rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6995

# **Abstract**

It is commonly believed that stock prices help firms’ managers make more efficient real investment decisions, because they aggregate information about fundamentals that is not otherwise known to managers. This paper identifies a limitation to this view. It shows that if informed traders internalize that firms use prices as a signal, stock price informativeness depends on the quality of managers’ prior information. In particular, managers with low quality information would like to learn about their own fundamentals by relying on the information aggregated in the stock price. However, in this case, the profitability of trading falls for informed speculators, who therefore reduce their trading volume, reducing the informativeness of prices. As a result, stock prices are not as useful in guiding capital toward its most productive use, leading to inefficient investment decisions. Using a sample of U.S. publicly traded companies between 1990 and 2010, the paper documents a positive correlation between the quality of managerial information and stock price informativeness. Contrary to the conventional view that less informed managers should rely more on stock prices when making investment decisions, the author finds no differences in the sensitivity of investment to stock prices for different levels of managerial information. The evidence suggests that while firms do learn from prices, the learning channel and its effects on real investment are limited.

This paper is a product of the Finance and Private Sector Development 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 apedrazamorales@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
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Conversely, control households have higher PMT scores than treatment households, in each village and overall – which may induce a downward bias in impact estimates. Also, there may also be some local spillover effects from beneficiaries to non-beneficiaries within villages, affecting SD and DID estimates.<sup>24</sup> Both higher PMT scores and local spillover effects would create a downward bias in the impact estimates; consequently it does not cast doubt on the significance of a positive impact.

Another concern would be the existence of an “Ashenfelter’s dip”, where beneficiaries become eligible due to a consumption dip immediately prior to program selection (Ashenfelter, 1978; Heckman & Smith, 1999). Given the design of the pilot cash transfer project in Niger, such an “Ashenfelter’s dip” effect is unlikely for a number of reasons. First, beneficiaries are not selftargeted into the program, which rules out self-selection bias. Second, targeting is conducted based on long-term assets and household characteristics which usually do not fluctuate rapidly in response to shocks. In fact, the PMT approach has been questioned precisely because it does not allow identifying households hit by temporary shocks (Del Ninno & Mills, 2014). Third, 30% of the villages population is targeted, which means that idiosyncratic short-term shocks would have to be particularly widespread and large– especially given the magnitude of the impact found– to generate a broad “Ashenfelter’s dip” effect. Fourth, beneficiaries are not only found to catch-up with non-beneficiaries, but to pass them in many cases. Fifth, in the “common support” sample, baseline characteristics between beneficiary and non-beneficiary are relatively similar. In fact we test the possibility of shocks affecting beneficiary households (and not non-beneficiaries) using a range of food security outcomes that are more correlated with exposure to short-term shocks than PMT scores (Schnitzer, 2016). In the common support sample, there are no significant differences between beneficiary and non-beneficiary households, suggesting that none of these groups have been particularly affected by shocks.<sup>25</sup> With these potential limitations in mind, we offer some additional robustness tests using Propensity Score Matching (PSM).

> 24 For evidence of such a spillover effect in other cash transfer programs, see Angelucci & De Giorgi (2009). 25 Results available upon request. The food security variables include the Household Hunger Scale; three Coping strategy indices; the Food Consumption Score; and the Household Dietary Diversity Score (see McBride (2015) for an analysis and discussion of these indicators). Unfortunately, the baseline survey only includes food security variables for the Tahoua region which includes 591 households in our common support sample.

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the aid context, these models speak more directly to the importance of aid targeting than to any non-linear growth effects of aid.

The rest of this paper is organized as follows. In the next section we review some of the existing evidence on poverty traps. In Section 3 we discuss saving-based poverty traps, and consider the role of foreign aid in helping countries to escape from such traps. In Section 4 we consider the evidence for technology-based poverty traps. Section 5 concludes.

# **2. Existing Empirical Evidence on Poverty Traps**

In this section we briefly review some of the few existing empirical studies relating to poverty traps. One strand of the literature provides very reduced-form evidence based on crosscountry data. If poverty traps are an important feature of growth dynamics, then over time one would expect to observe a bimodal distribution of per capita income, with a group of poor countries clustered around the low-level poverty-trap equilibrium, and a group of rich countries clustered around the high equilibrium (see Azariadis and Stachurski (2004) for details). In a series of papers Quah (1993a, 1993b, 1996, 1997) has used non-parametric methods to estimate the evolution of the distribution of per capita income across countries. His results suggest the emergence of such a bimodal distribution of income. This finding is supported by a recent paper by Bloom, Canning, and Sevilla (2003) which provides evidence that, after controlling for countries’ geographic characteristics, the evolution of income across countries is consistent with a bimodal ergodic distribution. There is some controversy, however, regarding the bimodality of the long-run income distribution: Kremer, Stock, and Onatski (2001) argue that the dynamics of the world income distribution is better characterized by a prolonged transition, during which inequality may increase, towards a single-peaked long-run distribution. A similar result is obtained in Azariadis and Stachurski (2004b), who uses a parametric model of income dynamics derived from an explicit growth model based on Azariadis and Drazen (1990). They also find that the long-run income distribution is unimodal, but that bimodality will appear during the transition. The last two papers are therefore only partly consistent with the idea of poverty traps, 4
27 The wage function for men and women is estimated by a maximum likelihood method using the probit estimates of 'abor market participation." The wage estimates indicate that human capital variables explain a substantial portion of the variation in wages.'9 Among the important determinants of productivity, education and experience are crucial; returns to experience, however, are higher for women than for men. Education at all levels influences both men's and women's productivity in Peru, although the results vary by region. For example, primary school has no influence on productivity in Lima, while it has a significant impact, at least for men, in other urban and rural areas.

