rafmacalaba/datause-encoder-data huggingface dataset:
First, to account for the potential biases arising from cohort effects, I follow the progress of individuals in a particular cohort (identified by decade) through several censuses, so that the errors due to unobserved differences among cohorts (decades) cancel out. Second, since the paper focuses on cross-country comparisons, period effects should not be of significant concern if assumed that changes in labor market conditions affect all immigrants in the same way irrespective of where they come from. Provided that were not so, because one of the three variables used to proxy occupational placement is constant over time, the impact of period effects could be identified by checking whether the results based on that index differed significantly from the ones based on the other variables. Part of the third source of bias, namely the mortality rate, is dealt with by imposing upper limits on the ages of individuals. The more troublesome concerns about return migration are addressed in sub-section 3.4.1. That analysis consists of comparisons of cohort sizes over time and identification of the countries with the highest rate of attrition. Re-estimating the regressions after excluding problem countries leads to results not significantly different from the main ones.

To isolate the country effects on immigrants’ performance, the strategy employed is to compare predicted occupational placement of individuals identical with respect to human capital endowments, but coming from different countries. This way, it is insured that all the variation in the compared levels of performance stems from the coefficients on country dummies, _β5_ . Thus, the aim of the 15 estimations based on equation (1) is to predict the occupational placement, _O_ ˆ _P_  _e predicted_ (ln _OP_ ) in 1980, 1990 and 2000 for hypothetical individuals with identical humancapital variables, who come from different countries, and are present in the US at the time of two or three censuses depending on when they migrated. Next, I calculate the predicted Occupational ˆ ˆ ˆ Placement Improvement  _OPi_  , _t_  ( _t_  10)  _OPi_  , _t_  10  _OPi_  , _t_ as the predicted change in the level of professional achievement of hypothetical individual _i_ from country _τ_ , between years t and t+10.

I construct two hypothetical individuals per country (Table 2), each with at least a Bachelor’s Degree earned before migrating, and having arrived at 25 years old, one in 1975, and the other in 1985.<sup>23</sup> The 1975 migrant is assumed to be captured by all three censuses, the 1985 one only by the two most recent ones. The individual arriving in 1975 is 30 years old in 1980 (therefore 40 in 1990, and 50 in 2000), while the one arriving in 1985 is 30 years old in 1990 (and 40 in 2000).

> 23 Because variable “years since migration” is identified by half decades, the 1975 and 1985 arrivals refer to intervals 1975-1979 and 1985-1989 respectively.

14
26 These water transfers initiated by large farmers using mostly their own capital (some farmers have also obtained bank loans) also benefits smaller farms in the vicinity through periodic water markets (i.e., during water scarce time) operating under two arrangements: one involving the sale of water at Rs. 20/hour and the other involving a somewhat a long-term arrangement. Under the latter arrangement the buyer has to deposit a refundable amount of Rs. 10,000/acre with the seller who, in turn, supplies water in lieu of interest payment (since the monthly interest in the village varies from 2 to 10 per cent, water payment amounts to Rs. 2400 to 1200/acre/year. This higher amount is not a problem as the buyer also grows high value crops like banana.) Notably, since these water marketing activities occur essentially in scarce water periods, they remain latent in periods with better rainfall and water supply conditions.

# **_Transferable Water Entitlements In Australia_**

Transferable water entitlements were introduced in several Australian states--in New South Wales, and South Australia in the early 1980s, and in the late 1980s in Queensland and Victoria. Data in Pigram et al. (1992) suggests a positive trend in quantities of water trade in all states over the period 1987-1988. The volumes transferred accounted for 5- 10% of the water use in these states. It should be emphasized that most water transfers were not permanent, but seasonal; of all transaction, 620 were of a temporary nature and 46 were of a permanent nature. Total volume transferred temporarily was 137.3 million **m**<sup>**3**</sup> **,** and total volume transferred permanently was 19.7 million **m**<sup>**3**</sup> **.** The data for the New South Wales state, which exists between 1984 and 1991 is summarized in Table 1. From the data it can be seen that annual transfers vary, although the trend is increasing, and the average unit value of water (in nominal Australian $) vary between I cent and 13 cents.

