rafmacalaba/data-use-mentions-extended huggingface dataset:
# 5.5 Composition of the innovation portfolio

Product innovations in the South Asia region are mainly related to quality upgrading and new functions, while a small percentage of them is associated with the introduction of cheaper products (except in Pakistan) (Table 13). Indeed, more than 90 percent of the firms introducing new goods in Bangladesh, Nepal, and Pakistan increase the quality of their products. Innovations related to products that include different inputs, a new technology or design are relatively more important in countries such as Nepal and Pakistan.

<u>Table 13. How innovative are new products? (% of product innovation firms)</u>

|**Types of Innovation**|**Bangladesh**|**Pakistan**|**India**|**Nepal**|**ECA**|**Africa**|
|---|---|---|---|---|---|---|
|New Function|63%|95%|66%|76%|67%|64%|
|Cheaper|40%|76%|22%|37%|-|42%|
|Better Quality|94%|96%|74%|99%|-|89%|
|Different Inputs|59%|77%|54%|70%|68%|58%|
|New Tech or Design|46%|75%|41%|81%|54%|54%|

Source: Author’s elaboration from Enterprise Survey (2014) There is a lot of heterogeneity in the region in terms of the type of process innovation that firms conduct (Table 14). For example, in Bangladesh, most of the innovations are related to the introduction of a more efficient technology or the adaptation of a new technology; while in India, they are geared to automate manual processes
generally makes deposit information publicly available at the country level (e.g. deposits held by Tanzanians in all foreign banking centers combined and deposits held in Cayman banks by all foreigners combined), it has traditionally restricted access to deposit information at the bilateral level (e.g. deposits held by Tanzanians in Cayman banks) to central bank staff and external researchers working under a confidentiality agreement with the BIS. In the main analysis, we use a dataset with restricted information at the bilateral level up until 2010, which allows us to break down each country’s total foreign deposits into deposits in havens and deposits in non-havens. In an auxiliary analysis, we exploit recently released information at 5
24 variable increases income inequality. In fact, for agricultural income, land owned accounts for the single largest share (38 percent) of agricultural income inequality in Egypt.

Table 10 suggests that it is the close relationship between land owned - which is distributed very unevenly - and agricultural income which skews the distribution of agricultural income in favor of the rich. Unfortunately, however, the findings in Table 10 do not address the key question of causality. In other words, is it inequality in landownership which leads to unequal agricultural income distribution or is it uneven agricultural income distribution which causes the high concentration of land ownership? To adequately answer this question for rural Egypt would require more data, specifically, panel data on how changes in the distribution of agricultural - and other sources of - income are related to changes in the ownership of land.<sup>32</sup>

# VI. Conclusion

This study has used decomposition analysis on two new, nationally-representative household data sets from Egypt and Jordan to examine the impact of different sources of income - including nonfarm income -- on rural income inequality. Four key conclusions emerge.

First, the study shows that nonfarm income has very different impacts on poverty and inequality in the two study countries. While in Egypt the poor -- that is, those in the lowest quintile group -- receive almost 60 percent of their total per capita income from nonfarm income, in Jordan the poor receive less than 20 percent of their income from this source. With respect to inequality, nonfarm income represents an inequality-decreasing
Policy Research Working Paper 11025

# **Abstract**

This paper fills a gap in research on private equity investments in emerging markets and developing economies. It provides descriptive evidence and examines the distribution of returns across sectors such as finance, technology, and resource-intensive industries like mining, where significant variation exists. Using data from the International Finance Corporation, the analysis finds that return distributions exhibit “fat tails”, with a notable presence of investments yielding extremely high returns alongside others generating little or no return, highlighting the importance of diversification. The analysis reveals that firm-specific factors account for the largest share of return variability, with country and sector factors playing a smaller role. Gross domestic product growth and real exchange depreciation are significantly related to returns, with median elasticities of 0.35 and −0.67, respectively.

This paper is a product of the 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 fmoelders@ifc.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
169-221.

SEYMOUR, G. & PETERMAN, A. 2018. Context and measurement: An analysis of the relationship between intrahousehold decision making and autonomy. _World Development,_ 111 **,** 97-112.

SIJBESMA, C., VERHAGEN, J., NANAVATY, R. & JAMES, A. 2009. Impacts of domestic water supply on gender and income: results from a participatory study in a drought-prone region in Gujarat, India. _Water Policy,_ 11.

