rafmacalaba/datause-encoder-data huggingface dataset:
The evidence to date strongly suggests that duration of stay in the host country, age when first migrated, education level before migrating, human capital acquired during migration and ties to the host and home countries all play some role in return migration. Evidence also suggests that return migrants earn more than non-migrants and that they are more likely to grow their own business that employs others.

# **III Method**

The likelihood of migrants returning is estimated using a probit model, where 1 is equal to those who “...plan to return to Romania (to live and work there permanently in the future)?” and 0 otherwise. The variables of interest are mostly taken from the International Agency for Source Country Information (IASCI), Soros Foundation and are based on previous findings in the literature. A number of push and pull factors are included such as whether the migrant has family in Romania, whether they remit home, whether there was already a network in the host country that assisted the migrant in finding a job or residence and whether the migrant has previously invested in the host country. The number of years being a migrant is included in our model to capture integration within the host country as well as the individual’s preference for being a migrant and the benefits this brings.

In keeping with previous theoretical and empirical work in the literature, we estimate an earnings gap between current earnings in the host country and estimated earnings in Romania. Estimated predicted earnings of migrants were based on using the estimated coefficients from a Heckman earnings model that used individual and household labor information from the 2012 Romanian Household Budget Survey (HBS). Only a very basic earnings equation could be estimated since much information could not be matched between the HBS and the IASCI. The earnings equation is consistent with a priori labor theory, e.g. a concave relationship between age and earnings and the more educated earning more.<sup>7</sup> Although we capture the relative earnings position of migrants, it would be interesting also to capture relative deprivation of migrants. The data allow us to calculate whether migrants have a better, same or worse occupation in the host country than they held in Romania prior to migrating and test the theoretical models of

> 7 All earnings are in Euros.  HMS earnings data was converted into Euros based on the average foreign exchange between the Euro and Romanian Lei being 0.23 between 2010 and 2012

> (http://www.freecurrencyrates.com/exchange-rate-history/RON-EUR).  The HBS does not contain individual level information on earnings, instead only reporting income from employment at the household level.  We based our sample on those households who had just one full-time worker to avoid the incomplete information problem of not knowing which worker earned what in the household.  While this approach means losing many observations it does mean the sample we are left with contains more accurate earnings information.

9
declines by about 27% in the period 1952-2009. The increase in the disconnection between the financial sector and the real economy implies a larger interconnectedness within the financial sector. We suggest that this increase in interconnectedness within the financial sector may have dampened the sensitivity of the real economy to monetary shocks. We present a stylized model that illustrates how interbank trading can reduce the sensitivity of lending to the entrepreneur’s net worth, thereby dampening the credit channel transmission of monetary policy. We then present some empirical evidence consistent with the predictions of the model.

Importantly, we take here an entirely positive perspective. We are not addressing the question of whether a change in the connection between the financial sector and the real economy is good or bad.<sup>5</sup> We proceed in three steps. First, we propose as measure of the structural transformation of the financial sector, the extent to which it is _connected_ to the real economy. The _Measure of Connectedness_ is the share of the credit market instruments represented by claims whose _direct counterpart_ belongs to the non-financial sectors (households, non-financial firms and government). We compute the measure of connectedness for each major player in the U.S. financial system for the period 1952-2009, as well as an aggregate measure, using data from the Flows of Funds. The 27% drop in the aggregate measure over the period 1952-2009 is mostly driven by a composition effect, namely the rising importance of financial institutions that are relatively “disconnected” from the real economy (such as the issuers of AssetsBacked-Securities).<sup>6</sup> Second, we propose a stylized model to illustrate how interbank trading, by increasing the liquidity of investment projects, reduces the sensitivity of lending to monetary policy shocks. In particular, we focus on the balance sheet transmission mechanism of monetary policy. According to this channel, changes in the nominal rate affect the net present value

> 5We do think that this is a very interesting question and we will explore it in our future research.

> 6We also investigate the relation between our measure and the share of finance in U.S. GDP reported by Philippon (2012). While they capture very different concepts, we interestingly find a very high correlation between a series equal to one minus our measure of connectedness and the share of finance in non-defence U.S. value added.

