rafmacalaba/datause-encoder-data huggingface dataset:
Source: World Bank, Global Logistics Indicators Survey, 2005.

Several of these global logistics indicators turned out to be highly correlated with one another, so they are included in the analysis one at a time to avoid multicollinearity problems. In addition, several have very sparse data (notably the time to resolve customs appeals and the frequency of port shutdown). Based on a preliminary assessment of the indicators, a smaller subset was selected for inclusion in the analysis. In addition, in line with the basic gravity model framework, in which trade between countries is an increasing function of their size or income and a decreasing function of distance between the countries (Frankel and Rose 2002), the following indicators are also included:<sup>10</sup>

- GDP of exporting country.

- GDP of importing country.

- Distance between countries (kilometers).

A dummy variable for regional trade agreements or trade blocs is also included, as well as additional variables that are described as they enter the model.

# **Augmented Gravity Model Results**

Specifications 1–3 in table 3 contain the initial set of models fitted, before any of the global logistics indicators or additional variables are included. Specification 1 contains regressions of bilateral trade on GDP in the exporting and importing country. Specification 2 adds the distance variable, and specification 3 adds GDP per capita of the exporting and importing country. The results are qualitatively consistent with those of earlier studies, particularly de Groot et al. (2004, table 2). Also as expected from earlier research, the dummy variable for regional trade agreements (included in specification 4) is significant.

Next, specifications 5–13 in table 3 include, one at a time, the set of global logistics indicators described in the previous section. Most have coefficients with the expected negative sign and are statistically significant; three are not statistically significant. Thus it can be concluded that most of the global logistics indicators have a significant effect on bilateral trade, in the expected direction.

> 10 The data for GDP are from the World Bank’s World Development Indicators database. The data for distance are from Gleditsch and Ward (2001), except for Hong Kong (China), for which the source is <u>http://www.wcrl.ars.usda.gov/cec/java/capitals.htm.</u>

14
**Figure 1: Delays in BREGS wage payments**

<!-- Start of picture text --> number of months since worked<br>round 1<br>round 2<br>1 2 3 4 5 6 7 8 9 10 11 12<br>.5<br>.4<br>.3<br>.2<br>share of owed wagesactually received (%)<br>.1<br><!-- End of picture text --> _Note_ : Sample restricted to participants who have not been fully paid BREGS wages owed to them. _Source:_ Authors’ estimates based on BREGS Survey.

25
by Bloom and Van Reenen (2007), the survey uses an interview-based evaluation tool that defines and scores from 1 (“worst practice”) to 5 (“best practice”) across 18 management practices that are grouped along four key areas. They are: i) operations management techniques - measuring the degree to which the firm acted upon encountering a problem in the production process; ii) systematic performance monitoring – capturing the extent to which the firm monitored production performance indicators and how many indicators; iii) appropriate target setting – measuring the time horizon of production targets (short versus long term); and iv) talent management (incentives) – measuring whether managers are offered performance bonuses. The sample of interviewed firms is restricted to the manufacturing sector and encompasses firms with 50 to 5,000 employees. The data set employed for the current analysis encompasses 11,930 manufacturing firms surveyed between 2006 and 2014. Table A4 in the Annex presents the summary statistics of overall management score by country.

Figure 15 presents the average managerial score of firms across countries. The United States has the highest average followed by Japan, Germany and Sweden. China and Vietnam show up in the second half of the distribution (Figure 13), ranked respectively at 20th and 24<sup>th</sup> , among 29 countries. The (average) ranking varies depending on the subcomponents of the overall management quality score: operations, monitoring, targets and incentives. Japan is the top 1 in target management, while the United States has the highest mean score in operations and target incentives (see Figure A1 in the Annex for ranking figures of all subcomponents).<sup>13</sup>