The accumulated experience with the transfer of water among users (mainly water agencies providing water to agricultural farmers) both over time and across regions can shed light on relative importance of potential problems associated with water transfers. Pigram et al. (1992) surveyed the agencies involved in water transfers and the results are presented in Table 2.

The range of potential problems in table 2 may be indication of the set of benefits and concerns associated with the implementation of transferable water rights elsewhere. While some potential problems, such as capacity and water and soil salinity, are associated with the physical short years than in regular rainy years. In such events available water is transferred from staple to perennial crops.
<!-- Start of picture text --> Daughters' Primary Completion Estimates by Birth Year<br><!-- End of picture text --> <!-- Start of picture text --> Intercept Linear Coefficient<br>6 .02<br>4 o-—- -e- -- CCC — ooo<br>><br>2 -.02 T<br>><br>a<br>04<br>><br>-.2<br>-.06<br>59 60 61 62 68 #69 #70 71 72 59 60 61 62 68 69 70 71 72<br>Birth Years<br><!-- End of picture text --> Omitted Birth Year 1957-58 <!-- Start of picture text --> © Comparison Cohort @ Fullly Exposed Cohort<br><!-- End of picture text -->
revive, in collaboration with other partners, its support for building research capacity in developing countries and sub-regions where such capacity is still weak or non-existent.

Working with development partners, the Bank needs to give greater weight to their views on priority areas for research, and then shape its own programs to enable good proposals to benefit from a global comparative perspective and methodology that facilitates cross-country learning. This is the pattern, for example, in the ongoing study on Peace and Development, a multi-disciplinary study that brings together case material from some 20 countries. A recent research initiative with the Economic Research Forum develops several thematic studies of mutual interest to countries in the Middle East and North Africa, such as regional economic integration, equity and inequality, and the management of natural resources and economic diversification, which can also benefit from a more global perspective.

The Bank needs to find ways of engaging more actively in researcher exchanges with developing regions. We need to use programs such as the Visiting Research Fellows Program to increase the diversity of our research engagements. Also important are new opportunities for research staff to be able to spend some longer periods in the field, whether as part of exchanges with other research institutions or through well-designed development assignments to address particular issues of importance to both operations and the research program.

The assets of researchers in developing countries make them vital partners. Their knowledge of development challenges at local levels complements the that of the Bank’s researchers. This is especially important in addressing political economy issues. Researchers in developing countries also can play a vital role in the task of identifying, collecting, and using data, such as the research department’s LSMS household datasets and enterprise survey datasets, which appear to be under-utilized by developing country researchers and policy makers. Importantly, researchers in developing countries are on the “front line” in identifying key development challenges and are uniquely placed to translate new research findings into effective policies in a local context.

Following our comparative advantage in access to experiences across countries in the world, the production of high quality, rigorous empirical research (including development data and analytical tools) and dissemination of the findings in an accessible and impartial form is 34
Table 3: **Land rights, land distribution, and land rental in** the sample

||Total|**Prov**<br>Guizhou|**ince**<br>Hunan|Yunnan|
|---|---|---|---|---|
|**Villagelevel**|||||
|Noactiontakenonpopulationincrease|73%|93%|43%|48%|
|Noactiontakenonpopulationdecrease|68%|86%|43%|43%|
|Landrentalallowed (villageleaders)|64%|68%|79%|39%|
|Share withcertificate|92%|98%|69%|98%|
|Landrentalallowed (individuallevel)|90%|87%|95%|91%|
|**Households'preferred**land**policy**|||||
|"Two nos"policy|41%|45%|28%|43%|
|Largeadjustmentandthen"twonos"|21%|23%|14%|21%|
|Continuingsmalladjustments|29%|24%|44%|28%|
|Continuinglarge scaleadjustments|10%|7%|14%|13%|
|SourceOwncomputationfromSSB/CCER/WB2|001HouseholdSurvey||||

27
Berkowitz, Daniel, Chen Lin, and Yue Ma, 2015, Do property rights matter? Evidence from a property law enactment, _Journal of Financial Economics_ 116, 583-593.

Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan, 2004, How much should we trust differences-in-differences estimates? _Quarterly journal of economics_ 119, 249-275.

