rafmacalaba/datause-encoder-data huggingface dataset:
the issue of PPP factors was solved and a critical number of recent surveys was reached in 2017, MENA regional poverty rates were reported again. In the 2018 reference year, about 7 percent of MENA population were found to live below the extreme poverty line of USD1.90 2011 PPP per day, which more than tripled from the 2 percent reported in 2013.

Figure 1. Regional extreme poverty rates in the MENA region at $1.9 2011 PPP poverty line, 2010-2018 reference years <!-- Start of picture text --> 8%<br>7%<br>6%<br>5%<br>4%<br>3%<br>2%<br>1%<br>0%<br>2010 2011 2012 2013 2015 2018<br>headcount poverty rate<br><!-- End of picture text --> Source: PovcalNet, May 2020.

Monitoring progress on the World Bank’s goal of boosting “shared prosperity”—defined as the income growth of the bottom 40 percent in any given country—is even more demanding in terms of data requirements. Measuring consumption/income growth across two points in time requires two comparable welfare aggregates constructed from two recent household surveys (Atamanov et al., 2016). Currently, only four countries in the MENA region meet these criteria and are included in the global shared prosperity database (2012-2017) (Figure 2).

As mentioned above, besides collecting detailed consumption data, HBSs collect information on aspects of human and social development. Thus, the usefulness of these surveys goes well beyond monitoring monetary poverty. Collecting household and individual characteristics along with locational attributes makes these surveys important for policy makers who want to design and understand the impact of public policies on socio-economic well-being. For example, the World Bank uses household and individual characteristics from HBSs to prepare profiles of the poor, or estimate the bottom 40 percent at country, regional, and global levels. These individual and household characteristics are harmonized to enable crosscountry analyses and are used in all World Bank flagship reports on poverty, shared prosperity, and economic mobility, and in different briefs.<sup>6</sup>

> 6 For example, the World Bank produces regular Poverty and Shared Prosperity reports, which provide a global audience the latest and most accurate estimates on trends in global poverty and shared prosperity. The reports also provide in-depth research into policies and interventions that can make a difference for the world’s poorest. The World Bank also produces bi-annual Country Poverty and Equity Briefs (PEBs), which highlight poverty, shared prosperity, and inequality trends, and provides the context of poverty and equity conditions in more than 100 countries.

4
**Table 1. Fiscal interventions included in the CEQ and child poverty analysis and child-relevant budgets**

Panel a. Fiscal interventions included

||Included<br>in the<br>CEQ<br>analysis<br>(yes/no /<br>grouped*)|In child<br>poverty<br>analysis<br>(Yes/No)|
|---|---|---|
|**Direct effects on children**|||
|**Child and Family Benefits**|||
|**_Contributory_**|||
|Pregnancy& childbirth|Yes|Yes|
|Bringingupa child upto the age of 2|Yes|Yes|
|Other contributory /1<br>|Grouped|Yes|
|**_Non-contributory and means-tested_**|||
|Child allowance|Yes|Yes|
|Allowance for raisinga child upto the age of 1|Yes|Yes|
|School-age children enrolled in 1stgrade|Yes|Yes|
|Other non-contributorymeans-tested/2|Grouped|Yes|
|**_Non-contributory and non-means-tested_**|||
|Birthgrant|Yes|Yes|
|Allowance for mothers who are regular tertiary students (children until one<br>year)|Yes|Yes|
|Allowance for raisingtwins until oneyear of age|Yes|Yes|
|Other non-contributorynon-means-tested/3|Grouped|Yes|
|**In-kind transfers**|||
|Education|Yes|No|
|Health|Yes|No|
|Childcare|No|No|
|**Direct taxes**|||
|Child tax deduction|Yes|Yes|
|**Indirect effects on children**|||
|**Socialprotection**|||
|Social exclusion|Yes|Yes|
|Disability|Yes|Yes|
|Old agepensions|Yes|Yes|
|Survivors|Yes|Yes|
|Unemployment|Yes|Yes|
|**Indirect subsidies**|Yes|Yes|
|**Indirect taxes**|Yes|Yes|

