rafmacalaba/data-use-mentions-extended huggingface dataset:
shorter delays at customs (although higher productivity firms do report shorter delays).

These results provide a strong case for a policy focus on the constraints identified by firms as most costly. In the next piece, we examine how the relative importance of different public input constraints reported by firms varies across countries in the sample.

# **8.  Which elements of the business environment matter most for firms and how do they vary across countries?**

In this section, we use the country averages of the costs of public inputs reported by firms, _Rij_ , to assess which elements of the external environment are more problematic for firms across the region and in each country. We also compare the country average evaluations with those in other countries at a similar level of development.

The data that we use in this section pools the surveys of the SAR countries used above with data from the surveys available from the World Bank’s Enterprise Surveys portal. We consider only formal sector firms in the analysis. Altogether, the surveys cover almost 120,000 firms from over 230 different surveys in 126 countries over the period 2000-2010. Of these, about 16,000 firms were from the 8 SAR countries. We use these firm-level data to calculate country average evaluations, i.e., country means.

In order to correct for differences in survey samples when comparing reported constraints across countries, we construct the “conditional country means” for each constraint for a standard firm. The controls included in the estimations are the same as those used above: size (log employment), and dummy variables for whether the firm is newly established, expanding employment, has substantial foreign ownership, is a significant exporter or importer, and is located in a small city.<sup>6</sup> Log employment is centered on ln(30). The estimations are identical to the country regressions reported earlier; the country conditional means are, in fact, the estimated intercepts

> 6 Also as previously, we include a dummy
Expected asset losses �<sup><u>�Hazard� ∙ �Exposure� ∙ �Asset vulnerability�</u></sup> Risk to wellbeing � Socioeconomic resilience Socioeconomic resilience Socioeconomic resilience ( _resilience_ for short in this paper) measures the ability of an economy to minimize the impact of asset losses on wellbeing and is one part of the ability to _resist_ , _absorb_ , _accommodate_ and _recover in a timely and efficient manner_ to asset losses (the qualitative definition of resilience from the United Nations) _._ In an idealized case of perfect risk‐sharing across the population, no irreversible impacts on human capital, and no pre‐existing inequality, welfare losses are equal to asset losses. Socioeconomic resilience at 50% means that welfare losses are twice as large as asset losses, and could be reduced by half if inequality disappeared, losses were perfectly shared, and irreversible impacts were avoided.

We develop and use a model to estimate expected asset losses and expected welfare losses, and quantify socioeconomic resilience in 117 countries. The model builds on Hallegatte et al. (2016b), which only considered river floods and 90 countries. Here we add new countries thanks to new socioeconomic data, and we introduce additional hazards – coastal floods and storm surge, windstorm, earthquakes, and tsunamis – using the risk assessment provided in the Global Assessment Report (UN‐ISDR 2015). We also simplify the model – focusing on the factors that were found to have most influence on our previous assessment and disregarding other factors – and improve our modeling of insurance and social protection.

Like all models, ours is incomplete and our assessment provides a partial view of resilience. For instance, we do not include many non‐economic components such as the link between disasters, conflicts, and state fragility. Nevertheless, our quantification informs on the ability of economies to deal with natural disasters and on the prioritization of policy options to improve resilience.

We find that some policy options can reduce welfare losses by increasing socioeconomic resilience
# III. RESULTS

Table 1 shows the main results, covering the 1974–2009 period. All data are averaged over four years. The dependent variable is the average annual growth rate of real GDP per capita; aid is measured as net Official Development Assistance (ODA) as a percentage of GDP.<sup>22</sup> When we do not account for diminishing returns to aid by including aid squared, the coefficient of the interaction term is negative and significant at the five‐percent level (column 1). When we include aid squared, the interaction becomes significant at the one‐percent level (column 2).<sup>23</sup> According to column 2, for any increase in _ΔAid_ , the effectiveness of this change in aid disbursements decreases with the share of the period the recipient country has spent on the UNSC two periods before (i.e., when the aid has been committed). The causal effect of a one percentage point increase in aid as a percentage of GDP on yearly economic growth is 0.64 percentage points higher if the recipient has not served on the UNSC compared to if it has served two years (i.e., 1/2 of the four‐ year period). Compared to the average growth rate of about 1.34 percentage points in our sample, this is a substantial reduction of almost half the average growth rate. The results thus support our hypothesis that aid committed during times of short‐term political importance is indeed less effective.