VAZ, A., PRATLEY, P. & ALKIRE, S. 2016. Measuring Women's Autonomy in Chad Using the Relative Autonomy Index. _Feminist Economics,_ 22 **,** 264-294.

2
ones. We also find that conflict reduces sales by decreasing both domestic and foreign demand. In contrast, other potential channels—such as increased credit constraints, corruption, or crime—do not appear to explain our findings. Taken together, these findings indicate that conflict reduces sales through lower output, intensified informal competition, and diminished demand, while profits remain stable due to firms’ adaptive cost-cutting responses.

A key advantage of using a harmonized global dataset is the ability to compare the effects of conflict on firms across countries with different structural and institutional characteristics. Our results show no meaningful heterogeneity in the effects of conflict exposure across countries with varying levels of per capita income, state fragility, or dependence on agriculture and natural resources. However, we do find substantial variation along other dimensions. Conflict exposure has more negative effects—reducing both sales and profits—for firms operating in countries with lower economic complexity, greater openness to international trade, lower bureaucratic quality, higher levels of corruption, or involvement in the production of illegal drugs. Importantly, the impact of conflict also differs depending on a country’s initial conflict status. Higher conflict exposure reduces both 2
so, we estimate the size of this premium based on each of the different approaches – Duranton’s algorithm, the AI, the two cluster algorithms, and the NTL approach – to defining metro areas.

# _4.1. Empirical framework_

4.1.1. Estimation strategy Similar to previous papers for other countries (Duranton, 2016; Chauvin _et al_ ., 2017; Quintero and Roberts, 2018), we identify the strength of Indonesia’s agglomeration wage premium, using the following basic regression:

> 22 See Rosenthal and Strange (2004) and Combes and Gobillon (2015) for excellent reviews of this literature.

> 23 Chauvin _et al_ . (2017) also present estimates of the strength of agglomeration economies for the United States for purposes of comparison.

> 24 See also Roberts (2018a).

> 25 We do not attempt to distinguish between the possible different underlying sources of agglomeration economies. These sources include the various matching, sharing and learning mechanisms and are discussed in detail by e.g., Duranton and Puga (2004).

> 26 Specifically, we use data from the August 2014 round of SAKERNAS. These data are representative for Indonesian districts. This cross-sectional framework leaves us unable to control for sorting of workers based on time-invariant unobservable characteristics as their ability (as distinct from their level of education) and motivation. In this sense, we fall short of the standards of what researchers have been able to achieve in terms of the identification of agglomeration effects for countries such as the United States (Glaeser and Mare, 2001), France (Combes _et al_ ., 2008), or Spain (De la Roca and Puga, 2017) where detailed panel data sets allow the researcher to control for time-invariant unobservable characteristics of workers.

16
greater damage from climate shocks than manufacturing and services firms. Weaker performing firms are more affected by natural disasters in India and Indonesia (Pelli et al. 2023, Xie 2022). In the aftermath of large storms, capital gets reallocated towards more productive firms (Pelli et al. 2023) and industries with a larger comparative advantage (Pelli and Tschopp 2017). In the US, smaller firms and less-productive establishments were less likely to survive being damaged by Hurricane Katrina (Basker and Miranda 2017). Given this evidence suggesting that climate shocks have more adverse impacts on smaller or less productive firms, it is also important to understand which types of firms are most exposed to shocks, a question which has not been examined systematically in the existing literature.

The remainder of this paper is structured as follows: Section 2 describes the data and methodology used for the meta-analysis. Section 3 presents the results and Section 4 concludes.

# **2. Data and Method**

# **2.1 Data**

We use data from multiple sources to analyze the relationship between flooding and extreme heat and relative wealth, proxied by the Relative Wealth Index (RWI). A similar dataset for firms is constructed using the most recent Indian Economic Census.

**Relative wealth.** The Relative Wealth Index, developed by Meta’s Data for Good team, uses a combination of machine learning algorithms, satellite data, ground survey data, and other publicly available datasets to estimate the wealth distribution at granular spatial resolution. Each RWI data point represents the center of a 2.4 km by 2.4 km square. It uses cross-sectional household-level data from the nationally representative Demographic and Health Survey from multiple countries linked to additional data such as satellite imagery (Chi et al. 2022). The Demographic and Health Survey (DHS) is a series of nationally representative surveys conducted in multiple countries, including South Asian countries.

**Firm size in India.** We use the most recent cross-sectional firm-level data
introduction of the FPE program. From the left panel of the figure, we can see that, while the secondary school gross enrollment rate was on the increase between 2001 and 2011, the increase was much faster between 2006 and 2010. The figure further shows that, while the net enrollment rate increased through out the period, the gross enrollment rate plateaued between 2011 and 2015, and then began the upward trend. The year 2007 is when the first cohort of FPE children entered secondary school level.