11
Table 6: Correcting for Potential Attrition Bias

|**Panel A: Heckman two-step **|**model – Total sample**||
|---|---|---|
||Contract Interaction|Price Interaction|
|Treated|-0.018|0.106|
||[-0.060,0.026]|[0.035,0.177]|
|Treated*Contract|-0.018<br>||
||[-0.060,0.026]||
|Treated*No-Contract|0.055<br>0.037||
||[0.001,0.105]<br>[0.001,0.071]||
|Treated*Health Insurance Price||-1.289|
|||[-2.245,-0.336]|
|F-Statistics|14.36<br>14.36|14.18|
|**Panel B: Heckman two-step **|**model - Trust sample with trust control**<br>Contract Interaction|Price Interaction|
|Treated|-0.019|0.092|
||[-0.063,0.023]|[0.008,0.165]|
|Treated*Contract|-0.019||
||[-0.063,0.023]||
|Treated*No-Contract|0.048<br>0.029||
||[-0.011,0.110]<br>[-0.009,0.068]||
|Treated*Health Insurance Price||-1.190|
|||[-2.185,-0.118]|
|F-Statistics|10.60<br>10.60|10.45|
|**Panel C: Heckman two-step **|**model – Trust sample but no trust in model**<br>Contract Interaction|Price Interaction|
|Treated|-0.020|0.091|
||[-0.063,0.023]|[0.009,0.165]|
|Treated*Contract|-0.020||
||[-0.063,0.023]||
|Treated*No-Contract|0.048<br>0.029||
||[-0.010,0.110]<br>[-0.009,0.066]||
|Treated*Health Insurance Price||-1.186|
|||[-2.187,-0.127]|
|F-Statistics|10.97<br>10.97|10.81|
|**Panel D: Correcting sample **|**attrition bias by re-weighting**<br>Health Insurance|Pension|
|Treated|0.101***<br>-0.031<br>0.003|-0.032|
||(0.033)<br>(0.020)<br>(0.047)|(0.023)|
|Treated*Price|-1.350***<br>0.046||
||(0.437)<br>(0.266)||
|Treated*No-Contract|0.061**|0.069**|
||(0.026)|(0.032)|
|No. of Observations|4,587<br>4,587<br>2,925|2,925|

Source: RUMiC Migrant Survey 2015 and 2016, RUMiC Information Intervention Field Experiment, 2016. Notes: All regressions are using the Model 3 specification (see note to Table 2). For Heckman model, the 95% bootsraping percentile confidence intervals are in the square brackets, which are calculated by pair cluster bootstrap with 1000 replications. The sample size of Panel A is 4,587, and the sample size of Panels B and C is 2,881. For reweigting results, robust standard errors clustered at workplace of household head are in parentheses. Significance codes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

34
Policy Research Working Paper 8980

# **Abstract**

The latest push for industrialization in Ethiopia has attracted much academic and public interest. This paper assesses Ethiopia’s competitiveness and attractiveness as an investment destination by comparing domestic productivity and input costs to a sample of manufacturing exporting countries. The paper documents that, in a comparison with Kenya, India or Vietnam, the labor cost advantage of Ethiopian firms is more than offset by low productivity.

However, Ethiopia appears competitive when compared to Bangladesh. Capital, firm size, or sectoral composition do not explain the low productivity of the Ethiopian manufacturing sector. Ethiopian firms, however, have worse management, particularly in the area of labor management. The paper concludes by discussing the potential for labor - interventions to increase productivity and create the con dition for further industrialization.

This paper is a product of the Office of the Chief Economist, Africa Region, commissioned as part of the _‘Industrialization for Jobs in Africa’_ regional study. 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/prwp. The author may be contacted at stefano.caria@bristol.ac.uk.

_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
limited in the benchmark model compared to the case when traders fail to internalize that firms learn from prices.

In columns (1) and (3) I control for stock price informativeness and its interaction with Q. As in previous studies (Chen et al. (2007), Bakke and Whited (2010) and Foucault and Fresard (2013)) I find that a firm’s investment is more sensitive to Tobin’s Q when its stock price is more informative. These results suggest that firm managers learn from the private information aggregated into the stock price when making investment decisions, but market prices does not seem to provide more guidance to managers with low quality of information.

# **5. Conclusions**

In this paper I find limits to the ability of secondary markets to inform and guide firms’ real investment decisions. The economy is modeled as a strategic game between firms and informed speculators. Before making an investment choice, firms use stock prices to update their priors about their own fundamentals. Informed traders are strategic in that they internalize the firms’ inference problem. In this setting, I show that informed trading volume depends on the quality of managers’ prior information. In other words, the amount of private information that is aggregated into the stock price through the trading process is a function of managers’ initial information. Learning from prices is limited because prices are endogenously less informative for firms with low quality of managerial information, which are precisely those firms that would like to learn more from the stock market in the first place. In turn, real investment efficiency falls as the market signal is not as useful in helping managers differentiate between good and bad projects.

Using a sample of U.S. publicly traded companies, I document a positive correlation between the quality of managers’ information and stock price informativeness. I show that less informed managers do not rely more on the stock price to make investment decisions. Collectively, the evidence is suggestive of limits to the ability of firms to learn relevant information from stock prices.