> 22. The original source for GDP per capita growth is the World Bank’s World Development Indicators; ODA is total net ODA in current US$ from table 2 of the OECD’s Development Assistance Committee in percent of GDP in current US$, taken from the World Development Indicators (see the Technical Appendix to Clemens et al. 2012). Data for the 2006–2009 period are from Minasyan (2016) and World Bank (2016).
holds in the previous 12 months and identied the age, gender, educational attainment, and the destination country of the remittance sender.

This study uses 7,108 NLSS III households from both the cross-section and panel components that have complete information on the variables used in the analysis. Among the households in the sample, 33.1% (2,212) reported having at least a member abroad in 2010-11. Characteristics of migrants are reported in table (A.1). Migrants tend to be young (mean age of 28), dominated by males (84.4%) and mostly composed of the daughters/sons of the household head. They tend to have achieved some grade level of education, 53.7% have completed between grades 1 and 10, while 20.3% have completed grade 10 (School Leaving Certicate) and intermediate level (high school). India and the Middle East seem to be the favored destinations for most of the migrants, 44.4% and 24.7%, respectively. Although remittances coming from abroad tend to be relatively large, there are very little dierence in total and non-wage household incomes (Figure 2); households with a migrant have slightly higher total and non-wage incomes.

A total of 16,879 working age adults (ages 18-60) are used for the analysis. For the analysis of time allocation in household activities, 4,985 boys and 5,195 girls (both ages 6-17) are added in the sample. Adults from households with and without migrants dier on demographics, household composition, their labor market outcomes, and communities where they reside. Table 1 reports the descriptive statistics of the 16,879 adults used in the analysis. Adults from migrating families, both male and female, are less likely to be employed in wage-employment but are more likely to be involved in self-employment activities compared to the adults from non-migrating families. Consistent with the theory (for the bottom 70% of the
# **Section 1: Why Do Management Capabilities Matter?**

The persistence in productivity differences between firms, within the same industry, is striking and puzzling. For example, labor productivity for plants in the 90<sup>th</sup> percentile, within narrowly defined four-digit US manufacturing industries, is four times as high as plants in the 10<sup>th</sup> percentile. Likewise, the difference in Total Factor Productivity (TFP) is twice as high, when controlling for other factors (Syverson, 2011). These differences persist over time and are robust to controlling for plant-specific prices in homogeneous goods industries. Such TFP heterogeneity is not an artifact of the US data; it is also evident in several other countries. It is well-known that variations in productivity trump any other source of heterogeneity in firm performance: in the United States, firms in the 90th percentile of the productivity distribution produce twice as much with the same set of inputs as firms in the 10th percentile, whereas in China and India this gap is 5:1 (Hsieh and Klenow 2009). Often such within-industry productivity differentials are attributed to “hard” technological innovations, as embodied in patents or in adoption of advanced equipment. Bloom et al. (2016a) provide an alternative explanation, which is also the focus of this paper, that is, productivity differences reflect variations in management practices.

