rafmacalaba/datause-encoder-data huggingface dataset:
which was about $13 a day in 2005.<sup>8</sup> Yet it is likely that many people in developing countries living below the US poverty line, and the Milanovic-Yitzhaki standard, would be deemed “middle class”. Consider the two most populous countries. The closest concept to “middle class” in China is “Xiaokang;” eventually achieving the “Xiaokang society” is the goal of China’s reforms, instigated in 1979 under Deng Xiaoping’s leadership. The Government of China’s National Bureau of Statistics set a minimum income for Xiaokang in 1991; when converted to 2005 PPP $’s, Xiaokang requires $2.24 per day in rural areas and $3.47 a day in urban areas; I estimate that over 500 million Chinese were Xiaokang by 2005 (using _<u>PovcalNet</u>_ <u>), which (as we</u> will see) far exceeds the number living above the US poverty line. It is clear that many people who would be deemed “poor” in the US are thought of as middle class in China.

This is true in India too. It is often claimed that 300 million people are now “middle class” in India; see, for example, the Wikipedia entry on the “Standard of Living in India” (although I have had little success in tracking down the origin of that number). The surveys done by the National Council of Applied Economic Research (NCAER) are often used in defining India’s middle class; based on that source, Sridharan (2004) gives a range of definitions implying that 100-250 million people are middle class in India around 2000. From the most recent NCAER survey, Shukla (2008) deems 25 million households (about 120 million people) to be middle class in 2007-08. As we will see, all these estimates far exceed the likely number of people in India who are not poor by US standards.

One can also question the relevance of other definitions found in the literature. It seems implausible that a definition of the middle class relevant to developing countries would be centered on the median, which might more reasonably be deemed a lower bound. And, while some degree of arbitrariness in such measurement efforts is inevitable, one might wonder what meaning can be attached to the Banerjee-Duflo bounds of $2 and $10.

The definition proposed here is close to the Banerjee-Duflo definition, but I try to give the bounds more meaning. The developing world’s middle class is defined as those who are not deemed “poor” by the standards of developing countries but are still poor by the standards of rich countries. For the lower bound I will use the median poverty line in poverty lines for 70 national poverty lines, drawn from in-country poverty measurement studies by the World Bank and national governments; the data on national poverty lines are described in Ravallion et al. (2008).

> 8 I have used the line for a family of four from the web site of the <u>US Department of Health and Human Services.</u>

4
Figure 6: Poverty Rate by Agricultural/Nonagricultural Household and Livelihood Type, 2009 and 2014/15

- (a) Poverty Rate for Agricultural/Nonagricultural Households

<!-- Start of picture text --> 80%<br>60%<br>40%<br>20%<br>0%<br>Urban Rural Urban Rural<br>2009 2014/15<br>Agriculture Non-Agriculture Agriculture Non-Agriculture Agriculture Non-Agriculture Agriculture Non-Agriculture<br><!-- End of picture text -->

- (b) Poverty Rate by Main Livelihood Type in Rural Sudan

<!-- Start of picture text --> 2009 2014/15<br>60%<br>40%<br>20%<br>0%<br>Crop farming Animal Wages and Owned Property Transfers* Other<br>husbandry salaries business income<br>enterprise<br>Poverty rate<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15 and NBHS 2009.

_Note_ : *Transfers include main livelihoods from remittances, pensions, aid, and transfers from family members.

Reducing poverty, particularly for agricultural households, requires reforms that will boost incomes from agricultural activities. This paper aims to examine the relationship between agriculture and household welfare. With the significant loss of government revenue following the secession of South Sudan, agricultural growth has the potential to improve the welfare of Sudanese households. This entails not only increasing the agricultural productivity of Sudanese farmers but also improving access to markets, thereby allowing farmers to cheaply obtain productivity-enhancing inputs and get better prices for their marketable agricultural surplus.

