rafmacalaba/datause-encoder-data huggingface dataset:
-7 -

**Figure** 3

<!-- Start of picture text --> NET ODA PER CAPRITA<br>CAnnoe averrge)<br>40 W4<br>35 34_ 4<br>30<br>25 227<br>20<br>15~~~~~~~~~~~~~~~~1.<br>'10<br>S<br>0<br>OJIMAS Kwea M Iewi Tarmanis Uganda<br>1291-935 i 1 a&- go<br><!-- End of picture text --> Source: IMF and World Bank data, and authors' estimates.

Comparisons of aid receipts as a percentage of GDP across countries could be somewhat misleading, however, since the GDP levels (measured in U.S. dollars) are affected significantly<sup>by</sup> the extent of exchanre rate adjustments. This applies equally to intertemporal comparisons<sup>within</sup> a single country, particularly if exchange rate changes have been large. Hence,<sup>we also look at</sup> trends in aid per capita in these countries. Figure 3 shows average aid flows per<sup>capita for the five</sup> countries in the two periods under consideration. This indicator also confirms<sup>that aid flows</sup> increased considerably in all countries when they embarked upon reforms. Figure 3 reveals<sup>that in</sup> per capita terms, while Tanzania was the largest recipient of aid in the pre-reform period, during the period of reform Malawi has become the largest recipient, with aid amounting to US$44 per capita. Furthermore, in per capita terms, Ghana and Kenya got amounts that were roughly comparable to Tanzania, and only Uganda got an amount that was significantly lower (about US$23).
— Table 6: Robustness Checks Land Values

||m|ean|98<sup>_th_</sup>|percentile|
|---|---|---|---|---|
|_β_1 : _H ×_log_θ_|0.114***|0.0907***|0.112***|0.0816***|
||(0.0294)|(0.0347)|(0.0321)|(0.0309)|
|_H ×_∆TW/acre|1.732***|—|1.905***|—|
||(0.593)||(0.636)||
|_H ×_∆logNC/acre|—|-0.0301|—|-0.0331|
|||(0.0531)||(0.0432)|
|_R_<sup>2</sup>|0.782|0.781|0.782|0.780|
|Observations|7,844|7,798|7,844|7,798|

_Notes:_ See notes to Table 2. Dependent variable is log(land value per acre). Compare columns 1 and 2 to column 2 of Table 4, and columns 3 and 4 to column 8 of Table 4. ∆TW/acre is the difference in no. of tubewells per acre cultivable command area ( CCA) between head and tail outlets and ∆logNC/acre is the difference in log no. of cultivators per acre CCA between head and tail outlets (available for 446 of 448 channels, which accounts for 46 fewer observations in the corresponding regressions).

regressions (see Table 6). While this control attracts a significantly positive coefficient, so that relatively greater access to groundwater does seem to enhance the head-of-channel land value premium,<sup>33</sup> the lobbying power effect is undiminished.

A final issue is that head-tail differences in the size distribution of family landholdings may be correlated with head-tail differences in average land productivity, another example of the potential incompleteness of _Zc_ . There are three necessary elements to the argument, each of which may be questioned: (1) substantial variation in average land quality between head and tail outlets of the same channel; (2) larger returns to consolidating landholdings in areas with higher average land productivity; (3) consolidation that mostly occurs through land purchases. Under these conditions, we could observe larger landholdings on higher productivity sections of the channel, thus reversing the arrow of causation relative to our interpretation of the land value regressions (but, of course, not of the DPR regressions).

On element (1), we reiterate that median channel length (the maximum distance between

> 33It is also possible that this effect is not causal and that tubewell density is merely correlated with, e.g., unobserved land productivity. The interpretation, however, is irrelevant for the robustness test.

36
# **6. Analytical results**

# _6.1. General results_

Table B1 presents the quantile regression results and, as a reference, OLS results. Estimates for the 10<sup>th</sup> , 25<sup>th</sup> , 50<sup>th</sup> , 75<sup>th</sup> , and 90<sup>th</sup> quantiles are presented to identify differences between low earners and high earners conditional on observables. Most of the variables enter with the expected sign at all quantiles. In the following, each variable’s impact on wages is discussed.