Bloom et al. (2016a) propose that some forms of management practices are like “technology”, in the sense that they raise firm productivity. Bloom and co-authors have significantly advanced the empirical foundations for measuring management practices across firms and countries, which has allowed core theories in many fields such as international trade, labor economics, industrial organization and macroeconomics to incorporate firm heterogeneity as a central component. While a frontier topic globally, there exists little evidence on managerial practices of firms in Croatia and the relationship between management and productivity. Nevertheless, there is some evidence on management in countries from the Europe and
**Figure 1: Regulation around the World**

<!-- Start of picture text --> Overall Regulation Index Product Market Regulation Index<br>INL EAP SAS LAC AFR MNA INL EAP SAS LAC AFR MNA<br>Labor Regulation Index Fiscal Burden Index<br>INL EAP SAS LAC AFR MNA INL EAP SAS LAC AFR MNA<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>.6 .8<br>.6<br>.4<br>.4<br>.2<br>.2<br>0 0<br><!-- End of picture text --> _Note: AFR: Africa region, EAP: East Asia and Pacific region, INL: OECD, LAC: Latin America and the Caribbean region, MNA: Middle East and North Africa region, SAS: South Asia region_ 33
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Source: Authors’ calculations The Gini index follows a U-shaped pattern: Countries first diversify then they start specializing at around PPP GDP per capita of 20,000 (constant 1985 US dollars). This result is in line with the literature on diversification and economic development (Imbs and Wacziarg, 2003; Klinger and Lederman, 2004 and 2006; and Cadot et al.,2011). Imbs and Wacziarg (2003) find a turning point at PPP GDP per capita 8,675 (constant 1985 US dollars) and they note that their turning point occurs around the level of income for Ireland in 1992. Since we use data on PPP GDP per capita in constant 2017 US$, the value of the turning point in our estimation is not directly comparable. We thus convert PPP GDP per capita (constant 2017 US dollars) into constant 1985 dollars to compare with Imbs and Wacziarg (2003) using the U.S GDP implicit price deflator provided by the U.S Bureau of Economic Analysis who use GDP per capita in constant 1985$. Our turning point corresponds roughly to Ireland’s income level in 2000. Countries to the left of the turning point include Ghana, <!-- Start of picture text --> to Ireland’s income level in 2000.<br><!-- End of picture text --> <!-- Start of picture text --> Countries to the left of the turning point<br><!-- End of picture text --> <!-- Start of picture text --> Ghana,<br><!-- End of picture text --> 12
# **Diversification and Efficiency of Investment By East Asian Corporations**

Stijn Claessens, Simeon Djankov, Joseph P.H. Fan, and Larry H.P. Lang

## **1. Introduction**

The East Asian financial crisis has at least in part been attributed to the extensive diversification of corporates, which is associated with misallocation of capital investment towards less profitable and more risky business segments. While a plethora of anecdotal evidence to support this argument has surfaced in the aftermath of the crisis, there was little discussion preceding the crisis. Quite the opposite, the rapid expansion of East Asian corporations through entry into new business segments was seen as an important contributing factor to the East Asian Miracle (World Bank (1994)). In this paper, we examine the efficiency of investment by diversified corporations in nine East Asian countries, using unique panel data of more than 10,000 firns over the 1991-96 pre-crisis period. We offer three contributions to the literature. First, we document the degree of diversification on the corporate sector in Hong Kong, Indonesia, Japan, Korea, Malaysia, the Philippines, Singapore, Taiwan, and Thailand, countries which have achieved enviable rates of economic growth over the last three decades. Second, we distinguish between vertical and complementary diversification and study the differences across the nine countries. Finally, we investigate whether diversification in East Asia has hurt economic efficiency.

