rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 7**

# **Normalized Difference Vegetation Index (NDVI)**

|Independent|Brazil<br>|India|
|---|---|---|
|Variables|||
|January NDVI|-452.|-3780|
||(1.16)|(2.32)|
|April NDVI|643.|14300|
||(1.53)|(5.13)|
|July NDVI|-1860|3140|
||(5.68)|(1.50)|
|October NDVI|630.|-8480|
||(1.64)|(4.27)|
|R<sup>2</sup>|.399|.736|

The NDVI variables have been added to the regression in column 6 in Table 4 for Brazil and column 6 in Table 5 for India. The dependent variable is net revenue per hectare. Only the added variables are shown here.

56
24

# Table 5.1 - Vietnam: Direct Foreign Investment (DFI) Comparative Incentives

|_ASEAN_<br>_countries_|_Tax Incentives_|_Import Tariff_|_EPZ and/other Free_<br>_trade_<br>_Instruments_|_Other incentives and/or conditionality_|
|---|---|---|---|---|
|Indonesia|None|Duty drawback|Bonded zones|Joint ventures required of DFIs|
|Malaysia<br>l_____________|Five-year tax holiday<br>_____________________________|Exemption for machinery<br>and raw materials for approved<br>projects.|Several EPZs|Loans from government-sponsored banks available<br>R & D expenditures tax deductible|
|Philippines|Five to eight years tax holiday|- Exemption for machinery<br>- Post-operative tariff protection<br>available|Several EPZs|Tax incentives for projects in<br>underdeveloped areas<br>-R & D expenditures tax deductible|
|Singapore|Five to ten years tax holiday|- Exemption for nearly all<br>imports including for FDI.|Several Free Trade<br>Zones|Loans at concessionary rates for targeted industries<br>- R & D expenditures tax deductible|
|Thailand|Three to five years tax holiday|Exemption for machinery and<br>selected raw materials|Two EPZs|- Tax credits for projects outside Bangkok<br>- Infrastructure support for large projects|
|_Viet-Nam_|Two years tax holiday plus<br>another two years tax reduction<br>at 50 percent|Exemption for machinery and<br>raw materials for approved<br>projects|Two EPZs<br>Other EPZs planned|- Lower corporate rates for FDls on "priority industries" list<br>- 100 percent owned FDls allowed|

Sources: Vietnam: Foreign Industrial Law and Decrees (1991) and 1992 provisions.

ASEAN: ASEAN Fiscal Incentives, Business International and other sources.
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE the population was getting access to traditional latrines (figure 15). The lack of sanitary facilities and poor hygiene is a major contributory factor to Sierra Leone‘s exceptionally high maternal, infant, and child mortality rates (UNICEF 2008). Moreover, Sierra Leone has not been able to reduce its reliance on surface water, which is higher than in comparable fragile states. Reliance on open defecation has decreased only marginally, but it is expected to decrease in the upcoming years due to the implementation of WASH programs.

**Table 9. Benchmarking water and sanitation indicators**

||Unit|Fragile states|Sierra Le|one|Low income<br>countries|
|---|---|---|---|---|---|
|||Mid 2000s|2000|2008|Mid 2000s|
|Access to piped water|% pop|17.9|11.3|7.6|9.3|
|Access to standposts|% pop|9.4|16.3|13.7|17.1|
|Access to wells/boreholes|% pop|54.5|44.3|49.8|39.3|
|Access to surface water|% pop|18.1|28.1|28.3|34.2|
|Access to septic tanks|% pop|11.2|6|3|4.7|
|Access to improved latrines|% pop|29|16|8|18.3|
|Access to traditional latrines|% pop|36.8|55|64|38.5|
|Open defecation|% pop|23.1|23|24|38.3|
|<br>|<br>|||2007<br>||
|Domestic water consumption|liter/capita/day|29.6||40|50.9|
|Revenue collection|% sales|94.6||77|94.1|
|Distribution losses|% production|31.5||40|34.8|
|Cost recovery|% total costs|57.2||61.4|89.5|
|Operating Cost recovery|% operating costs|80.1||85.8|125.2|
|Total hidden costs as % of revenue|%|169||297|67|
|||Sierra Leone<br>2007|Countries wi<br>scarce w<br>resourc|th non-<br>ater<br>es<br>Oth|er developing<br>regions|
|Average effective tariff|U.S. cents per m3|22||80|3.0 – 60.0|