The model presented here may have implications for firms’ decision about whether to be financed through public or private equity. More specifically, depending on the information gap between traders and the firm’s managers, the firm might benefit from an IPO or might be better off being held privately. In the same way, the quality of managers’ information should determine the choice of outside speculators to either become private or public equity investors. When managers have low quality information, speculators’ trading profits are limited, despite knowing the true value of the fundamental. In this case, the speculator 22
(those with fewer than 10 workers). While the model accurately reflects the majority of plants operating in the informal sector, as observed in the data, it slightly overestimates the number of informal plants. In the model, the fraction of informal plants is around 77%, compared to 66% in the data. Similarly, the mean size of formal plants in the model is 14.1 workers, while in the data, it is 10.1. To sum up, only a small fraction of plants are formal and they are bigger in terms of employment compared to informal plants. Formal plants account for 67.1% of all employment and 72.5% of aggregate output in the benchmark economy.

Before comparing the non-targeted life-cycle profiles in the model and the data, we first discuss the mean sizes at the initial period. In the data, informal plants employ an average of 1.9 workers at birth, whereas in the model, the mean size of informal plants at birth is 1.5 workers. The model also closely matches the overall mean size at birth. In Mexico, the overall mean size at birth is 2.1 workers, compared to 2.9 workers in the model.

Figure 3: Plant size distribution in the benchmark economy <!-- Start of picture text --> (a) Fraction of Plants (b) Employment Shares<br>data model data model<br><= 10 11-20 21-50 51-100 101+ <= 10 11-20 21-50 51-100 101+<br>1<br>.4<br>.8<br>.3<br>.6<br>.2<br>.4<br>.1<br>.2<br>0 0<br><!-- End of picture text --> Source: INEGI Notes: This figure compares the overall plant size distribution in the benchmark economy with the data for different size categories.

Next, we compare the life-cycle profiles of formal and informal plants in the benchmark equilibrium with those observed in the data. Figure 5 shows that the model is able to replicate the life cycle of the average plant in Mexico. It also closely matches the life cycle of informal plants observed in the data. However, for formal plants, the model predicts a lower growth profile compared to the data, resulting in an overall growth rate in the model that is slightly lower than in the data. The growth rates in the 20
migration has served as a vehicle to the patriarchal model where emigrants were bound for Saudi Arabia and the Gulf, and to the individualistic one when they were destined to Europe. In other words, migration is hypothesized to have brought about normative changes, whether towards reinforced control of the family over its members or towards increased individual autonomy.

# **5.  Upstream diffusion of demographic models by migrants**

What would have been the level of fertility had no international migration taken place? Comparisons over time and space offer an imperfect but workable method to answer the question.

<!-- Start of picture text --> Figure 3 : Remittances and Birth Rates in Egypt<br>correl = + 0.623<br>2,50<br>2,00<br>1,50<br>1,00<br>0,50<br>0,00<br>Remittances<br>-0,50 (1970 prices)<br>-1,00<br>-1,50<br>Birth Rates<br>-2,00<br>-2,50<br>1965 1970 1975 1980 1985 1990 1995 2000 2005<br>Source: see Table 6<br>Standardised Index<br><!-- End of picture text --> Let us compare firstly Morocco and Egypt over time. The last three decades have been a period of intense emigration for both countries. Annual flows of emigrants are not recorded, 18
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Cambodia and Lao PDR 23
the recipient is thus likely to require a higher price for the service provided. As a result, an increase in the recipient’s income will reduce the probability of transfer. If the transfer does take place, then the amount of the transfer could rise, fall, or stay the same depending on the remitter’s elasticity of demand for the services of the recipient.

The empirical findings of Cox (1987), Cox and Rank (1992) and Cox _et al_ . (1998) suggest a positive relationship between the size of transfers and the recipient’s pre-transfer income, rejecting the altruism behavior of remitters. It is perhaps questionable whether the Cox (1987) theoretical framework has as much relevance to the Vietnamese context as it does for the case of the United States. However, it is worth noting that Secondi (1997), using data for China, also finds that altruism alone cannot explain the observed transfers and that exchange may indeed be involved. In the context of China, where much of the financial flows appear to be transfers from adult children to their elderly parents, child-care is found to be one of the main services that parents render to their adult children in exchange for money (Secondi, 1997).

The above motives are certainly not mutually exclusive and an individual migrant may have more than one motivation for remitting home at any given point in time. Lucas and Stark (1985), for instance, propose “tempered altruism” or “enlightened self-interest” to refer to transfers motivated by a combination of altruism and self-interest. This is based on the view that remittances are part of a self-enforcing contractual arrangement between a migrant and his or her family, which is of mutual benefit. The migrant adheres to the arrangement as long as it is in their interest to do so (Lucas and Stark, 1985). For example, using data drawn from the National Migration Study of Botswana, their analysis suggests 7