To accomplish the first two contributions, we use the inter-industry commodity flow data in the US input-output table as a common benchmark to construct vertical relatedness and complementarity indices between the primary and the secondary businesses of all sample firms in the nine countries (see Fan, Lang and Stulz (1998)). These measures allow us to describe, for any pairs of businesses, the possibility of vertical integration, joint procurement, and/or sharing marketing and distribution in each firm. We document the degree of diversification in the corporate sector, distinguish between vertical and complementary diversification, and study the differences across the nine countries
<mark>“We will not be able to reach our goal unless we have data to show whether or not people are actually lifting themselves out of poverty [.] Collecting good data is one of the most powerful tools to end extreme poverty. We pledge, working alongside our partners in countries and international organizations, to do something that makes common sense and is long overdue: to conduct surveys in all countries that will assess whether people’s lives are improving.”</mark><sup>6</sup> Alleviating data deprivation and collecting household survey data for the robust measurement of living standards every three years in each of the 78 IDA countries imply that a total of 390 surveys will need to be carried out over the period of 2016-2030. To achieve this, the WBG would need to commit to working with the national statistical offices (NSOs) on existing household survey operations that are already integrated into the national statistical systems (NSSs). The NSOs would be supported in line with the recommendations of the “World Bank Shared Strategy for Household Surveys: Tracking the Twin Goals and Informing Investment and Policy Decisions” (henceforth referred to as the World Bank Household Survey Strategy).

Achieving this ambitious objective will require a significant infusion of financial and human capital into the household survey agenda. The objective of this paper is to estimate the resource requirements to implement 390 household surveys across 78 IDA countries from 2016 to 2030 for monitoring and understanding the progress towards the Twin Goals and the SDGs, and to provide direct technical assistance to national statistical offices (NSOs) on all facets of survey design, implementation, and dissemination towards timely production of quality household survey data. Our approach to costing household survey data production is unique, as it makes use of detailed data on actual survey implementation and technical assistance costs from a group of countries around the world, unlike previous attempts at costing household survey data gaps.<sup>7</sup> The budget envelope for achieving the objective laid out by
dimensions of personality. Taken together, the five first dimensions – Openness, Conscientiousness, Extraversion, Agreeableness and Emotional Stability – are usually referred to as the Big Five Inventory (BFI), which have been shown to approximate the main dimensions used to describe personalities (John and Srivastava, 1999). Whereas the BFI concerns very broad personality traits, the module also collects data on three other more specific dimensions of behavior – Grit, Decision-Making and Hostility Bias. Interviewees 64
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error in (14) does not present second-order serial correlation.

We rely on two main sources of data. Inequality data come from Dollar and Kraay’s database on inequality. This database expands the inequality data used in their 2002 paper and contains 953 observations of the Gini coefficient for 137 countries. We acknowledge that the Gini coefficient is less than perfect and that other measures, such as the income

> 11 Clearly, equation (6) would also be subject to a similar transformation.

-10-
- 14 Kanyanda S, Markhof Y, Wollburg P, Zezza A. Acceptance of COVID-19 vaccines in sub-Saharan Africa: evidence from six national phone surveys. _BMJ Open_ 2021; **11** : e055159.

- 15 Solís Arce JS, Warren SS, Meriggi NF, _et al._ COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries. _Nat Med_ 2021; published online July 16. DOI:10.1038/s41591-021-01454-y.

- 16 Africa CDC. COVID-19 Vaccine Perceptions: A 15 country study. 2021 https://africacdc.org/download/covid-19-vaccine-perceptions-a-15-country-study/ (accessed May 18, 2021).

- 17 Razai MS, Oakeshott P, Esmail A, Wiysonge CS, Viswanath K, Mills MC. COVID-19 vaccine hesitancy: the five Cs to tackle behavioural and sociodemographic factors. _J R Soc Med_ 2021; **114** : 295–8.

- 18 Himelein K. Weight Calculations for Panel Surveys with Subsampling and Split-off Tracking. _Stat Public Policy_ 2014; **1** : 40–5.

- 19 Ambel A, McGee K, Tsegay A. Reducing Bias in Phone Survey Samples. Effectiveness of Reweighting Techniques Using Face-to-Face Surveys as Frames in Four African Countries. Washington D.C.: World Bank, 2021 https://documents.worldbank.org/en/publication/documentsreports/documentdetail/859261622035611710/reducing-bias-in-phone-survey-samples-effectivenessof-reweighting-techniques-using-face-to-face-surveys-as-frames-in-four-african-countries.

