rafmacalaba/data-use-mentions-extended huggingface dataset:
POLICY RESEARCH WORKING PAPER 2033

# Su mni ary findings

The East Asian financial crisis has been attributed in part to the corporate diversification associated with the misallocation of capital investment toward less profitable and more risky business segments. Much anecdotal evidence to support this view has surfaced since the crisis but there was little discussion of it before the crisis. Quite the contrary: The rapid expansion of East Asian firms by entering new business segments was viewed as contribuiting to the East Asian miracle.

Claessens, Djankov, Fan, and Lang examine the efficiency of investment by diversified corporations in nine East Asian countries, using unique panel data from more than 10,000 corporations for the pre-crisis period, 1991-96. They:

**-** Document the degree of diversification in the corporate sector in Hong Kong, Indonesia, Japan, the Republic of Korea, Malaysia, the Philippines, Singapore, Taiwan (China), and Thailand, countries that have achieved enviable rates of economic growth over the past three decades.

* Distinguish between vertical and complementary diversification and study the differences across nine countries.

Investigate whether diversification in East Asia has hurt economic efficiency.

Their study tests the learning-by-doing and misallocation-of-capital hypotheses related to the types and degrees of diversification in East Asiar. countries. Firms in Indonesia, Korea, Taiwan, and Thailand appear to have suffered significant negative effects of vertical integration on short-term performance; the same countries gained significant short-term benefits from complementary expansion. The results suggest that the misallocation-of-capital hypothesis is appropriate for Korea and Malaysia; the learning-by-doing hypothesis for Indonesia, Taiwan, and Thailand.

Firms in more developed countries succeed in vertically integrating and improve both short-term profitability and market valuation. Firms in more developed countries are ultimately more likely to benefit from such diversification (learn faster, to improve their performance). And diversification by firms in less developed countries is subject to more misallocation of capital.

- This paper -a product of the Economic Policy Unit, Finance, Private Sector, and Infrastructure Network is part of a larger effort in the department to
Figure A6: Average quality of imported digital goods due to full liberalization <!-- Start of picture text --> Ivory Coast<br>Mali<br>Burkina Faso<br>Nigeria<br>Zimbabwe<br>Seychelles<br>Mauritius<br>Lesotho<br>Zambia<br>Swaziland<br>Benin<br>Namibia<br>Mauritania<br>South Africa<br>Morocco<br>Malawi<br>Gambia<br>Senegal<br>Botswana<br>Cape Verde<br>Burundi<br>Angola<br>Niger<br>Africa average<br>Ethiopia<br>Mozambique<br>Togo<br>Tanzania<br>Madagascar<br>Uganda<br>Libya<br>Ghana<br>Congo<br>Tunisia<br>Rwanda<br>Comoros<br>Egypt<br>Central African Rep.<br>Kenya<br>Ex-post Ex-ante<br>92 94 96 98 100 102<br><!-- End of picture text --> Notes: Figure depicts expected change in the quality of digital goods due to the implementation of a full tariff liberalization towards all countries. Product quality by source country is estimated using the Feenstra and Romalis (2014) methodology, drawing on data for 2019 and is assumed to be constant. Estimated product quality changes are driven by changes in the shares of digital goods imports from each source country due to the implementation of the full tariff liberalization.

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representative household assumption and its associated theoretical shortcomings are dropped almost entirely. However, these accomplishments entail higher data requirements and computational costs, and may require other dimensions of the model to be scaled back (e.g., the authors are only able to consider three sectors and four commodities).

Cogneau and Robilliard analyze a variety of sector-level shocks to Madagascar’s economy including, among others, shocks to the world price of cash crops. Without exception, they find that the impact of poverty and inequality is small. They give three reasons for this. Firstly, they argue that many of the households are diversified in their sources of income and so the impact of sector-specific shocks are muted. Secondly, to the extent that households are not initially diversified, they may respond to the external shock by changing their allocation of labor, in an effort to equate factor returns across sectors, thereby spreading the impact beyond the directly affected sector. Finally, they make the point that the second-round benefits of any favorable development tend to accrue to those individuals who control the factors of production in the economy. Thus a disproportionate share of the gains go to the wealthy, preserving the initial degree of inequality. This stability of the overall income distribution in the face of policy interventions has long been observed by those working in this field (e.g., Dervis, de Melo and Robinson, 1982).<sup>8</sup>

