rafmacalaba/data-use-mentions-extended huggingface dataset:
# 1. Introduction

Since 1990 economic growth in Sub-Saharan Africa has averaged just 0.8% annual growth in per capita income. By comparison, global growth rates over the same period were double that, and regions like East Asia scored per capita growth rates of more than 6% per annum.<sup>1</sup> This anemic growth record saw African economies and other slow growing lower income countries’ incomes slipping further behind the rest of the world. Meanwhile, the number of Africans living in poverty – measured using the global poverty line – has grown by tens of millions since 1990. In contrast, countries like China have averaged per capita growth of around 7.6%, lifting over 750 million people out of poverty.<sup>2</sup> If current trends in economic growth and poverty rates persist, a large group of low income, slow growing or stagnant economies, found mostly in Africa, will never eradicate extreme poverty, nor will they catch up to the rest of the world. Furthermore, the number of those living in extreme poverty globally is set to begin to rise again by 2035 after decades of decline.

What can explain this extraordinary divergence in economic destiny? What might the implications be for the twenty-first century? And why is documenting this divergence so important for informing policy? In this paper we examine these questions and offer reflections on the measurement of economic divergence. We also consider why this finding is not more widely understood. Indeed, various studies over the past two decades have drawn the opposite conclusion – namely that world incomes are converging, and that the evidence supports a finding of ‘unconditional convergence’ – specifically that the overall result is one of convergence regardless a country’s starting point. For example, ‘The New Era of Unconditional Convergence’ argues that the era of _unconditional divergence_ is over, and that developing countries have been growing fast enough to be driving overall global convergence in incomes (Patel et al. 2021).<sup
to test the limited-location-choice mechanism. The main effect of global oil and gas reserves is excluded because it does not vary over countries and is hence absorbed by the time fixed effects.

It is possible that oil-and-gas-related FDI flows and political violence are the result of the discovery of one of these valuable resources. Therefore, we also control for large oil and gas field discoveries within a country using a data set obtained from the BP Statistical Review of World Energy. The variable for discovery is a dummy coded as one if a major oil or gas field was found in the country, and zero otherwise. A _major gas/oil discovery_ is a discovery of a field that contains at least 500 million barrels of oil or 79 million m<sup>3</sup> of gas (Halbouty, 2001).

We proxy oil and gas rents with a global price index of oil and gas prices ( _gasoilindex_ ). This index is constructed using data from the BP Statistical Review of World Energy. We take 2003 as the base year and subtract 100 from the index to ease the interpretation of our results. We interact this price index with battle-related deaths to test for the effect of rents on the responsiveness of resource-related FDI to political violence. The main effect of our global oil and gas price measure is excluded because it does not vary over countries and is hence absorbed by the time fixed effects.

Column (1) of Table 7 shows the baseline model for the oil and gas industry. Similar to column (3) in Table 4, the results show that the effect of political violence on oil and gas FDI is comparable to the effect on total resource-related FDI flows (see columns 3-4, Table 1): battle-related deaths do not have a significant effect on oil-and-gas-related FDI. In columns (2) and (3), respectively, we add the moderator for hydrocarbon reserves and moderator for the price index. Figures 5a and 5b present the effect of these moderators, as estimated in model 3. The moderating effect
is the aggregate of intermediate inputs into production and the elasticity of substitution between G and factors is zero 17 Land is used only in rural goods. Both rural and urban labor produce rural goods because the value of agricultural output has two components. The first is on-farm output produced with land and rural labor. The second is the additional value of output between farm gate and FOB, which is produced by urban labor and capital.

> 16 A similar model has been used by Chia et al (1992) to investigate poverty issues in Cote d'Ivoire.