**Figure 15. Average management score: 1(“worst practice”) to 5(“best practice”)**

<!-- Start of picture text --> 5<br>4.5<br>4<br>3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>Source: Own elaboration based on WMS dataset.<br>Note: The country score is calculated as a simple average across firms, for each country<br>United States Japan Germany Sweden Canada Singapore UK France Australia Italy Mexico Poland Nigeria Chile New Zealand Portugal Argentina Republic of Ireland Spain China Greece Turkey Brazil Vietnam Zambia India Colombia Mozambique Ghana<br><!-- End of picture text --> Some of these subcomponents of overall management quality are especially relevant to innovation. In principle, innovation activities are, by definition, long term and highly uncertain. In

> 13 In a separate analysis - also drawing from WMS data, Malaysia is benchmarked against a quite similar set of countries and its average management practices is also ranked below the United States and Japan, but in line with large Australasian countries and Southern Europe. For further details, see World Bank (2017) chapter 4.

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This is shown in the first row of Table 1, which shows that this growth in migrant stock is only 9 percent of the recorded growth in remittances over this time period.<sup>4</sup> Figure 3 plots remittance growth against migration growth for the 68 developing countries for which data on both are available over the 1990–2010 period (it drops 5 countries with remittance growth above 5,000% in order to see the other countries). We see there is a positive correlation between remittance growth and migration growth (Pearson correlation of 0.27, Spearman rankorder correlation of 0.39). However, we also see that many countries have remittance growth rates that vastly exceed their migration growth rates, and that there is tremendous heterogeneity across countries in this extent. Table 1 also reports data for the 15 developing countries with the largest absolute increases in recorded total remittances over the 1990 to 2010 period. These countries all have remittance growth rates that exceed their migration growth rates, but the share of remittance growth accounted for by migration growth alone ranges from less than 1 percent in Nigeria to over 79 percent in Egypt.

# _3.1.3. Growth in the Income that Migrants Earn_

Equation (5) shows remittance growth will exceed migration growth if the incomes that migrants are earning grow. Data on the earnings of migrants at a global level over time are not available. We therefore proxy the growth in income earned per migrant with the growth in per capita GDP in the main destination country in which migrants from that country reside in 2010. We use the UN bilateral migration data to determine this country. Then, as an example, we see that the main destination country for Mexicans was the United States, and that U.S. real per capita GDP grew 23.2 percent over the 1990–2010 period. We do the same calculation for all 167 developing countries in the United Nations database, and then construct a weighted average growth in income for developing country migrants by weighting the country-specific migrant income growth rates by the share of their migrants in the developing country total.

> 4 Of course one might also worry about measurement error in the migration stock data. Typically this data comes from national censuses. To the extent that there have been improvements in measurement over time, these improvements are likely to result in greater coverage of migrants in more recent years, making the numbers we use an upper bound of the share of remittances that can be accounted for by growth in the migrant stock.

13
# 1. Introduction

Welfare<sup>1</sup> disparities are generally found to play an important role in explaining the frustration and dissatisfaction that people may have with the societies in which they live; in the most extreme cases, these disparities are directly linked to the outbreak of civil conflicts (Hirschmann and Rothschild, 1973; Muller, 1985; Esteban and Ray, 1999, 2008, 2011). In this regard, the Middle East and North Africa (MENA) region represents an important exception, as in the face of apparently low and stagnant inequality, dissatisfaction is rampant ( _inter alia_ , Verme et al., 2014, Cuesta Leiva et al., 2016, and Devarajan and Ianchovichina, 2018). What mechanisms might explain this puzzle? Drawing on Moroccan data, we show that absolute measures of welfare disparities—notably absolute polarization—are significantly correlated to perceptions of deprivation, and hence more suitable in explaining the growing discontent characterizing the central and lower deciles of welfare distribution.

At the end of the last decade, the MENA region was the only region in the world with a high incidence of large declines in average subjective well-being, with steeper declines for the top 60 percent of the population, representing mostly the middle class, than the bottom 40 percent, representing the poor and vulnerable (Arab Barometer, 2019). This pattern was especially pronounced in the countries that had experienced the Arab Spring—the Syrian Arab Republic, Libya, Tunisia, the Arab Republic of Egypt, and the Republic of Yemen—but the ranks of the unhappy swelled in nearly all Arab countries (Dang and Ianchovichina, 2016).

