rafmacalaba/datause-encoder-data huggingface dataset:
# **TABLES TO BE INSERTED IN THE TEXT**

**Table I. Potential impacts assessed through qualitative work**

|**Area**|**Description**|
|---|---|
|Project impact on individual<br>households|Focuses on how the project might have influenced farming practices,<br>use of resources (fertilizers, labour), household income, household<br>consumption,familyrelationships|
|Project impact on receiving<br>communities|Deals with the economic, environmental and social impacts the<br>project mayhave had on the receivingcommunity|
|Non project factors that impact<br>productivity<br>_Source: Behrman(2009)_.<br>**Table II: Attrition Rates by Type**|Sheds light on the degree to which impact on productivity can be at-<br>tributed to the project, by providing information on non-project re-<br>lated factors that took place parallel to the project intervention and<br>are believed to have impacted productivity in the intervention areas.<br>This includes similar projects in the area, rising prices of inputs,<br>availabilityof markets,weather conditions and so forth.<br>**of Beneficiary**<br> <br> <br>|
||Control Group<br>BeneficiaryGroup<br>Total|
|Non-attrited(in finalpanel)|**381**<br>**256**<br>**637**|
|Attrited(onlyat baseline)|80<br>140<br>220|
|Total|461<br>396<br>857|

## **Table III. Outcome Variables**

|_Agricultural Variables_||
|---|---|
|Change in Land (Garden) Size|The total size of cultivated (not total land)|
|Change in Title|1 if the land was purchased with a title, 0 if not|
|Change in Productive Assets (ln)|Natural log of the value of productive assets|
|Change in kg Maize|Kilograms of maize produced|
|Change in Maize Yield|Kilograms of maize produced / acre of land|
|Change in kg Tobacco|Kilograms of tobacco produced|
|Change in kg Cassava|Kilograms of cassava produced|
|Change in Livestock|Livestock holdings in tropical livestock units|
|Total Crop Value (ln)|Natural log of the total selling value of all crops|
|_Welfare Variables_||
|Change in Months of Reserve Food|Months for which current food supply would last|
|Change in Lean Season Meals|Number of meals household eats in lean season|
|Change in Consumption Assets (ln)|Natural log of the value of consumption assets|
|Income Better than Before|1 if respondent believes income is better than before moving<br>(better than 5 years ago for control groups), 0 if not|
|Economic Well-Being Better than Be-|1 if respondent believes economic well-being is better than be-|
|fore|fore moving (better than 5 years ago for control groups), 0 if not|

32
- [5] Binswanger, H., J. Dayantha, T. Balaranaia and D. Sillers, 1980. The Impacts of Risk Aversion on Agricultural Decisions in Semi-Arid India, working paper, World Bank, Washington DC.

- [6] Conley, T. G. and C. Udry, 2003. Social learning through networks: The adoption of new agricultural technologies in Ghana, _American Journal of Agricultural Economics_ 83, 668-673.

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- [8] Cox, D. R., 1975. Partial Likelihood, _Biometrika_ 62, 269 - 276.

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- [10] Feder, G. and G. T. O’Mara, 1982. On information and innovation diffusion: a Bayesian approach, _American Journal of Agricultural Economics_ 64, 145-147.

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- [12] Gin ́e, X. and S. Klonner, 2004. Dynamic Lending with Limited Commitment and Uncertain Borrower Types: Theory and Evidence, working paper, Cornell University Department of Economics.

- [13] The Hindu, 2001. Boon for small-scale fishermen, _The Hindu_ , March 22, 2001.

- [14] Kurien, J., 1994. Kerala’s marine fisheries development experience. In B. A. Prakash, editor, _Kerala’s economy: Performance, problems, prospects_ , 195-214. New Delhi: Sage.

- [15] Munshi, K., 2003. Social Learning in a Heterogeneous Population. Technology Diffustion in the Indian Green Revolution, unpublished manuscript, Brown University.

30
# **Figure 12: China’s overall, rural and urban maternal mortality ratios, 1991 – 2009**

<!-- Start of picture text --> Deaths per 100,000 live births<br>120<br>112.5<br>100<br>88.9<br>80<br>Rural<br>Total<br>60 Urban<br>45.9<br>40 34<br>31.9<br>20 26.6<br>0<br><!-- End of picture text --> Source: Ministry of Health, _China Health Statistical Yearbook,_ 2009 (1991 – 2008 data) Ministry of Health, China Health Statistical Digest, 2010 (1990, 2009 data) 24
# SIMULATING ALTERNATIVE INFRASTRULCTUR PRICING POUCIES IN THE PRESENCE OF PRIVATE PROVISIONS: NIEIN AND INDONESIAN MANUJFACTURINCG

# I. INTRODUI

Our purpose in this report is to explore the welfare implications of alternative pricing schemes for publicly supplied electricity when public supply is unreliable and competes with private provision. This will be done by calibrating models based on simple alternative assumptions about the firm's production technology and then simulating the responses of the individual firms in our sample to hypothetical changes in NEPA's pricing policy in Nigeria and PLN's pricing policy in Indonesia.

