rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 0

# **Abstract**

This paper develops a framework for the quantitative analysis of individual income dynamics, mobility and welfare. Individual income is assumed to follow a stochastic process with two (unobserved) components, component representing measurement error or transitory income shocks and an Autoregressive (AR(1)) component representing persistent changes in income. The analysis uses a tractable consumption-saving model with labor income risk and incomplete markets to relate income dynamics to consumption and welfare, and derive analytical expressions for income mobility and welfare as a function of the various parameters of the underlying income process. The empirical application of the framework using data on individual incomes from Mexico provides striking results. Much of measured income mobility is driven by measurement error or transitory income shocks and therefore (almost) welfare-neutral. A smaller part of measured income mobility is due to either welfare-reducing income risk or welfare-enhancing catching-up of low-income individuals with high-income individuals, both of which have economically significant effects on social welfare. Decomposing mobility into its fundamental components is thus seen to be crucial from the standpoint of welfare evaluation.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at Wmaloney@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
# **ADR Holdings of U.S. Based Emerging Market Funds**

# **1. Introduction**

It is estimated that in 2003 U.S. investors allocated eleven to twelve percent of their total equity portfolio to non-U.S. equities. Institutional investors’ purchases of foreign equities in 2003 amounted to $1,300 billion according to the U.S. Federal Reserve’s Flow of Funds. Institutional investors are also responsible for the large interest in the trading of American Depositary Receipts (ADRs). ADR programs are set up by U.S. depositary banks and are claims against the ordinary shares that trade in the home market as discussed in detail later in this paper. In 2003, the combined dollar trading value of listed depositary receipts on the New York Stock Exchange, the American Stock Exchange and Nasdaq totaled $630 billion.<sup>1</sup> Mutual funds such as Fidelity Management and Putnam Investment are among the largest U.S. investors in depositary receipts.

U.S. investors interested in investing in a foreign firm (one based outside the U.S.) can do so by purchasing ADRs in the U.S., or by purchasing the underlying stock in the home market of the firm, or by doing both. This paper examines the factors that affect an institutional investor’s choice of investment security, i.e. investing in the ADR versus investing in the underlying shares of an emerging market firm. Thus, we are interested in the question of how a fund manager, once she has decided to invest in a company cross-listed in the U.S., chooses to divide that investment amount between the ADR share and the non-ADR shares (primarily the underlying domestic share that trades on the home stock exchange of the issuer) of that company. The factors that affect fund managers’ choice of a particular firm

> 1 See “Depositary Receipt Market Review 2003,” Bank of New York website.

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I POLICY RESEARCH WORKING PAPER 2198

# Summary findings

There have been extensive theoretical studies of firms' responses to environmental regulations and enforcement but few empirical analyses of firms' expendirures on pollution abatement in response to different reguilations and enforcement strategies.

Wang and Chen empirically analyze the pollution abatement efforts of Chinese industrial firms under a system combining pollution charges and abatement subsidies.

Using data on China's top industrial polluters and on regional development in China, they find that the combination of charges and subsidies used in China has provided effective incentives for the most heavily polluting industrial firms to abate pollution.

Chinese industries operate under a unique pollution control system, a rnarket-based instrument combining emissions charges and abatement subsidies. This combination of clharges and subsidies has given firms incentive to invest in wastewater treatment facilities. The pollution levy, although low, has significantly improved investments in abatement.

Wang and Chen found that the more pollution a firm generates, the more likely it is to invest in pollution abatement.

This study was only of top polluters, which are closely monitored by environmental agencies, so the results may not be valid for other sources of industrial pollution.

- - This paper a product of Infrastructure and Environment, Development Research Group is part of a larger effort in the group to identify appropriate policies for environmental regulation in developing countries. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Hua Wang, room MC2626, telephone 202-473-3255, fax 202-522-3230, Internet address hwangl@worldbank.org. Policy Research Working Papers are also posted on the Web at http://www. worldbank.org/html/dec/Publications/Workpapers/home.html. October 1999. (28 pages)

_The Policy Research Working Paper Series disse7ninates 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 filly 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. 7They do not necessarily represen_ **_t_** _he view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
57 only the risk of death or disability to an insurance company and consequently still bears the risk of longevity; b) the pension fund buys the old-age pensions from the insurer too. The pension fund still manages the accumulation of retirement capital.

4. _Non-autonomous pension funds_<sup>55</sup> : All the risks are covered by an insurance company.

5. _Savings associations_ : By construction, this form bears no actuarial risks, because only old–age savings are accumulated.