However, at first glance, a mismatch is apparent between this generalized dissatisfaction of Arab world citizens and inequality. The latter has been stagnant or declining since the end of the 1990s (Hassine, 2015; Hlasny and Verme, 2018), although some works question the veracity of the data arguing that inequality is underestimated (Alvaredo et al., 2018; van der Weide et al., 2018). Likewise, while often identified as one of the root causes of social instability and conflicts, polarization has been moderate and stable throughout the period (Esteban and Ray, 2011). Interestingly, recent evidence suggests that polarization translates into open conflict in the MENA

> 1 Since Sen introduced the idea of capabilities after his critiques of utilitarianism and more generally, welfarism, one needs to be cautious theoretically. However, as Sen (1999) acknowledges, multi-dimensional functioning comparisons on capabilities space may not always be possible, and income may be used as a proxy variable while keeping in mind the underlying space of functionings and capabilities. This is our approach here. For a discussion and application of capabilities and human development indicators to the MENA region with emphasis on Egypt, please see Khan (2011).

2
# Resarch Coponents of World Iank Education Projects, since 1982.

|Area|Country and Loan/Credit<br>|Nllumber|Aproved<br>-<br>||Research Coponents<br> <br>||Current Status of Studies,<br>including<br>Comleted<br>Dociments/Data|
|---|---|---|---|---|
|ASIA<br>ASIA<br>ASIA|China CR 1908-CIU<br>IndiaLN3045-IN<br>Indonesia Lh2101-IlD|1968<br>1989<br>1982|Teacher Training<br>Proiect.<br>Three studies<br>to be<br>copleted<br>by September 1990: (i)<br>cost<br>effectiveness<br>of delivery<br>system<br>for teacher<br>training;<br>(ii)<br>training<br>of dual subject<br>tewchers,<br>effectiveness<br>andcost-saving<br>advantages;<br>(iii)<br>ethods to increase femle<br>participation<br>in teacher training<br>system (psra.2.8<br>p.13)<br>Vocational<br>Training<br>Project.<br>Thereareanumber<br>of components to improve the planning<sup>and</sup><br>wrnagementof the National<br>Vocational<br>Training<br>System: (i)<br>monitoring<br>andevaluation<br>system<br>between states will<br>trace emptoyment outcomes<br>andfinancing<br>of programes;<br>(ii)<br>Central Staff Training<br>and Research<br>Institute<br>millconduct studies<br>to evaluate<br>the<br>self-employment<br>scheme of the project(Dec<br>1994)<br>trace Industrial<br>Training<br>Institute<br>certificate<br>holders<br>in the labormrket(Sept 1995),<br>study the qnlity<br>of ITI instruction<br>(Dec 1994)<br>andto review the accredition<br>standardsof<br>ITI (July<br>1995). Ipara. 2.23-2.28<br>pp.23-251.<br>Second Teacher Training.<br>Series of<br>inter-related<br>studies:<br>(i)<br>integration<br>of schooLs and colleges:<br>(ii)<br>expansion of secondary teacher training<br>cotleges;|(i):<br>TOR prepared and study tem<br>formd.<br>(ii)<br>id<br>i):<br>Task forces being organized<br>to draft<br>TORs.<br>Nostudies<br>yet initiated.<br>Noinformation<br>suiplied|

