rafmacalaba/datause-encoder-data huggingface dataset:
firms, but also in attracting new firms to specific locations. Local scale effects and competition are significant, raising the probability of exit, while discouraging entry, although the effects are smaller. Sectoral diversity in a location is not as robust – and is even associated with increasing exit.

Three additional methodological insights emerge that should influence future research in this area. First, agglomeration externalities appear to influence firm turnover beyond productivity channels. Thus studies of firm analysis should take these agglomerating into account too; a firm’s own productivity is not sufficient in explaining the patterns.

Second, both absolute and relative productivity are important influences on firm turnover. For example, while the negative impact of productivity on firm exit found in other empirical studies is confirmed for Moroccan firms, relative efficiency is also found to be important. More specifically, it was found that at both province and commune level, it is insufficient for firms to be productive to avoid exit, firms must also keep pace with the productivity of the leading firms in their sector. Relative efficiency also appears to matter for firm entry with firms attracted to regions with low productivity and low productivity gaps in the local region.

Third, the unit of geography used also matters. For exit, the effects do not vary so much by whether one looks at effects at the province or commune level. However, for entry it makes a difference. While competition is less localized, the benefit of having productive neighbors is stronger with smaller geographic units.

26
<!-- Start of picture text --> Percentageoa . Gansof stunted population at commune level wihr<br>0.379 to 0.429 # Was<br>0.317<br>0.146 to 0.3 7917 a, % Jayi e<br>Misia eA<br>SHB pues<br>15 OTR?<br>ERS I<br>‘ ited SE"<br>\ as<br>Oe em<br>| aa Ge e “a<br>—<br>ce j<br>| eA: a cy<br>bel be a<br>; wep ee: ae 4<br>anes<br>4 we ts<br>Rae a<br>om :<br>ace<br>Ea<br><!-- End of picture text -->

<!-- Start of picture text --> Number ofto stunted population 192,859 at commune level 2 a<br>9,724iG308 toeal 5,738 y‘me aS i<br>ae rf<br>Hoes og 5<br>jee<br>Lee. Bi<br>$<br>a<br>e ?aor Se<br>. x<br>) ee<br>var Sal b<br>% = ig ‘et ‘ ©<br>f yg Gere<br>o- OF ta ty<br>A a x ee<br>\ age RoeS. . eee<br>A ae.<br>ln Pee<br><!-- End of picture text -->
financing to its most efficient use. Tybout finds the marginal return on projects by **firms** rationed from credit to have a higher return than firms favored under government credit schemes. Tybout notes that rent-seeking activities to receive favored government treatment can further decrease efficiency. It should be noted that government provision of credit is not limited to countries without well-functioning financial institutions. One-third of all credit extended in the U.S. in the 1980s has been subsidized by the federal government (Gale, 1991).

Stiglitz (1990) notes direct government intervention in credit markets may be unlikely to be successful: If informational problems are the barrier to the development of an effective capital market, then there is no reason to presume that govermnental lending agencies will be in a superior position to address these problems. Indeed, the lack of incentives for government bureaucrats to monitor loans may exacerbate the problem.<sup>13</sup> Further, the political system may lack the willpower to enforce the collection of goverment debts, turning government loans effectively into government grants.

Instead, Stiglitz suggests that more basic government efforts in establishment of an infrastructure - - - both physical and legal may do more toward reducing credit risks. Physical improvement of transportation networks and irrigation can directly reduce the risks faced by agrarian economies. The establishment of a legal infrastructure would provide the legal means to einforce credit contracts.

Policies which increased the accessibility of collateral to borrowers may also be warranted. For example, land titling in rural areas might allow the use of land as collateral. This type of policy could reduce information costs of borrowing without the consequent distributional effects of policies such as those suggested by Bernanke and Gertler. In this regard, policies which lead to the accumulation of wealth also lower costs of asymmetric information. Further, lending institutions which also serve as 13Joseph E. Stiglitz, "Peer Monitoring and Credit Markets,' The World Bank EconoMic Review vol. 4 no. 3 (September 1990), p. 362.

