rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Many countries now gather survey microdata at the household and individual level. The rapid expansion of household surveys at frequent intervals that are comparable over time and across countries has facilitated poverty monitoring in the developing world: coverage increased from 13 countries in the 1990s to over 60 countries in 2011 (Serajuddin _et al_ . 2015). One of the main uses for these surveys is to study poverty and welfare within countries and over time. However, most of the available micro data are cross-sectional (i.e., they do not track individuals and households over time and therefore only provide aggregate welfare trends). Panel datasets that follow individuals over several periods of time are less readily available, which limits the understanding of the factors that explain movements out of poverty, into poverty, and the duration of poverty (Dang et al. 2019).

This paper proposes a machine learning approach with a multiple imputation technique to overcome this empirical challenge. We propose using a Least Absolute Shrinkage and Selection Operator method with multiple imputation by Predictive Mean Matching (LASSO-PMM) to estimate intra-generational economic mobility using cross-sectional data.<sup>1</sup> The LASSO-PMM approach we propose estimates a model of household-level income<sup>2</sup> using a set of time-invariant and deterministic harmonized variables as controls to provide estimates of welfare dynamics. Compared to previous methods in the literature, LASSO-PMM makes fewer assumptions and estimates not only poverty transitions, but also income changes. We present, validate, and apply the LASSO-PMM method on harmonized microdata. Specifically, we validate the method using 36 panels in four Latin American countries (Argentina, Chile, Nicaragua, and Peru) and apply the

> 1 We developed a Stata command to implement the LASSO-PMM, which can be accessed here. The command makes use of Wilbur Townsend’s lassoregress Stata command.

> 2 For simplicity we refer to income as the welfare measure throughout this paper.

2
Harhoff, D., K. Stahl, and M. Woywode (1998) “Legal Form, Growth and Exit of West German Firms”, _Journal of Industrial Economics,_ 46:453–488.

Hopenhayn, H. (1992). “Entry, Exit, and Firm Dynamics in Long Run Equilibrium” _Econometrica,_ 60(5):1127-1150.

Jovanovic, Boyan (1982) “Selection and the Evolution of Industry” _Econometrica,_ 50(3): 649-70.

King, R. and Levine, R. (1993) “Finance, Entrepreneurship, and Growth: Theory and Evidence”, _Journal of Monetary Economics_ , 32(3): 513-542.

Keil, T. and Pe'era, A., (2012). “Are all startups affected similarly by clusters? Agglomeration, competition, firm heterogeneity, and survival”, _Journal of Business Venturing_ , 28 (3):354–372 Klapper, L., Laeven, L., and Rajan, R. (2006). “Entry Regulation as a Barrier to Entrepreneurship”. _Journal of Financial Economics_ . 82(3): 591-629.d Klapper, L., and Love, I., (2010a) The Impact of the Financial Crisis on New Firm Registration, Policy Research Working Paper 5444.

Klapper, L., and Love, I., (2010b) The Impact of Business Environment Reforms on New Firm Registration, Policy Research Working Paper 5493.

Klapper, L., Love, I., and Randall, D. (2014) New Firm Registration and the Business Cycle, Policy Research Working Paper 6775 Klapper, L. and Richmond, C. (2011) “Patterns of Business Creation, Survival and Growth Evidence from Africa”, Policy Research Working Paper 5828 Levine, R. (2005) “Finance and Growth: Theory and Evidence.”, in in Handbook of Economic <u>Growth, (editors) Philippe Aghion and Steven Durlauf, (p. 865 - 934) Elsevier Science, The</u> Netherlands.

Lucia Foster, John Haltiwanger, And C. J. Krizan (2006) “Market Selection, Reallocation, And Restructuring In The U.S. Retail Trade Sector In The 1990s”, _The Review of Economics and Statistics,_ 88( 4):748-758.

Melitz, M (2003). “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity”, _Econometrica_ , 71(6):1695-1725.

McPherson, M. A. (1995). “The hazard of small firms in Southern Africa”, _Journal of Development Studies,_ 32(1):31–54.

Olley, S. and Pakes, A. (1996) “The Dynamics of Productivity in the Telecommunications Equipment Industry”, _Econometrica_ , 64 (6):1263-1297.