> 6More young and qualified teachers were recruited. Between 2000 and 2007, the proportion of teachers with tertiary qualifications increase from 6 percent to 14 percent, but teacher quality, as measured by teacher performance on SACMEQ reading test, declined (see Moshoeshoe, 2015).

7
# Figure 4: External positions in LICs

# (A) Current account balance

<!-- Start of picture text --> Percent of GDP<br>0<br>All LICs<br>-2<br>Commodity exporters<br>-4 Others<br>-6<br>-8<br>-10<br>-12<br>-14<br>-16<br>2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text -->

## (B) Current account funding

<!-- Start of picture text --> Ratio of current account deficit<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>LICs Metal exporting LICs<br>Foreign direct investment<br>Official development assistance<br><!-- End of picture text -->

# (C) Foreign direct investment

<!-- Start of picture text --> Standard deviation of FDI flows<br>6<br>Median for non-LIC EMDEs<br>5<br>4<br>3<br>2<br>1<br>0<br>LICs Commodity Commodity Metal exporting<br>exporting LICs importing LICs LICs<br><!-- End of picture text -->

# (D) Median foreign reserves

<!-- Start of picture text --> Months of import coverage<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>LIDCs LICs<br><!-- End of picture text --> Source: International Monetary Fund, World Bank.

A. LICs= Low-income countries. Figure shows median current account balance in percent of GDP. The sample represents a total
level. For example, Anand and Ravallion (1993) attribute roughly two-thirds of the elasticity of life expectancy with regard to average income to the positive effect of income on public health spending and roughly one-third to the decrease in incomemeasured poverty associated with higher mean incomes.

We look at the direct relationship between the infant mortality rate and life expectancy and per capita GDP growth and the share of social sector spending in GDP. We control for initial income, a measure of income inequality -- as given by a country's Gini coefficient for income, a measure of political freedom -- as measured by the Gastil index, the quality of bureaucracy, and regional and decade dummies. We instrument for per capita GDP growth, social spending share of GDP, political freedom and income inequality. The instruments are the average of the previous five year lags of each endogenous variable except for per capita GDP growth rate for which we use the predicted values from the IV growth regression (Table 6, column 4). _Table 7_ reports the OLS and IV results from two regressions explaining infant mortality and life expectancy. As usual, we concentrate on the IV estimates. Essentially, we get qualitatively similar results from the two regressions **30.** Life expectancy and infant mortality -- the latter significantly -- improve as a result of income growth. Further, the share of social spending by govermnent is associated with higher life expectancy and lower infant

> **30** This is not unexpected as some **life expectancy data are extrapolated** from infant mortality **rates and** therefore may not contain any additional information (See Pritchett and Summers (1996)). We thank Lant Pritchett for this observation.

36
better reflects what people eat. The evidence to date on the health effects of dietary patterns is consistent with the WHO and GBD findings, suggesting a focus on plant-based diets and lower level of food processing (FAO and WHO 2019) **.**

**While there is evidence of diets changing globally over time, there are no harmonized metrics monitoring diet quality or how diets are evolving** (Verger et al. 2023) **. Diets are assessed in a variety of ways, based on the population of interest and type of data available** . For example, breastfeeding practices are measured for infants and children 0-23 months of age. Measures distinguish those who are exclusively breastfed up to six months and continue to be breastfed while receiving complementary food up to 23 months from those who entirely stop breastfeeding at any time and receive instead breast-milk substitutes such as commercial infant formula and/or animal milk. For LMICs, the micronutrient adequacy of the diet is estimated using updated standardized methods for the assessment of minimum diet diversity (MDD) in children 6-23 months of age (WHO and UNICEF 2021) and minimum diet diversity in non-pregnant women of reproductive age (15-49 years, MDD-W) (FAO 2021). However, diet diversity is also assessed among other age groups (Arimond et al. 2021) and pregnant women (e.g. by the most recent Demographic Health Surveys). Since the consensus on standardized metrics for monitoring healthy diets is just emerging (Verger et al. 2023), current evidence focuses on dietary risk factors associated with negative nutrition and health outcomes, such as inadequate consumption of fruits, vegetables, legumes, whole grains, meat, dairy and fiber, or overconsumption of salt, trans-fatty acids, red meat, processed meat and sugarsweetened beverages.