-38 -
<!-- Start of picture text --> Presence of explicit deposit insurance Share of countries with all top banks rated?<br>by per capita income level by per capita income level<br>0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.00 0.10 0.20 0.30 0.40 0.50<br>0-2500 0-2500<br>2500-5000<br>2500-5000<br>5000-8000<br>5000-8000<br>8000+<br>8000+<br>High income<br>High income<br><!-- End of picture text --> <!-- Start of picture text --> Accountability of bank management Share of assets in listed banks<br>by per capita income level<br>by per capita income level<br>0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70<br>2.60 2.65 2.70 2.75 2.80 2.85 2.90 2.95<br>0-2500 0-2500<br>2500-5000 2500-5000<br>5000-8000<br>5000-8000<br>8000+<br>8000+<br>High income<br>High income<br><!-- End of picture text --> Figure 3: _Selected indicators of market discipline preconditions by per capita income level (PPP), group means_ (Source: World Bank Regulation and Supervision Survey) Thus, when the data, fragmentary though it is, is interpreted through this prism, we can find surprisingly little evidence of systematic difference in the extent to which most positive and negative prerequisites for market discipline are present as between poor and rich, large and small countries. To be sure, systemic crises caused by macroeconomic, exchange rate or fiscal instability tend to swamp the discriminating function of the market as between banks that are soundly-run and the rest, and these events are more prevalent in poor and small economies.

24
34

- 9/ All figures are from World Bank (1988). Also see Faini et al. (1988).

- 10/ In all packages, it is assumed that there is sufficient credibility in the monetary policy of the Central Bank (because money supply is kept from rising) so that the Central Bank has full control over the money supply and is therefore able to sterilize the effects of capital flows. Because our model is not well suited for adjustment under highly inflationary conditions -- no durable goods, portfolio decisions for incremental flows rather than for the total portfolio and exogenous expectations -- we do not consider an adjustment package that would rely heavily on the inflation tax.

- 11/ Of course, this does not account for the fact that the economy has a higher volume of debt than in BR.

- 12/ The slightly lower growth in E3 is also due to the shift towards consumption because of the strong wealth revaluation in that package. (Wealth changes are fairly similar for E2, E4 and E5.)

- 13/ Sachs (1986) contrasts the experience with adjustment of East Asian and Latin American countries, noting the pressures to abandon stabilization in Latin America because of the high income per capita loss during 1980-5 which he estimates at about 20S for 8 Latin American countries.

- 14/ Since the distribution of income is assumed to be uniform within each socioeconomic group, this is the only approach we can take to define poverty. It would be easy to postulate a lognormal distribution of income within each socioeconomic group as in de Helo and Robinson (1982). Such an approach, however, would not add much to the present discussion since within-group variance ls exogenous and there is little information on variances among the socioeconomic classes defined here.
the variable “male” as a dummy that takes the value 1 if the respondent is a male and 0 otherwise. The variable “age” is constructed as an ordinal variable that captures the fouryear age interval to which the respondent belongs. It ranges from 1 to 19, where 1 stands for the interval “5 to 9 year old” and 19 stands for “more than 80 year old”. We create the variable “education” as an ordinal variable that ranges from 1 to 4 where 1 stands for “less than primary completed”, 2 for “primary completed”, 3 for “secondary completed” and 4 for “university completed”. Finally, we define the variable “urban” as a dummy that takes the value 1 if the respondent lives in a urban area and 0 if she lives in a rural area.

