rafmacalaba/datause-encoder-data huggingface dataset:
risk tolerance, and skills, which are all factors positively associated with migrating, and the income earned from self-employment could fund the costs of migrating.

This new and causal evidence on the be- paper provides descriptive relationship tween self-employment and internal and international migration. We begin by outlining how migration theory offers uncertain predictions for both selection into migration on the basis of attributes that also determine the self-employment decision, as well as for the likely causal effect of self-employment on subsequent migration. We then put together panel data on almost 140,000 individuals from eight countries to descriptively examine the association between self-employment and migration. Contrary to several theoretical predictions and the priors of many academics,<sup>4</sup> we find that the selfemployed are less likely to migrate than either wage workers or the not employed, and that this holds conditioning on age, gender, education and income. This relationship is stronger for internal migration than international migration, in part reflecting the much lower likelihood of international migration.

We then attempt to put together data from as many randomized experiments that increase self-employment as possible and measure the causal impacts of these programs on migration. We were able to do this for seven different experiments, exhibiting a wide range of increases in self-employment (between 2 and 41 percentage points), and enabling migration to be measured over time horizons of up to 10 years. In we do that which more did aggregate, find programs generated self-employment reduce migration. However, the impacts are typically modest, are restricted to internal migration, and require several years for impacts on migration to materialize. One reason for the limited impacts is that these programs have not been expressly targeted at those with high likelihoods of migration, and in some settings, there is very little migration among the control group to deter. This is particularly the case for international migration, which is an extremely rare event.<sup>5</sup> Nevertheless, we find no evidence of programs that increase self-employment subsequently increasing migration. The results therefore provide some support for fostering entrepreneurial activities in home regions as a way to reduce migration pressures, but also suggest the need to carefully target such programs at individuals with high propensities to migrate if they are to have more than modest effects.

This paper contributes to two main literatures. The first is the existing literature on migration and self-employment that has largely focused on different questions. The

> 4At the 12th Conference on Migration and Development, McKenzie asked the audience of experts on migration and development whether they thought that the self-employed would be more, or less, likely to migrate than wage workers. Only one person thought the self-employed would be less likely, and the vast majority thought they were more likely to migrate.

> 5Only 0.6 percent of the global population is estimated to migrate over a five-year period (Abel and Sander, 2014).

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reductions in the marginal cost of diversifying, pooling, and trading risk might also work on the intensive margin, and therefore disproportionately help those who are already using these services. Thus, policies affecting the risk services offered by the financial system could substantively impact the intergenerational persistence of relative incomes.

# **2.3.3 Savings behavior**

We conclude by reviewing theories of how savings behavior affects intergenerational income dynamics. At one level, this is the most obvious vehicle through which richer dynasties remain comparatively rich; richer parents give more assets to their children than poorer ones. Under standard assumptions, the intergenerational correlation of income will be positive if inheritance (saving) is a positive function of parental income [ ∂ a(i,t)/ ∂ (y(i,t-1)] >0, i.e., a’>0. Rich parents bequeath more money than poor parents, which perpetuates relative income differences. The positive correlation between parental and child wealth, however, does not necessarily imply that cross-dynasty relative income differences persistent in the long-run. Holding labor income constant, if the inheritance (saving) function is a concave function of parental income, a’>0 a’’<0, then average savings rates a/y decline as income y rises. Consequently, relative income differences between dynasties decline across generations (Stiglitz, 1969).

In this setting, inequality persists in the long-run when rich parents bequeath (save) a higher _percentage_ of their incomes to their children than poor parents. With a convex bequest (saving) function, a’>0 and a’’>0, average saving rates a/y rise with income, putting upward pressure on wealth inequality (Bourguignon, 1981).<sup>3</sup> With no financial market frictions and no cross-dynasty differences in labor income, convex bequest preferences can produce long-run persistence in cross-dynasty relative income differences.<sup>4</sup>

> 3 Endogenous fertility decisions can also motivate the assumption of a convex savings function in per capita terms as shown by Atkinson (1980). If poor dynasties have more kids than rich ones, cross-dynasty per capita wealth inequalities can persist across generations even if all dynasties have the same aggregate savings rate. Galor and Weil (2000) note, however, fertility does not fall with wealth, rather fertility falls with human capital, which is not perfectly correlated with wealth. This line of inquiry also suggests that financial market policies could affect fertility decisions.