# **_Outcomes of interest_**

**Second, the outcomes of interest are all forms of malnutrition and diet-related co-morbidities such as type II diabetes and hypertension** defined as including undernutrition (such as child wasting,
30. A further factor driving the international contagion was the collapse in international commodity prices, especially oil, as a result of the fall in demand in the advanced economies. This led to a serious fiscal crisis in most oil-exporting countries. As illustrated in Figure 21, oil prices had initially reached a real price, in 2005 dollars, of about $110 per barrel, up from $60 a year earlier. In the run-up to the crisis, the commodity price hikes further compounded the effect of the collapse in the financial sector and pushed these advanced countries further into recession. Loose monetary policy also triggered these commodity price increases, as it diverted demand away from U.S. Treasury bills.<sup>18</sup> In environments with tight commodity markets and declining interest rates, there is a perverse incentive to convert commodities into assets.

**Figure 21: Real Price of Brent Crude**

<!-- Start of picture text --> 120<br>100<br>80<br>60<br>40<br>20<br>0<br>Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1<br>1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010<br>Sources: International Monetary Fund, International Financial Statistics; and U.S. Department of<br>Commerce, Bureau of Economic Analysis.<br>Price per barrel, 2005 US$<br><!-- End of picture text -->

> 18 Caballero, Farhi, and Gourinchas (2008).

23
their professional experience. The data are available for 2005–2014. We exclude countries with populations below 500,000, since they can be very different from the rest of the countries. Country-level data related to public procurement competitiveness are not available.

Measures of firm-level tax compliance costs are from the _World Bank Enterprise Surveys_ (WBES) conducted during 2006-2014.<sup>7</sup> We use four variables: whether the firm was visited or inspected by tax officials; the frequency of such visits; whether a gift or informal payment was expected or requested in any of the inspections or meetings with tax officials; and the extent to which tax administration is an obstacle to business operations. Firm-level procurement practices and costs are also taken from the WBES. The two variables of interest are: whether the firm has secured or attempted to secure a government contract over the last year; and whether the firm had to pay a bribe to get the contract. We restrict the sample of countries to those that are present in at least two survey waves.

Our main country-level controls comprise: GDP per capita, expressed in terms of purchasing power parity (PPP) from the _World Development Indicators_ (WDI) database and the overall polity score, from the _Polity IV_ database, which measures political competitiveness, executive recruitment, and constraints on executive action. To measure the level of development we use also gross secondary school enrollment; the number of Internet users from WDI; measures of the rule of law and government effectiveness from the _Worldwide Governance Indicators_ (WDI) database; and a measure of business freedom from the Heritage Foundation. In addition we use the time to enforce a contract, the time and number of procedures to start a business, and the time and number of procedures to register a property from DB database to construct a measure of the ease of doing business in a country.

Our firm-level controls are taken from the WBES and comprise firm productivity (real sales per employee) and a vector of indicator variables for whether a
2009 data and the 2009 forecast that is the unpredicted piece of new import protection that one could associate as being related to the crisis. Furthermore, according to column (6), Argentina actually experienced a smaller increase in imports covered by TTBs in 2009 than the model predicts based on a time trend alone – i.e., not controlling for any of the sizable macroeconomic factors during the recession which would make conditions even more likely for an increase in TTBs. And Turkey’s _value_ measure actually fell slightly (‐9.25%) in 2009 compared to 2007 according to column (4), despite the model predicting a slight increase from the 2007 realized value.<sup>22</sup> Thus while there was an increase in import protection during the crisis – at least as measured by the stock of imported products subject to TTBs in 2009 being higher in 2007 – my interpretation of the preliminary evidence is that it is difficult to support a claim that the increase was _caused by_ the crisis, given underlying, pre‐crisis trends already apparent in the data.

# **4. Exporters and Foreign Use of Antidumping**

Thus far I have addressed the question of TTB use from the perspective of the policy‐imposing economies and the scope of their imported products likely to be affected by use of such policies over time. In this section I switch my focus to the _incidence_ of such policies, and I ultimately take the perspective of the exporters directly and negatively impacted by imposed TTBs. Furthermore, in this section I restrict my attention to the antidumping policy instrument.

I structure this section in two parts.<sup>23</sup> In the first subsection, I examine the use of antidumping from the perspective of each G20 policy‐imposing economy to identify trends and potential heterogeneity in the application of the policy across different categories of foreign export targets. In the second subsection, I re‐orient the analysis to the perspective of the exporters themselves. This allows

>