# **3.2 Weather data**

We obtain our weather data by computing a Standardized Precipitation Index (SPI) for each region. To do so, we first rely on the Inter-Governmental Panel on Climate Change (IPCC) dataset<sup>9</sup> that provides measures of monthly precipitations at the 0.5 degree level over the entire 20th century. We calculate monthly regional precipitation by placing the grids within regions. We then fit these rainfall data to estimate a gamma distribution. For each year in each region, the SPI is subsequently computed as the standard deviation of rainfall, i.e., as the variation of rainfall around its regional historical mean, as predicted by the gamma distribution. As such a SPI greater than 2 (1) indicates an extremely (moderately) wet event. Conversely, a SPI lower than -2 (-1) indicates an extremely (moderately) dry event (Hayes, Svoboda, Wilhite and Vanyarkho (1999)).

In terms of using SPI to capture the appropriate weather shocks that may affect migration, the choice of time frame is important. To the best of our knowledge, Dercon (2004) is the

> 9This dataset is available at http://www.cru.uea.ac.uk/cru/data/hrg/.

14
<!-- Start of picture text --> Latin America & Carib. Mid. East & North Afr. South Asia<br>2000-2030 2000-2030 2000-2030<br>10 10 10<br>8 8 8<br><!-- End of picture text --> <!-- Start of picture text --> 2000-2030 2 8 | & 8 2 8 B= a<br>6 7 = «iia af, = 4 =z 4<br>oan m a 2 HL oH | 2-22 2288 2 =<br>> mm = Potitit mitt > BEES BEE=<br><!-- End of picture text -->

<!-- Start of picture text --> 25 _ = seeS$ag eec-s5u8 eene§ug senséug seneséag seossaeg Product<br>— 2" an 2 7 =. an an = Small ruminant meat<br>4‘ 20 cage 523% eeg¢ 233 2892 F828 a= beetPigmeatig<br>s } SSP4 SSPS5 ssP4 SSP5 SSP4 SSP5 TM Poultry meat<br><!-- End of picture text --> <!-- Start of picture text --> & Cent. Asia Sub-Saharan Africa East Asia & Pac. = Dairy milk<br>10 | | 2000-2030 2000-2030 2000-2030 = Small ruminant milk<br>. | | il 10 10 10 i r]<br>HH =| F i= 8 8 3-748 | | | | |<br><!-- End of picture text -->

<!-- Start of picture text --> ;<br>-_<br>Elgg555 Figg f& 6 gS76 SSe6 i | tae!<br>555 § ee Fe 2 = Bane = 2 lilHL |==<br>SSP4 SSPS ° SS== S==5= ° ==> -See ° | a<br><!-- End of picture text -->

<!-- Start of picture text --> osu 2e osm 2 osm 2 osue osu e osue<br>Z Z Z ind ind Z<br>SSP4 SSP5 SSP4 SSP5 SSP4 SSP5<br><!-- End of picture text -->
- _5_ Labor Market Regulations, Industral Rdations, and Enterprise Performance 43

- 5.1 The Position of C;overnment Officials, 43 _5.2_ Managers' Views, 45

- 5.2.1 Restrictive Labor Regulations, 45

- 5.2.2 Recruitment and Tainin& 46

- 5.3 Workers' Views of Unions, 49

- 5.4 Trade Union Perspective, 50

- _5.5_ The Nonuoperative Outcome, 51

- 6 The Productuinty Effects of Labor Force Strucure, Enterprise Ownership, and Foreign Trade Variables _53_

- 6.1 Overall Production Function Estimates, 56

- 6.2 Industry-Specific Production Function Estimates, 70

- 7 Econometric Estimates of the Determinants of Wages 101

- 8 Industrial Wages and the Extended Family 107

- 8.1 Size of Extended Families, 108

- 8.1.1 Nuclear Families, 108

- 8.1.2 Non-nuWdear Families, 110

- 8.2 Relationship Between Earnings and the Extended Family, 112

- 8.3 Secondary Income, 114

- 9 Summary and Policy Implications

117

- 9.1 Summary, 117

- 9.2 Policy ImpLications, 119

References 123 Index 125
rankings has been to either restrict the set of individual preferences or restrict the set of voting rules (see, e.g., Black (1948); Dasgupta and Maskin (2008, 2020)). We have good news: truthful voting remains a Nash equilibrium for the classes of preferences and voting rules that produce acyclic (or even transitive) social outcomes (Theorem 2, Corollary 1).