> **17** There are some differences between the present model and that of Chia et al (1992). In our model labor is not differentiated by skill level; our model imposes a constant elasticity of substitution among factors equal to 0.5 in all rural goods (coffee, cocoa, cotton, forestry, fisheries, other agricultural exports, food crops) and to 0.8 in all urban goods (the 21 sectors of industry and services), while the corresponding elasticity varies between 0.4 and 0.9 across sectors in Chia et al; our model is based on the 1995 input-output matrix of Cote d'lvoire and 1996 national accounts data, while that of Chia et al uses 1986 data. Both models assume zero substitution between primary factors and intermediate goods.
Policy Research Working Paper 8823

# **Abstract**

This paper looks at how changing food prices affect child undernutrition in Ethiopia. It derives height for age (stunting) and weight for height (wasting) as indicators of child undernutrition from the two most recent years of the Livings Standards Measurement Survey and utilizes market prices for key cereals, teff, wheat, and maize at the zone level across all regions of the country. Using a panel data fixed effects model, the analysis finds that, contrary to previous studies, rising crop prices are positively associated with improved child stunting rates for children between ages 6 months and 5 years, while the results for wasting are not conclusive. These results suggest that across the board policy interventions that seek to suppress cereal price increases may have adverse effects on poverty reduction in the long term by undermining potentially positive impacts on child nutrition.

This paper is a product of the Macroeconomics, Trade and Investment 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 pbrenton@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
a selection of the babies surviving into late pregnancy as shown by the increase in miscarriages and the small decrease in stillbirths.

> 10 They use VRS data (1980-2003) and their most complete specification uses fixed-effects for province, time (month-year of conception) and province trend (the interaction between province and time). The size of the effect in this specification was of - 0.28 gr per additional casualty.

11 This differs from the approach used by other studies analysing the effects of war on human capital (e.g. Akresh, Lucchetti, and Thirumurthy 2012; Weldeegzie 2017) that rely on comparisons of conflict-affected areas with those where the conflict was absent, before and after a war, as this setup may not be appropriate for countries where violence is geographically widespread and distinguishing between affected and non-affected areas is not straightforward, or where the ongoing nature of the conflict complicates the determination of the before and after periods, as in fact is the case in Colombia. (For instance, 67\% of the Colombian municipalities are classified by the local research centre CERAC (Restrepo et al. 2014) as experiencing ‘interrupted’ conflict during the 2000s, which means that the presence of armed groups has been sporadic.) 12 To address this concern, (Dagnelie, Luca, and Maystadt 2018) use a mineral price index as an instrument variable for local violence intensity.

5
**Figure 11: "Do you approve of the job performance of the leadership of your country?" (% of adults)**

||**2010**|**2019**|
|---|---|---|
|**Baghdad**|**33%**|**12%**|
|**Sunni**|**13%**|**21%**|
|**Shia**|**62%**|**6%**|
|**Kurds**|**50%**|**11%**|

Source: Gallup World Poll, 2019.

We conduct a preliminary analysis to see if Iraqi governorates with lower approval ratings of the country’s leadership were also more likely to see violence during the ISIS conflict (figure 12). Data from Iraq Body Count provides us with governorate-level death counts for years between 2010 and 2016. The death count soars in 2013 (our proxy for the transition period before ISIS control) in the Western governorates where ISIS gained control. We pool the death count between 2013 and 2016, and cross-tabulate it with governorate-level approval ratings in the preceding years (2010-2012). While there may be several factors at play, the data find that these governorates also had poorer approval ratings and witnessed higher death rates in the ISIS years.

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# **3 Data**

## **3.1 Annual Survey of Industries (ASI) Data**

Our primary dataset consists of firm-level surveys from the Annual Survey of Industries (ASI). The period of coverage for each ASI survey is the accounting year that ended on any day during the fiscal year: the 1985-86 survey (which we refer to as the 1985 survey) refers to the factory’s accounting year that ended on any day between April 1, 1985 and March 31, 1986. We obtained firm-level data for all available years between 1985 and 2004. Data were not available for 1995. In addition, the way in which input data were collected and made available for the years 1996 and 1997 did not make it possible to construct certain key variables for those two years that were consistent with the other years. Therefore, we restrict our analysis to the firm-level data for the remaining 17 years between 1985 and 2004 (1985 through 1994 and 1998 through 2004).