# **V. Micro-Macro Syntheses**

While the approach of Cogneau and Robilliard is innovative and ambitious, it remains beyond the reach of most CGE modelers seeking to obtain some basic insights into the links between trade and poverty. This is particularly true in larger countries, where much greater sector detail is required, and where survey data may involve well in excess of 100,000 households. It is also complicated by the fact that researchers with expertise in CGE modeling are typically different from those with expertise in household modeling. As a result, it can be difficult to
(e.g. Bates, 1981; Krueger, Schiff and Valdes, 1988; van de Walle, 2001; Anderson and Masters, 2009; Bates and Block, 2010).<sup>41</sup> Following recommendations by the World Bank and the International Monetary Fund (Badiane et al., 2002), African cotton sectors have however seen their share of reforms.<sup>42</sup> Figure 5, which uses the database constructed by Delpeuch and Leblois (2011), depicts the proportion of competitive cotton markets in SSA countries in 1990 and in 2008. On the one hand, this figure shows that an increasing number of markets have become competitive; but on the other, it also points to the fact that, in many countries, markets remain noncompetitive. Indeed, while many countries have undertaken reforms over the past two decades, the nature of these reforms has widely varied across regions, ranging from farreaching market and price liberalizations, to only marginal adjustments.

Schematically, reforms have come in two waves: a first series of far-reaching reforms in ESA in the mid-1990s and a second series of more modest reforms in WCA in the course of the 2000s.<sup>43</sup> As illustrated in figure 6, a number of countries in ESA have privatized their parastatals, liberalized prices and allowed for competition.<sup>44</sup> Private stakeholders have however reacted to these reforms in different ways. While the Tanzanian cotton market is considered to be relatively highly competitive (Poulton, 2009); markets in Uganda, Zambia and Zimbabwe are said to exhibit lower degrees of competition (Brambilla and Porto, 2009; Poulton and Hanyami-Mlambo, 2009).

Resistance to market reforms has been much stronger in WCA, where the reforms implemented in the late 1990s and early 2000s have given rise to ‘hybrid markets’ characterized by regulation and mixed ownership (figure 7). While parastatals have often been privatized to some extent, including by allowing farmer associations
lagged outcome variable) using a single baseline and single follow-up survey. The power will also depends on the correlation between baseline profits (revenues) and follow-up profits (revenues). McKenzie (2011, 2012) examines several firm surveys and finds autocorrelations in the 0.3 to 0.5 range, reflecting the fact that firms experience a large amount of idiosyncratic variation in their profits and revenues from one period to the next (Fafchamps et al, 2012). This autocorrelation is likely to be stronger for larger firms (see Bloom et al, 2012), and weakest for the smallest microenterprises. We assume an autocorrelation of 0.5, and ignore any survey attrition, which would further weaken power. However, we also ignore any reduction in heterogeneity that comes from stratified randomization or other methods to improve power (Bruhn and McKenzie, 2009).<sup>5</sup> Nevertheless, these calculations provide a useful approximation of the approximate power of different studies. Group-randomized studies add a further complication for power comparisons, since their power is reduced relative to individual level randomization by a factor which depends on the intra-group correlation. Our solution is to provide a range of power, where the higher number would occur if the intra-group correlation is zero (equivalent to individual randomization) and the lower number would occur if the intra-group correlation is one (equivalent to having only one observation per group). Given the heterogeneity in firms, it is likely the intra-group correlation is relatively low, making actual power closer to the higher number.

Many funding agencies consider 80% to 90% power an appropriate target (Duflo et al, 2008) and power of 80% or more is the standard in medical trials (Schultz and Grimes, 2005). Table 5 reveals that many, if not most, business training experiments fall well below these levels in terms of power to detect a 25 percent or 50 percent increase in profits or revenues. For a microenterprise earning $25 per month (about $1 per
<br> South Asia Sub-saharan Africa Latin America Fitted values<br>100<br>80<br>60<br>WBL_Index<br>40<br>20<br><!-- End of picture text --> Source: Authors from World Bank, Women, Business, and the Law (2023) database. Note: Used data comes from the World Bank’s time series on Women Business and the Law index (1971-2023) for 122 countries belonging to the MENA, OECD high-income, South Asia, Latin America, East Asia, Sub-Saharan Africa, and Europe and Central Asia regions. The latest year, 2023, is considered. The index scores are based on the average of each economy’s scores for the 8 topics included in the aggregate score: mobility, workplace, pay, marriage, parenthood, entrepreneurship, assets, and pension. The index scores range between 0 and 100 and a higher score indicates more gender equal laws. The corresponding values for female labor force participation rate for each country are from the World Bank, World Development Indicators database.