_Source_ : AICD water supply and sanitation database downloadable from **http://www.infrastructureafrica.org/aicd/tools/data** .

Access figures calculated by AICD using data from the 2000 Multiple Indicators Cluster Survey as published by the JMP in March 2010 and the 2008 Demographic and Health Survey Note: A country is considered non- scarce water resources is the renewable internal freshwater resources per capita is greater than 3,000 mm Domestic water consumption, revenue collection, distributional losses, cost recovery, and total hidden costs only reflect the values for GWVC as there is not data available for SOWALCO — = data not available.

32
# The Role of Social Ties in Factor Allocation<sup>_∗_</sup>

Ulrik Beck Benedikte Bjerge Marcel Fafchamps Keywords: social networks; allocative efficiency; land markets; factor markets JEL codes: C21, D85, O12

> _∗_ Ulrik Beck (corresponding author) is a senior economist at the Danish Economic Councils, Copenhagen, Denmark; his email address is urb@dors.dk. Benedikte Bjerge is an economic consultant; her email address is benediktebjerge@gmail.com. Marcel Fafchamps is a senior fellow at the Freeman Spogli Institute for International Studies, Stanford University, Stanford, USA; his email address is fafchamp@stanford.edu. We benefitted from comments from seminar participants at the Universities of Oxford, UC-Berkeley, Paris 1, Gothenburg and the Goethe University in Frankfurt, as well as conference participants at the CSAE conference 2014, the International Economic Association’s World Congress 2014 and the Nordic Conference in Development Economics 2015. The authors also thank Dany Jaimovich, Thomas Markussen, Carol Newman, John Rand, M ̊ans S ̈oderbom and Finn Tarp and three anonymous referees for their very valuable comments. Finally, we thank Jean-Louis Arcand and Dany Jaimovich for making the Gambia Networks Data 2009 available to us.
**Table** 7: **Land price regressions at the plot level with and without household fixed effects**

|||_Technique_||
|---|---|---|---|
||OrdinaryLeastSquares|Randomeffects|Householdfixed effects|
|Area|0.011***<br>(18.91)|0.009***<br>(17.76)|0.007***<br>(10.38)|
|Areasquared|-0.000001***<br>(12.39)|-0.0000002***<br>(12.00)|-0.0000003***<br>(7.19)|
|Valueofimprovements(log)|0.061***|0.047***|0.023***|
||(9.00)|(7.83)|(2.97)|
|Lengthofpossessioninyears|0.004*|0.005**|0.013***|
||(1.68)|(2.08)|(3.31)|
|Plotisregistered|0.372***|0.331***|0.294***|
||(5.80)|(5.31)|(3.21)|
|Plot hasbeenpurchased|<br>0.001|<br>0.104|0.281***|
||(0.01)|(1.51)|(2.68)|
|Plothas beenoccupied|0.048|0.085|0.119|
||(0.35)|(0.62)|_(0.55)_|
|Plotacquiredthroughag.Reform|0.293|0.209|-0.531|
|before1990|(0.98)|(0.70)|(1.02)|
|Plotacquired throughag.Reform|0.040|0.201*|0.442**|
|<br>after 1990|(0.37)|(1.86)|(2.44)|
|Pre-1990agrarianreformand|-0.532*|-0.204|1.107**|
|agr.Reformtitle|(1.70)|(0.65)|(2.05)|
|<br>Plotisundulating|0.168**|0.127*|0.139|
||(2.38)|(1.78)|(1.25)|
|Plotishilly|0.071|0.064|0.216+|
||(0.97)|(0.84)|(1.69)|
|Plotissteep|-0.109|-0.161|-0.031|
||(0.91)|(1.30)|_(0.15)_|
|Plot usedforperennials|0.538***|0.392***|0.004|
||(4.80)|(3.65)|(0.03)|
|Plotusedforpasture|0.436***|0.368***|0.128|
||(6.44)|(5.68)|(1.43)|
|Plotusedforforest|-0.008|0.109|0.131|
||(0.04)|(0.67)|(0.71)|
|Plotisinfallow|<br>-0.288|<br>-0.335**|-0.614***|
||(1.61)|(2.00)|(2.62)|
|Houseplot|<br>-0.635***|<br>-0.580***|-0.388**|
||(4.60)|(4.78)|(2.46)|
|Constant|<br>8.964***|<br>9.042***|8.989***|
||(93.18)|(92.79)|(63.08)|
|Numberofobservations|3062|3062|3062|
|R-squared|0.26|0.20|0.17|
|Numberofhouseholds|1926|1926|1926|