<mark>Besley, Timothy. 1995. "Property rights and investment incentives: Theory and evidence from Ghana."</mark> _<mark>Journal of Political Economy</mark>_ <mark>103(5), 903-937</mark> _<mark>.</mark>_ Besley, T. J., Ghatak, M. 2010. “Property Rights and Economic Development.” In Handbook of Development Economics vol. 5, p. 4525-95.

Bliss, M.A., Gul, F.A., 2012. Political connection and cost of debt: some Malaysian evidence. J. Bank. Finance 36 (5), 1520–1527.

Boubakri, N., Cossett, J.C., Saffar, W., 2008. Political connection of newly privatized firms. _J. Corp. Finance_ 14 (5), 654–673.

Boubakri, N., Guedhami, O., Mishra, D., Saffar, W., 2012. Political connections and the cost of equity capital. _J. Corp. Finance_ 18 (3), 541–549.

Brandt, L., T. Tombe, X. Zhu. 2013. Factor market distortions across time, space and sectors in China. _Review of Econ. Dynamics_ 16(1), 39-58.

Burt, Ronald. 1998. “The Gender of Social Capital.” _Rationality and Society_ 10(1), 5-46.

Cai, Hongbin, Hanming Fang, Lixin Colin Xu. 2011. “Eat, Drink, Firms, Government: An Investigation of Corruption from Entertainment and Travel Costs of Chinese Firms.” _Journal of Law and Economics_ 54, 55-78 Calomiris, Charles W., Raymond Fisman, and Yongxiang Wang, 2010, Profiting from government stakes in a command economy: Evidence from Chinese asset sales, _Journal of Financial Economics_ 96, 399-412.

Caro, Robert. _The Power Broker: Robert Moses and the Fall of New York._ New York: Knopf.

Caves, Douglas, Christensen, Laurits, Diewert, Erwin, 1982. Output, Input, and Productivity Using Superlative Index Numbers. _Economic Journal_ 92, 73–96.

Chan, K.S., Dang, V.Q.T., Yan, I.K.M., 2012. Chinese firms' political connection, ownership, and financing constraints. _Econ. Lett_ . 115 (2), 164–167.

Chen, Ting, James KS Kung. 2018. “Busting the `Princelings’: The campaign against corruption in China’s primary land market.” _Quarterly Journal of Economics_ 133, 185-226.

Chen Charles, Li Zengquan, Su Xijia, and Sun Zheng, 2011. Rent-seeking incentives, corporate political connections, and the control structure of private firms: Chinese evidence. _J. Corp. Fin_ . 17 (2), 229–243.

Claessens, S., Feijen, E., Laeven, L., 2008. Political connections and preferential access to finance: the role of campaign contribution. _J. Financ. Econ_ . 88 (3), 554–580.

Cull, Robert, and Lixin Colin Xu, 2005, Institutions, ownership, and finance: the determinants of profit reinvestment among Chinese firms, _Journal of Financial Economics_ 77, 117–146.

Cull, Robert, Wei Li, Bo Sun, Lixin Colin Xu. 2015. “Banks, the Government, and Financial Constraint: Evidence from a Large Sample of Chinese Firms,” _Journal of Corporate Finance_ 37
Figure 1 shows indexes of dissatisfaction with privatization in Latin America for the years 1998 and 2005 as reported by Latinobarómetro, an annual public opinion survey carried out in 18 countries in the region. The poll asked the public in several Latin American countries whether privatization had been beneficial for the country. As early as 1998, negative opinions about privatization averaged 40 percent, and seven years after privatization had peaked in the region, more than 60 percent of respondents in all reporting countries expressed negative views.<sup>2</sup> If we believe that the efficiency argument is so strong, why are there such high levels of negative opinion about the benefits of privatization? That is the central question of this paper.

In what follows, we first describe the trends in privatization in developing countries and the indicators used to measure the extent of privatization. Next, we briefly discuss the economic theory behind privatization. We do not attempt a comprehensive review; there are many good and exhaustive books on this topic, chief among them those by the distinguished late JeanJacques Laffont and his colleague Jean Tirole. Rather, we simply illustrate a few basic points concerning the economics of privatization. We then present empirical evidence on the main effects of privatization and, finally, draw conclusions.