The sampling universe for the ASI is all firms that are registered under Sections 2m(i) and 2m(ii) of the Factories Act, as well as firms registered under the Bidi & Cigar Workers Act, and a number of utility and service providers. We include only manufacturing firms in our analysis. All firms that have 20 or more employees (10 or more employees if a power source is used) are required to register.<sup>3</sup> The sampling frame is derived from the registry list of each state’s Chief Inspector of Factories, and all but four small states are covered.

The ASI divides firms into two sectors - the “census” sector, in which firms are surveyed every year, and the “sample” sector, in which firms are sampled every few years. Between 1985 and 2004, the inclusion of firms in the census and sample sectors, as well as the sampling strategy, changed several times. To ensure that our results are representative of
As far as Puzhehei water quality project is concerned, only 7% respondents have ever heard the project being proposed before. Thirty-nine percent of the households are currently living within the project domain, making them direct project beneficiaries. When responding why they perceive the project as important, income increase, lake view improvement, swimming condition improvement, environment protection, health benefit, tourism benefit, and next generation benefit are all frequently mentioned by the respondents (19%, 36%, 16%, 36%, 39%, 36%, and 46%, respectively). A majority of respondents (87%) have confidence that the project would prevent lake water from further deterioration. 56% of the interviews are conducted individually and the remaining 44% of interviews are conducted in a group setting.

# **5. WTP Estimation**

# 5.1 Analytical Framework

We followed the estimation strategy proposed in Wang and He (2010) to conduct the empirical analysis, which is summarized below. Suppose an individual i’s WTP is Vi, which is a random variable with a cumulative distribution function F(t). The mean value of Vi is  i and the standard variance is  i. The WTP model can be written as, Vi=μi+  i (1) where  i is a random term with a mean of zero. Individual i knows his valuation distribution. When given a price tij, the probability for the person to say “yes” to the offer tij will be, Pij = Prob(Vi>tij)=1-F(tij) (2) Once Pij, the probabilities for individual i to agree to the price tij, is known to a researcher, either by assigning numerical values to the verbal MBDC data or by directly asking individuals of their numerical likelihood information as did with the SPC approach, equation (2) can be estimated for each individual.

The estimation model can be constructed as follows: Pij = 1-F(tij) + λi
bundle|11.72|10.08|11.72|
|Percentage poi|nt change from th|e lowest point (Augu|st-October|10.78|12.54|
|'97) to the peak|(September-Dec|ember '98)||(164%)|(164%)|

Notes on adjustments: a) To adjust the three estimates from Gardiner, the amount necessary to make 11.47 equal 9.75 (1.72) was subtracted for all three periods. The adjustment of food share is not crucial as his food share (0.7) is quite close (0.8) and the largest relative food price shift occurred after Feb 1998.

b) For the IFLS 2+ the Aug-Oct 1997 figure was adjusted to reflect "expected" poverty in that period if poverty in Feb. 1996 was 9.75 by subtracting an adjustment for economic growth between Feb 1996 and Aug 1997 using the real per capita consumption expenditures time a semi-elasticity of poverty. Their 1998 estimate of 19.9 was adjusted by first subtracting an equivalent amount for renormalization (11-6.37) and then adding an amount for the fact that their prices were only 15 percentage points above CPI, when using CPI prices but a food share of .8 suggests prices 31 percentage points higher than CPI inflation (using formula above).

c) All figures are adjusted from column A to B (or vice versa) using the ratio of 11.34 to 9.75.