Compared to other African countries that produce the same main crops as Sudan produces, Sudan has lagged in productivity growth (Figure 7). For instance, sorghum, Sudan’s most commonly produced crop, has seen yields increase from below 500 kg per ha in 1995 to almost 700 kg per ha in 2017. However, this is lower than yields in Nigeria (1,200 kg per ha) or Ethiopia (2,600 kg per ha), the latter of which has seen a dramatic increase in yields over the past couple of decades, up from 1,200 kg per ha in the mid1990s. Similarly, among the major producers of millet in Africa, Sudan has the lowest yields despite 7
household surveys (e.g., TFPM 2016). Furthermore, given the large and frequent shocks that could potentially affect welfare and induce displacement (e.g., Favari et al. 2021), high frequency data collection is necessary to differentiate between potential mechanisms affecting displacement.

Given the substantial constraints on traditional data collection and the need for high frequency data, we turn to rare, non-traditional survey data.<sup>7</sup> This paper utilizes the most geographically complete survey covering the Republic of Yemen following the start of the conflict- the mobile Vulnerability and Assessment Mapping Survey (mVAM) conducted by the WFP. The monthly data begin in August 2015, just months after the escalation of violence and the peak in forced displacement. The data provide a rare opportunity to study household decisions during an active conflict, with important implications for governments and humanitarian and development organizations.

The primary purpose of the mVAM is to assess the food security situation in the Republic of Yemen each month. On average, it takes about 20 minutes to complete. The survey is conducted via mobile phone and respondents are reached via random digit dialing (RDD). The survey is stratified by governorate/capital city, where potential respondents continue to be contacted until a sufficient number of completed responses have been met in each governorate (aside from the island of Soccatra).<sup>8</sup> The number of minimum responses per governorate is determined by its share of the total population. The number of respondents surveyed each month is approximately 2400.<sup>9</sup> The survey is a rolling panel, where the call center initially acquires a list of 2400 phone numbers that answered the survey in the initial month. In the second month, the same 2400 phone numbers are contacted for an identical survey. Of these 2400, approximately 400 on average are not reached and have to be replaced for the second survey via random digit dialing, and approximately 200 do not respond for two months in a row. However,

> 7Hoogeveen et al. (2019) also take a non-traditional data approach, interviewing 500 households faceto-face in Mali after displacement and conducting monthly follow-up interviews over a year to identify factors related to whether households return to their origin regions.

> 8The Republic of Yemen has 333 districts that fall within 20 governorates and one municipality (capital city).

> 9See Appendix 1 for the number of responses by governorate. All appendices are available online at ”https://sites.google.com/site/sharadtandon222222/”.

9
<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Public Sector Banks Private Banks<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> Note: Data is from Reserve Bank of India. Years refer to respective fiscal years.

There has been a long downward trend in the high NPA level since the late 1990s. The PSBs had a higher non-performing asset ratio at that time. As PSBs gradually reduced their NPAs, the NPAs continued to grow at private sector banks till early 2000s. In recent years the ratio of nonperforming assets has increased in PSBs. Besides, there is also a stark contrast between the profitability of PSBs and private banks. Public sector banks have continued to record negative profitability ratios since March 2016. Return on assets of public sector banks was -0.1 percent in September 2017, and its return on equity was -2.0, compared to 1.4 percent and 11.9 percent respectively for private banks.<sup>43</sup>

**Figure 24: Gross NPAs to Gross Advances of the Banking System**

> 43 The allocative efficiency of the public-sector-dominated Indian banking sector is considered to be low, holding back potential investments and economic growth. Banerjee et al (2004) characterize the Indian public banks as “lazy”, since the lending decisions of their managers are not based on the current or expected profitability of firms; they under-lend to the private sector and overinvest in government securities. They explain that the employees of the public-sector banks are treated as public servants by law, and hence if they take decisions which result in direct financial gain to a third party, they may be held guilty of corruption. The bankers thus choose to lend less to the private sector; and disproportionately more to the government. Gupta et al (2015) too show that the Indian public banks allocate a larger share of their assets into government securities; and in doing so they respond more to the level of fiscal deficit than to market signals or even the SLR ratio.