Nigeria is representative of those developing countries where the public sector is inefficient and private provision of electricity by manufacturers compensates for public sector inefficiency. The use of private generators among Nigerian manufacturers is virtually ubiquitous even though the Nigerian government did not encourage the use of generators in order to protect NEPA's monopoly. In our Nigerian sample collected in 1988, 89% of the firms produced some of their power needs intemally. We also observed, however, that many firms underutilized their plant and also their generators. This is because of the recessionary conditions which prevailed in the 1980's.

In Indonesia, PLN is fairly efficient but its ability to supply power in a reliable manner
There is also work related to other aspects of our study than entry regulation. Kumar, Rajan and Zingales (2000) find that the average size of firms in human capital intensive industries (and in R&D intensive industries) is larger in countries that protect property rights (patents). Using survey data from five transition countries on the reinvestment of profits by entrepreneurs, Johnson et. al. (2002) examine the importance of property rights. They find lower investment by entrepreneurs in countries with weak property rights. Claessens and Laeven (2003) find that growth of industries that rely on intangible assets is disproportionally lower in countries with weak intellectual property rights. Our finding that there is less entry in R&D intensive industries when intellectual property is weakly enforced echoes their findings. Finally, others have also found that financial development seems to foster entry (see Black and Strahan (2002) or Rajan and Zingales (1998)).

There is a substantial literature on entry into an industry (possibly by a firm from another industry) as distinguished from firm creation. It is the latter sense in which we use the term “entry”. It would take us too much out of our way to describe the literature on industry entry, so we refer the reader to Gilbert (1989) for a comprehensive survey. Note that there are technological determinants of entry into an industry such as minimum scale, etc., which also affect firm creation. We assume these determinants carry over countries so they are absorbed by industry indicators. Our focus then is on environmental determinants of firm creation.

The paper proceeds as follows. In Section I we describe the data and in Section II we present the empirical methodology. We present the empirical results in Section III. We conclude in Section IV.

# **I. Data**

# _1.1 Amadeus database_

Central to our analysis is the firm-level Amadeus database. Amadeus is a commercial database provided by Bureau van Dijk. It contains financial information on over 5 million private 7
Table 5. Changes in output by sector and region following NDC implementation (top three sectors with the largest output reduction in each region are reported), % change w.r.t. post-COVID baseline in 2030

|Region\most|To<br>|p1<br>|Top2<br>|<br>|Top3<br>||
|---|---|---|---|---|---|---|
|impacted sector|Sector|Change, %|Sector|Change, %|Sector|Change, %|
|China|Coal<br>extraction|-6.0|Gas extraction<br>and distribution|-3.0|Wearing apparel<br>and leather<br>products|-0.2|
|Rest of East Asia<br>& Pacific|Air<br>transport|-10.6|Coal extraction|-8.6|Other transport|-5.8|
|Europe|Coal<br>extraction|-33.2|Air transport|-2.5|Gas extraction and<br>distribution|-2.2|
|Europe &<br>Central Asia|Coal<br>extraction|-15.7|Gas extraction<br>and distribution|-1.1|Non-metallic<br>minerals|-0.6|
|Latin America<br>and Caribbean|Coal<br>extraction|-18.3|Gas extraction<br>and distribution|-3.3|Refined oil|-1.3|
|Middle East &<br>North Africa|Coal<br>extraction|-5.7|Gas extraction<br>and distribution|-0.3|Wearing apparel<br>and leather<br>products|-0.2|
|Rest of High<br>Income<br>Countries|Coal<br>extraction|-18.5|Gas extraction<br>and distribution|-3.3|Textiles|-1.2|
|South Asia|Coal<br>extraction|-6.1|Wearing apparel<br>and Leather<br>products|-0.4|Gas extraction and<br>distribution|-0.4|
|Sub-Saharan<br>Africa|Coal<br>extraction|-8.9|Gas extraction<br>and distribution|-0.2|Meat products<br>(inc. fisheries) and<br>other food|-0.2|
|United States|Coal<br>extraction|-25.0|Gas extraction<br>and distribution|-4.9|Other transport|-0.5|
|World|Coal<br>extraction|-10.9|Gas extraction<br>and distribution|-2.2|Air transport|-0.9|