Source: Own computation from 2000 World Bank/University of Wisconsin survey.

Notes: Dependent variables is the self-reported selling price of the land in C5 *** significant at 1%; ** significant at _5%;_ * significant at 10% Region dummies included but not reported. Absolute value of robust t-statistics in parentheses.

29
## **INTRODUCTION AND APPROACH'**

# **Background**

The demand for real money balances in Venezuela has experienced sharp, recurrent fluctuations, as the economy adjusted to external and internal shocks. Real money balances (as measured by Mi)<sup>2</sup> was 41 percent higher at the end of 2001 than in 1993, but it was still 7 percent bclow the peak reached at the end of 1997. Despite the severity of these fluctuations, real money balances expanded at an annual rate of about 4 percent since 1993 (see Figure "Venezuela: Real Money Balances").

**Venezuela: Real Money Balances***

**Venezuela: Standard Deviation of**

<!-- Start of picture text --> 16000 - Changes in Real Money Balances*<br>2500 -<br>14000 -<br>2000-<br>4L2000 -<br>21500-<br>L.100<br>=E A!1000<br>8000-<br>500-<br>6000-<br>4000 . . cr c-r o. co r- cc Co<br>cn 1-4 - I r-- co 0% CD 0 % % 0 a,0 0' a, 0<br>a,'a, a,ol rI lr4 *j fRolling standard deviation using a 4 quarter window.<br><!-- End of picture text --> <!-- Start of picture text -->
*MI  deflated by the  CPI  index.  Quarterly frequency.<br><!-- End of picture text -->

The severity of shocks to real money balances as measured by the rolling standard deviation of its changes, also fluctuated greatly during the 1993.1-2001.4 period, multiplying eightfold from a low in 1994 to a peak in 1996, and falling thereafter by about one fifth (see Figure "Venezuela: Standard Deviation of Changes in Real Money Balances").

'This paper has been prepared in the context of the "Venezuela-Growth and Sustainability" study at the World Bank. I wish to thank Professor Andrew Harvey (Cambridge University) for providing guidance on recent developments in structural time series modeling. Vicente Fretes-Cibils and Marcelo Giugale, at the World Bank, provided strategic guidance as well as resources that greatly facilitated the preparation of this paper. Valeriano Garcia and Mauricio Villafuerte (IMF) have kindly provided suggestions and feedback. Responsibility for its contents and for any errors remains solely with the author.

**2** This section focuses on Ml because no satisfactory empirical results have been obtained using base money and M2 as measures of money demand.

