rafmacalaba/datause-encoder-data huggingface dataset:
to provide biased estimates of poverty rates (as measured by household consumption) and may only be able to help track poverty trends over time under special conditions (Dang _et al._ , 2017a).

We end this section on a cautious note that even if all the challenges discussed above are overcome, to our knowledge, several other technical challenges remain with the usage of wealth assets to measure poverty. For example, it remains unclear how many assets are sufficient in constructing a wealth index. Assets may be more comparable within a country, but it is unclear to what extent assets are comparable across countries (for cross-country analysis)? It also remains unclear how to take into account the issue of quality versus quantity of assets (e.g., the ownership of one brand-new luxurious car can be different from that of three old and cheap cars).<sup>9</sup>

# **III. Poverty Estimates for Project Targeting** _Motivation_

Identifying poor households that are beneficiaries of social transfer projects is a common task which is also known as proxy means testing, but completing this task is not simple when there are no data on household consumption. Put differently, this case shares a similar constraint with that of the previous section: consumption data are not collected because of various costs or logistical constraints. One major difference with proxy means testing, however, is targeting a subset of the poor population that are to benefit from (government or NGO) subsidies rather than measuring poverty for the whole population. Another important feature is that proxy means testing is (mostly) implemented when there are two sources of household survey data: one is a nationally representative survey that collects both household consumption data and the variables (xij, that are used in Equation (1)), and the other is the special and (much) lighter survey conducted to collect only the variables (xij) for the purpose of proxy means testing.

> 9 Ngo (2018) offers a method to construct a utility-based living standards index based on the values of assets that may address this issue to some extent (assuming that asset quality is correctly measured by asset values). But one practical challenge with this approach is that it requires the collection of asset price data.

11
Policy Research Working Paper 9815

# **Abstract**

Many developing countries face severe electricity constraints, which are reflected in low electrification rates, frequent and prolonged outages, and high electricity tariffs, all of which result in low electricity consumption that impedes economic development. This study estimates the impact of electricity consumption on value added through reduced form equations for three sectors: agriculture, manufacturing, and services. It uses panel data on 126 countries for 1996–2014 from the International Energy Agency and World Development Indicators databases. To control for endogeneity and reverse causality bias in the ordinary least squares estimators, the study applies two-step difference and system panel generalized method of moments estimation techniques, which improve the ordinary least squares estimates by applying lags of the explanatory variables as instruments that are not correlated with the error term and account for countries’ fixed effects generating bias in the coefficients. The estimation results indicate that electricity consumption has a significant and positive impact on the manufacturing sector’s value added in non-highincome countries (with an elasticity of 0.022). By contrast, the electricity consumption elasticities are insignificant in agriculture and services in non-high-income countries, as the production technologies of these industries vary substantially across income groups compared with those in manufacturing. Finally, using all the countries in the sample produce positive and significant results for all sectors, with the highest elasticity of 0.036 in manufacturing.

This paper is a product of the Sector Economics and Development Impact, International Finance Corporation. It is part of a larger effort by the World Bank Group 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://www.worldbank. org/prwp. The authors may be contacted at shovhannisyan@ifc.org and kstamm@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
Table 3: Nationwide impact of MNREGA on monthly fires

||Cropla|nd Fires|
|---|---|---|
|Post-MNREGA|0.096<sup>_∗∗_</sup>|0.213<sup>_∗∗∗_</sup>|
||(0.044)|(0.051)|
|Districts|558|558|
|Months|144|144|
|N|80,352|80,352|
|Avg. monthly fres pre-MNREGA|5.46|5.46|
|District FE|X|X|
|Year _×_ Month FE|X|X|
|Weather Controls||X|

Notes: Each column represents separate regressions. In all columns the outcome is monthly cropland fires. In all columns the coefficient can be interpreted as the approximate percentage change in fires after MNREGA was statutorily implemented in a district. In all columns the base regression is a Poisson of fires in district _i_ in month _m_ in year _y_ that includes district fixed effects and year by month fixed effects. In column 2 I include controls for the monthly average cloud cover, precipitation and minimum and maximum temperature in district _i_ in month, year _y_ . N refers to the number of districts _×_ months included in each regression. The sample is a balanced, monthly panel of districts in India from 2003 to 2014. Heteroskedasticity robust standard errors clustered at the district level are in parentheses. (* p _<_ .10 ** p _<_ .05 *** p _<_ .01).