3
<!-- Start of picture text --> o<br>Ol<br>me<br>£~<br>ie)° ood oon os<br>r<br>52<br>= a =<br>9 na “en =<br>£ =e “=<br>2<br>2<br>ow<br>o<br>0 5 10 15 20<br>Years of Schooling (Fathers)<br>nos SONS en se Daughters<br><!-- End of picture text --> <!-- Start of picture text --> 9<br>we<br>=<br>oe)<br>o<br>E<br>e<br>Ww<br>G<br>£<br>mo<br>5 oT<br>=<br>w<br>eC<br>2<br>©<br>Oo<br>Cc<br>om<br>oY<br>ily<br>[=<br>o<br>0 5 10 15 20<br>Years of Schooling (Fathers)<br>os SONS =o Daughters<br><!-- End of picture text -->
regulation of labor (Botero et al. 2004), and financial development (La Porta et al. 1998, Claessens and Laeven (2003), and Beck et al. (2005)) Through these different channels, we expect legal origin should also have an impact on firm size and growth over the lifecycle. For our sample of countries, we use **_Legal Origin_** , a set of three dummy variables to capture the differences between three influential legal traditions British common law, French civil law, and Socialist law - using data from La Porta et al. (1998, 1999).<sup>19</sup> Acemoglu, Johnson, and Robinson (2001) argue that many countries, especially former colonies, did not design the legal system to protect property rights. Instead, its purpose was to facilitate the extraction of resources from the indigenous population. Thus, two systems with the same legal origin may in practice offer very different protections. Acemoglu et al. (2001, 2002) and Engerman and Sokoloff (1997) contend that European colonization offers a natural experiment to test this hypothesis. Europeans set up extractive systems in colonies that were not attractive for colonial settlement, either because of high settler mortality due to natural causes at the time of colonization, or because the indigenous population was relatively large. In colonies where settlement was feasible, countries set up the judicial systems to protect the property rights of the settlers. This theory emphasizes the role of geography (latitude and natural endowments) and disease environment (which affected the settler mortality) in shaping property rights.

To measure geographical endowments, we use **_Latitude,_** which is the absolute value of the latitude of the country scaled between zero and one, from La Porta et al. (1998). Countries closer to the equator have a more tropical climate that is inhospitable to European settlers, and

> 19 Three countries – Ethiopia, Iran, and Yemen – were re-classified from French Civil law in La Porta, Lopez-deSilanes, and Shleifer (2008) to English Common Law in Djankov, McLiesh, and Shleifer (2007). We retain the original classification but our results are not significantly different with the change.

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decentralization substantially reduced the amount of public funds diverted by powerful individuals. However, since citizens were not aware of these diversions, they concluded that corruption had increased. Crook and Manor based upon evidence from Karnataka conclude that political decentralization reduces grand theft but increases petty corruption in the short run but in the long run both may go down. Olowu (1993) also considers political centralization as a root cause of endemic corruption in Africa. Fiszbein (1997) based upon a review of political decentralization in Colombia concludes that competition for political office opened the door for responsible and innovative leadership that in turn became the driving force behind capacity building, improved service delivery and reduced corruption at the local level.

A few studies show that administrative decentralization reduces corruption. Wade (1997) finds that over-centralized top down management accompanied by weak communication and monitoring systems contributes to corruption and poor delivery performance for canal irrigation in India. Kuncoro (2000) finds that with administrative decentralization in Indonesia, firms relocated to areas with lower bribes.

Increased fiscal decentralization is also shown to be associated with enhanced quality of governance as measured by citizen participation, political and bureaucratic accountability, social justice, improved economic management and reduced corruption by Huther and Shah (1998).

Thus, a small body of theoretical and empirical literature (for a survey of this literature, see Shah, Thompson and Zou, 2004) confirms that localization offers significant potential in bringing greater accountability and responsiveness to the public sector at the local level and reducing the incidence of grand corruption. However, a systemic examination of the root causes of corruption is not available, and hence the results from the literature must be seen as tentative and subject to further scrutiny.

5
<!-- Start of picture text --> Figure 2: Sub-national expenditures as % of GDP Transition<br>Developing<br>20<br>18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>19801981198219831984198519861987198819891990199119921993199419951996199719981999<br><!-- End of picture text --> Source: IMF Government Finance Statistics (various years) and World Bank Fiscal Indicators.