Money Demand in Venezuela, Mario A. Cuevas 2
This research is policy relevant. First, if the _mixture_ of financial institutions and markets matters—and not only the development of financial institutions and markets, then this advertises financial structure as an independent indicator of the ability of the financial system to provide growth-enhancing services to the economy. Second, if the optimal mixture changes as an economy develops, then this advertises the costs of policy and institutional impediments to the evolution of the financial system. Third, this work suggests that the sensitivity of economic activity to bank and securities market development changes with economic development. This implies that the estimated OLS elasticities from past research of the impact of changes in bank or stock market development on economic development will yield misleading information about countries incomes far from the sample average income. Past studies do not account for the _evolving_ importance of banks and markets during the process of economic development.

This paper builds on earlier studies of financial structure. Demirguc-Kunt and Levine (2001), for instance, find that financial structure is not robustly linked with economic growth. We do not reject this finding. Rather, we show that economies tend to increase their relative demand for the services provided by securities markets during the process of economic development and that _deviations_ of actual financial structure from an economy’s optimal financial structure—financial structure gaps—are associated with lower levels of economic activity. Thus, while earlier work focused on actual financial structure, we focus on the financial structure gap and stress the evolving importance of banks and markets.

This paper is one step in deriving a better understanding of the dynamic relationships among economic development, financial institutions, and securities markets. In this paper, we show that both the “supply” of securities market services and the economic development “returns” to securities market development increase as economies grow. This suggests that the relative demand for securities market services increases with economic development. But, we do not identify the causal impact of banks, markets, and financial structure on economic outcomes. Although this paper advertises the desirability of understanding the policy and institutional determinates of the Financial structure gap, much work remains.

5
**Table 1. Percentage of wage and self-employed workers, sample countries**

||Wage/ salaried<br>workers|Self-employed<br>workers|Number<br>employed|
|---|---|---|---|
|Armenia|87.87|8.98|1,047|
|Bolivia|51.85|32.89|1,788|
|Colombia|53.20|35.62|1,735|
|Georgia|84.13|10.23|958|
|Ghana|35.67|50.80|2,181|
|Kenya|56.59|33.03|2,419|
|Ukraine|88.83|6.96|1,307|
|Vietnam|56.34|28.32|2,366|

_Note_ : “Employed” includes employers and unpaid workers. _Source_ : STEP Surveys (2014).

In keeping with standard practice, the top 1 percent hourly earners were removed from the analytic sample to avoid potential outliers, and extremely low wages were imputed to those reporting zero hourly earnings.<sup>10</sup> Finally, cases missing information on any of the key variables were dropped from the sample. The proportion missing constitutes less than 0.1 percent of the sample.<sup>11</sup> This is a small proportion of observations; we believe that excluding them would not bias the estimates due to nonrandom loss of sample. The effective sample size for the empirical analysis ranges from 849 to 1,953 observations across the countries in our sample.

# **_Description of the sample_**

The general landscape of the labor market in each country is presented in Tables 2 and 3. Overall, labor force participation across the sample of countries is high and ranges from 49.5 percent in Armenia to 84.3 percent in Ghana. However, the employment rate is fairly low in Armenia (27.8 percent) and Georgia (25 percent), while it is above 50 percent in the other countries.<sup>12</sup> The low employment rate observed in Armenia and Georgia impacts the effective sample size used to estimate the returns to education and the relationship between various skills and hourly earnings.

The average hourly earnings in 2011 purchasing-power-parity-(PPP)-adjusted U.S. dollars range from approximately US$2.15 in Ghana to US$3.60 in Bolivia and Ukraine. There are also

> 10 A value of 0.00001 was imputed to workers who reported no hourly earnings.

> 11 In the case of Ghana, about 800 cases are missing data on the noncognitive skills measures. These items were administered in English and respondents reported inadequate English language skills to complete this battery. As a result, data from Ghana are not included in estimating the effects of noncognitive skills.

> 12 According to UN data, labor force participation among males and females in Armenia in 2012 was about 51 percent and 73 percent, respectively. Corresponding figures for Georgia in 2012 were 56 percent and 75 percent, respectively

> (http://unstats.un.org/).