We reinterpret our basic model in the context of _intrapersonal bargaining_ to shed light on the occurrence of cyclic individual choice, which constitutes a violation of rationality. Following the psychological literature of “choice under conflicts” or “reason-based choice” (Simonson, 1989; Shafir, Simonson, & Tversky, 1993), economists studying intrapersonal bargaining problems generally assume that an individual facing a choice problem has multiple rational inner selves holding preferences that may be conflicting (Bernheim and Rangel (2004); Benhabib and Bisin (2005); Eliaz and Spiegler (2006); Fudenberg and Levine (2006); De Clippel and Eliaz (2012); Ambrus and Rozen (2015)). In this literature, several reasons are advanced to explain systematic violations of rationality.<sup>8</sup> Our analysis complements this line of research by offering a non-cooperative solution to an intrapersonal bargaining problem where the decision-maker’s rational selves can be viewed as different criteria for ranking the alternatives in the choice set. We then show that cyclic individual choice can emerge as the _equilibrium_ outcome of interactions between these selves, especially if they hold conflicting preferences or priorities over the choice menu. Our analysis explains why such irrational choices can exist in reality (see, e.g., Section 6), especially when the number of selves is small.<sup>9</sup> Our findings imply that ranked voting addresses several shortcomings of traditional voting systems such as plurality and range voting<sup>10</sup> , used in most presidential elections, parliamentary institutions, and international organizations. Vote-splitting and manipulability are the most well-known limitations of these traditional systems. For example, evidence suggests that vote-splitting has tainted the outcome of many elections, including the U.S. presidential elections in 1992 and 2002, the 2016 primary elections of the

> 8For a comprehensive survey on bounded rationality in choice theory, interested readers can refer to de Clippel and Rozen (2023).

> 9The occurrence of a cyclic social ranking is rare when the number of voters (or selves) is large (Balasko & Crès, 1997).

> 10These voting rules consist of selecting the alternative, if any, which receives the most support from the electoral body. The support can be in terms of the number of “yes” votes (plurality) or a score factor (range voting). A tie-breaking rule is generally applied in case there are many candidates who receive the same support.

5
_Figure 4. Gender differences in poverty rates_ _4.a. Poverty rates difference (male‐female). All ages_ _4.b. Poverty rates difference (male‐female). 25‐34 age group_ <!-- Start of picture text --> Male poverty is higher Male poverty is higher<br>Female poverty is higher<br>Female poverty is higher<br>20 15 10 5 0 5 20 15 10 5 0 5<br>Total poverty >35<br><!-- End of picture text --> Notes: The dark lines indicate a total poverty rate higher than 35 percent. Total sample 89 countries Source: WB Staff’s calculations based on GMD.

In later life, gender differences in global poverty rates tend to disappear and the percentage of poor women is slightly lower than that of men. Again, the global numbers mask regional differences, partly because of the population composition in each region, and life‐expectancy differences by sex. For example, the difference in poverty rates between women and men age 60 and above is small across all regions, except for Sub‐Saharan Africa, where men are 8 percentage points poorer than their female counterparts and where they represent a larger share of the poor than women. However, in this region, this age group accounts for about five percent of the population and four percent of the poor population, with no gender differences in either population shares.