39
4 TABLE **2** PERCENT OF WOMEN WORKERS BY LABOR INTENSIVE SECTORS, 1993

||ExporU<br>Pmoducton|Tota|l Works|**Tota**|**l**Women<br>W|<br>orkers|
|---|---|---|---|---|---|---|
|||No of<br>Workers|%**Dist**|No of<br>Women<br>Workers|% Dist|Women<br>Workers<br>Parlicipati<br>on|
|**i.**<br>**Texble**|18.0|582,197|16.3|<br>309,145|18.4|<br>53.1|
|2.<br>Garment|44.7|351,959|9.8|268,652|16.0|<br>76.3|
|3.<br>Footear|75.6|231,135|6.5|170,918|10.2|<br>73.9|
|Subtotal<br>(1-3)|35.1|1,165,291|<br>32.6|<br>748,715|44.6|<br>64.3|
|4.<br>Food|12.0|516,740|14.5|<br>201,739|12.0|<br>39.0|
|5.<br>Cgarettes|6.4|185,612|5.2|147,201|8.8|<br>79.3|
|6.<br>Electronics|<br>34.9|107,260|3.0|55,626|3.3|<br>51.9|
|Subtotal<br>(1-6)|22.4|1,974,903|<br>55.3|<br>1,153,281|69.0|<br>58.4|
|Manufactunng|21.6|3,574,809|<br>100.0|1,678,878|100.0|<br>47.0|

_Source: BPS, Industrial Statistics, 1993_
**Figure 13. Contribution by each firm characteristic to explain total variance of markup performance across firms**

<!-- Start of picture text --> 13.6%<br>21.4%<br>6.4%<br>11.3%<br>7.9%<br>34.7%<br>4.7%<br>capital intensity Export status R&D status age ownership size region<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on SBS data set. _Note:_ The pie chart reflects markup variance decomposition results. It decomposes the variance of markup performance across firms after removing the proportion accounted for by omitted factors, NACE 4-digit sector, and year contributions.

# 4.4. WHAT WOULD BE THE PRODUCTIVITY DIVIDENDS FROM MARKUP REDUCTION?

**While it became evident that aggregate market power in Romania has been rising, it is important to analyze its consequences for economic growth.** An indirect way to measure this is to estimate the productivity dividends that would come from markup reduction. The association between markup and productivity can be positive or negative, depending on the nature of competition in the specific markets/sectors. As shown by Autor et al. (2017) and Van Reenen (2018), there are markets—such as high-tech sectors or knowledge-intensive ones— where the nature of competition tends to emphasize the importance of network effects, economies of scale and scope, and intangible assets, so the most productive and dynamic firms exert higher markups and obtain larger market shares. On the other hand, in certain markets, where recouping fixed costs (such as R&D) because of higher-risk investments is not that relevant, high-markup firms are not necessarily more productive and efficient. Another way to look at this contrast is through the inverted-U relationship of innovation and competition to an increase in productivity, as explored by Aghion et al. (2005). According to this view, stronger competition (proxied here by smaller markups) encourages neck-and-neck firms to innovate— the “escape competition” effect—and therefore to increase productivity growth. At the same time, this view discourages laggard firms from innovating (the Schumpeterian effect), therefore reducing productivity growth. The combination of the two effects—escaping competition and discouraging innovation—explains the inverted-U relationship of competition and innovation with productivity growth.

**To assess the correlation between productivity growth and markups in Romania, the analysis uses firm-level estimations of TFPR and markups obtained so far.** Thus it estimates the specification presented in Aghion, Braun, and Fedderke (2008) as follows: <mark>ΔdnTl R = φ</mark> 0 <mark>+ φ</mark> 1 <mark>μ + η + ρ + γ + υ ,                              (13)</mark> ccjjiijj ccjjiijj jj jj cc ccjjiijj where the left-hand-side variable represents TFPR growth of firm _i_ in NACE 4-digit sector _j_ in year _t_ , −1 is the lagged value of firm _i_ ’s markup in sector _j_ in year _t_ , _ηt_ , _ρj_ , and _γc_ are iij μμ

27
observe any significant discontinuity along time invariant characteristics of municipalities.

Another potential concern is that municipalities that receive higher transfers (and choose to spend more of that additional revenue on municipal preschool) might have higher demand for preschool services, both private and public. To examine this possibility, in the last three columns of Table 5 we perform balancing tests on pre-determined municipal characteristics that would be expected to be strongly correlated with preschool demand, notably the share of females that were economically active and income per capita (both measured in 1991). In addition, we examine for the presence of discontinuities with regard to the racial composition of the population. The results are reported in the last three columns of Table 6. Reassuringly, they do not point to the presence of any significant discontinuity along these pre-determined municipal characteristics.<sup>11</sup> To the best of our knowledge, and as is also noted by Brollo et al. (2013), there are no other relevant policy discontinuities affecting the outcomes of interest. We can therefore use a fuzzy RD design as a (local) source of exogenous variation in the neighborhoods of the seven FPM thresholds considered.