# **II. Trends in Privatization in Developing Countries**

Since the 1984 privatization of British Telecom, developed and developing countries have adopted privatization programs to different extents. To date, almost every developing country has divested some SOEs to the private sector; cumulative proceeds raised by privatizations during the period 1988–2005 approach US$530 billion in constant 2000 U.S. dollars.

Considering first the two most widely used indicators, proceeds and number of deals, we see that privatization started slowly in the 1980s, with fewer than 89 transactions per year and proceeds of less than US$4.0 billion. Two decades after the privatization of British Telecom, however, proceeds approached US$56 billion per year, and the number of transactions had increased to about 200 per year. During those two decades, developing countries witnessed various waves of privatization.

> 2 These years were chosen simply because 1998 was the peak year of the second and last big wave of privatization in Latin America and 2005 is the most recent year for which information on attitudes toward privatization is available in Latinobarómetro.

2
# **1 Introduction**

Prominently positioned among the Millennium Development Goals, universal primary education has become a central tenet of the international development effort. As far back as the 1970s, the most readily available policy tool for promoting enrollment has been the removal of school fees. This type of policy was implemented in Kenya and Nigeria in the 1970s, in Zimbabwe and Tanzania in the 1980s, and in Ethiopia, Malawi, and Uganda in the 1990s. More recently, this policy has been aggressively pursued by international development organizations as a key tool in achieving the goal of universal primary education, as evidenced by over a dozen additional countries removing school fees since 2000 (Kattan and Burnett, 2004; World Bank, 2009). The accelerated proliferation of these fee-removal programs over the past few decades highlights the importance of gaining a greater understanding of the consequences of these reforms; however, a recent systematic review (Snilstveit et al., 2016) by the International Initiative for Impact Evaluation (3ie) concluded that little is known about the long-term impact of reducing school fees.

This article evaluates the returns to a nationwide free primary education (FPE) program in Ethiopia. The removal of school fees in grades one through ten is found to generate an increase in schooling. Using a two-stage least squares (2SLS) model, each additional year of schooling generated by the reform is found to reduce fertility by more than 0.4 births.

The article uses data from the Ethiopian census and from three rounds of the Demographic and Health Survey (DHS). The increase in schooling is identified by combining two dimensions of variation, the timing of the reform and geographic variation in schooling outcomes for cohorts who completed their education prior to the introduction of the reform. Motivated by the work of Bleakley (2010), Lucas (2010, 2013), and Lucas and Mbiti (2012a,b), the identification relies on the concept that although the FPE policy itself is applied uniformly across the country, the intensity of the reform in a specific location depends on the pre-existing characteristics of that area. In this setting, as proposed in Chicoine (2019), removing school fees from an area of high pre-reform educational attainment will have a small impact relative to removing the same fees in an area with a low pre-reform education level.

Investigating the mechanisms through which the increase in schooling leads to a reduction in fertility for Ethiopian women can yield an increased understanding of the household fertility decision-making process. The increase in schooling generated by the FPE reform is found to increase literacy rates, and it leads to women working in higher-quality jobs and wanting fewer children. However, as exposure to the reform increases, women are no more likely to use contraception, and there is no evidence of an increase in empowerment. The totality of these findings suggests that the decline in the ideal number of children and the associated increase in economic activity are the central mechanisms through which the increase in schooling 1
calculate the contribution of structural transformation (as proxied by the rate of net employment creation) to the change in the intersectoral productivity gap between 2014 and 2015 based on the following equation:

> S = ∑L ∆∅ +<sup>∑</sup> L ∆∅ −<sup>∑</sup> L ∆∅ −<sup>∑</sup> L ∆∅ ,  (19) NN NN NN NN NN NN NN NN rr rrN rr rrN rr rrSSr rrSSr rrN rr rrN rr rrSSr rrSSr ∆LLPP where S = contribution of structural transformation to the change in the intersectoral labor productivity gap in region rr , L r = labor productivity of -type enterprises in sector in region ∆LLPP kk , ∅r = employment share of rr -type enterprises in sector kk in region , = informal or formal,  jj kk = industry (I ) or services (S ), and ∆ = 2014 −2015. rr kk jj rr kk It is not feasible to measure the change in labor productivity at the enterprise level. jj II SS Given this, Equation 19 provides a partial picture of the total change in the intersectoral productivity gap over time (using a standard shift-share decomposition framework). Assuming own-sector productivities remain constant, that is, ∆L N = ∆L N = ∆L N = ∆L N = NN NN NN NN 0, Equation 19 approximates the contribution of structural transformation to the change in the rr rr rr rr regional intersectoral productivity gap. We calculate ∆∅N , ∆∅ , ∆∅ , and ∆∅ and NN NN NN NN labor productivities based on IBES data. rr SSr SSr SSr