Age and tenure. Both age dummies _yrgroup2_ and _yrgroup3_ are positive and highly statistically significant at all quantiles (10<sup>th</sup> , 25<sup>th</sup> , 50<sup>th</sup> , 75<sup>th</sup> , and 90<sup>th</sup> ), hence, impacting wages in RCI positively. The age dummies proxy for general, as opposed to firm-specific experience. The regressions show that workers with general experience receive a premium, and the premium increases with increased skill level. Tenure is also positive and the estimated coefficients are non-constant as they increase with tenure. Tenure over 12 years ( _tenure3_ ) is significant for all quantiles, while 6-12 years of tenure ( _tenure2_ ) is highly insignificant in the upper quantiles (75<sup>th</sup> and 90<sup>th</sup> ). At the 90<sup>th</sup> quantile, the coefficient even becomes negative, though it is statistically insignificant. One would expect tenure to have a positive effect on wages as it captures a pay-off to experience. In Côte d’Ivoire this pay-off does not seem to be systematic and, therefore, possibly delinks experience from wages.<sup>14</sup> Education. Completed basic education with diploma generally enters with the expected positive sign but is statistically insignificant at all quantiles (with respect to no completed education). This may be explained by the fact that we only analyze wages in the manufacturing sector (see more below). Completed higher education relative to no completed education is positive and significantly different from zero at the 1 percent level across all quantiles. The premium obtained by highly educated workers ranges between 33 percent (10<sup>th</sup> ) and 105 percent (90<sup>th</sup> ), and increases almost linearly with the quantile, _vis-à-vis_ workers with no education.<sup>15</sup> Why is there not a premium to basic education as compared to no completed education— a result that strongly differs from prior expectations and from previous results in the literature (see survey-table in appendix E)? In Verner (1999c) the return to basic education is generally found significant and positive. When restricting the 1995 sample to males only (as we have

> 14 The result is robust to different splines.  In particular, introducing a dummy for 0-1 years of tenure and a dummy for 2-5 years of tenure along with _tenure2_ and _tenure3_ does not change the result.

11
ahead of rural progress. For the remaining five opportunities, rates of improvement in rural areas were moderate or negative, but always superior to the urban rates.

|**Table 4. Human Opportunity Index for Eg**<br>|**ypt, Rural **<br>|**2000 and**<br>|**2009**<br>|||
|---|---|---|---|---|---|
|_Opportunity_|_2000_|_Circa_<br>|_Annual_<br>|_Deco_|_mposition_|
|||_2009_|_rate of_<br>_change_|_Access_|_Equality of_<br>_opportunity_|
|Completeprimaryeducation on time|78|85|0.9%|77%|23%|
|Complete secondaryeducation on time|53|60|0.9%|69%|31%|
|School attendance, 9-15|---|87|---|---|---|
|Water|70|85|1.6%|81%|19%|
|Sanitation|11|17|0.6%|78%|22%|
|Lightingenergysource|97|98|0.1%|62%|38%|
|Cookingenergysource|63|97|3.8%|61%|39%|
|Non-overcrowding,0-5|43|55|1.3%|64%|36%|
|Telephone|8|64|6.3%|70%|31%|
|Assisted birth delivery|55|79|3.1%|82%|18%|
|Post-natal care,0-5|16|24|1.0%|92%|8%|
|Prenatal care,0-4|48|73|3.1%|82%|18%|
|Immunization vaccines,0-4|89|85|-0.5%|42%|58%|
|Non-wasting,0-4|87|75|-1.5%|75%|25%|
|Non-stunting,2-17|65|67|0.3%|33%|67%|
|Non-underweight,0-17|77|82|0.7%|67%|33%|
|_Aggregate HOIs_||||||
|IOHa9-15-HousingServices|36|77|4.6|71%|29%|
|IOHa0-4-HousingServices|22|64|4.7|82%|18%|
|IOHa0-4-Nutrition & ECD|12|12|0.0|---|---|
|_Source_: HIECS 2000 and 2009 surveys, EDH|S 2000 and|2008, an|d authors’ ca|lculations.||