> 4 Cross-country empirical observations do not support the assumption of aggregate convex savings behavior. Convex saving behavior implies a positive association between inequality and growth. Consider two economies that are equivalent except economy A has an equal distribution of income, while economy B has a skewed distribution of income. With convex savings, economy B will save more and enjoy a higher average per dynasty income level in the next period. Income inequality, however, is not positively associated with growth (Alesina and Rodrik (1994),

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The evaluation design above does not distinguish explicitly the potential effect of the Access to Information pilot from the effect of participatory budgeting. As pointed out above, one of the selection criteria for the municipalities in the pilot ATI, was the existence of a functioning participatory budgeting process. However, the implementation of participatory budgeting meetings was not uniform across the seven treatment municipalities. For example, the Monte Plata municipality had functioning participatory budgeting, whereas San José de los Llanos and Hato Mayor municipalities did not.<sup>11</sup> It is important to point out, however, that the participatory budgeting process is also present in the control municipalities. According to the baseline survey about 25% of the households in both treatment and control municipalities knew what a participatory budget process was, and most of them heard about it through friends and family (43% and 38% in control and treatment).<sup>12</sup> Approximately 53% and 60% of respondents in the control and treatment municipalities, respectively, who reported knowing what a participatory budget process was, reported that participatory budget meetings were being undertaken in their municipality (this difference is statistically significant at the 10% level but not at the 5% level), and approximately half of them had participated in at least one meeting, with no significant differences between the two groups. Among those people that had participated in the meetings, 76% in the control municipalities and 60% in the treatment municipalities had expressed their preferences during these meetings (statistically significantly different at the 5% level).

To the extent that the participatory budgeting processes were already in place in both treatment and control municipalities and their effects on people's perceptions have already taken place prior to the implementation of the ATI, then one could reasonably argue that the application of the DiD estimator is likely to yield an estimate of the impact of the ATI program that is "net" of the effects of participatory budgeting on key outcome indicators. However, this statement needs to be qualified further, since participatory budgeting was "work in progress" with possibly measurable impacts on people's perceptions and attitudes during the life of the ATI project.

> 11  See Lima (2011). 12 See Table 1 Appendix B of Lima (2011).

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tracking than random assignment. I explore a variety of alternative explanations for these results: time-varying student selection into dormitory or non-dormitory status, differential time trends in student performance between dormitory and non-dormitory students, limitations of GPA as an outcome measure, and direct effects of dormitory assignment on GPAs. I conclude that the results are not explained by these factors.

The mean effect size of 0.13 standard deviations is substantial for an education intervention. McEwan (2013) conducts a meta-study of experimental primary school interventions in developing countries. He finds average effects across studies of 0.12 for class size and composition interventions and 0.06 for school management or supervision interventions. Replacing tracking with random assignment thus generates gains that compare favorably to many other education interventions, albeit in different settings. The direct pecuniary cost is almost zero, yielding a particularly high benefit to cost ratio.

I then use randomly assigned dormitory-level peer groups to estimate directly the effect of living with higher- or lower-scoring peers. I find that students’ GPAs are increasing in the mean high school test scores of their peers. Low-scoring students benefit more than high-scoring students from living with high-scoring peers. Equivalently, own and peer academic performance are substitutes, rather than complements, in GPA production. This is qualitatively consistent with the effects of tracking. Peer effects estimated under random assignment can quantitatively predict features of the GPA distribution under tracking. However, the predictions are sensitive to model specification choices over which economic theory and statistical model selection criteria provide little guidance. This prediction challenge reinforces the value of cross-policy evidence on peer effects. I go on to explore the mechanisms driving these peer effects. I find that peer effects operate largely within race groups. This suggests that peer effects only arise when residential peers are also socially proximate and likely to interact directly. However, peer effects do not appear to operate through direct academic collaboration. They may operate through spillovers on time use or through transfers of soft skills.

This paper makes four contributions. First, I contribute to the literature on optimal group design in the presence of peer effects. Models by Arnott (1987) and Benabou (1996) show that the effect of peers’ characteristics on agents’ outcomes influences optimal class4
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

# **Box 4.2 . Mobile sharing arrangements in Uganda**

As of 2009, Uganda’s telecommunications market had five mobile operators and several ISPs. MTN and UTL are the two national wireline operators and are also significant mobile operators. Both companies also have operations in Rwanda and thus have a common interest in establishing a communications link across the border from Uganda to Rwanda. In 2007, MTN constructed a fiber-optic cable from Kigali, Rwanda, to the border with Uganda. The company also recently announced a deal with its competitor UTL to jointly develop the fiber optic network on the Uganda side—a good example of competing operators forming a cooperative arrangement to lower the costs of developing fiber optic networks outside major urban areas. However, such an arrangement raises concerns for the market in Rwanda since the only fiber optic connection to the country will be jointly controlled by the only two network operators in Rwanda. Such concerns may ease as more licenses are issued in Rwanda and competition grows.