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15 _Figure 5. Annual Investment per Thousand Connection_ <!-- Start of picture text --> 80<br>60 - ---- - - - - - -- - - - - - - - --- -- -- -- -- -- -- -- -- -- --- -----/<br>1987 1988 1989 1990 1991 1992 1993 1994 1995 1996<br>Source: Author's Calculations based on data from SEDAPAL Arnual Reports<br><!-- End of picture text -->

# **IV. THE EFFECTS OF THE REFORM ON PERFORMANCE AND WELFARE**

This section shows the impact of the reforms described in the previous section on several performance measures and then compares their welfare effects with those that might have been achieved under the concession.

_i. The Effects of the Reform on Performance._ Additional funding from the World Bank project and higher water tariffs enabled SEDAPAL to add close to 200,000 additional water connections between 1989 and 1996 (figure 6). This growth in connections only allowed the company to keep pace with Lima's rising population, however, and the coverage ratio of 70 percent barely changed (figure 7). Moreover, the quality of service does not seem to have improved. For example, field interviews suggest that some districts of Lima received no water service for more than two months in 1997.
# **3.2. Services have become more productive**

Revolution in ICT technologies has made services more productive. In some countries, the real sources of growth in output per worker have emerged in services than manufacturing. Table 1 shows output per worker and total factor productivity growth sectorally over the period of 19902006 for India and China. Both countries have experienced relatively high TFP growth in services. However, India‘s growth experience has been leveraged by service TFP growth whereas China‘s in Industry. The two countries have taken different growth paths, and India‘s growth experience has shown that over 7 percent annual growth can be achieved by a developing country on the foundations of service productivity growth. With lack of disaggregated data on employment by sectors for developing countries, we plot the service output per worker for selected advanced economies in Figure 4 which suggests a rising trend.

**Table 1. Sources of Growth in Output per Worker in India and China: Total Economy and Major Sectors, 1990-2006,(average annual percentage change).**

||Total|Economy|Agri|culture|Ind|ustry|Ser<br>|vices|
|---|---|---|---|---|---|---|---|---|
||1990-<br>2000|2000-<br>2006|1990-<br>2000|2000-<br>2006|1990-<br>2000|2000-<br>2006|1990-<br>2000|2000-<br>2006|
|**India +**|||||||||
|Output Per Worker|4.1|4.5|1.3|1.4|3.3|2.7|4.9|4.6|
|TFP|1.8|2.1|0
,403|30|9.29|
|CoGen|917|30|7.44|
|Diesel|1070|30|11.13|
|Gas CC|917|30|7.44|
|Gas OC|603|30|12.14|
|Geothermal|4,362|30|n.a.|
|HFO|1,250|30|9.81|
|Hydropower|See below|35|n.a.|
|Nuclear|4,102|60|n.a.|
|Solar|2,500|25|n.a.|
|Wind|2,000|25|n.a.|

_Sources_ : IEA, (U.S.) Energy Information Administration, International Renewable Energy Agency, and World Bank technical specialists.

_Note_ : n.a. = not applicable.

As with the AICD, a single cost was assumed in every country for each technology irrespective of location or size, with the exception of hydropower (Table A1.3). There were more categories for technologies than in the AICD study, which had three costs in total for all generation plants using fossil fuels (“oil,” coal, and gas). Plant-level data on hydropower generation capacity were obtained to estimate a weighted-average capital cost of hydropower in each country based on three capacity categories. Hydropower plants were assumed to be refurbished after 35 years at a cost of 35 percent of the cost of a new plant. The life of each existing hydropower plant was checked, and if the plant was less than 35 years old in the reference year, the capex in Table A1.3 was annualized over 35 years. If the plant was 73
organized and are represented by lobbyists at the industry level. We test these two hypotheses by using FDI data at their project-level.

For the first scenario, we proxy the big firms by those having the largest aggregated announcements across the period at the country pair level. We argue that removing the top 1 percent of those firms should leave the effect of PTAs exogenous to the remaining smaller firms. Table 6 confirms that our baseline and discipline level results are robust to removing the top 1 percent of announcements.