39
# **Annexes**

**ANNEX A:** LIST OF DEVELOPING AND LEAST DEVELOPED COUNTRIES ELIGBLE FOR THE ECU GENERALIZED SYSTEM OF PREFERENCES

## **Eligible Developing Countries**

1. Albania 36. Fiji 71. Oman 2. Algeria 37. Gabon 72. Pakistan 3. Anguilla 38. Ghana 73. Panama 4. Antigua and Barbuda 39. Grenada 74. Papua New Guinea 5. Argentina 40. Guatemala 75. Paraguay 6. Aruba 41. Guyana 76. Philippines 7. Bahamas Islands 42. Honduras 77. Qatar 8. Bahrain 43. Hong Kong 78. Saint Lucia 9. Barbados 44. India 79. Saudi Arabia 10. Belize 45. Indonesia 80. Serbia 11. Bermuda Islands 46. Iran 81. Seychelles 12. Bolivia 47. Iraq 82. Singapore 13. Bosnia and Herzegovina 48. Jamaica 83. South Africa 14. Botswana 49. Jordan 84. South Korea 15. Brazil 50. Kenya 85. Sri Lanka 16. British Virgin Islands 51. Korea 86. St. Helena Island 17. Brunei 52. Kuwait 87. St. Kitts and Nevis 18. Cameroon 53. Lebanon 88. 19. Cape Verde 54. Libya 89. Suriname 20. Cayman Islands 55. Macedonia 90. Swaziland 21. Chile 56. Malaysia 91. Syria 22. China 57. Marshall Islands 92. Thailand 23. Colombia 58. Mauritius 93. Tokelau 24. Congo 59. Mexican 94. Tonga 25. Cook Island 60. Micronesia FS 95. Trinidad and Tobago 26. Costa Rica 61. Mongolia 96. Tunisia 27. Côte d'Ivoire 62. Montenegro 97. Turkey 28. Croatia 63. Montserrat 98. Turks and Caicos Is. 29. Cuba 64. Morocco 99. United Arab Emirates 30. Dominica 65. Namibia 100. Uruguay 31. Dominican Republic 66. Nauru 101. Venezuela 32. Ecuador 67. Netherlands Antilles 102. Vietnam 33. Egypt 68. Nicaragua 103. Zimbabwe 34. El Salvador 69. Nigeria 35. Feather 70. Niue

88. St. Vincent and the Grenadines 93. Tokelau 94. Tonga 95. Trinidad and Tobago 98. Turks and Caicos Is.

99. United Arab Emirates 100. Uruguay _Source:_ UNCTAD TRAINS Database.

## **Eligible Least Developed Countries (duty-free access)**

1. Afghanistan 18. Haiti 2. Angola 19. Kiribati 3. Bangladesh 20. Laos 4. Bhutan 21. Lesotho 5. Burkina Faso 22. Liberia 6. Burundi 23. Madagascar 7. Cambodia 24. Malawi 8. Central African Rep. 25. Maldives 9. Chad 26. Mali 10. Dem. Rep. of Congo 27. Mauritania 11. Djibouti 28. Mozambique

28. Mozambique 12. Equatorial Guinea 29. Myanmar 30. Nepal 13. Eritrea 31. Niger 14. Ethiopia 32. Republic of Benin 15. Gambia 16. Guinea 17. Guinea-Bissau 33. Ruanda 34. Samoa 35. Saô Tomé and Prìncipe 36. Senegal 37. Sierra Leone 38. Solomon Islands 39. Somalia 40. Sudan 41. Tanzania 42. Timor-Leste 43. Togo 44. Tuvalu 45. Uganda 46. Union of Comoros 47. Vanuatu 48. Yemen 49. Zambia _Source:_ UNCTAD TRAINS Database.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 42
(EITI), which has the goal of verification and full publication of company and government revenues from oil, gas, and mining, and 6 countries have issued one or more EITI reports.