Table 16 presents an overview of the classification of pension funds. The number of non-autonomous pension funds has more than halved, but the number of individuals in such funds has remained constant. This reduction is mainly due to a decrease in the number of “Institutions of firms” (almost -40 percent since 1994) and of “Institutions for one employer”. Autonomous pension funds with reinsurance contracts show an increase in both the number of funds by almost 20 percent and the number of covered individuals by more than 85 percent. They almost doubled their relative market share. This could suggest a general tendency toward handing over potential maximum losses. However, autonomous pension funds with reinsurance count less than 800 members on average. In comparison, autonomous pension funds have on average 2,500 members. This higher number allows them to bear more risk on their own.

**Table 16  Risk Coverage** **<u>of Pension Funds under BVG</u>**

|Characteristics|1994||2002||Change|2002|
|---|---|---|---|---|---|---|
|||%||%|%|#Members/PF|
|autonomous|||||||
|# Pension Funds|663|14.0%|457|14.4%|-31.1%||
|# Members|1,196,376|36.9%|1,143,142|34.5%|-4.5%|2,501|
|autonomous<br>(with|||||||
|reinsurance)|||||||
|# Pension Funds|488|10.3%|583|18.4%|19.5%||
|# Members|242,167|7.5%|450,479|13.6%|86.0%|773|
|Semi-autonomous|||||||
|# Pension Funds|2,120|44.8%|1,404|44.3%|-33.8%||
|# Members|592,556|18.3%|504,291|15.2%|-14.9%|359|
|non-autonomous|||||||
|# Pension Funds|1,300|27.5%|620|19.6%|-52.3%||
|# Members|1,186,639|36.6%|1,205,320|36.4%|1.6%|1,944|
|savings association|||||||
|# Pension Funds|156|3.3%|106|3.3%|-32.1%||
|# Members|21,617|0.7%|8,146|0.2%|-62.3%|77|

Source: Bundesamt für Statistik (2004b), p.32, T8 Table 17 combines the two characteristics “Administrative Form” and “Degree of Risk Coverage” for the most recently available data. As Table 16 shows, the autonomous and the non–autonomous pension funds cover the largest number of individuals. Within the group of autonomous institutions, the _mixed forms_ (544,841) and the _institutions of firms, holdings or parent companies_ (401,738) encompass the largest number of covered

> 55Non-autonomous pension fund = Kollektive Pensionskasse (german)
_Table B.1. Share of PSCT received by each crop, 2010_

|**Crop**||||||||||**Korea,**<br>|||||**United**<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Argentina***|**Brazil**|**Canada**|**China**|**Colombia**|**Indonesia**|**India***|**Japan**|**Kazakhstan**|**Rep**|**Mexico**|**Philippines**|**Turkey**|**Ukraine***|**States**|**Vietnam**|
|**Banana**||||||0.0%|24.2%||||||||||
|**Barley**|||11.8%|||||1.0%|-2.3%|1.4%|1.9%||12.6%|6.9%|0.3%||
|**Cassava**||||||0.0%|||||||||||
|**Cocoa**||||||0.0%|||||||||||
|**Coconuts**||||||||||||0.2%|||||
|**Coffee**||2.9%|||2.1%|0.0%|||||0.1%|||||-2.6%|
|**Cotton**||37.4%||29.4%|||9.6%||39.1%||0.4%||6.3%||4.9%||
|**Groundnuts**|||||||3.4%||||||||||
|**Lentils**|||18.1%||||||||||||||
|**Maize**|9.8%|8.5%|8.2%|29.5%|45.8%|23.5%|4.1%||-8.2%||27.4%|-6.5%|6.3%|20.3%|41.7%|4.9%|
|**Palm**|||||7.4%|-5.6%|||||||||||
|**Plantains**|||||10.1%||||||||||||
|**Potatoes**|||||||12.6%||0.1%||||28.1%||||
|**Rapeseed**|||34.5%|3.9%|||3.6%||||||||0.0%||
|**Rice**||8.3%||-0.9%|32.9%|76.2%|33.4%|89.0%|-17.5%|88.3%|0.2%|70.7%|||1.5%|82.3%|
|**Sorghum**|||||||||||15.7%||||1.4%||
|**Soybeans**|71.3%|11.9%|1.1%|4.0%||0.6%|3.9%|0.4%||10.3%|0.7%||||25.2%||
|**Sugar**||24.7%||4.1%|1.6%|5.2%|5.2%|7.5%|||28.4%|35.6%|||11.1%|16.0%|
|**Sunflower**|4.9%||||||||-17.6%||||3.4%|39.8%|||
|**Tea**||||||||||||||||-0.6%|
|**Tobacco**|||||||||||||0.1%||||
|**Wheat**|14.0%|6.4%|26.4%|29.9%||||2.0%|106.4%||25.1%||43.3%|33.0%|13.9%||
|**Total PSCT**<br>**(US$,**<br>**millions)**|**-5,097**|**1,074**|**272**|**28,337**|**112**|**5795**|**-**<br>**21,348**|**1551**|**80**|**2,422**|**610**|**2,129**|**4,170**|**-647**|**3,334**|**2,142**|