What else do we know of men and women in the age group where the greater differences can be observed? We know that about 68 percent of women and men age 18 or above are married, and that by age 70 percent of the poor of that same age are married<sup>23</sup> . For those of a younger age, we find that ten percent of poor girls are married, compared with five percent of poor boys. For both, these rates are higher than those in the total population (six and two percent respectively). Figure 5 summarizes marital status for the poor. On average, we see small and not significant differences in poverty rates between women and men when looking at marriage and cohabitation. Differences arise when looking at those who have

> 23 Globally, the average mean age of marriage is of 26 years of age (source World Bank Genderstats)

13
# **Table A8.3 The Effect of PMT on Height-for-Age of Children 24 to 60 Months: Using Exposed Share of Eligible Life to Measure Program Exposure**

||Poole|d OLS|Com<br>Fixed|munity<br>-Effects|
|---|---|---|---|---|
|Year (2000=1)|-0.055|-0.043|-0.022|-0.028|
||[0.058]|[0.058]|[0.071]|[0.073]|
|Age exposed to the program*Year|-0.107|-0.127|-0.079|-0.064|
||[0.117]|[0.118]|[0.172]|[0.175]|
|Gender (boy=1)|-0.014|-0.012|-0.005|-0.006|
||[0.040]|[0.040]|[0.041]|[0.041]|
|Age|0.005***|0.005***|0.005**|0.005**|
||[0.002]|[0.002]|[0.002]|[0.002]|
|Mother education (years)|0.019***|0.018***|0.013**|0.013**|
||[0.005]|[0.005]|[0.005]|[0.005]|
|Father education (years)|-0.004|-0.005|-0.002|-0.002|
||[0.005]|[0.005]|[0.005]|[0.005]|
|Mother height (cm)|0.056***|0.055***|0.054***|0.055***|
||[0.004]|[0.004]|[0.004]|[0.004]|
|Father height (cm)|0.034***|0.033***|0.034***|0.034***|
||[0.004]|[0.004]|[0.004]|[0.004]|
|Head of HH (male=1)|0.027|0.032|-0.024|-0.016|
||[0.067]|[0.067]|[0.068]|[0.069]|
|Number of children 5-14 yo in HH|-0.054***|-0.051***|-0.032|-0.030|
||[0.019]|[0.019]|[0.020]|[0.020]|
|HH owns private toilet (Yes=1)|0.143***|0.128***|0.118**|0.108**|
||[0.043]|[0.044]|[0.048]|[0.048]|
|HH has sanitation (Yes=1)|0.382***|0.302***|0.357***|0.318***|
||[0.053]|[0.063]|[0.071]|[0.076]|
|Type of community (urban=1), C|-|0.145**||-0.015|
|||[0.058]||[0.093]|
|Distance to district capital (km), C|-|-0.001||-0.002|
|||[0.001]||[0.001]|
|Distance to bus station (km), C|-|<br>0.002||<br>0.002|
|||[0.003]||[0.005]|
|Land w/ technical irrigation (%), C|-|0.019||-0.176|
|||[0.115]||[0.182]|
|Comm. has asphalt road (Yes=1), C|-|-0.008||0.192**|
|||[0.053]||[0.087]|
|C h Y=1 C||0062||0015|
|omm. as sewerage system (es),|-|-.||-.|
|||[0.047]||[0.075]|
|Comm. has piped water (Yes=1), C|-|<br>0.019||<br>0.020|
|||[0.049]||[0.078]|
|Constant|-15.727***|-15.487***|-15.415***|-15.602***|
||[0.813]|[0.817]|[0.873]|[0.890]|
|R-squared|0.15|0.15|0.26|0.26|

Notes: _Exposed Share_ is the share of a child’s eligible life exposed to the program and is calculated as described on page 11. n is 3221. (C) indicates community-level variables. Robust standard errors are in parentheses. (*), (**) and (***) indicate significance respectively at 10%, 5%; and 1%.

45
the prediction accuracy of each tree in the remaining sample. The decision of which variable will be used to split the data (i.e constitute a _node_ ) (see Figure 4) and how the node will be split is based on the maximization of the entropy criterion value.<sup>9</sup> Breiman (2001) shows that the prediction accuracy measured by the out-of-bag error decreases as the number of trees increases. We therefore implement 1,000 bootstrap replications. In Section 5.1, we discuss the robustness of our results to different selections of: (i) bootstrap replications and (ii) the share of observations assigned to training vs. testing samples.