Nonetheless, these improvements have not yet been reflected consistently in many governance indicators since data started becoming available. Figure 19 compares the governance indicators of 1996—the first year of the series—and 2006. Actually, all indicators worsened. But a closer look shows that resource-rich countries—oil-rich countries in particular—were among the main causes of the worsening in governance and also that these indicators were particularly low there during growth acceleration. These results highlight the importance of the political economy issues associated with mineraland oil-rich countries in Africa as found in the recent empirical literature (see, for example, Kaufmann, Kraay, and Zoido 1999; Dufrénot, Sanon, and Diop 2006; Ndulu et al. 2007; Seldadyo, Nugroho, and de Haan 2007).

# **IV. Key Areas of Actions to Sustain Growth**

The avoidance of economic collapses will continue to depend on good policy, leadership, and aid. To sustain an accelerating growth, however, the region will have to tackle several barriers and constraints to greater productivity and investment. Addressing these barriers will require both continuing reforms and greater external assistance.

Firm studies such as Eifert, Gelb, and Ramachandran (2005) highlight one of the barriers to greater productivity. Research shows that efficient African enterprises can compete with Chinese and Indian firms in factory floor costs (figure 20). They become less competitive, though, because of higher indirect business costs, including infrastructure (figure 21). In China, indirect costs are about 8 percent of total costs, but in African countries they are 18–35 percent.

Building the African private sector will be crucial both for growth and for fostering a national consensus for growth-oriented policies. Improving the investment climate and enhancing the capacity of African entrepreneurs to invest and engage in business are central to this effort.

Africa remains a high-cost, high-risk place to do business. Overall, the cost of doing business in Africa is 20–40 percent above that for other developing regions, including the costs associated with bureaucracy, corruption, risk, and essential business services (figure 22). During 2006/07, the average rank of African countries was 136 in the Doing Business indicators (figure 23), but four middle-income countries rank in the top third: Mauritius, 32; South Africa, 35; Namibia 43; and Botswana, 51. Value-chain analysis by Subramanian and Matthijs (2007) also indicated several choke points in the supply chain for African firms: high cost of import logistics and time, low speed to market delivery, high cost of export logistics, and high incidence of rejects.

40
than land it rents in or residential or swidden land.<sup>28</sup> We make a distinction between “landless” and “non-cultivating” households; the latter include those who rent out all their land.<sup>29</sup> The unemployment rate among the landless is low. Based on adults who report that they did not work because they could not find work in the last 12 months, only 2.6% of landless rural households in 2004 had an unemployed adult, and this was almost identical in 1993 (2.5%).<sup>30</sup> The rate was lower in the MD (2.1% in 2004; 1.6% in 1993). However, time-use data might well reveal higher rates on underemployment, depending on the season.

Table 1 provides summary statistics. For rural Vietnam, the landlessness rate increased by two-thirds over the period, to slightly more than 12% in 2004. (These are population weighted; the proportion of households who were landless rose from 8.4% to 13.6%.) The rise in landlessness naturally put upward pressure on the inequality of land holdings.<sup>31</sup> In all years, the poverty rate is higher for those with land than for the landless. Similarly, mean consumption is higher for the landless. There was a sharp contraction in the incidence of poverty, which occurred at roughly the same rate for those with land as the landless. Over 1993-2004, the proportionate gain in consumption was higher for those with land; the ratio of the mean consumption for the landless relative to those with land fell between 1993 and 2004 from 1.33 to 1.24, though within the period it fell (to 1.21 in 1998) then rose (back to 1.33 in 2002).

> 28 We tested two alternative definitions, one that excludes auction land and one that additionally excludes land contracted from state farms.  The second gives a slightly higher landlessness rate (14.5% of households) in 2004 but as it can only be calculated for 1998 and 2004 we stick with the first definition.