Figure 12 compares the actual intersectoral productivity gap and the predicted intersectoral productivity gap calculated as the sum of the actual intersectoral productivity gap and the contribution of structural transformation to the predicted change in the intersectoral productivity gap in each region. At the national level, the contribution of structural transformation to the change in the intersectoral productivity gap is positive, increasing the gap from 111.8 thousand cedis per worker to 158.3 thousand cedis per worker. For most regions, the contribution of structural transformation to the change in the regional intersectoral productivity gap is positive. As a result, accounting for the contribution of structural transformation, the disparity in regional intersectoral productivity gaps increases. Measured in standard deviation terms, the disparity in regional intersectoral productivity gaps increases from 55.0 thousand cedis per worker to 75.6 thousand cedis per worker.

The disparity in regional intersectoral productivity gaps could be smaller if the negative own-sector effect were to outweigh the positive contribution of structural transformation. For this reason, our results here should only be taken as suggestive evidence that the disparity in regional intersectoral productivity gaps is not lowered by the differing pace of structural transformation across regions.

22
Table 2: Number of exporting firms and destination markets

|Destination|**2007**|**2008**|**2009**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|**2017**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Number of exporting firms to LAC|5,615|5,815|5,696|5,098|5,255|5,520|5,508|5,450|5,604|5,612|5,738|
|Number of exporting firms to LAC only|3,383 3|,713|3,669|3,114|3,286|3,526|3,535|3,462|3,518|3,523|3,621|
|Share of LAC firms in total firms|0.68|0.69|0.70|0.69|0.71|0.73|0.71|0.70|0.69|0.68|0.68|
|Share of LAC onlyfirms in total firms|0.41|0.44|0.45|0.42|0.44|0.46|0.46|0.45|0.43|0.43|0.43|

Source: Author’s calculation based on Colombian customs data.

Note: This table displays Colombian firm-level export dynamics to Latin America and the Caribbean (LAC), measured in absolute (number of firms) and relative terms (share in total number of exporting firms). The third and fourth rows report the number and the share of firms among those exporting to some LAC country that exports exclusively to the region.

19 HS 6-digit products in 2007 to 26 products in 2017. Third, the increase in product diversification is driven by the expansion of regional export relationships, rather than extraregional ones. Put in perspective, Colombian exporting firms show a relatively high level of product diversification; Peruvian firms, for example, only export, on average, 8.5 HS 6-digit products (Fugazza et al., 2018).

Table 3: Number of exported products per firm

||Me|an<br>|Med|ian<br>|Maxi|mum<br>|
|---|---|---|---|---|---|---|
||LAC|All|LAC|All|LAC|All|
|2007|19.5|17.4|10.0|9.0|128|131|
|2008|20.5|18.3|10.0|9.0|133|133|
|2009|22.8|20.1|11.0|10.0|180|181|
|2010|23.3|20.8|11.0|10.0|175|175|
|2011|26.0|22.9|12.0|11.0|193|195|
|2012|27.0|23.7|13.0|11.0|202|204|
|2013|29.0|25.7|13.0|11.0|207|209|
|2014|29.4|26.1|13.0|12.0|202|204|
|2015|29.6|26.0|13.0|11.0|207|208|
|2016|28.6|25.4|13.0|11.0|182|186|
|2017|26.6|23.6|12.0|11.0|153|155|

Source: Author’s calculation based on Colombian customs data. Note: This table reports the number of exported HS 6-digits products by Colombian firms over time and by LAC and world destination markets.

Colombian exporting firms are increasingly diversified in their sales markets. Table 4 shows that the average share of the sales market grew from 6.4 in 2007 to 7 in 2017. What is more, Colombian firms export to an additional five destination markets beyond the region, resulting in a total of 12 destination markets globally (columns 2 and 3).

10