# **_3.2 Regional Differences_**

_Better Opportunities for Metropolitan Children Relative to Their Peers in Other Regions._ Table 5 presents the HOIs across the four main regions in 2000 and 2009, and at the bottom of the table shows three aggregate HOIs (two for housing services 0-4 and 9-15 years of age, and a third one for nutrition plus early childhood development). The evidence indicates that in 2009 the three overall aggregate HOIs were highest in Metropolitan areas (95, 97 and 18 points, respectively) and lowest in Upper Egypt (64, 74 and 10 points, respectively), with Lower Egypt and Frontier Governorates in between. Moreover, the gaps between the best and worst region have been substantially reduced between 2000 and 2009, particularly in housing services, but much less in nutrition and early childhood development. However, based on the same aggregate HOIs, Lower Egypt offered in 2009 better opportunities than Frontier Governorates for children 0-4 years of age, but not for aggregate opportunities of housing services of children 9-15 years of age (79 points in Frontier Governorates compared to 75 in Upper Egypt). In contrast, a count of the number of opportunities for which each region offered the first best _or_ second best opportunities (see Table A-4) shows the lead of the Metropolitan region with 14 HOIs (of 16 HOIs in total), followed by Lower Egypt with 11, Upper Egypt with four, and Frontier Governorates with only three. This would suggest considerable lack of opportunities in the Frontier Governorates, compared to Upper Egypt and Lower Egypt, contradicting the regional ranking based on aggregate HOIs. However, we should follow the interregional ranking based on the more rigorous aggregate HOIs that shows that Frontier Governorates offers better 15
# **Appendix A: Internal and External Conditions for Innovations**

# _Intrafirm determinants of organizational innovations_

In a survey of literature, Wan et.al. (2003) noted the internal environment favorable to the survival of new ideas and innovations in an organization include (i) frequent internal communications, (ii) decentralized structure, (iii) organizational resources, (iv) belief that innovation is important and should be supported and rewarded, (v) willingness to take risks and (vi) willingness to exchange ideas. Of the list of six determinants, it would appear that the government through tax and other financial incentives would be able to provide incentives for firms to increase the organizational resources available for innovation. The other five determinants would depend much on the leadership and the prevailing culture within the firm, as well as the external environment -- to the extent that the external environment affects the internal culture or values of the firms.

# _The external environment for innovation and creativity_

Baumol (2002) lists the following as amongst the most important business conditions for technological innovation: (i) oligopolistic competition among large, high-tech business firms, with _innovation rather than price serves as the prime competitive weapon_ ; (ii) routinization of innovation to minimize the uncertainty of the process; (iii) incentives for productive entrepreneurship rather than innovative rent seeking behaviors; (iv) the rule of law; and (v) markets for technology selling and trading. As the above are all features of a capitalist or free market economy, Baumol argues for the free enterprise economy as the best system to foster innovations.

Baumol’s analysis is undoubtedly US-centric and takes for granted the existence of laws, institutions, market size and human capital that serve to facilitate innovation. Other conditions would include (i) adequate human capital (ii) society’s tolerance for learning through failure; (iii) bankruptcy laws which do not excessively penalize business failures; (v) ease of communications and travel for the dissemination of ideas; and (iv) domestic markets that are sufficiently large to support oligopolistic product competition. Moreover, as technological innovation depends on spillovers facilitated by networks of firms and individuals, the existence of a sufficiently large localized industrial cluster is a necessary condition for specific types of R&D activities.

40
- Pallais, A. and E. G. Sands (2016). Why the referential treatment? evidence from field experiments on referrals. _Journal of Political Economy 124_ (6), 1793–1828.

- Spinnewijn, J. (2015). Unemployed but optimistic: optimal insurance design with biased beliefs. _Journal of the European Economic Association 13_ (1), 130–167.

- Stanton, C. and C. Thomas (2015). Landing the first job: The value of intermediaries in online hiring. _The Review of Economic Studies_ , rdv042.

- Tervi ̈o, M. (2009). Superstars and mediocrities: Market failure in the discovery of talent. _The Review of Economic Studies 76_ (2), 829–850.

25
accepted draw) and a covariance matrix Σ, which is calculated as the inverse Hessian at the mode. The second component is the likelihood function, which must be evaluated before the MH algorithm starts running.

We apply the Kalman filter to the linear state-space form, which includes a state equation describing the evolution of states based on their past values and a measurement equation showing how states are related to data, to get the likelihood.

for t=1,...,n where Σ<sup>_s_</sup> _t_<sup>andΣ</sup><sup>_m_</sup> _t_<sup>arecovariancematricesofstateandmeasurementshocks</sup> _εt,s_ and _εt,m_ . Kalman filter simply operates on two steps: It first makes a prediction about a new state **st** based on the state transition equation and then corrects for it when new data **yt** become available. If **yt** are missing, Kalman filter still guesses **st** using the prediction step. Then, it predicts missing **yt** using the measurement equation. This feature of the Kalman filter makes adding mixed frequency or missing observations possible. For example, we have energy balances available at the annual frequency. We simply feed what we have to the Kalman filter and leave missing quarterly observation as NaN. Similarly, public/private investment data are not published after 2016 Q2. We feed only available data to the filter and leave the remaining values as NaN. Kalman filter fills these missing observations by using the best available information.