Notes: Top three sectors with the largest reduction in output are reported for each region. Numbers in brackets indicate corresponding change in output. Source: ENVISAGE simulations Consistent with the observed changes in production patterns, exports and imports of fossil fuels are impacted the most (Figure 20). With lower global demand for fossil fuels, volumes of trade for these commodities are expected to decline, accompanied by a moderate reallocation of trade to places with less stringent environmental regulations. China, as the world’s largest net importer of fossil fuels with a relatively low carbon price consistent with its NDC target, increases imports of fossil fuel commodities. Both Sub-Saharan Africa and East Asia, being large net energy exporters, suffer reductions in exports of fossil fuels. In the case of Sub-Saharan Africa, reduction in exports of fossil fuels is compensated for by an increase in exports of other goods and services, including agricultural commodities.

32
**_E. And the banking system vulnerabilities are rising_**

**_In recent years before the global crisis, resistance to nominal appreciation in the face of upward pressure on the real exchange rate, with financial conditions, offered the banks a one-way bet._** In this environment, attempts to sterilize capital inflows lead to high domestic interest rates while exchange rate pressure is up towards an appreciation. With ample global liquidity and low global interest rates, Russia’s banks and corporations were able to borrow cheaply abroad, earn high interest rates by lending at home, and expected to make additional capital gain on their foreign exchange exposure (Figure 11).

**Figure 13: Russia: Increased forex borrowing by banks (A) and corporations (B)**

<!-- Start of picture text --> Commercial bank borrowing, US$ bn Net borrowing of corporations, US$ bn<br>80 80<br>60<br>60<br>40<br>40<br>20<br>20<br>0<br>0<br>-20<br>-20<br>Net borrowing of corporations, US$ bn<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> Source: Central Bank of Russia.

**_Time and again, as observed among other oil exporters that did not accept the real exchange rate consequences of their spending plans, this resulted in rising bank fragility_** . The high foreign exchange exposure that resulted threatened to wipe out large parts of bank equity and a substantial part of deposits if and when oil prices unexpectedly fall and the anticipated currency appreciation evaporates and rapidly turns into depreciation.

**_The banking sector entered the global financial crisis in a relatively strong position._** At the end of 2008, Russia’s banks enjoyed relatively sound financial indicators, with strong solvency (average capital adequacy ratio of 15.5%), high profitability (return on equity of 22.7%), low non-performing loans (2.5% of total loans)<sup>4</sup> and adequate liquidity (short term liquidity ratio of 72.9%).

**_However the financial sector indicators masked exposures to liquidity and credit risks._** Many Russian banks relied, indeed, excessively on nontraditional (foreign) external sources of funding. The loan-deposit ratio of the Russian banking system increased from

> 4 Non-performing loans in Russia are defined as debt-service charges overdue by more than 60 days, excluding refinanced and restructured loans.

17
# **Managing Food Price Volatility in a Large Open Country: The Case of Wheat in India**

**Christophe Gouel**<sup>**1**</sup> **Madhur Gautam**<sup>**2**</sup> **Will J. Martin**<sup>**3**</sup>

_JEL classification:_ F13, O13, Q11, Q17, Q18 _._

> 1 INRA, Économie Publique, and CEPII (christophe.gouel@grignon.inra.fr)

> 2 World Bank, Agriculture Global Practice, The World Bank (Mgautam@worldbank.org).

> 3 International Food Policy Research Institute (IFPRI) (w.martin@cgiar.org).

A revised version of this paper has been accepted for publication in Oxford Economic Papers.
6 _4. Income._ The subtlest argument refers to the role of per capita income. Following Wheare (1964), decentralization is often viewed as a luxury good since, as Prud’homme (1995) argues, at low levels of income the basic needs of the population are universal and require no fine preference matching, while as income grows people start demanding different kinds of public goods. Oates (1972) and Panizza (1999) indeed find evidence of a positive correlation between per capita income and fiscal decentralization. Wallis and Oates (1988) on the contrary find no such evidence for the USA states and argue that the relationship can be reversed in rich countries: as societies become richer, they pay more attention to equalization of wealth, which is usually implemented by fiscal redistribution through central government transfers. Combining the two arguments and viewing Russia as a medium-income country, one would expect a positive relationship between income and fiscal decentralization in Russian regions.