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17 _Rm_  '  '' as in Figure 5. When _Rm_ is very low the regulations reduce welfare because the distortion in the labor choice is very large and from the social point of view, it is worth paying the cost of the trip to the bank to eliminate this distortion for high productivity buyers.

Figure 5: Welfare gain from eliminating bonds as a function of the real rate of return on money ( _Rm_  _R_  1).

# <u>Numerical example</u>

> To illustrate, we assume that  is uniformly distributed in the range 0.01  1 and  0.001. Later we use parameters that are consistent with recent US data. There are large differences between the two. In recent US data the top 1 percent makes about 15 percent of total income while here they make about 3 percent of income. But this

example allows for a smooth variation in the number of bond users and it is useful for illustrating the different concepts of welfare used.
Policy Research Working Paper 8686

# **Abstract**

This study presents a novel set of indicators on outcomes of foreign direct investment spanning 63 developing countries and 10 areas that matter for development. Building on decade-long data collection by the World Bank Enterprise Surveys, the indicators highlight systematic differences between foreign multinational enterprises and domestic firms across countries in competitiveness outcomes such as productivity, innovation, export orientation, as well as the extent to which they promote inclusiveness through job creation, gender empowerment, or supply linkages. Although there appears to be no striking trade-off between competitiveness and inclusiveness of foreign multinational enterprises, their premia differ substantially across regions and income groups. Differences in some key drivers of competitiveness, such as productivity, innovation, and skills transfer, appear to be increasing with income, although premia in most outcomes are stronger in lower-middle-income or low-income markets, highlighting the relevance of foreign multinational enterprises for socioeconomic progress in these contexts. Moreover, outcomes of foreign multinational enterprises in areas such as export orientation, skills transfer, and physical capital accumulation are more consistent across countries, whereas in other areas the outcomes display wide variation, suggesting potentially higher sensitivity to investor motivations, sectors, seasonal trends, and business environments. Policy efforts should take these differences into account, to devise investment strategies that not only seek to increase capital flows, but also enhance the benefits that could be derived from them.

This paper is a joint product of the Economics and Private Sector Development Vice Presidency of the International Finance Corporation (IFC), member of the World Bank Group, and the Investment Division of the Organisation for Economic Cooperation and Development (OECD). 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/research. The authors may be contacted at aragoussis@ifc.org and iza.lejarraga@oecd.org.

_The
to prospective inflation. Each measure has advantages and drawbacks.

# 2.1. Survey-based measures

Surveys of households and firms . Among advanced economies, commonly referenced surveys of households’ inflation expectations include the University of Michigan’s Surveys of Consumers (monthly frequency for the United States), the European Commission’s consumer survey for the countries of the European Union (monthly), and the Bank of England’s consumer survey (quarterly) for the United Kingdom.<sup>2</sup> High-frequency surveys of households’ or firms’ inflation expectations are also conducted by Australia, Canada, the Czech Republic, Italy, Japan, the Republic of Korea, New Zealand, and Sweden. Among EMDEs, survey-based measures of households’ or firms’ expectations are produced by central banks in East Asia (for example, Indonesia, the Philippines, and Thailand), Europe and Central Asia (for example, Kazakhstan and Turkey), and India and South Africa.<sup>3</sup> Surveys of professional forecasters. The most commonly used survey of professional forecasters is produced by Consensus Economics, which incorporates the views of more than 700 professional forecasters in 85 advanced economies and EMDEs. Consensus Economics publishes short-term expectations at a monthly frequency and long-term expectations at a semi-annual or quarterly frequency.<sup>4</sup> Other surveys of professional forecasters include the Survey of Professional Forecasters by the Federal Reserve Bank of Philadelphia, which provides data on expectations up to 10 years ahead, and the European Central Bank’s Survey of Professional Forecasters. Central banks in several other economies (for example, Argentina, Brazil, Iceland, Indonesia, Israel, Mexico, South Africa, and Turkey) also produce professional survey-based measures of inflation expectations. Surveys of inflation expectations in EMDEs typically have smaller samples than those in advanced economies, but the number of EMDEs included in Consensus Economics’ surveys has increased over time, from seven in 1990 to 52 in 2018.<sup>5</
Berman, N., and J. H ́ericourt, 2010, “Financial Factors and the Margins of Trade: Evidence from Cross-Country Firm-Level Data,”Journal of Development Economics 93(2), 206-217.