10
<mark>Goldstein, Markus, and Christopher Udry. "The profits of power: Land rights and agricultural investment in Ghana."</mark> _<mark>Journal of political Economy</mark>_ <mark>116(6), 981-1022.</mark> Hadlock, Charles, and Joshua Pierce, 2010, New Evidence on Measuring Financial Constraints: Moving Beyond the KZ Index, _Review of Financial Studies_ 23, 1909-1940.

Hallward-Driemeier, Mary, Lant Pritchett. 2015. “How Business is Done in the Developing World: Deals versus Rules.” _J. of Econ. Perspectives_ 29(3), 121-140.

Harrison, Ann, Marshall Meyer, Peichun Wang, Linda Zhao, Minyuan Zhao. 2019. “Can a tiger change its stripes? Reform of Chinese state-owned enterprises in the penumbra of the state.” NBER Working Paper 25475.

Hayek, Friedrich. 1960. the constitution of liberty. Chicago: Chicago University Press.

Heckman, James J., V. Joseph Hotz. 1989. “Choosing among Alternative Nonexperimental Methods for Estimating the Impact of Social Programs: The Case of Manpower Training,” _Journal of the American Statistical Association_ , 84 (408), 862-874.

<mark>Hornbeck, Richard. "Barbed wire: Property rights and agricultural development."</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>125 (2), 767-810.</mark> Hsieh, Chang-Tai, and Peter Klenow, 2009, Misallocation and Manufacturing TFP in China and India, _Quarterly Journal of Economics_ 124, 1403-1448.

Huang, Zhangkai, Lixing Li, Guangrong Ma, L. Colin Xu. 2017. “Hayek, Local Information, and Commanding Heights: Decentralizing State-Owned Enterprises.” _American Economic Review_ 107(8), 2455-2478.

Jacoby, Hanan G., Guo Li, and Scott Rozelle, 2002, Hazards of Expropriation: Tenure Insecurity and Investment in Rural China, _American Economic Review_ 92, 1420-1447.

Jimenez, E. 1984. “Tenure security and urban squatting.” _Review of Economics and Statistics_ 66(4), 556-67.

Johnson, Simon, John McMillan, and Christopher Woodruff. 2002. “Property Rights and Finance.” _American Economic Review_ 92(5), 1335–56.

Johnson, S., Mitton, T., 2003. Cronyism and capital control: evidence from Malaysia. _J. Financ_ . Econ. 67 (2), 351–382.

Lanjouw, Jean O., Philip I. Levy. 2002. “Untitled: A Study of Formal and Informal Property Rights in Urban Ecuador.” _Economic Journal_ 112, 986-1019.

- Li, Hongbin, Lingsheng Meng, Qian Wang, Li-An Zhou, 2008, Political connections, financing and firm performance: Evidence from Chinese private firms, _Journal of Development Economics_ 87, 283–299

- Li, Kai, Tan Wang, Yan-Leung Cheung, and Ping Jiang, 2011, Privatization and risk sharing: Evidence from the split share structure reform in China, _Review of Financial Studies_ 24, 24992525.

Libecap, Gary, Dean Lueck. 2011. “The demarcation of land and the role of coordinating property institutions.” _Journal of Political Economy_ 119(3), 426-467.

Lin, Justin Yifu, Fang Cai, and Zhou Li., 1998, Competition, policy burdens, and state-owned 39
# **3 Data and Empirical Specification**