_Notes:_ A green box indicates that a crop receives more than 20% of the total PSCT in that country; a yellow box indicates that a crop received less than 20% of the total PSCT paid in a country, it is shown by the yellow color. A red box indicates that a crop contributes negatively to the total PSCT—that is, the country paid subsidies to all other commodities except that crop.

* identifies countries with negative PSCT values.

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Clearly, they contribute to market segmentation, where skilled workers are scarce, and all but a fortunate few of the unskilled face high rates of unemployment. However, it is the seemingly excessive and inefficient use of capital by public enterprises that represents their greatest economic impact.<sup>6</sup> Table 13; Net Financing to Parastatal Sector (CFAF billion) (negative numbers imply public subsidy of parastatals)

|Enterprise<br>Type Year<br>|1982<br>|<br>|<br> 1983|1984|1985|1986|1987|11988|
|---|---|---|---|---|---|---|---|
|**iOOX**State-Gwned<br> <br>(number)|1<br>-49.51<br>(8)|-66.31<br>(9)j|-40.4<br> <br>(8)|-114.3|<br>(8)|-97.2|<br>(9)|-83.81<br>(9)|-85.21<br>(9)|
|Majority<br>State-Owned<br>(number)|**-17.6**<br>(20)|13.0<br>**(20)**|-14.8<br>(20)|-28.8<br>(20)|-3.7<br>(20)|9.3<br>(21)|14.8<br>(22)|
|Minority<br>State-Owned<br>(number)|24.4<br>(35)|-8.6<br>(37)|-30.8<br>(37)|48.2<br>(38)|14.4<br>(38)|29.8<br>(40)|41.4<br>(43)|
|Total<br>||-42.71|-87.9|1-86.01-|134.91|-86.5|-43-7|-29.0|

Source: **BOF** While no comprehensive data are available on linkages to the private sector, it is clear that public enterprises have altered the course of private investment through linkages offered and those denied. Public enterprises encourage private investment through direct purchases and contracting out for services. The decision of what intermediate inputs and services to integrate in public enterprises is a significant one for private sector development -- particularly if the purchasing or contracting fosters competition instead of allocating rents.

In Cote d'Ivoire, some public enterprises did indeed "crowd in" private enterprise.' For instance, private suppliers to the large agricultural parastatals were encouraged to invest; unfortunately the crowding in often did not foster competitiveness. For example, the government encouraged entrepreneurs to launch (or jointly venture) into production of packaging for agricultural - products granting them enduring monopolies over such supplies. The result has been that private enterprises were created and expanded with considerable dependence on their protective linkages to the public sector and are now a burden rather than an asset. More recently, even competitive suppliers have been seriously hindered by the poor payment' and management record of most public enterprises.

3' As a rough indication of the low capital productivity of public enterprises, in 1988 they had (according to BDF data) a value added to investment ratio of only .37, compared to .72 for large private corporations.

_n_ This micro-crowding phenomenon in no way suggests that the overall level of private investment is larger than if a private alternative to public enterprise has been pursued.

' The catalog of problems private enterprises have with parastatal receivables ranges from payment in **kind** (which SODESUCRE has employed) to non-payment (which abounds).

25
then increased the tariff rates for the other regions in our model so that the overall weighted average collected tariff rate is unchanged. We used the trade flow data, disaggregated by regions and sectors of our model to weight the tariff rates. This adjustment has the impact of raising the collected tariff rates for the regions in our model where positive tariff rates apply. The resulting adjusted tariff rates are reported in Table 4.

# **Share of Market Captured by Multinational Service Providers**

It was necessary to calculate the market share of multinational firms in the services sectors by region of the model. Take the banking sector as an example. We need to know the share of the market captured by Tanzanian, EU, African and Rest of the World firms, where the countries in the regions are defined in table 1. This entailed acquiring a list of all banks operating in Tanzania along with their market share, and, when the bank is owned by multiple parties, allocating the ownership across the regions of our model. The database Bankscope was sufficient for this task in most cases, but websites of the banks had to be consulted to allocate ownership shares in several cases. The results, by region and sector, are presented in table 6. Documentation of the results, with listing by firm, sector and region, and the data sources are presented in appendix B.