Then we move to the industry level lobbying hypothesis. To determine which industries lobby most for trade related issues, we rely on the US Lobbying Disclosure Act of 1995, which requires any lobbying activities engaged within the US to be disclosed.<sup>15</sup> The Center for Responsive Politics publishes the full reports of the disclosed lobbying activities indicating the client name (including foreign entities), lobbyist name, contribution level and year, and more interestingly industry classification and the general issue that is lobbied for. Among the possible lobbied issues we focus on the ones which description might relate to the content of PTAs, they are tariffs, trade, labor antitrust and workplace, environment, copyright patent and trademark. Table 7 gives the level of contributions of each lobbied issue by sector and relative to the sector-level total contribution in 2003. The sectors that are relatively more engaged in “PTA-related” lobbying turn out to be Health, Construction and Defense. Table 8 shows that our results are robust to removing these three lobbying intensive sectors from our estimation sample.

# ii. Composition concerns

There is evidence supporting the existence of templates for the content of trade agreements, which are championed by the US and the EU the two major economies

> 15 The US Lobbying Disclosure Act of 1995 contributes to the “public awareness of the efforts of paid lobbyist to influence the public decision making process in both the legislative and executive branches of the Federal Government.”

20
A further bias and potential underestimate stems from the exclusion of Nigeria’s nomadic population, estimated to amount to 9.4 million<sup>18</sup> , and households living in IDP camps, due to the non-permanent nature of the settlement and challenge to trace households across visits and waves. This represents a sizable share of Nigeria’s population considering that about 2.1 million<sup>19</sup> Nigerians are estimated to be internally displaced and likely more deprived. Despite their limitations, these are the best currently available data sets, and the results presented in this study hence the best estimates currently available, though likely underestimates.

# 5. Results

## 5.1 Descriptive Results

## _5.1.1 Monetary Child Poverty in Nigeria_

Figures 1 and 2 present the headcount poverty rates based on Nigeria’s national poverty line for different age groups as an aggregate and differentiating by urban and rural sector respectively. Figures 3 and 4 present the same statistics but instead using the USD 1.90$ a day international poverty line. Irrespective of the poverty line four points stand out: (1) poverty increases the younger the children (2) aggregate child poverty has been increasing over the three waves of the GHS survey<sup>20</sup> (3) aggregate child poverty rates are driven by up to almost three times higher and rising poverty rates for children in rural areas and (4) the child poverty gap between those living in the northern versus the southern regions is comparable to the rural urban gap. For children aged 0-4 the rural poverty rate according to the national poverty line is 62% whereas in urban areas it is 19%. Poverty rates according to the international poverty line are slightly higher but show the same gap.

> 18 Figures based on National Commission for Nomadic Education estimates retrieved 29<sup>th</sup> May 2019: - <u>http://www.ncne.gov.ng/
I1 _Figure 4. Average Monthly Difference over Spot (L/mt)_ <!-- Start of picture text --> 2MI~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>W-9 / 97_z<br>-300<br>-PositionS bstbn<br><!-- End of picture text --> _Sowrce: Authors'_ calculations from LIFFE monthly data from December 1990 through May 1997.

Even if the forward price exceeds spot, the net benefits of forward sales may be negative if there are significant costs associated with selling forward. The net benefits of forward sales of cocoa are determined by the relation between the spot price and forward prices, by storage costs including financing, and by the availability of physical cocoa for export. We present two models of forward sales (denoted by Ml and M2). The gross benefits of the first model are only due to regular sales, that is selling forward evenly throughout the year, and not to the existence of the forward premium. The second model, is to quantify the income benefits (forward premium) that private exporters might obtain by selling forward in a liberalized export system. In the first model, CAISTAB sells forward through the _PVAM._ The net benefits of Ml are the difference between the average price realized by selling forward regularly and the spot price, less the unit costs of operating the _PVAM._ In the model M, there is no forward premium because CAISTAB does not hold the physical cocoa