- Besedeˇs, T., 2013, “The Role of NAFTA and Returns to Scale in Export Duration,” CESifo Economic Studies 59(2), 306-336.

- Besedeˇs, T., and T.J. Prusa, 2006a, “Ins, Outs, and the Duration of Trade,” Canadian Journal of Economics 39(1), 266-295.

- Besedeˇs, T., and T.J. Prusa, 2006b, “Product Differentiation and Duration of US Import Trade,” Journal of International Economics 70, 339-358.

- Besedeˇs, T., and T.J. Prusa, 2011, “The Role of Extensive and Intensive Margins and Export Growth,” Journal of Development Economics 96, 371-379.

- Besedeˇs, T., B.-C. Kim, and V. Lugovskyy, 2014, “Export Growth and Credit Constraints,” European Economic Review 70, 350-370.

Braun, M., 2003, “Financial Contractibility and Asset Hardness,” Working Paper.

- Braun, M., and B. Larrain, 2005, “Finance and the Business Cycle: International, InterIndustry Evidence,” Journal of Finance 60(3), 1097-1128.

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14 the Gini coefficient. Figure 4.2 shows the Lorenz curves for Rural, Urban excluding Lagos, Lagos Urban and Nigeria for 1985/86 and 1992/93. The figure shows that Lagos urban distribution has actually improved. The worsening in national income distribution is thus caused mainly by the worsening rural distribution. The Gini coefficients for rural, other urban and Lagos

**Table 4.2: Distribution of Per Capita Food Expenditure by Population Deciles (Constant 1985/86 Naira Per Annum**

|Decile|1985/86**PCE**|**1992/93 PCE**|**Percentage Change**|
|---|---|---|---|
|First five percent|99.20|53.85|-45.7|
|Second five percent|145.89|110.41|-24.3|
|Second Decile|186.50|163.89|-12.1|
|Third Decile|224.17|236.95|5.7|
|Fourth Decile|260.98|309.41|18.5|
|Fifth Decile|295.78|374.01|26.45|
|Sixth Decile|353.27|461.17|30.54|
|Seventh Decile|408.63|567.06|38.77|
|Eighth Decile|482.14|698.77|44.93|
|Ninth Decile|595.65|947.83|59.13|
|Tenth Decile|1047.96|1472.48|40.51|
|Nigeria|391.63|554.92|41.7|

Source: NCS, 1985/86 and 1992/93 urban are shown in Table 4.3. In Lagos urban, the Gini coefficient increased from 0.340 to 0.399. For other urban excluding Lagos, the value increased from
**-** **<u>Table 1: Changes in cropped area, Ukraine 2004 2018 as well as yields and output prices in 2018</u>**

||<br>Cultivated<br>|<br>land F29<br>|<br>T<br>|<br>hree-year averag<br>|<br>e F50 (2016-201<br>|8)<br>|
|---|---|---|---|---|---|---|
||(million<br>2005|s of ha)<br>2018|Yield<br>dt/ha|Price<br>UAH/Q|Cost<br>UAH/ha|Profit<br>UAH/ha|
|Wheat|6.36|6.61|43|403|12,044|4,095|
|Barley|4.20|2.49|37|390|10,302|3,346|
|Maize|1.45|4.58|75|379|18,459|8,180|
|Sunflower|3.51|6.11|24|896|13,128|7,426|
|Soybeans|0.42|1.72|22|966|14,784|5,207|
|Roots, veg. & forage|5.19|3.59|||||
|Total agricultural crops|24.07|27.68|||||

Source: Own computation from form 29 and 50 of state statistics database 15
to define two groups: highperforming IPAs (those with positive, significant effects in attracting FDI) and other IPAs (those with insignificant or negative, significant effects). It then considers which IPA characteristics significantly differ between the two groups, and in turn, reflects on what type of institutional setting, strategic focus, organizational framework and service provision is most conducive for IPAs to attract FDI.