We use the software Dynare 5.3 on Matlab to simulate posterior distribution of 85 model parameters. We run two chains of MH algorithms with 50,000 iterations each, and burn in the first half of the accepted draws. We choose the scaling parameter of the proposal distribution such that the acceptance ratio is around 0.25.

# **B.5 Prior distributions**

Tables B4 and B5 report the prior and posterior distributions of all estimated model parameters. We generally choose loose priors to let the data speak as freely as possible. We take the means of prior distributions of household preference parameters from Smets and Wouters (2007) and Christoffel et al. (2008). The elasticity of substitution between highand low-skilled labor is centered at 1 _._ 5 with a standard deviation of 0 _._ 5 (Cantore et al. 2017).

The prior distributions of the domestic Armington elasticity (between domestic and foreign goods) and the corresponding elasticity of the RW are taken from Albonico et al. (2019). The mean of the prior distribution of the elasticity of substitution between core and energy consumption goods comes from the posterior mean of oil and core consumption elasticity in An et al. (2011). The mean elasticity of substitution between value-added and energy is 0 _._ 5 – a common value in the literature reported by Van der Werf (2008). The standard deviation is 0 _._ 1.

67
# **3.1 Globalization Dynamics**

I start by estimating the globalization dynamics at the sector level. Figure (2) plots the results for 1965-2021. Note that these dynamics are obtained with different estimations by sector and period.<sup>7</sup> It shows that globalization started earlier for manufacturing, with positive dynamics already in the 1960s. Agriculture started its globalization process in the early 1980s, and services only took off in the second half of the 1990s. From figure (2) (a), we see that by 2015, international trade in manufacturing grew 314% ([exp(1.42) _−_ 1] _∗_ 100%) with respect to domestic trade since the year 1965. This growth is equivalent to a tariff reduction of 29.97% ([exp(1.42/(1 _−_ 5)) _−_ 1] _∗_ 100%), as shown in figure (2) (b), when taking a value for the elasticity of substitution for (1) from the literature, _σ_<sup>_k_</sup> = 5.<sup>8</sup> Note that the 316% growth of international trade with respect to domestic trade during 1965-2015 results from an average annual growth rate of 2.88% over 50 years or an annual equivalent tariff reduction of 0.71%.

During the same period, agriculture grew 172%, or had an equivalent tariff reduction of 22.15%. While services grew 63.3%, or had a tariff equivalent reduction of 11.5%. Note that services display a positive trend after the mid-1990s, but agriculture and manufacturing show a positive trend before the 1990, with the trend being very similar for the periods 1965-1990 and 1990-2015. These similar trends show that using the long WIOD data until 1990 and EORA after that year (as explained in the data section) allows for a smooth transition between datasets and consistently estimated dynamics for 1996-2015. Also, note that there was a stagnation period in the dynamics of agricultural products in the late 1980s. Then in the early 1990s, we observed a decline and quick recovery in this sector’s globalization dynamics and subsequent continuation of the trend observed since the late 1970s. The services dynamic started improving around 1990 and accelerated in the 2000s, showing how recent globalization is in this sector compared with manufacturing and agriculture.

The Global Financial Crisis in 2007-2008 interrupted the positive globalization dynamics during the Great Trade Collapse. This event is captured in figure (2a), providing a precise magnitude of around 13% decrease in trade of all sectors between 2008 and 2009

> 7The dynamics for the manufacturing sector during 1965-1990 and 1990-2015 are obtained in different estimations since different datasets are used. 8The interested reader may refer to Felbermayr et al. (2015) for a discussion of the sensitivity of the results in counterfactual gravity analysis to the choice of the elasticity of substitution.

11
# **1 Introduction**

Migration is often a temporary phenomenon, either by choice or because permanent stays in destination countries are legally prohibited. In fact, temporary migration is the rule rather than the exception in many of the major migrant-sending countries across the globe (Dustmann and G ̈orlach 2016). This is the case of low-skilled migration from South Asia: migrants from the region go to work in the Gulf countries and Southeast Asia on the basis of fixed term employment contracts and have to return home when employment overseas ends.