> 29 The surveys have different land modules, although comparisons are still feasible. A limitation of the 2002 survey is that it only gives the amount of land that is cultivated by households, whether they have long term use rights to the land or are renting it in.  While we know if the household rents-out land, we cannot identify how much and, hence, the total amount over which the household has use rights.

> 30 For 1993 one can also calculate an unemployment rate for whether work was wanted but could not be found in the last 7 days; this gives an even lower rate for rural areas of 0.5% (Gallup, 2004).

> 31 We found that the 2004 Lorenz curve was nowhere above that for 1993 for either annual crop land or perennial land.

19
Table 3: DiD estimates for the effect of terrorist attacks against civilians in Mogadishu

||Log of core<br>consumption<br>(1)|Log of food<br>consumption<br>(2)|Poverty<br>status<br>(3)|Poverty<br>gap<br>(4)|Experienced<br>hunger<br>(5)|Police<br>competence<br>(6)|
|---|---|---|---|---|---|---|
|Diff-in-diff<br>coefficient (β)|-0.326***<br>(0.118)|-0.415**<br>(0.159)|1.617***<br>(0.597)|0.115***<br>(0.039)|0.813<br>(0.659)|-0.881**<br>(0.365)|
|Wave|Yes|Yes|Yes|Yes|Yes|Yes|
|Exposed/control|Yes|Yes|Yes|Yes|Yes|Yes|
|District fixed<br>effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Characteristics of<br>household & head|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling<br>characteristics|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1,532|1,532|1,532|1,532|1,516|1,498|

Source: Authors’ calculations based on data from the SHFS and ACLED. Note: Estimated coefficients from an OLS or probit regression. Characteristics of household refer to size and receiving remittances, while those from household head refer to age, sex and literacy. Dwelling characteristics include tenure, roof and floor material, water source and sanitation type. Drought affected status and humanitarian assistance are not included due to the lack of variation in the data within Mogadishu. Standard errors considering sampling weights in parenthesis; *** p<0.01, ** p<0.05, * p<0.1.

In terms of the effect of incidents on self-reported outcomes, the coefficient for experiencing hunger is positive but not statistically significant (column 5 in Table 3). A reduction of food consumption is likely to be associated with a larger share of household reporting to have experienced hunger. However, a non-significant result could be explained by the recall period considered in the survey instrument. The question asked to households referred to whether they had experienced hunger over the last month. As such, hunger reported by households covers between 1 and 7 days after an incident and at least 3 weeks before. We also find a deterioration of perception on police competence (column 6 in Table 3). The predicted probability of households perceiving police as being competent decreases on average by 0.34 point among exposed households in Mogadishu.

For the IV estimates, we use the same preferred specification as in the DiD approach, which includes fixed effects, characteristics of the household, household head and dwelling, as well as the drought affected status of households. The results also show a negative immediate effect on core consumption (per capita deflated) attributable to terrorist attacks from Al-Shabaab against civilians (column 1 in Table 4).

Overall, point estimates are larger with the IV approach compared to DiD estimates. Yet, the former estimates are less precise and have larger standard errors in the second stage since it only considers part of the variation in the treatment status of households, 16
O.A.M.D.G.

reported in Data Table 2. This is a crude approximation of the business and selfproduction income a household is enjoying. Given the risks under which the households in our sample are operating, the assumption of a real cost of capital of 14%<sup>11</sup> - while being a reasonable assumption for corporate Colombia - for private households is probably on the conservative side. We will therefore test the sensitivity of our results, first, with the total income measure derived above, and second, using only reported salary as income.