37
42 with the Battle of the Sexes (see Osborne and Rubinstein, 1994, Chapter 6). Let me instead illustrate this with the Prisoner’s Dilemma game of life I, as described above.

Suppose that before playing that game, player 1 has the choice of enacting a law, which says that the citizens should choose A and the police, that is player 3 should choose R. Actually, in this approach what the law says is unimportant. All that matters is the fact that enacting the law is costly to player 1 (who, it is assumed here, does the enacting). Suppose, the cost of enacting the law is 1 to player 1. Under this formulation what we have is a two-period game. In period 1, player 1 chooses to enact the law (burn money) or not do so, and then, in period two, they play the Prisoner’s Dilemma Game of Life I (PDGL).

This full game, in which the person who enacts the law is also a part of the game, will be called the Economy Game with Law Enactment. The game is illustrated below. The game starts at the node w, where player 1 chooses to do “Nothing” or “Enact” the law, which inflicts a cost of 1 on player 1.

Once this choice is made, players 1 and 2 play the PDGL, which is illustrated below. The only difference between playing the PDGL after enactment and playing after doing nothing is that, after enactment, player 1 earns a payoff of 1 less than what he would have earned otherwise. This is the cost of enactment he has to bear. Hence, the 3 players playing (A, A, L) leads to the payoff of (7, 7, 0), if no law is enacted. But if the law is enacted, the same action triple leads to the payoff of (6, 7, 0).

How will this game be played? To answer this, note that if player 1 enacts the law, that is, burns money, it must be because he expects that by virtue of doing this the game will end up at (A, A, R) instead of (B, B, L). Burning money was worth it because it signals to the other player that he is now expecting the game to go to (A, A, R). Hence, the law helps to take the economy to the Nash equilibrium outcome that is good for the citizens. The law in this case is very effective. Its role is purely that of signaling what the one who enacts the law will do and this is a powerful driver of everybody’s behavior.

Unfortunately, forward induction has other complications. A question that quickly arises is: If burning money can take a player to his or her preferred equilibrium, why is it necessary to burn money? Even if he or she does not burn money, that fact that he or she could have, should be enough to take the economy to the equilibrium outcome where the player who could have burned money is better off. In other words, it is not really necessary to enact the law. The mere fact that he or she can enact the law is enough to influence behavior.
While the wage gap can be evaluatWd either at union or nonunion mean characteristics, this study employs all workers' mean characteristics. The use of all-worker means, X=(PX, + (I-P)X,), where P is the proportion of union observations, corresponds to asking the question: for a worker with average characteristics, what is the predicted wage differential between his/her working in the union and nonunion sectors (Hirsch and Addison 1986)?

To test the statistical significance of the wage gap the predicted union log hourly wage of nonunion workers is computed using the estimated union wage structure and nonunion characteristics _(predictedl union InW of nonunion worker =X,$,j3 + v.,)._ The estimated union wages and actual wages of nonunion workers are then tested for statistical difference employing a standard t-test.

# Data

The data used in this analysis come from the 1989 household survey conducted by the the National Institute of Geographical Statistics and Information _(Instituto Nacional de Estadistica Geografia e Informatica),_ known as the National Income and Expenditure Household Survey _(Encuesta Nacional de Ingreso-Gasto de los Hogares)._ The survey covers 11,545 households and contains 57,332 individual observations. The geographical coverage includes each of the 31 Mexican states, representing 260 _municipios_ (counties) and the Federal District. Each household is identified by the state and the _municipio_ in which it is _5_
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<!-- Start of picture text --> 2.0 m Non-commodity exporters<br>4.5 =’ Commodity exporters<br>1.0<br>05<br>0.0<br>05<br>~<br>-1.0<br>25 83 2 5<br>Ee? = > as<br>eS 82 s<br>ax 68c<br><!-- End of picture text -->
# _Figure 5. Perceptions of unfairness in LA by subgroup, 1997-2015_