## **3.1 Data on Child Health**

I use micro data on infant and neonatal mortality from country-specific DHS (DHS Program, 2015). All other information on children and mothers is also obtained from the DHS. The survey questionnaires are standardized which allows for cross-country analysis and each survey consists of a nationally representable sample of women of childbearing age (15 to 49 years). The women answer detailed questions regarding their personal characteristics, family situation, the survival of their children, etc. In these surveys, mothers give detailed information on the birth dates and death dates, if applicable, of all their children ever born (up to 20 children) so that a panel of children’s infant and neonatal mortality can be obtained. One country survey is thus enough to secure a panel of children. For the purpose of analyzing the effects of debt relief under the HIPC Initiative I select all 31 HIPCs for which there is at least one survey available between the country’s Decision Point date and mid-2014 and all countries that participate in the IMF’s PRSP approach to economic and social development or are Sub-Saharan African or Latin American countries where at least one survey has been conducted between 2004 and mid-2014. If there are more than one survey that fulfill these criteria, I choose the latest available survey to maximize the number of post-Completion Point observations. Table 2 presents a complete list of the 56 countries in the sample and information on the DHS used in the empirical analysis.<sup>5</sup> The main child health outcomes in this paper, infant mortality and neonatal mortality, are defined as a baby dying before reaching his or her first birthday or month of life, respectively. Children born less than twelve months prior to their mother’s interview are dropped since it is not possible to know whether they will survive their first birthday. Also, only children born after their country’s year of independence are included in the sample. This gives a sample of 1,770,705 children born to 501,800 mothers between 1967 and 2013 in the 56 countries. The sample average infant mortality rate is 7 percent and the neonatal mortality rate is 3.5 percent, indicating that half of the children that die before turning one year in fact live less than one month. Figure 3 presents the yearly infant and neonatal mortality rates of the group of HIPCs and non-HIPCs. The differences between the groups are greater for infant mortality than for neonatal mortality and the general trend is decreasing for both mortality measures. Since there appears to be some differences in the trends between the two groups, I control for country-specific linear time trends in the model specification presented below.

Table 3 shows descriptive statistics on infant and neonatal mortality across different sub-groups of the sample as well as other variables in the analysis (see Section 3.2 for more information on the other

> 5I use the standard DHS unless stated otherwise.

7
Handbook (1998), and the Singapore Investment Guide (1998).2 We exclude 852 companies across the nine countries which have proxy ownership that cannot be traced to a specific owner. In all cases, we collect the ownership structure as of December 1996 or the end of the 1996 accounting year. We end up with 2,980 companies for which have complete ownership information and where we can trace the ultimate owners.

The coverage of the sample does not differ significantly across the nine countries, as shown in Table 1. Typically, we cover about three-quarters of total market capitalization even though the share of firms in our sample relative to the total number of listed firms is sometimes (Korea, Malaysia, Taiwan, Thailand) less than 50%. This is because we always cover the largest hundred firms in terms of market capitalization, i.e., the average firm in our sample is larger than the average listed firrn on the stock markelt.

Following on La Porta, Lopez-de-Silanes, and Shleifer (1998), we analyze the control pattern of companies by studying all ultimate shareholders who control over t'en percent of the votes. We also use a twenty percent cut-off (originally suggested by Berle and Means), thirty percent cut-off, and forty percent cut-off. The four ownership cut-off levels are used for robustness purposes, but also help us compare the concentration of ownership across countries, size, and age of corporations. Consistent with the previous literature, however, the twenty percent cut-off is used as the benchmark.

In the majority of cases, the principal shareholders are themselves corporate entities, not-for-profit foundations, or financial institutions. We then identify their owners, the owners of their owners, etc. We do not distinguish among individual famnily members and use the family group as a unit of analysis. The exception is the Suharto family in Indonesia, which we discuss in Section VI.

Our definition of ownership relies on control rights, and not on cash flow rights. This distinction can make an enormous difference in the analysis. Suppose, for example, that a family owns 11% of the stock of publicly-traded Firm A, which in turn has 21 % of the stock of Firm B. Since we look at control rights, we would say that the famnily controls 11% of Firm B-the weakest link in the chain of voting rights. In contrast, we would say that the family owns about 2% of the cash flow rights of Firm B-the product

> 2 While significant sources of ownership information exist on the internet (see La Porta, Lopez-de-Silanes, and Shleifer, 1998 for details) we used exclusively hard copy reports, which proved more efficient given the number of companies covered in the database.

7