(http://www1.worldbank.org/publicsector/decentralization/fiscalindicators.htm) <!-- Start of picture text --> Figure 3: Sub-national expenditures as % of Transition<br>consolidated government expenditures Developing<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>19801981 1982 19831984 198519861987 1988198919901991 1992 19931994 199519961997 19981999<br><!-- End of picture text --> Source: IMF Government Finance Statistics (various years) and World Bank Fiscal Indicators. (http://www1.worldbank.org/publicsector/decentralization/fiscalindicators.htm) 12
# **1. Introduction**

Low student learning is an important feature in Pakistan, as it is in much of the developing world (Andrabi et al. 2007; Glewwe and Kremer 2006; Hanushek and Wößmann 2007). The shortfalls relative to national curricular standards are exacerbated by the fact that a large share of children, particularly rural, female, and poor children, obtains little or no formal schooling in the country, likely seriously impairing their long-term socioeconomic prospects. In addition, in general, the factors and processes that produce real and sustained gains in student learning are not as well understood as the factors and processes that produce real and sustained gains in school participation and attainment. Evidence also shows that the factors and processes behind learning can differ from the factors and processes behind participation (Andrabi et al. 2007). This background makes the search for effective policy solutions to improve learning outcomes pressing yet tenuous.

In this study, we use five semiannual rounds of standardized test data to characterize and explain the evolution of student learning over a period which spans three academic years (2007/08-2009/10). The tests were conducted in low-cost private schools supported by public cash subsidies under an innovative test-based accountability program in the province of Punjab, Pakistan administered by the Punjab Education Foundation (PEF), called the Foundation Assisted Schools (FAS) program. The data are repeated cross-sections at the student level and longitudinal at the school level. Such data are relatively rare for a developing country. Additionally, the data are unusual for a low-income setting given that they are generated from what appears to be a well-designed and robust quality assurance testing system integrated into the administration of the program, providing a high degree of confidence that the test scores can be interpreted as sound estimates of underlying learning.

Initiated in 2005, the FAS program provides conditional cash subsidies to low-cost private schools with the objective of offering private schooling opportunities for children from low-income households and raising the level of learning in low-cost private schools. The cash subsidies are provided monthly and on a per-student basis, with essentially no conditions on how the subsidy is to be used by the program school. The subsidy amount is purposely set low to
# **Business Practices in Small Firms in Developing Countries**<sup>**#**</sup>

David McKenzie, _World Bank_ Christopher Woodruff, _University of Warwick_ _Keywords:_ business practices; small enterprises; productivity; management _JEL codes:_ O12; L26; M20; O17; M53.

> # We gratefully acknowledge funding from the Knowledge for Change Trust fund, as well as the funders and authors of the underlying surveys which we use. We thank Miriam Bruhn, Nicholas Bloom, Raffaella Sadun, John Van Reenan, and participants at the AEA annual meeting, Bocconi, Collegio Carlo Alberto, and Warwick for useful comments. Anna Luisa Paffhausen provided excellent research assistance.
Using the income reported in surveys we concluded above that the global income distribution had moved from a twin-peak to a single-peak. This also holds for the global distribution of income which adjusts for missing top incomes (Figure 5). Not surprisingly, the top-heavy Pareto imputation stretches out the top tail and makes it thicker from around $PPP 40,000. Further down the distribution, mass appears to shift from around $PPP 3,000 to $PPP 5,000, which are the top fractiles in the poor countries.

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14 other hand the saii2ple is divided in half and the regression is run only on those observations where z is above its mean then this automatically doubles the fraction of the sample in which the slope is positive and hence raises the estimated coefficient to £ =.1*2.1.691( =m6 Since the estimated impact on the sample in the bottom half of z values is zero, the difference between the estimates depends on the fraction above the threshold and the average slope above the threshold. So, if the division of the sample along characteristic z fails to produce substantatively different results then either a very small fraction of the sample lies above the threshold region (which does not deny a threshold above which population growth is **detrimental)** or the difference in impact above and below the threshold is not very large (perhaps because characteristic z (as measured) is ibt really an important interactive term). This result is true whatever the true functional form of the increased impact of population growth. lI Data and Methods Output per worker is taken from the Penn World Tables 5 (Summers and Heston, 1991). Output is re2a GDP measured at 1985 "real' dollars, that is adjusted for variations across countries in purchasing power. Output levels using these PPP currency conversions should be comparable over time and across countries"l.