# (a) By age:

# (b) By gender:

<!-- Start of picture text --> 100<br>18-24 25-40<br>41-64 65+<br>90<br>80<br>70<br>60<br>1997 2000 2005 2010 2015<br>%<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>Females Males<br>90<br>80<br>70<br>60<br>1997 2000 2005 2010 2015<br>%<br><!-- End of picture text -->

# (c) By educational attainment:

# (d) By labor status:

<!-- Start of picture text --> 100 Less than Primary<br>Complete Primary<br>90 Complete Secondary<br>Complete Tertiary<br>80<br>70<br>60<br>1997 2000 2005 2010 2015<br>%<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>Employer<br>Employee<br>90 Self-Employed<br>Unemployed<br>80<br>70<br>60<br>1997 2000 2005 2010 2015<br>%<br><!-- End of picture text --> Note: This figure presents the share of individuals that perceived income distribution as unfair or very unfair according to four categories of age (18-24; 25-40; 41-64 and 65+), gender, maximum educational achievement and labor status. Each line refers to the average of 18 LA countries for which data is available.

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_JULY 2018_ _4_ able to isolate and understand the impact of these large loans on the growth of the participating enterprises.

The remainder of this paper is organized as follows. Section II describes our main data source and empirical strategy. Section III reports on the selection that was observed for the WEDP services; section IV analyzes the start-up and survival of firms in the sample and section V outlines the characteristics of firms and their owners who were able to borrow. Section VI presents the results of receiving a loan on firm performance. Section VII concludes.

# **II. Data and Empirical Strategy**

# _A. Data_

The data used in this paper come from the Women Entrepreneurship Development Project (WEDP) impact evaluation sample of firms. In order to build a sample for the impact evaluation the research team relied on the registration database of WEDP firms that was being collected by the Federal Micro and Small Enterprise Development Agency (FeMSEDA) from the beginning of 2014. Before a baseline survey could be carried out we had to wait until enough firms were registered for the WEDP program for an appropriate impact evaluation (IE) sample to be established. However, this waiting period meant that by the time of the baseline survey there were some firms that could have already received WEDP loans or training. Since the baseline survey began in October 2014 the research team collected information about whether any WEDP services had been taken-up at the time of baseline and we relied on asking retrospective information about some key variables during the baseline survey to be able to determine pre-program levels.

Between October and December 2014 baseline data were collected from WEDPregistered firms in six Ethiopian cities and formed a sample of 2,369 female entrepreneurs. The WEDP baseline questionnaire contained a set of questions on household demographic characteristics, socioeconomic status, business sales, profits, costs, employees, entrepreneurial profile (e.g., age, place of birth, education level), and questions designed to elicit an entrepreneur’s business knowledge and level of financial literacy.

A follow-up survey was conducted between December 2016 and February 2017 for the same firms approximately 2 years after the baseline for 2,139 firms which is 90% of the baseline sample. The attrition rate of firms that were either not found, owner died or refused to be surveyed for the follow-up survey was 10%. The rate of survey attrition was similar across firms that received a loan and those that did not. The survey questionnaire elicited business performance, business practices and further entrepreneurial and psychological characteristics of the female business owners.
averaged over the last five years and taken from WBL; urbanization rate or the percentage of country’s population living in urban area last year taken from WDI; paternity leave defined analogously to maternity leave as (log of 1 plus) number of days of paid paternity leave mandated by law averaged over the last 5 years and taken from WBL; and the percerntage of seats in the lower house of parliaments that are held by women (average value over the last five years prior to the ES<sup>8</sup> ) obtained from Inter Parliamentary Union (Women in Parliament). Firm-level controls all taken from the ES include firm-size measured by (log of) number of workers at the firm; (log of) age of the firm; labor productivity equal to (log of) real annual sales of the firm in the last year (in 2009 USD) divided by the number of workers; dummy variable equal to 1 if the firm has one or more female owners and 0 otherwise; dummy variable equal to 1 if the firm’s top manager is female and 0 otherwise; percentage of firm’s annual sales last year made abroad (Exports); percentage of firm’s ownership that is with foreign entities (Foreign ownership); dummy variable equal to 1 if the firm provides training to its workers and 0 otherwise; dummy variable equal to 1 if the firm experienced losses due to crime last year and 0 otherwise; dummy variable equal to 1 if the firm was registered when it started operations and 0 otherwise; dummy variable equal to 1 if the firm has its own website and 0 otherwise; severity level of inadequately education workers (on 0-4 scale) as an obstacle to firm’s current operations as reported by the firms; severity level of access to finance (on 0-4 scale) as an obstacle to firm’s current operations as reported by the firms; severity level of tax rates (on 0-4 scale) as an obstacle to firm’s current operations as reported by the firms.