## **Outline**

Section 2 of this report reviews the literature. It first considers the broader literature on the effect of IPAs in attracting foreign investment. It then also considers the potential that gravity models hold for the analysis of FDI dynamics. To identify the characteristics that may shape the functioning of IPAs we build on the qualitative literature reviewed by Heilbron and Kronfol (2020) and consider four broad pillars: institutional arrangement; strategic alignment and focus; organizational framework and resourcing; and investor service delivery. Section 3 provides a summary of the data used in this analysis. This describes the process for harmonizing different types of sectoral FDI, and merging this to the WB-WAIPA survey on IPA characteristics. Section 4 summarizes the empirical strategy, which includes the structural gravity model of FDI and the gravity model with country-interaction effects. Section 5 provides the results, and Section 6 concludes.

## **Added value of this paper**

This paper provides a modest contribution to two distinct bodies of literature. The first relates to economic literature analyzing the impact of IPA to attract FDI. These studies commonly rely on an IPA’s specific sectoral targeting strategy combined with sectoral FDI data. Yet, because sectoral FDI is often not available for most countries, researchers either restrict their analysis to FDI inflows from the United States 2
costs, and are generally levied on imports but not exports. Relaxing the symmetry assumption, we define ad-valorem trade costs when shipping from origin city o to destination city d as τod ≡ where countries are indexed by k; ĉ<sup>j</sup> is an estimate of the freight rate per unit of distance for each road, rail, or sea link segment j reflecting the labor, fuel, maintenance j and depreciation costs of freight transport; dod is distance; and the set of segments J is chosen such that it minimizes the trade cost τ̂od.<sup>8</sup> When cities are in the same country and k(o) = k(d) the interpretation of advalorem trade costs is straightforward: transport costs are the sum of transport costs along each segment, divided by the value of the shipment. We set S = $50,000 or the notional value of a shipment provided to survey respondents who quote border costs to the World Bank’s Doing Business Indicators. Border and tariff costs are added only when the origin and destination are in different countries, or when k(o) ≠k(d). For country k, borderkexport refers to the sum of quoted export border and documentary import compliance costs and borderk refers to the sum of quoted import border and documentary compliance costs, all from the Doing Business Indicators. The average ad8 The selection of the minimum cost route is performed using the shortest_path function in the NetworkX package, which makes use of Dijkstra’s (1959) algorithm.

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allowed them to instill a quantitative approach to their off-site monitoring, with techniques that can summarize large amounts of data into a few quantitative assessments. Although the reliance on databases is not a perfect substitution for onsite supervision, the process for off-site monitoring has particularly been facilitated by those jurisdictions that utilize the PCR to produce more reliable and useful estimations of bank risk; for example, those that use PCRs in techniques, such as risk assessment and early warning systems, credit portfolio models and stress tests, and need detailed data on banks credits.

- **Supervisory risk assessment and early warning systems** . Based to a large extent on the experience with credit scoring models and rating systems by the banking industry, supervisors developed their own systems to quantify and rank banks’ performance in general, and to measure their loan portfolio quality in particular. In a review of the supervisory approaches for bank monitoring in G10 countries, Sahajwala and Van den Bergh (2000) grouped the diverse systems into four categories: supervisory bank rating systems, financial ratio and peer group analysis systems, comprehensive bank risk assessment systems and statistical models.<sup>23</sup> To the extent that credit risk is the major driver of bank losses, the ability to count with detailed PCR data enhances the quality of these systems and the reliability of their assessments. For example, the off-site rating system used at the Bank of Italy (the PATROL rating system)<sup>24</sup> measures credit quality and loan concentration with PCR data.

- **Bank portfolio credit risk** . When monitoring banks and assessing their capacity to withstand losses for credit risk, the adequacy of their provisions and capital levels can be evaluated by means of transition matrices and portfolio credit risk models (CreditRisk+<sup>25</sup> , Internal Ratings Based approach - IRB -, etc.) that incorporate PCR data. Transition matrices are useful devices to analyze the dynamics of portfolio credit risk - they show how borrowers risk