Once all the trees composing the random forest have been generated, the predicting variables can be ranked by importance score. This score measures the increase in the misclassification rate in the forest when the observed values of a given variable are randomly permuted in the out-of-bag samples (Genuer et al., 2010).

# 3.2.2. LASSO Stability Selection

The second type of supervised machine learning method used in this paper is LASSO Stability Selection. This algorithm aims to detect which variables have the highest probability of being selected by LASSO (Meinshausen & Bühlmann, 2010; Jayachandran et al., 2021), which is a method commonly used to select a reduced set of variables to predict a dependent variable.

LASSO (Least Absolute Shrinkage and Selection Operator) is a model selection method, which aims to balance between goodness of fit and parsimony. It imposes a constraint to the coefficient of the penalty function (the regularization parameter λ), which causes some coefficients to shrink towards zero. In so doing, LASSO keeps variables that are most associated with the dependent variable and drops the least associated ones.

In a nutshell, LASSO Stability Selection performs multiple repetitions of LASSO. The procedure, first, draws a 50 percent subsample without replacement from the training sample, then runs the LASSO algorithm on the complete list of independent variables. To be consistent with the Random Forest method, we choose to run 1,000 replications. We can compute the rate of appearance of each variable being selected in each LASSO run from the 1,000 repetitions. This yields the probability that the variable is chosen by LASSO as a predictor of the dependent variable (in our case, it is the interest to migrate). We can rank predictors in a descending rate of appearance.

> 9 The entropy criterion is given by the formula: E= −∑ci=1 pi . log ( pi) with _c_ being the number of unique classes and pi the prior probability of each given class (Schonlau, 2020).

13
Steen, R.C., 1986. Nonubiquitous transportation and urban population density gradients. Journal of Urban Economics 20: 97–106.

Storeygard, A. 2012. Farther on down the road: transport costs, trade and urban growth in sub‐Saharan Africa, manuscript.

Stull, W., 1974. Land use and zoning in an urban economy. American Economic Review 64: 337–347.

Sullivan A. 1986. A general equilibrium model with agglomerative economies and decentralized employment. Journal of Urban Economics, 20: 55–74 Taaffe, E., R. Morrill, and P. Gould. 1963. Transport expansion in underdeveloped countries: a comparative analysis. Geographical Review, 53: 503‐529 Venables, A. 2010. Economic Geography and African Development. Journal of Regional Science 89(3): 469‐ 483.

Venkataraman, M. (2014). Analysing Urban Growth Boundary Effects in the City of Bengaluru. Indian Institute of Management, Working Paper No : 464.

Walker, R. 2004. Industry Builds out The City: The Suburbanization of Manufacturing in the San Francisco Bay Area, 1850‐1940. Published in The Manufactured Metropolis by Robert Lewis (ed.) Temple University Press: 92‐123 Wheaton, W. 1978. Price‐Induced Distortions in Urban Highway Investment. Bell Journal of Economics, 9: 622‐632.

Wheaton, W. 1998. Land use and density in cities with congestion. Journal of Urban Economics, 43: 258– 272.

Wheaton, W. 2004. Commuting, congestion, and employment dispersal in cities with mixed land use. Journal of Urban Economics, 55: 417–438.

White, M. 1976. Firm suburbanization and urban subcenters. Journal of Urban Economics 3: 323–343.

Zhang, W. and K. Kockelman. 2014. Urban Sprawl, Job Decentralization, and Congestion: The Welfare Effects of Congestion Tolls and Urban Growth Boundaries. Proceedings of the 93rd Annual Meeting of the Transportation Research Board, 2014.

Zhang Y. and K. Sasaki. 1997. Effects of subcenter formation on urban spatial structure. Regional Science and Urban Economics, 27(3): 297–324 39