# 3.7 _Robustness controls_

> 8 We use average values over the last 5 years for which data are available to avoid loss of sample due to missing data for some countries in a single year.

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**Figure 12: Underpricing, constant 2014 US$ per kWh billed at benchmark performance**

|-$0.04<br>Seychelles||
|---|---|
|-$0.04<br><br><br>Central African Republic||
|-$0.04<br>Uganda||
|-$0.03<br>Benin||
|-$0.03<br>Cape Verde||
|-$0.03<br>Togo||
|-$0.02<br>Gabon||
|-$0.02<br>Congo, Rep.||
|-$0.01<br><br>Niger||
|-$0.01<br>Ghana||
|-$0.01<br>Tanzania||
|$0.00<br><br>Cameroon||
|$0.00<br>Mauritius||
|Liberia|$0.00|
|Mozambique|$0.01|
|Lesotho|$0.01|
|Zambia|$0.02<br>|
|Swaziland|$0.03|
|Kenya|$0.03<br>|
|Malawi|$0.04|
|Sierra Leone|$0.04|
|South Africa|$0.04|
|Zimbabwe|$0.04|
|Côte d'Ivoire|$0.04|
|Mauritania|$0.05|
|Burkina Faso|$0.05<br>|
|Nigeria|$0.06|
|Mali|$0.06|
|Senegal|$0.06|
|Madagascar|$0.07|
|Gambia, The|$0.08|
|São Tomé and Príncipe|$0.09|
|Burundi|$0.09|
|Rwanda|$0.09|
|Sudan|$0.10|
|Ethiopia|$0.10<br>|
|Botswana|$0.13|
|Comoros|$0.16|
|Guinea|$0.16|
|-$0.05<br>$0|.00<br>$0.05<br>$0.10<br>$0.15<br>$0.20|

_Source_ : World Bank staff calculations based on utility annual reports and financial statements.

# 6.5 Breakdown of regional quasi-fiscal deficits into hidden costs

Figure 13 presents the total hidden costs across the 39 countries. Underpricing is the dominant hidden cost. For all countries except South Africa, underpricing represents a weighted average of 40 percent of the hidden costs. T&D losses represent 30 percent of hidden costs, while bill collection inefficiencies and overstaffing represent 20 and 10 percent respectively. South Africa is treated separately because of its 50
distance defined over the set of baseline variables.<sup>16</sup> After that we randomized the firms into two groups in each block of four firms: two firms were invited to the job fairs, one firm on each of the days, at random. The other two firms in the group were assigned to the control group, who were not to be invited to the fairs.

Additionally, we assigned treatment using a re-randomization method. Following the recommendations of Bruhn and McKenzie (2009), we will control in our estimations for the baseline covariates used for re-randomization (that is, the set of variables described in Table 2) and for the baseline covariates used to construct the randomization blocks.<sup>17</sup> With this sample we have 78% power to detect a small treatment effect on the total number of pay-roll employees (that is, only 0.2 standard deviations), using a significance level of 0.05%.

# **3 Design and implementation**

## **3.1 The job fairs**

We invited treated job-seekers and treated firms to attend two job fairs. The first fair took place on October 25 and 26, 2014. The second fair took place on February 14 and 15, 2015. We ran two fairs to ensure that each job-seeker and firm would have the chance to participate in at least one of them. The job fairs were held at the Addis Ababa University campus, a central and well-known location in the capital city. To minimize congestion, each job fair lasted two days and only half of the firms and job-seekers were invited to attend on each day. The firms that were invited to attend on Saturday 25th (Sunday 26th) of October were then invited to attend on Sunday 15th (Saturday 14th) of February. On the other hand, job-seekers invited to attend on the Saturday (Sunday) of the first fair were also invited to attend on the Saturday (Sunday) of the second fair. This ensured that, in each job fair, job-seekers were exposed to a different pool of firms and firms were exposed to a different pool of job-seekers.