Using labor force growth intead of either population growth (as most studies do) or growth of the working age population (ManLiw, Romer, Well 1992) -is empirically and conceptually important. It is empirically important as it allows the decomposition of changes in output per worker into factor accumulation and per worker productivity, which is performed 1 Since the focus is on growth rates, nothing of substance would change if World Bank constant pice local currency GDP data were used as the cross country correlations in growth rates are very high beween the two sere..
indicators and weights based upon 2008 expenditures only.

|**Table 10. DAU F**<br>**Expenditure Function**|**iscal Need Compensat**<br>**Provinces**<br>**Need Indicator**|**ion: Alternatives -**<br>**Weight ( share in**<br>**aggregate provincial**<br>**spending in prev. 5 years**|
|---|---|---|
|General Administration|Population (66.6%) and<br>area (33.3%)|52.3|
|Law and order|Population|0.7|
|Education|School age population|7.4|
|Health|Weighted population with<br>higher weights for age<br>groups 0-5 and 65+|6.1|
|Social Protection and<br>Welfare|No. of unemployed|1.1|
|Housing|No. of people in public<br>housing or no of units of<br>public housing|7.6|
|Roads and Transportation|Km of Roads|10.0<br>20|
|Agriculture and forestry|Area|6 2|
|Other services<br>ALL|population|9.7<br>100.0|

|**Table 11. DAU Fiscal**<br>**Expenditure Function**|**Need Compensation:**<br>**(c1..5)**<br>**Need Indicator**|**Alternatives – Cities**<br>**Weight ( share in**<br>**aggregate city group.**<br>**spending in prev. 5 years**|
|---|---|---|
|General Administration|Population|29.3|
|Law , order and fire protec|Population and prop.values|1.0|
|Education|School age population|30.4|
|Health|Weighted population with<br>higher weights for age<br>groups 0-5 and 65+|9.0|
|Social welfare and<br>protection|population below poverty<br>line or unemployed|1.3|
|Housing|No. of people in public<br>housing or no of units of<br>public housing|7.6|
|Roads and Transportation|Km of Roads|10.0|
|Water and Sewer|No. Residential and<br>comm/ind units|9.2<br>21|
|Other services|population<br>1.0||

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studying only.<sup>33</sup> Unfortunately, our data do not allow us to further investigate these channels.

However, the ban seems to have caused detrimental effects on a sub-group of the affected cohort, that is, those who have relative lower earnings potential. Indeed, we find that earnings are 6.2 percent lower among those affected by the law who are in the bottom 20<sup>th</sup> percentile of the log-earnings distribution.

We believe the results found in this paper has some policy implications: (1) it informs policy makers how the law affected time allocation between working and schooling activities in the short term; (2) it shows that the ban has persistent effects on time allocation between schooling and working activities; (3) it suggests that the returns to experience for the unaffected cohort were similar to the returns to education for the affected cohort; (4) it can have detrimental effects among those with lower earnings potential; and finally (5) it suggests that policies that reduce individuals’ choice-sets should be accompanied by compensating or complementary policies—for example, a conditional cash-transfer program or an apprenticeship program—for those to whom it is more likely to cause harm.<sup>34</sup>

# **Conclusions**

This is the first paper to investigate the short- and long-term effects of a child-labor ban. The increase in the Brazilian minimum legal age of entry into the labor market from 14 to 16 in December 1998 was interpreted as a natural experiment. Several years of Brazilian household surveys and a fuzzy RDD were used to estimate the ITT impact of the ban on the outcomes of interest.

> 33 Indeed, Emerson, Ponczek and Souza (2016) find that only studying improves learning as compared to studying and working in Brazil.

> 34 The Brazilian CCT _Bolsa Família_ was in a pilot stage at that time (Glewwe and Kassouf, 2012) and the Brazilian apprenticeship program was launched only in December 2000 (Corseuil et al. 2012).