5. _Urbanization_ . Wallis and Oates (1988) found that urbanization is associated with higher fiscal decentralization. As the size of municipalities grows, they become able to provide a wider range of public goods and services, which could not be provided within small rural communities. The study by the Inter-American Development Bank (1997) finds that among categories of government expenditure the most decentralized are urban transportation, water supply, waste and urban street management. In the course of urbanization the weight of these expenditure items in total government spending grows rapidly, facilitating overall fiscal decentralization. World Bank (2000) report suggests that decentralization may be viewed as a means of maintaining political stability when pressures for more autonomy become stronger. Insofar as educated middle class emerging in cities becomes more exposed to global aspirations and voices demand for more autonomy, urbanization may contribute to decentralization. Besides,
# **2. Overview of the reforms**

Table 1 summarizes the results of a survey on the extent to which there is at least some corporate private sector participation in electricity distribution, telecoms, water & sewerage, and railways across regions of the world.<sup>6</sup> On average, a smaller proportion of the developing countries are sharing the responsibility for service delivery in these industries than in developed countries. In general also, it seems that among developing countries, the richest countries have been more systematic at engaging in reforms to attract the corporate private sector.<sup>7</sup>

**Table 1: How present is the private sector in Network industries?** **<u>(Share of countries in total number of countries)</u>**

||**Electricity**<br>**Distribution**|**Water &**<br>**Sewerage**|**Railways**|**Telecoms**|
|---|---|---|---|---|
|**Developing Countries**|36%|35%|37%|48%|
|**Developed countries**|43%|80%|65%|83%|
|**Sub-Saharan Africa**|28%|20%|47%|41%|
|**East Asia**|20%|64%|43%|38%|
|**Eastern Europe**|48%|62%|20%|58%|
|**Latin America**|61%|41%|56%|67%|
|**Middle East**|6%|18%|20%|23%|
|**South Asia**|13%|13%|17%|50%|

Source: Estache and Goicoechea (2004) There is unfortunately no equivalent encompassing survey for the type of regulation used in reforming economies. There are however a few partial surveys which all point to an increased concern for the explicit consideration of efficiency in the choice of regulatory regimes. The largest survey is for a sample of about 1000 contracts in Latin America.<sup>8</sup> The database contains detailed information on the characteristics of these concessions, including general details about the projects (sector, activity, year of award), the award criteria, size and duration of the concession, information with respect to the institutional context and degrees of freedom of the regulator, the type of regulatory framework put in place (price cap, rate of return, no regulation), and several details of the concession contract like arbitration clauses, nationality of operators, among others. In this rather encompassing data base, 56% of the contracts were regulated under a price cap regime, 20% under rate of return regulation. For 24% of the contracts, the regime is a hybrid one meaning that roughly 80% of the contracts signed in that region will require at some point some consideration of the efficiency gains achieved by the operators. Equivalent but smaller surveys for utilities were conducted in Africa and Eastern Europe. In all cases of water or electricity distribution covered by these surveys a price cap of some sort was adopted. Even if there may have been a selection bias in those surveys,

> 6 More details in Estache and Goicoechea (2004).

> 7 From now “private sector” is meant to mean “corporate private sector” since we ignore the role of small scale operators in the survey.

> 8 For a full description, see Guasch (2003).

3
the interaction between individual and average formality are unchanged. We also use interaction with industry-level formality, and find that out results are robust to this alternative measure. These findings lend support to the hypothesis that tax compliance matters for access to credit through an “information channel”. They are also consistent with the evidence in de la Torre et al. (2007) who, using data from recently fielded banking surveys, find that informality and low quality of balance sheets are the foremost constraint to SMEs lending in Argentina and Chile.

To provide a more nuanced understanding of how formality relates to credit, we use alternative dependent variables, such as indicators taking value 1 if firms report to finance their working capital or new investment with external (EXTFIN) or informal sources (INFFIN). External sources include financing from banks, leasing arrangements, and credit cards while informal finance sources include financing from family and friends, and money lenders.<sup>14</sup> Consistent with the results estimated for CREDIT, firms with better compliance rely more heavily on external financing. Coefficients are significant both in OLS and FE. Estimated effects with FE are small, indicating that a one-standard deviation increase in formality is associated with about 2% of a standard deviation increase in the probability of obtaining external finance.

More formal firms appear also to resort less to informal ways of financing. FE estimates are in this case larger than OLS, reflecting the likely negative correlation between development of informal credit networks and enforcement/tax compliance at the country-level (table 8). Note that formal and informal finance appear to be substitutes to some degree. In particular, reliance on informal (external) finance decreases (increases) linearly with size. For example, informal sources might fulfill completely the financing needs of smaller firms but as scale increases they might not satisfy credit demand. Informal, larger firms that lack proper documentation might then find themselves credit constrained.

# **VI. Robustness checks**

In our context, FE estimation provides an important robustness checks to the coefficients estimated with OLS, since they correct the bias driven by the correlations between country-level enforcement and development of financial markets. However, even after the country-effect is accounted for, a causal interpretation of the impact would be misplaced if the estimates still

> 14 Results are unchanged if we include trade financing in the definition of informal finance.

11
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