<u>Source: Information and Communications for Development 2009.</u> For years, EAC suffered from a shortage of international connectivity traceable to its lack of undersea fiber optic cables. Since 2009, this situation has changed with the advent of three new cable systems. SEACOM (South Africa–East Africa–South Asia–Fiber Optic Cable)<sup>9</sup> was the first of the new East African cables, arriving in July 2009. It has landing stations in Kenya and Tanzania; Uganda and Rwanda can access the cable through connections via Kenya. The East African Submarine Cable System (EASSy)<sup>10</sup> is scheduled for deployment in mid-2010); its landing stations will include Kenya, Sudan, and Tanzania with access via terrestrial backhaul for Burundi, Ethiopia, Rwanda, and Uganda. The TEAMS cable will link the United Arab Emirates and Kenya.

Connectivity to the landing stations of these new undersea cable systems has spurred upgrading and expansion of domestic backbones and cross-border agreements for onward transmission throughout the region. Burundi is building a national backbone assistance that will have two international exits via Rwanda and Tanzania.<sup>11</sup> Rwanda has around 230 kilometers of fiber optic cable in its national backbone, which is expanding to complete a 400-kilometer link from the capital to Gatuna on the border with Uganda. From there it connects to Ugandan fiber optic links, which run to the Kenyan border and on to the landing station in Mombasa. Rwanda has connectivity to the new SEACOM cable and will also have access to EASSy (figure 4.2).

> 9 http://www.seacom.mu

> 10 http://www.eassy.org

> 11 http://web.worldbank.org/external/projects/main?pagePK=64312881&piPK=64302848&theSitePK=40941& Projectid=P094103

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34 kind of policy only served to increase the inceitives of financial institutions to abuse publicly-provided insurance. For this reason, required along with deregulation was increased supervision and monitoring of banks. But as Diaz-Alejandro (1985) noted, the Latin American countries largely failed to anticipate this need. Lax government monitoring permitted an environment of lax lending policies. What emerged was a vicious cycle of government bailouts and inefficient intermediation. In this regard, there is a strong parallel with the savings and loan crisis in the U.S.

# V. Evidence

We have argued that an important aspect of growth is a decline in the premium for external finance. As economies develop, therefore, one should expect an evolution from self-finance to formal credit relationships. Indeed, at least since Gurley and Shah- (1956) and Goldsmith (1968), development economists, have believed that financial deepening was an important aspect of growth. Much of recent growth theory, however, has ignored financial considerations. In this section, we update the evidence on financial deepening. The general motive is to confirm the potential relevance of the kind theories we have been describing. One way our analysis differs from much of the previous work is that we make explicit use of panel data techniques: that is, we exploit information from both the time series and the cross-section.

Our work is non-structural in the sense that we do not estimate a formal statistical model. Further, we do not deal with the all- important question of causality, so that the linkages between the financial system and the real economy are not explicitly identified. Rather, we seek, at least initially, to develop robust generalizations at the level of descriptive statistics. As is true of much non-structural econometric work, our results cannot verify hypotheses, but they are capable of refuting theories. The spirit of our empirical work is to present facts which constitute a benchmark to discriminate between viable and implausible theories.
|||OECD Hi|gh growth d|efinition|||Birch Hi|ghgrowth d|efinition||
|---|---|---|---|---|---|---|---|---|---|---|
||qld|q25|q50|q75|q90|qld|q25|q50|q75|q90|
||(1)|(2)|(3)|(4)|(5)|(1)|(2)|(3)|(4)|(5)|
|HGFExperience|0.018|0.027|0.101***<br>|=0.278***|0.691***|0.012|0.032|0.111***|0.282***|0.527***|
||(0.017)|(0.019)|(0.033)|<br>(0.057)|~—(0.211)|(0.024)|(0.021)|(0.027)|(0.062)|(0.165)|
||0|0|0|0|0||||||
|Initial age|-0.005<br>|-0.010<br>|-0.067***<br>|-0.141***<br>|-0.307***<br>|-0.004<br>|-0.012<br>|-0.071***<br>|-0.141***<br>|-0,.293***<br>|
||(0.009)|(0.010)|(0.012)|(0.027)|(0.088)|(0.010)|(0.008)|(0.015)|(0.038)|— (0.080)|
|Initialsize|0.045***|0.058***|0.049***|0.026|-0.043|0.044***|0.053***|0.041***|—-0.003|-0.088*|
||(0.006)|(0.007)|(0.012)|(0.028)|(0.069)|(0.010)|(0.010)|<br>(0.013)|<br>(0.019)|(0.050)|
|InitialagexInitialsize|-0.004<br>|=-0.044**|-0.095***|-0.322***|-0.925***|-0.001|-0.044***|-0.102***|-0.326***|-0.944***|
||(0.017)|<br>(0.017)|<br>(0.030)|<br>(0.062)|(0.167)|(0.017)|<br>(0.011)|<br>(0.024)|(0.044)|(0.182)|
|Initial averagewage|0.041<br>|0.045<br>|0.104***<br>|0.147**<br>|= 0.429**<br>|0.044<br>|0.049<br>|0.092**<br>|0.138***<br>|0.224**<br>|
||(0.030)|(0.031)|(0.035)|(0.071)|_—(0.187)|(0.032)|(0.032)|(0.039)|(0.040)|— (0.112)|
|Agglomeration centerdummy|0.043<br>|0.037<br>|0.007<br>|0.031<br>|0.471**<br>|0.041*<br>|0.038<br>|0.013<br>|0.033<br>|0.423**<br>|
||(0.030)|(0.036)|(0.039)|(0.060)_|—(0.239)|(0.021)|(0.041)|(0.045)|(0.095)|(0.176)|