40
Easterly, William, Norman Loayza, and Peter Montiel. 1997. "Has<sup>Latin America's Post</sup> Reform Growth Been Disappointing?" Journal of International Economics 43: 287-311.

Easterly, William, and Ross Levine. 2002 " Tropics, Germs, and Crops:<sup>How</sup> Endowments Influence Economic Development". University<sup>of Minnesota. Mimeo..</sup> Estevadeordal, Antoni and Alan M. Taylor. 2002. " A Century of Missing.Trade?".. American Economic Review Vol 92(1):383-393 Fischer, Stanley. 1993. " The role of Macroeconomic Factors in Growth"<sup>Journal of</sup> Monetary Economics 32:485-512 Frankel, Jeffrey A., and David Romer. 1999. "Does Trade Cause<sup>Growth?" American</sup> Economic Review 89(3): 379-399.

Giiliches, Zvi. and Hausman, Jerry A. 1986. "Errors in Variables in Panel<sup>Data". Journal</sup> of Econometrics. Vol 3:93-118.

Grubel, Herbert G., and P.J. Lloyd. 1975. Intra-Industry Trade: The<sup>Theory and</sup> Measurement of International Trade in Differentiated Products. New York:<sup>John Wiley &</sup> Sons.

Irwin, Douglas (2000) " How Did the United States Become a Net<sup>Exporter of</sup> Manufactured Goods" NBER Working Paper.

Jones, Charles (2000)"Comment on Rodriguez-Rodrik, 'Trade Policy<sup>and Economic</sup> Growth: A Skeptic's Guide to the Cross-National Evidence'." NBER Macroeconomics Annual 2000 (MIT Press).

Knight, Malcolm, Norman Loayza, and Delano Villanueva. 1993.<sup>"Testing the</sup> Neoclassical Theory of Economnic Growth: A Panel Data Approach."<sup>IMF Staff Papers</sup> 40: 512-541.

Krugman, Paul. 1979. "Increasing Returns, Monopolistic Competition and<sup>International</sup> Trade." Journal of International Economics 9: 469-479.

Lane, Philip, and Aaron Tornell. 1999. "The Voracity Effect." American<sup>Economic</sup> Review 89: 22-46.

Leamer,-Edward 1984. Sources of Comparative Advantage: Theory<sup>and Evidence.</sup> Cambridge Massachusetts: MIT Press.

Levine, Ross, and David Renelt. 1992. "A Sensitivity Analysis of Cross-Country<sup>Growth</sup> Regressions." American Economic Review 82(4): 942-963.

17
<!-- Start of picture text --> Loglinear and quadratic model − Second Stage Loglinear and quadratic model − Second Stage Loglinear and quadratic model − Second Stage<br>AIDS AIDS AIDS<br>QUAIDS QUAIDS QUAIDS<br>3.5 4.0 4.5 5.0 5.5 6.0 3.5 4.0 4.5 5.0 5.5 6.0 3.5 4.0 4.5 5.0 5.5 6.0<br>Expenditure Expenditure Expenditure<br>Loglinear and quadratic model − Second Stage Loglinear and quadratic model − Second Stage Loglinear and quadratic model − Second Stage<br>AIDS AIDS AIDS<br>QUAIDS QUAIDS QUAIDS<br>3.5 4.0 4.5 5.0 5.5 6.0 3.5 4.0 4.5 5.0 5.5 6.0 3.5 4.0 4.5 5.0 5.5 6.0<br>Expenditure Expenditure Expenditure<br>Loglinear and quadratic model − Second Stage<br>AIDS<br>QUAIDS<br>3.5 4.0 4.5 5.0 5.5 6.0<br>Expenditure<br>0.04<br>0.4 0.015<br>0.03<br>0.3 0.010<br>0.02<br>Exp. share food 0.2 Exp. share education 0.01 0.005<br>Exp. share school material<br>0.1 0.00 0.000<br>0.0 −0.01 −0.005<br>0.2 0.25<br>0.10<br>0.20<br>0.1 0.05<br>0.15<br>0.00 0.10<br>0.0<br>Exp. share health care Exp. share medicine 0.05<br>−0.05 Constrained Health Services<br>0.00<br>−0.1<br>−0.10<br>−0.05<br>0.15<br>0.10<br>0.05<br>0.00<br>Constrained Medication −0.05<br>−0.10<br>−0.15<br><!-- End of picture text --> Figure 5: ECs estimated on subsample of untreated households using non-labor transferred income as instrument for income
regions have invested in agricultural development but, in the absence of appropriate institutional arrangements, agricultural employment in these regions may not be resilient to climate shocks. Additionally, climatic changes over recent decades may also have changed the adequacy of pre-colonial institutions that persist. Thus, it is expected the impact of weather shocks to be contingent on pre-colonial modes of subsistence. To examine this, data from ethnographic atlas are used to identify the primary mode of subsistence local ethnic groups relied on in the pre-industrial era. For each individual, a dummy variable is defined to indicate if they live in a region where ethnic groups historically relied on: (i) agricultural; (ii) hunting, fishing and gathering; or (iii) pastoralism or, in other words, herding. A location is considered as relying on a given mode of subsistence in the pre- industrial era if the share of the population that relied on that mode of subsistence is greater than the sample median. The climate shock variables are interacted with these dummy variables to examine how the impacts on employment vary with the predominant pre-colonial modes of subsistence.