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# **Table 1: Summary statistics**

|Variable|Obs|Mean|Std Dev|Min|Max|Mean|Std Dev|Source|
|---|---|---|---|---|---|---|---|---|
|||At o<br>|bservation le<br>|vel<br>||At count<br>|ry level<br>||
|Net interest margin, %|147,761|3.662|1.891|0.310|14.100|4.729|2.242|Bankscope|
|Net interest income to total assets, %|152,997|3.137|1.556|0.000|11.146|3.459|1.486|Bankscope|
|log(gross loans, million USD)|150,345|5.316|1.812|1.142|10.934|5.688|1.417|Bankscope|
|Equity / total assets, %|152,641|11.743|8.620|1.606|77.778|13.470|4.717|Bankscope|
|Noninterest expense / total assets, %|154,624|4.267|6.821|0.000|50.000|4.313|2.670|Bankscope|
|Cash / total assets, %|154,460|5.948|7.670|0.000|42.857|11.991|8.347|Bankscope|
|Loan loss provisions / gross loans, %|127,296|2.346|3.011|0.000|23.140|4.394|2.680|Bankscope|
|Noninterest income / revenue, %|152,770|24.171|23.122|0.000|100.000|38.888|13.481|Bankscope|
|Inflation, %|155,175|3.040|2.898|-1.674|23.642|5.304|3.188|WDI|
|Money market rate, %, monthly average|153,381|2.523|2.868|0.001|13.560|5.446|3.602|WDI|
|Country Lerner Index|152,164|0.217|0.235|-1.750|0.480|0.272|0.101|GFDD|
|Depth of credit information|155,171|7.071|1.884|0.000|8.000|3.521|3.083|Doing Business|
|Legal right index|155,171|8.693|2.669|0.000|12.000|6.200|2.705|Doing Business|
|Recovery rate, %|154,994|68.431|20.842|0.000|92.900|33.916|24.369|Doing Business|
|GDP per capita (thousand, USD)|155,028|40.040|18.324|0.219|141.165|13.887|20.983|WDI|

Note: The mean and standard deviation at country level are based on pooled bank-level averages for each country, for the stable sample of 128 countries reflected in the regression analysis.

# **Table 2: Correlation matrix**

||[1]|[2]|[3]|[4]|[5]|[6]|[7]|[8]|[9]|[10]|[11]|[12]|[13]|[14]|[15]|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|[1] Net interest<br>margin|1.00|||||||||||||||
|[2] Net interest<br>income / TA|0.88|1.00||||||||||||||
|[3] log(gross loans)|-0.26|-0.27|1.00|||||||||||||
|[4] Equity / TA|0.32|0.22|-0.32|1.00||||||||||||
|[5] Noninterest<br>expense / TA|0.34|0.27|-0.21|0.41|1.00|||||||||||
|[6] Cash / TA|0.19|0.08|-0.18|0.13|0.10|1.00||||||||||
|[7] Loan loss<br>provisions / gross<br>loans|0.26|0.22|0.08|0.18|0.42|0.07|1.00|||||||||
|[8] Noninterest<br>income / revenue|0.06|-0.08|0.12|0.24|0.56|0.11|0.39|1.00||||||||
|[9] Inflation|0.50|0.38|-0.10|0.26|0.37|0.13|0.31|0.34|1.00|||||||
|[10] Money market<br>rate volatility|0.35|0.27|-0.07|0.20|0.33|0.01|0.26|0.28|0.67|1.00||||||
|[11] Country Lerner<br>Index|0.09|0.08|-0.06|0.03|-0.03|0.13|-0.04|-0.10|0.05|-0.01|1.00|||||
|[12] Depth of credit<br>information|-0.16|-0.07|-0.10|-0.12|-0.06|-0.11|-0.30|-0.29|-0.44|-0.28|-0.07|1.00||||
|[13] Legal right index|-0.03|0.04|-0.26|-0.08|-0.15|0.04|-0.40|-0.34|-0.28|-0.22|0.17|0.45|1.00|||
|[14] Recovery rate|-0.38|-0.25|-0.07|-0.24|-0.25|-0.19|-0.41|-0.38|-0.60|-0.49|-0.02|0.60|0.54|1.00||
|[15] GDP per capita|-0.41|-0.29|-0.08|-0.22|-0.29|-0.19|-0.47|-0.36|-0.60|-0.44|-0.01|0.45|0.52|0.75|1.00|

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