Technical education increases the labor market productivity of men and women. In Lima, women's wages increase by 25 percent and men's wages by 16 percent if they have had vocational training. But when gains from interregional migration are excluded, women's wage gains drop to 22 percent. In contrast, the wage increases of women with postsecondary diplomas do not change as a result of interregional migration. The returns to vocational training vary by region: wages of male workers with a secondary, postsecondary, or university diploma are 46 percent higher in rural areas than '<sup>8</sup> When normality tests (that is, skewness and kurtosis tests) of the log wage errors are carried out, the normality assumption is rejected for both men and women. Although this finding has severe adverse effects on the t statistics of an OLS method, the asy-mtotic property of maximum likelihood method reduces the severity of non-normality of the wage errors. The non-normality of wage errors suggests, however, that individual, household, and community heterogeneity influence individual wages.

'"The wage rate regression for rural women workers does not produce significance of any explanatory variable. This is not surprising, given the high standard error of the wage regression fitted for rural areas. The reason is perhaps because there are few women working in the wage sector in rural areas, and since most rural women work as teachers or clerks, the variation in wages is small.
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**Table 2. Distribution of official international remittances by income group of country, 1998 and 2003** (million US dollars)

|_Income group of country_|_1998_|_2003_|_Annual percent_<br>_change, 1998 to_<br>_2003_|
|---|---|---|---|
|Low income|17,196.8<br>(27)|22,863.8<br>(27)|+5.8|
|Lower middle income|18,711.4<br>(32)|31,785.3<br>(30)|+11.2|
|Upper middle income|13,957.2<br>(18)|20,181.8<br>(19)|+7.6|
|**Total**|49,865.2<br>(77)|74,830.9<br>(76)|+8.5|

Notes: All countries divided into income groups according to 2002 GDP per capita (constant 1995 US dollars), as calculated by World Bank, _World Development Indicators 2004_ . The groups are: low income, $735 or less; lower middle income, $736 to $2,935; and upper middle income, $2,936 to $9,075. Numbers in parentheses are countries reporting receipt of official international remittances.

Source: IMF, _Balance of Payments Statistics Yearbook_ (2004). World Bank, _World Development Indicators 2004_ .
-.46 -

> In 1989 the average annual compensation of EPF employees was 5,7r6 U.S. dollars<sup>2 8</sup> . This was 1.84 times the average annual covered wage of . members, which was 3,108 U.S. dollars in the same year<sup>29</sup>

# 5.2 Zambia

: The main social security institutions in Zambia are the following<sup>30</sup> a) The Provident Fund, established in 1966. It is a mandatory contribution system that provides lump-sums to registered persons ('members") that meet conditions of age, retirement, invalidity and to survivors of deceased members. It covers workers in the private sector, parastatals and non-pensionable workers in the public sector. b) The Civil Service Pension Scheme. This a pension system established by the government for its employees that are in the Public Service, including the Teaching Service and the Defense Forces. c) The Local Authorities Superannuation Fund, which covers senior employees in the local authorities and has only 26,000 members. d) Supplementary Schemes, established by other employers that contribute also to the Provident Fund. One of them is the Mukuba Pension Scheme, established by the Zambia Consolidated Copper Mines Ltd. (state owned).

b) The Civil Service Pension Scheme. This a pension system established by the government for its employees that are in the Public Service, including the Teaching Service and the Defense Forces.

## Coverage

In the early eighties, Zambia's population was estimated at 6.5 million, of which 45% was in urban areas. The labor force was 1,880,400, but only 391,000 were wage earners (Kalula 1985, page 593). The rest were independent workers in agriculture and in the informal sector, and workers of agricultural cooperatives. In 1989 the population estimate was adjusted to 8 million, and the formal sector workers to 360,000. Life expectancy was adjusted to 57.5 years in the 1990 census (ISSA 1990, p. 61).

> 28 Accordingto the EPF Annual Report, in 1989 the wage bill was MS 29.144 minlion and total personnel was 2,149.