Table 9 reports the coefficient estimates of this analysis. In each column, the omitted group is the rest of the sample that does not belong to a homeland with the corresponding historic mode of subsistence. So, for instance, in Column 1, the interaction term shows the relative impact of water shocks on employment of individuals living in regions which historically relied on agriculture against those that did not. The results confirm the heterogeneity of impacts by historic mode of production. The coefficient on the wet and dry shock variables for the base category in Column 1 imply that non-agricultural ethnic homelands experience no impact of wet or dry shocks on employ- ment. The interaction coefficient suggests that, compared to this base category, agricultural homelands experience negative (positive) impacts during dry (wet) shocks, respectively. This difference is large in magnitude and statistically significant. The negative impact of droughts is to be expected if there is persistence in the agriculturalreliance of the economies in these regions till today. Meanwhile, local norms and institutions in historically agricultural regions have also adapted to allow workers to benefit from surplus agricultural productivity during periods of excess water, for example through the development of traditional flood-based agri- cultural systems widely practiced in riverine regions of Africa such as the Niger, Senegal and Volta river basins.

On the other hand, Column 2 suggests that regions where the primary mode of subsistence was hunting, fishing and gathering are better off during the occurrence of both wet and dry shocks when compared to other regions. The impact of dry and wet shocks for the hunter-fishergatherer regions is statistically different, and the magnitude is large. In the case of dry shocks, it 17
Policy Research Working Paper 10261

# **Abstract**

This paper develops a novel methodology to construct a harmonized cross-country database of the state’s footprint in markets: the Businesses of the State database. The methodology of the database is built on three criteria—(i) a harmonized definition of state-owned enterprises, (ii) identification of direct and indirect state ownership linkages at the national and subnational levels across the corporate sector, and (iii) classification of economic activities depending on their efficiency rationale—which conceptualize a framework to trace state presence in the corporate sector across economic activities. The database is constructed leveraging different firm-level data sources including the ORBIS Global Database, as the primary data source, which is then complemented with supplementary data sources (EMIS Intelligence, Factiva, Worldscope, Pitchbook, among others) to mitigate ORBIS’s data limitations across countries and regions. The Businesses of the State database identifies an unprecedented number of firms with state participation across countries and economic activities, as well as providing novel insights on financial performance, economic performance, and governance of state-owned enterprises. A deep-dive analysis of 36 countries within the Businesses of the State database shows that 69 percent of state-owned enterprises operate in competitive activities (low efficiency-rationale for state participation), 16% are in partially contestable industries (moderate efficiency rationale), and 15 percent are natural monopolies (strong efficiency rationale). Furthermore, this analysis suggests that performance-based productivity of state-owned enterprises (revenue per worker) is negatively correlated with government control variables, such as government shareholding percentage and direct versus indirect government ownership.

