891|
|Improved sanitation|-0.010***|-0.035***|0|-0.043***|-0.068***|0|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u>

|**Table A12**<br>|**: Identifying**<br>|**the $3.20poo**<br>_Pakistan_<br>|**r based o**<br>_2018_<br>|**n covariates o**<br>|**f poverty**<br>||
|---|---|---|---|---|---|---|
|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Years of schooling|-0.001***|-0.010***|0|-0.022***|-0.030***|0|
|Asset ownership|-0.028***|-0.112***|0|-0.268***|-0.353***|0|
|Literacy|-0.009***|-0.085***|0|-0.205***|-0.281***|0|
|Not employed in the<br>agricultural sector|-0.007*|-0.057***|0|-0.115***|-0.166***|0|
|Access to electricity|0.001|-0.060***|0|-0.345***|-0.406***|0|
|Piped drinking water|-0.003|0.029***|0.0063|-0.055***|-0.024|0.0063|
|Improved sanitation|-0.006*|-0.066***|0|-0.196***|-0.256***|0|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u> 53
Companies and individuals looking to hire on oDesk fill out a job description, including the skills required, the expected contract duration, and some preferred worker characteristics. After oDesk’s founding, most of the jobs posted were hourly positions for technology-related or programming tasks (e.g., web development), but postings for administrative assistance, data entry, graphic design, and smaller categories have become more prevalent as the platform has grown. After a company posts a position opening, workers apply for the job and bid an hourly rate. Firms can interview workers via oDesk, followed by an ultimate employment relationship being formed.

We study the role of the Indian diaspora in facilitating oDesk contracts to India. Our data begin at oDesk’s founding in 2005 and run through August of 2010. The data were obtained directly from oDesk with the stipulation that they be used for research purposes and not reveal information about individual companies or workers. oDesk does not collect a person’s ethnicity or country of birth, so we use the names of company contacts to probabilistically assign ethnicities. This matching approach exploits the fact that individuals with surnames like Chatterjee or Patel are significantly more likely to be ethnically Indian than individuals with surnames like Wang, Martinez, or Johnson. Our matching procedure exploits two databases originally developed for marketing purposes, common naming conventions, and hand-collected frequent names from multiple sources like population censuses and baby registries. The process assigns individuals a likelihood of being Indian or one of eight other ethnic groups.<sup>7</sup> Several features of this work should be noted. First, some records cannot be matched to an ethnicity, either due to incomplete records for listed ethnicities (e.g., very obscure names) or to uncovered ethnic groups (e.g., African ethnicities). Second, this approach can describe ethnic origins, but it cannot ascertain immigration status. For example, a U.S.-based company contact with the surname Singh is assigned to be of ethnic Indian origin, but the approach cannot say whether the individual is a first- or later-generation immigrant. Third, while we focus on the Indian ethnicity, attempting to match on all nine ethnic groups is important given that some names overlap across ethnicities (e.g., D’Souza in the Indian context due to past colonization).

> 7 The ethnic groups are Anglo-Saxon, Chinese, European, Hispanic, Indian, Japanese, Korean, Russian, and Vietnamese. Kerr (2007, 2008) and Kerr and Lincoln (2010) provide extended details on the matching process, list frequent ethnic names, and provide descriptive statistics and quality assurance exercises. Stanton and Thomas (2011) further describe the oDesk platform.

8
traditional, and less inclined to assume lead roles in loan syndicates. Equity/assets is the bank’s total common equity to total assets ratio lagged by one year with a mean of 0.05. Less capitalized banks may be less aggressively entrepreneurial, and hence less inclined to take on lead roles in loan syndicates. Finally, Past relationship is a dummy variable that indicates whether a bank has already provided at least one syndicated loan to the borrower in a previous year with a mean of 43.6%. A past relationship with a borrower makes it less necessary to provide a loan through a foreign subsidiary to facilitate monitoring, and it makes it easier for a bank to take on a lead role in new loan syndicate as the bank has an information advantage regarding the borrower.

A multinational bank with foreign subsidiaries can engage in regulatory arbitrage in the area of capital regulation by originating syndicated loans through foreign subsidiaries located in countries with less stringent capital regulation. Figure 3 provides information on the extent to which loans have been ‘arbitraged’ over the 1995-2016 period in the sense that they have been originated through foreign subsidiaries located in countries with less stringent capital regulation than in the parent country. The share of ‘arbitraged’ loans reached a peak of about 8% in 2000, and generally was relatively high in the period 2000-2007 preceding the financial crisis, but has been more modest at less than 1% since then. The much lower share of arbitraged loans in recent years could conceivably reflect international convergence in capital regulatory standards, which would reduce the scope for such arbitrage. To see whether such convergence has occurred, Figure 4 displays the mean value and 10<sup>th</sup> percentile and 90<sup>th</sup> percentile values of the capital regulatory variable across countries for each of the World Bank regulation and supervision survey waves. The figure fails to provide evidence of international capital regulatory convergence over time.<sup>15</sup>

> 15 <mark>Alternatively, the use of foreign subsidiaries as a means to bring about lower capital regulatory standards may have declined over time as home-country regulations regarding banks’ international operations have tightened according to a confidential IMF survey covering the period 2006-2014 (see IMF, 2015). In the empirical work, a time trend in such regulations is controlled for by the inclusion of time fixed effects.</mark>

13