> 29 In 1989 total collection was MS 3,566 million, coming from 2.660 million active members, while contribution rates were 10% on the employer and 10% on the employee. Average compensation was adjusted by the variation of the consamer price index until March 1992. as reported in the IFS (1 have only up to January. the variation was 8.74%, check this). 30 ISSA (1990), pages 66-67.
# **Summary**

The phenomnenon "capital flight" has over the years received considerable interest from researchers as well as policy makers. Papers on capital flight deal with one or more of the four following questions: what is the right concept to use for measuring capital flight; what is the figure for capital flight following from this measure; can the occurrence and mnagnituide of capital flight be explained by certain (economic) variables; and what policy implications follow (e.g., which policy changes can he useful in reversing capital flight).

The purpose of this paper is not to add any new insights on which (if any) capital flight measure is the right one to use or what causes capital flight. Neither will the paper attempt to draw any policy iimplications. The sole purpose of this paper is to present recent estimates of capital flight using a number of alternative, existing methodologies.

The paper first discusses the various methodologies which have been used for measuring capital flight. The paper draws attention to the fact that, while the alternative methodologies may differ in their approach to meastiring capital flight, tne identities used in balance of payment data make these methodologies close in final measurement. In particular, the so called "World Bank Residual" and the "Dooley" methods, while in the past presented as very different approaches to meastring capital flight, are actually very close in measurement.

The paper discusses in detail the data used for calculating capital flight figures and the various adjustments which need to be made. It then presents aggregate capital flight figures using the various measures for a group of 84 developing countries. (Individual country data are available from the World Bank on a floppy disk, contact Ms. Shelley Fu, tel. (202) 473-3885, fax. (202) 477-0661). I'he figures calculated bear out that the various capital flight figures are highly correlated and, with the exception of a few years, show the same pattern of (increasing) capital flight until 1988, followed by a reversal (return of flight capital) in the years 1989-1991.

The paper also presents various regional aggregates of capital flight, and ranks countries and regions by the lev'el of capital flight relative to GDP. The paper tinds that capital flight is a much wider spread phenomenon than commonly asserted. When compared to the respective countries' GDP, capital flight is quite even distributed. The capital flight-GDP Lorenz-curve is even above the 45-degree line, indicating that countries with smaller GDP have more capital flight than one would expect if it were distributed over countries proportionately to GDP.
|**Unemployment**<br>**Insurance**|No unemployment<br>insurance|If you become unemploy<br>income of**MYR 800**until<br>mo|ed you receive a monthly<br>you find a job, for up to 6<br>nths|If you become unemploy<br>income of**MYR 1,200**unt<br>mo|ed you receive a monthly<br>il you find a job, for up to 6<br>nths|
|---|---|---|---|---|---|
|**Monthly Take-**|Equal to your current|**0.5% less**than your|**0.2% less**than your|**0.5% less**than your|**0.2% less**than your|
|**Home Salary**|take-home salary|current take-home salary|<br>current take-home salary|current take-home salary|current take-home salary|

Note: The respondent is shown Job A, and one of Jobs B1, B2, B3 and B4 (randomly assigned), and asked to choose their preferred job between the two.

### **c. Identification**

We seek to examine the effect of the combination of the attributes of the social insurance package offered, and earnings on the likelihood of selecting the baseline job. Causal identification is determined by the randomized allocation of the description of job B (from B1 to B4) to respondents, i.e. the attributes of the social insurance package and the earnings offered in job B are orthogonal to respondents’ observable and unobservable characteristics.

Prob (A) is the likelihood of the respondent _i_ preferring Job A, the baseline scenario B is the randomly allocated hypothetical job description _j_ from 1 to 4 to individual _i_ εε is the error term

## **5 Results**

# a. Descriptive Statistics

The survey data show that over 40% of respondents contribute to private health insurance (44.8%) and private retirement schemes (46.9%), while over 25% contribute to EPF (26.6%) and SOCSO (27.3%). The coverage of respondents by EPF and SOCSO is almost equivalent to that of the coverage of all employed persons (which includes formal and informal workers), estimated at about 30% (World Bank 2020). Moreover, the data also suggests that a relatively large share of respondents are already covered by private schemes. It is important to note that 44.9% of the respondents do not have other jobs outside of freelancing. Hence, they may have access to insurance or retirement savings through their other job(s).

There is a mismatch between what freelancers deem as very important, and what they currently have in their jobs – especially when it comes to protection. For instance, 68.9% of respondents stated that it is very important for them to have insurance for workplace injuries, 66% stated that it is very important for them to have pension income upon retirement, and 57.2% stated that it is very important for them to have health insurance. At the same time, 59.9%, 62.7%, and 57.8% reported that they do not have access to these in their current jobs. These findings suggest that there is a desire among gig workers for protection that they are not currently accessing through their current jobs.

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