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://www.worldbank.org/prwp. The authors may be contacted at adallolio@worldbank.org, tgoodwin@worldbank.org, mlicetti@worldbank.org, jan.orlowski@aiib.org, fpatinopena@worldbank.org, rfrancisralambot@worldbank.org, and dsancheznavarro@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
decline generated by the political changes were the driving factors of subsidy reforms that eventually overruled the other logics for maintaining subsidies.

Table 2: Average Regional Prices of Petroleum Products, in US$ (January 2013)

||Gasoline|Diesel|Kerosene|LPG|
|---|---|---|---|---|
|East Asia and Pacific|1.25|1.03|1.11|1.20|
|Europe and Central Asia|1.16|1.23|n.a.|n.a.|
|Latin America and the Caribbean|1.24|1.14|1.18|1.01|
|Middle East and North Africa|0.67|0.44|0.41|0.40|
|South Asia|1.25|0.91|0.86|1.44|
|Sub-Saharan Africa|1.24|1.22|0.96|0.97|

_Source:_ Elaborated from Kojima 2013.

_Note:_ The table is based on a sample of 63 countries. Data on Europe and Central Asia (ECA) countries may not be representative due to the small sample size.

# Who Reformed, When, How, and Why?

An overview of reforms undertaken during this period shows that of the eight countries considered, six implemented substantial reforms. In chronological order of reforms, these countries are the Islamic Republic of Iran, the Republic of Yemen, Jordan, Morocco, Egypt, and Tunisia. The other two countries of Djibouti and Libya did not implement any reforms and will not be covered in this section. In what follows we focus on the key reforms undertaken between 2010 and 2014, summarizing the background, contents, and outcomes of the reforms.

## Islamic Republic of Iran: December 18, 2010

On January 5, 2010, the Government of the Islamic Republic of Iran introduced the Targeted Subsidy Reform Act, a major subsidy reform designed to eliminate most subsidies and compensate the population with a cash transfer. Due to political and organizational constraints, the implementation of the act was delayed for almost a year and the reform was finally launched on December 18, 2010. The reform was originally planned to be implemented over a period of five years to coincide with the fifth five-year economic, social, and cultural development plan. The act estimated the expected net gain at 200 trillion rials but did not indicate the price increase to be applied to subsidized products. The reform was preceded by an extensive public relations campaign to educate the population on the costs and benefits of the reform (Guillaume, Zytek, and Farzin 2011). The government also made clear that protests would not be well received.

10
**FIGURE O.5a** Finance, insurance, real estate, and business services is contributing most to India’s structural change . . .

<!-- Start of picture text --> Each sector’s contribution to overall structural change<br>Agriculture Mining Manufacturing<br>1.0<br>0.5<br>0.0<br>–0.5<br>Public utilities Construction Wholesale and retail<br>1.0<br>0.5<br>0.0<br>–0.5<br>Transport and storage FIREBS Community and<br>1.0<br>social services<br>0.5<br>0.0<br>–0.5<br>0 2 4 6 0 2 4 6 0 2 4 6<br>Time period a<br>Percent<br><!-- End of picture text -->

**FIGURE O.5b** . . . and since 2000, manufacturing is helping its “within-sector” change

<!-- Start of picture text --> Each sector’s contribution to overall with-sector structural change<br>Agriculture Mining Manufacturing<br>2<br>1<br>0<br>–1<br>2 Public utilities Construction Wholesale and retail<br>1<br>0<br>–1<br>2 Transport and storage FIREBS Community and<br>social services<br>1<br>0<br>–1<br>0 2 4 6 0 2 4 6 0 2 4 6<br>Time period a<br>Percent<br><!-- End of picture text --> Source: Ahsan and Mitra, Chapter 1 in this book. Note:<sup>a</sup> Period values are: 1 = 1960–1969; 2 = 1970–1979; 3 = 1980–1989; 4 = 1990–1999; 5 = 2000–2004. FIREBS = finance, insurance, real estate, and business services.

21