Notes: The dependent variable is TFP growth over a three year window.All regressors measure their initial level. All regressor included in Table 4 are included in these estimations, but results are presented for the select few. Estimations report standard errors. clustered at the plant level.
# <u>5. Comparing the interventions’ effects on reading skills</u>

The school readiness intervention ultimately aimed to improve the home environment and subsequently improve a child’s ability to learn in a school environment. While its objectives were much broader than improving the specific reading skills assessed in TEGRA, an important question is did it have any effect on them? Two birth cohorts were exposed to both the school readiness intervention prior to starting school and the CLRW intervention once in school. Using data on mappings between communities and schools, we estimate the effect of the school readiness intervention on early grade reading scores. We find a positive effect for children with one year of exposure to the school readiness intervention at the end of two years of schooling in CLRW control schools and for girls with two years of exposure to the school readiness intervention at the end of one year of schooling.

# _Data_

In TEGRA, there are two sources of data on whether a student lived in a treatment or control community prior to starting primary school. First, the February round of the 2016 TEGRA collected data on which community 1<sup>st</sup> grade children lived prior to starting school. However, the information could not be collected for 17 treatment or control schools, and a further review of the data found that it may not have been reliable for all students. Second, 421 students in TEGRA were previously sampled in the 2014 TEHCI data. This source of data of where a child lived prior to primary school was reliable and almost all schools in TEGRA contained some of these students. We use this source to derive for each school the probability that a student was from a school readiness treatment community as opposed to a control community or unassigned community. Unassigned communities were also valid controls as the treatment (and control) communities were selected at random from all communities in Tonga. The indicator was calculated as the proportion of students in a school who came from a CPBA treatment community. This measure was highly correlated with an analogous indicator calculated using the first source of data on students’ communities.

32
economic characteristics of the mother and especially the education of the mother are likely to play a very important role. While these features imply that it is imperative to take into account the potential influences of these confounding factors in determining the impact of maternal education on ECD, the DML approach that addresses these issues explicitly reveals that in São Tomé and Príncipe at least, maternal education has a direct effect on ECD. Yet, one more intriguing result from the analysis in São Tomé and Príncipe is that maternal education has no relationship at all with child stature. This result appears to contradict much of the conventional wisdom derived from other countries regarding the effect of maternal education on child nutrition. However, it is important to bear in mind that the conventional wisdom is based more on correlations as opposed to established causal relations (Desai and Alva, 1998). A more recent study by Alderman and Heady (2017), addressing explicitly the problem of omitted variable bias in the relationship between parental education and child nutrition, finds that the nutritional returns to parental education are reduced substantially in models that include cluster fixed effects, which are typically omitted from conventional regression models, thus resulting in omitted variable bias in the estimated coefficients.

In conclusion, country-specific policy advice based on relationships observed from data in other countries may be quite risky, if not misleading. DML offers the opportunity to identify and validate causal relationships with country-specific observational data. Given the constraints faced by randomized control trials to address every possible question, DML provides a practical and feasible approach to reducing threats to internal validity to derive robust inferences based on observational data for evidence-based policy advice. The extent to which the DML estimates are close to the true causal or impact estimates has yet to be established and this can only be ascertained by comparing with causal estimates derived from an RCT. Nevertheless, there is promising evidence that estimates based on the DML represent an improvement over the usual OLS in the sense that they are closer (and sometimes quite close) to the impact estimates from the preferred instrumental variable method (Duflo, 2018).

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