A growing literature in contexts other than South Asia shows that temporary migration overseas impacts migrants’ labor market outcomes after return.<sup>1</sup> It has been reported that return migrants bring financial and human capital back home, which allows them to start up self-employment activities and gain access to higher-paying wage employment (Dustmann and Kirchkamp 2002; Piracha and Vadean 2010; Reinhold and Thom 2013; Wahba 2015; Bossavie, Gorlach, et al. 2021). On the other hand, temporary migration may be negatively associated with labor market outcomes after return: leaving the home country for several years may result in a loss of social networks that have been shown to be critical in accessing better employment. In addition, skills that are demanded and rewarded in the home country labor market may differ from those required for work overseas, which can generate job search frictions upon return (Coniglio and Brzozowski 2018). Therefore, the association between past migration overseas and labor market outcomes back in the home country is a priori ambiguous.

South Asian countries are among the top senders of international migrants globally, and emigration from the region is largely temporary. Although evidence on the labor market outcomes of return migrants has been growing in recent years, it remains very scarce for South Asia.<sup>2</sup> Temporary migration from South Asian countries differs from other contexts studied by the return migration literature in several respects. First, low-skilled workers from South Asia can only migrate on the basis of temporary contractual arrangements in the main destinations, primarily the Gulf countries and Southeast Asia, and have to return once their contract expires. Second, South Asian migrants are employed in very low-skilled brawnbased occupations at destination, which could limit human capital accumulation overseas as evidenced in other contexts, and reduce incentives to take up similar work after returning home. Third, the upfront monetary costs of migration from the region are very large relative to wages both at home and at destination (Ratha and Seshan 2018), which can reduce the

> 1For a recent review of the literature on this question, see Bossavie and Ozden (2022).

> 2To the best of our knowledge, the only exceptions are Arif (1998) and Ilahi (1999) who use data from the 1980s and 1990s in Pakistan, and more recently Bossavie, Gorlach, et al. (2021) and Bossavie, Gorlach, et al. (2022) in Bangladesh.

2
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Table 1: Descriptive statistics for women.

|Variable|Defnition|Mean|St. dev.|
|---|---|---|---|
|_Mine variables_||||
|distance|Distance to closest active or inactive mine (km).|246.4|211.0|
|distance to active|Distance to closest active mine (km).|363.6|247.2|
|active (20 km)|At least one active mine < 20 km.|0.016|0.125|
|inactive (20 km)|At least 1 inactive mine < 20 km, no active/suspended.|0.005|0.067|
|suspended (20 km)|At least one suspended mine < 20 km, no active.|0.013|0.113|
|_Main dependent variables_||||
|Working|1 if respondent is currently working.|0.659|0.474|
|Services|1 if respondent is working in the service sector.|0.036|0.187|
|Profess.|1 if respondent is a professional.|0.027|0.161|
|Sales|1 if respondent is working with sales.|0.168|0.374|
|Agric. (self)|1 if respondent is self-employed in agriculture.|0.276|0.447|
|Agric. (emp)|1 if respondent is employed in agriculture.|0.054|0.023|
|Domestic|1 if respondent is employed as a domestic worker.|0.010|0.101|
|Clerical|1 if respondent is employed as a clerk.|0.010|0.097|
|Skilled manual|1 if respondent is employed in skilled manual labor.|0.046|0.209|
|Unskilled manual|1 if respondent is employed in unskilled manual labor.|0.030|0.172|
|_Other dependent variables_||||
|Cash|1 if respondent is paid in cash.|0.462|0.499|
|Cash & Kind|1 if respondent is paid both in cash and in kind.|0.167|0.373|
|Kind|1 if respondent is paid in kind.|0.083|0.275|
|Not paid|1 if respondent is not paid.|0.289|0.453|
|Seasonally|1 if respondent is working seasonally.|0.320|0.467|
|All year|1 if respondent is working all year.|0.569|0.495|
|Occasionally|1 if respondent is working occasionally.|0.111|0.314|
|_Control variables_||||
|urban|1 if respondent is living in an urban area.|0.327|0.469|
|age|Age in years.|28.400|9.560|
|schoolyears|Years of education.|4.200|4.344|
|christian|1 if respondent is Christian.|0.591|0.492|
|muslim|1 if respondent is Muslim.|0.338|0.473|
|_Migration_||||
|non mover|1 if respondent always lived in the same place.|0.457|0.498|
|_Marital status_||||
|partner|1 if respondent has a partner.|0.671|0.470|
|N||512,922||

5