# **Share of Expatriate Labor Employed by Multinational Service Providers**

The impact of liberalization of barriers to foreign direct investment in business services sectors on the demand for labor in these sectors will depend importantly on the share of expatriate labor used by multinational firms. We explain in the results section that despite the fact that multinationals use Tanzanian labor less intensively than their Tanzanian competitors, if multinationals use mostly Tanzanian labor, their expansion is likely to increase the demand for Tanzanian labor in these sectors.20 We obtained estimates of the share of expatriate labor or specialized technology not available to Tanzanian firms that is used by multinational service providers in Tanzania from the survey mentioned above. We found that multinational service providers use mostly local primary factor inputs and only small amounts of expatriate labor or

> 20 See Markusen, Rutherford and Tarr (2005) for a detailed explanation on why FDI may be a partial equilibrium substitute for domestic labor but a general equilibrium complement.

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is that the Institutional Investor magazine’s sovereign risk ratings can be explained by a very small number of variables measuring the country’s repayment history, its external debt burden, and its history of macroeconomic stability. However, there are three key differences between our paper and this one. First, their dependent variable, the Institutional Investor rating, measures _perceptions_ of the probability of debt distress, whereas we attempt to explain the incidence of _actual_ episodes of debt distress.<sup>3</sup> Second, their sample consists mostly of middle- and upper-income countries. Third, as we will show in more detail below, we find that _contemporaneous_ policy, and only to a lesser extent a _history_ of bad policy and non-repayment, matters for the incidence of debt distress.

Finally, Manasse, Roubini and Schimmelpfennig (2003) is the recent paper most closely related to the analysis contained in this paper. They define a country being in a debt crisis if it is classified as being in default by Standard & Poor’s or if it has access to non-concessional IMF financing in excess of 100 percent of quota. They then use logit and binary recursive tree analysis to identify macroeconomic variables reflecting solvency and liquidity factors that predict a debt crisis episode one year in advance. Once again, the key difference with the analysis contained in this paper is that Manasse et. al. restrict their analysis to a sample of emerging market developing countries for which such data is available (especially the Standard & Poor’s data), whereas a special focus of this paper is the factors affecting debt distress in low income countries. Several of their key results, however, are broadly consistent with ours. They find that debt burden indicators and GDP growth, as well as a somewhat different set of measures of policies and institutions, significantly influence the likelihood of debt crises.

# **3.  Empirical Framework**

# **3.1  Identifying Debt Distress Episodes**

> 3 As documented in Reinhart et. al. (2003), country risk ratings such as these are only imperfect predictors of actual default episodes.

7
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# cheap.

These findings are important for three reasons. First, they help understand TPOs’ structure, organization and objectives, and how those differ across income groups. It is, for example, surprising that TPOs focused mainly on established exporters. The existing literature has shown that returns to export promotion are higher along the extensive margins, rather than the intensive margin (Volpe Martincus and Carballo, 2008). In principle, established exporters already know how to penetrate foreign markets. Non-exporters face higher information barriers, and the literature has shown that this is an important margin on which TPO programs can exhibit high returns (Lederman et al., 2016). Arguably, the global disruptions in supply chains due to the pandemic have increased the information needs for established exporters. In contrast, the focus on medium rather than small firms is less surprising.<sup>1</sup> To be able to penetrate foreign markets, scale is important and small firms may fall short by definition in this aspect. Second, they help us understand the responses of TPOs during the pandemic and how differences in budget can help explain how TPOs adapted. For example, the fall in marketing activities expenditures in low-income countries is probably linked to the slow adoption of virtual tools and the absence of a recovery plan being put in place at the beginning of the pandemic. This in turn may be explained by the much smaller budgets of TPOs in low-income countries. Finally, they provide a first look at what the post-COVID TPO world may look like, with virtual tools being more prominent. This combined with the large demand for training on e-commerce could potentially lead to a more inclusive export composition with smaller firms being able to participate more easily not only in export promotion programs, but also in world markets through electronic platforms.

The remainder of this paper is organized as follows. Section 2 describes the sample that responded to the TPO survey. We divide our sample into four groups: high-income countries (17 TPOs), upper-middle-income countries (20 TPOs), lower-middle-income countries (11 TPOs) and low-income countries (9 TPOs). Section 3 explores differences in TPO size 1Although the export promotion literature has shown that a focus on small exporters may generate higher returns (Volpe Martincus and Carballo, 2010).

3