> 11 Average real credit interest of Colombian banks, Banco de la República, 2001.
# **Table 8: Unorganized Sector Log (Employment)**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Horizontal Employment Spillovers from|_Othe_|_r-sector Emplo_|_yment Spillov_|_ers_|
|Organized Sector|0.2720***|0.2407***|0.2291***|0.0816***|
||(0.0321)|(0.0288)|(0.0286)|(0.0237)|
|Forward Employment Spillovers from|||||
|Organized Sector|0.7649**|0.1034|0.1616|2.7174***|
||(0.2978)|(0.2897)|(0.2757)|(0.9145)|
|Backward Employment Spillovers from|||||
|Organized Sector|1.1563***|1.2431***|1.3892***|1.6135***|
||(0.1831)|(0.1773)|(0.1699)|(0.3313)|
||_Own_|_-sector Produc_|_tivity Spillove_|_rs_|
|Average TFP of Horizontal-linked|||||
|Organized Plants||0.1107*|0.1051*|0.1171**|
|||(0.0630)|(0.0622)|(0.0556)|
|Average TFP of Forward-linked|||||
|Organized Plants||0.6123***|0.5691***|-0.3226|
|||(0.1484)|(0.1494)|(0.5059)|
|Average TFP of Backward-linked|||||
|Organized Plants||0.6162***|0.6008***|0.7733**|
|||(0.1457)|(0.1472)|(0.3157)|
||_Own_|_-sector Employ_|_ment Spillove_|_rs_|
|Forward Employment Spillovers from|||||
|Unorganized Sector|||0.3721|0.9449|
||||(0.3179)|(1.1316)|
|Backward Employment Spillovers from|||||
|Unorganized Sector|||0.9511***|1.2050**|
||||(0.1581)|(0.5338)|
|Constant|-17.4252***|-15.7545***|-33.1262***|-69.1314***|
||(3.8514)|(3.6970)|(5.4266)|(14.5748)|
|Observations|5888|5888|5888|5888|
|R-squared|0.0837|0.1030|0.1131|0.3488|

_Note: All specifications contain panel (state-industry) fixed effects as well as year fixed effects. Column 4 also includes state-year and industry-year fixed effects. Standard errors are clustered by the state-industry level. Significance at the 20% level is indicated by ++, at the 15% level by +, at the 10% level by *, at the 5% level by ** and at the 1% level by ***._ 37
construction can begin are critical elements in a firm’s planning process. A city will have an edge if it offers replicable processes for getting complicated permitting issues resolved, updated building/fire codes allowing for special technical requirements, and availability of ready-tooccupy office space. Tax regime also matters. In Dublin, the establishment of an International Financial Services Center in 1987 has generated a considerable influx of foreign firms. Aware of the rise in labor and overhead costs, the Irish government has recently begun an effort to spread development beyond Dublin, with similarly flexible tax regime and low regulation.<sup>23</sup> Good infrastructure is important to creative firms, particularly that critical to the direct operation of the firms. For biotech laboratories, Internet-based companies and large facilities housing multiple servers, non-interruptible power is critical. So is a reasonable rate structure. Making infrastructure information open to the high-tech and creative community is beneficial, as shown in the case of Seattle’s updated map of the major fiber optic network (Sommers and Calson 2000). In addition, it has been shown in the U.S. that availability of direct flights may be the number one priority in the location decisions of high-tech firms with above-average innovation (Echeverri-Carroll 1999). Proximity to airport facilities encourages services to clients, minimizes travel time, and provides welcoming opportunities to visitors.

Appropriate real estate also is essential for the development of creative industries and there may be cluster-specific preferences. Bioscience companies often need specially configured labs that may be less interchangeable than general office space (Walcott 2002). Developers must be willing to build such facilities on a speculative basis, given the high failure rate of startup firms. The real estate market also needs to provide space for companies from incubation to startup and through established maturity, as well as an array of amenities for employees. Software/internet engineers, on the other hand, prefer private offices and the ability to work at all hours. Therefore, access to backup power and telecommunication lines is critical. New server hotels – large facilities constructed specifically to house serves of multiple firms – are becoming a significant factor in local electric demand (Sommers and Calson 2000).

> 23 The corporate tax rate of 12.5 percent (10 percent for manufacturing and international services) was phased in by 2003, in addition to no restrictions on currency movements, no local content requirements and unlimited profit repatriation for foreign firms.  See Reason (2001).

16