At the beginning of both fairs, we gave job-seekers (i) a list of all firms invited to the fair and (ii) a list of recommended meetings. We created these recommended meetings using information on firms’ vacancies obtained from the phone survey which we ran shortly before the fairs (see the data section). After creating a ranking of workers for each vacancy and

> 16 These are listed in Table A.6 in the Online Appendix.

> 17 Details of these variables and how they are defined are contained in our detailed pre-analysis plan.

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- 20 -

(-= _mcd)_ is a positive function of the real rate of return on stocks (p), a negative function of the real rate of return on quasi-money (r), a negative function of the real rate of return on money (0), and a function of a vector (Zq,,) of exogenous variables that will be specified later. In equilibrium _mcd = mcs_ and for given all exogenous variables, p, and 0, the stock market determines the real rate of return on stocks p.

The stock market equilibrium is given by the following <!-- Start of picture text --> MCS<br>-mcs<br>- = nws<br>y<br>MCd_ mcd(0,r,p,Zqm)<br>y q<br>Mcs MCd<br>y y<br><!-- End of picture text --> and it is represented in Figure 14 where demand intersects supply. Along the demand curve, wealth-holders hold increasing levels of market capitalization relative to GDP at higher real rate of returns p. If the real rate of return on stocks p is higher than the equilibrium rate at which asset holders would be willing to hold the given stock then the real rate of return on quasi-money r and the real rate of return on money 0 would have to be higher for asset holders to be satisfied in holding the given stock of market capitalization at a higher real rate of return on stocks p.

**Figure 14: Stock market equilibrium**

<!-- Start of picture text --> p<br>t ___ ____ ____ ____ ____ ,__ _.________ n<br>_~~~~~~~~~~~~~~~~~~~p ,,w<br><!-- End of picture text -->
<!-- Start of picture text --> $ billions deals<br>1,800 3,000<br>Value (Emerging)<br>1,600 Value (Advanced)<br>2,500<br>Deals (Emerging)<br>1,400<br>1,200 2,000<br>1,000<br>1,500<br>800<br>600 1,000<br>400<br>500<br>200<br>0 0<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Source: Authors' calculations, based on Thomson-Reuters SDC Platinum<br><!-- End of picture text --> Figure 1: Total cross-border M&A deals by firms from advanced economies and emergingmarket economies, 1997–2010. The upward trend held by emerging market firms is evident in both absolute deal number and relative deal values.

# **2 Recent Trends in Emerging Economy M&A**

Emerging market multinationals have become far more assertive in their M&A activities on the global stage over the past decade. Save a dip during the global crisis in 2008 and 2009, the overall trend in cross-border M&A has been upward, especially in the post-dot-com period since 2001. This is evident both in terms of the total number of deals (increasing from 661 deals in 2001 to 2,447 in 2010), as well as—perhaps more dramatically—in the value of M&A deals concluded by emerging economy firms (the rise from $30 to $254 billion over this period represented an increase in shares from 6 to 29 percent) (see Figure 1).

Although not the focus of this paper, it is illuminative to consider, by way of comparison, whether patterns in emerging market M&A are also replicated in the other component of FDI, greenfield activity.<sup>2</sup> The share of emerging market greenfield investment did indeed rise between 2001 and 2009 (the latest year data are available), from 12 to 15 percent of total global greenfield activity. This rise, while clearly more modest, was nevertheless a significant absolute increase: the value of cross-border greenfield investment rose from an estimated $98 billion to $250 billion over the same period.<sup>3</sup>

> 2 Greenfield investment, as opposed to M&A, typically represents internal, organic corporate growth, while M&A activity, in addition to satisfying growth objectives, may capture other more complex corporate goals, such as strategic market penetration or the acquisition of new technology.

> 3Greenfield data were sourced from UNCTAD and fDi Markets but were, unfortunately, only available for 2003 through 2009. The value for 2001 given here is an exponential projection from the available time series, and is not meant to be an authoritative figure, but rather to give a sense of the magnitude involved.

4