# **4.2 FPM transfers and the provision of preschool services**

In this section, we implement (2) to examine the impact of transfers on the provision of municipal and private preschool services. The first column of Table 6 reports the estimated first-stage coefficient on the relationship between theoretical transfers and actual FPM transfers. The point estimate is positive, smaller than one, and estimated with a great degree of precision. The fact that the coefficient is smaller than one is in line with the findings of Brollo et al. (2013) and might reflect measurement error, e.g. due to differences between IBGE population estimates and those used by TCU. In the second column, we observe that similar results are obtained when including municipality fixed-effects.

In the third and fourth columns, we observe that theoretical transfers further exhibit a strong positive relationship with total federal transfers (not just FPM transfers) received by municipal governments. This evidence reassures us that the discontinuities of FPM transfers at the thresholds of interest are not offset by systematic changes in other fed-

> 11The balance tests reported in this section are especially relevant for specifications with state fixed-effects.

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# 1. **Introduction**

Many microfinance organizations lend predominately or almost exclusively to women. For example, 97 percent of Grameen Bank’s seven million borrowers are women<sup>1</sup> , as are 70 percent of FINCAs borrowers<sup>2</sup> , and 65 percent of ACCIÓN’s five million clients.<sup>3</sup> Many of the justifications are economic in nature. Women are argued to be poorer than men on average (e.g Burjorjee et al., 2002; FINCA, 2007), have less collateral, and hence be more credit-constrained (e.g. Khandker, 1998; SEAGA, 2002). Muhammad Yunus (2004, p. 4078) writes that “women have greater long-term vision and are ready to bring changes in their life step by step. They are also excellent managers of scarce resources, stretching the use of every resource to the maximum”.

But are women in low-income countries more constrained by finance than men? There is surprisingly little evidence on this question. Udry (1996) and others provide some evidence that men are favored in intra-household allocation of resources in rural households. Studies of microfinance clients in several countries suggest that femaleowned enterprises grow more slowly and generate lower profits than male-owned enterprises.<sup>4</sup> But the samples for studies of clients of microfinance programs reflect selection decisions both on the part of enterprise owners as to whether or not to apply for a loan, and on the part of lenders as to whether or not to lend. The limited work on returns to capital in the broader population of microenterprises seldom differentiates returns by gender, and is still subject to the problem that capital stock is not exogenously determined. We are unaware of any convincing evidence on gender differences in the productivity of incremental investments across households. We provide evidence on that in this paper, using data from a field experiment among microenterprises in Sri Lanka.

For the experiment, we sampled microenterprises with less than US$1000 in equipment and working capital in three districts in southern Sri Lanka. We provided random grants, half in cash and half as purchases of inputs or equipment selected by the

> 1 - <u>http://www.grameen info.org/bank/index.html</u> [Numbers as of May 2007], accessed August 15, 2007. 2http://www.villagebanking.org/site/c.erKPI2PCIoE/b.2604299/k.FFD9/What_is_Microfinance_What_is_ <u>Village_Banking.htm, accessed August 15, 2007.</u>

> 3 <u>http://www.accion.org/about_key_stats.asp</u> [all clients 1976-2006], accessed August 15, 2007.

> 4 Kevane and Wydick (2001) provide a survey of several studies of investment behavior of microenterprise owners by gender, with mixed results found. In their own work, they find no significant difference between male and female borrowers in Guatemala in their ability to generate increases in sales. See McPherson (1996), Mead and Liedhom (1998), and Grasmuck and Espinal (2000) for additional evidence on the size and growth of  enterprises owned by men and women.

2

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