- 20 Brubaker J, Kilic T, Wollburg P. Representativeness of individual-level data in COVID-19 phone surveys: Findings from Sub-Saharan Africa. _PLOS ONE_ 2021; **16** : e0258877.

- 21 Buguzi S. Covid-19: Counting the cost of denial in Tanzania. _BMJ_ 2021; : n1052.

- 22 Wilson SL, Wiysonge C. Social media and vaccine hesitancy. _BMJ Glob Health_ 2020; **5** : e004206.

- 23 Bursztyn L, González AL, Yanagizawa-Drott D. Misperceived Social Norms: Women Working Outside the Home in Saudi Arabia. _Am Econ Rev_ 2020; **110*
cannot easily minimize the objective function within the constraints.

The estimated parameters presented in table [A.3] show the expected signs (imposed by the model) and coefficient sizes. It is also worth noting that their impact decreases in 2007; those increasing (in absolute terms) are mostly negative, suggesting that being illiterate or having a low level of education in urban areas becomes more disadvantageous in 2007 in economic terms. Using eq. [10] we compute the fitted value, from which we obtain the results reported in table 2. With _θ_<sup>ˆ</sup> as an estimated parameter and _θdir_ the corresponding direct estimates, it is possible to define the Absolute Relative Bias (ARB): This index facilitates comparison of all models in terms of bias (figure 1). This plot demonstrates that the only method that produces a low ARB is the one using the algorithm, with consistent bias reduction when compared with the other methods.

Table 2: Estimated Indices with Fitted Values, Algorithm Method

|Id|20|00|20|07|
|---|---|---|---|---|
|nex|Rural|Urban|Rural|Urban|
|Gini|0.354|0.425|0.356|0.442|
||(0.004)|(0.003)|(0.006)|(0.005)|
|Theil|0.233|0.323|0.230|0.343|
||(0.009)|(0.007)|(0.011)|(0.009)|
|HCR|0.175|0.207|0.167|0.268|
||(0.004)|(0.004)|(0.013)|(0.006)|

_Source_ : Authors’ calculations based on Moroccan HBS 12
business sector productivity is slightly faster when measured with a weighted labor input than when measured with an unweighted input. Similarly, in the whole economy quality adjusted labor input grows faster than the numbers employed.

In addition to the impact of changes in the industrial structure of employment, the skill level of the labor force has been steadily increasing, with the average number of years of schooling of the population aged 25 to 64 rising by 1.2% per year. International experience suggests that an additional year of education raises the level of earnings (hence productivity) by 7%.<sup>2</sup> On this basis, the years of education series has been transformed to a human capital index by exponentiating the rate of return by the number of years of education. Such a transformation halves the growth rate of human capital relative to the growth of earnings.

# **_Income shares_**

A measure of total factor productivity also requires a weighting system to combine capital and labor, usually taken to be the share of value-added coming from labor and capital. Such data are provided by the Chinese input-output table but not in enough detail to isolate the sectors used in this study. Wu et al (2015) have provided a slightly more granular IO table covering 37 industries from 1990 to 2010, with nine observations corresponding to the published IO tables. These sectors do not correspond exactly to those used in this paper for two reasons: culture, entertainment and household services are classified as one sector by the CIP data while this paper has allocated culture and entertainment to the government sector. As the bulk of value-added in the CIP sector comes from household services, this sector has been allocated to the business sector. Secondly, the housing sector is kept as part of the real estate sector in the CIP data. In this paper, the housing sector is excluded from real estate by deducting capital consumption of housing from the CIP real estate sector and its addition to the government and infrastructure sectors. The overall result is
on land ownership. A few additional observations drop out once we control individual, spouse, couple and household characteristics due to missing values.

> 11 Unlike in section 2, where we reported the most recent WBL data (referring to 2020), the multivariate analysis draws on the data referring to 2015 (from the 2020 WBL edition), which are closer in timing to the DHS data in our sample (see Table A2).

17