rafmacalaba/data-use-mentions-extended huggingface dataset:
-23-

# United States

The 1977 Census of Manufactures is held every five years as in Brazil and Mexico, but the dates are different. The nearest census to 1975 is 1977. The Annual Survey of Manufactures 1975-76 (ASM) can be used to retropolate to 1975 for output and input values and for employment, but it contains no information on the quantity of output. In order to link 1975 and 1977 in quantitative terms, we were advised to use the detailed indices of shipments in constant 1972 dollars which are contained in the 1982 US Industrial Outlook for 200 Industries with ProJections for 1986 (US Dept. of Commerce, 1982, pp. 431-8), rather than the detailed releases of the Federal Reserve Bank, which is responsible for the monthly index of industrial production.

The 1977 Census of Manufactures presents cost information only for inputs which are directly related to the production process as well as fuel and energy consumption and contract work. It does not provide information on the cost of most purchased services, as is done in Brazil and Mexico. As a result, the census definition of value added is bigger than that in the National Accounts. This is indicated in the introductory notes to the General Summary volume of the Census reports (pp. XXV-XXVII of the 1977 Census), which gives a very rough reconciliation of the census information and the national accounts. The treatment there is rather perfunctory, given the wealth of statistical information at the disposal at the US Dept. of Commerce and the existence of a very detailed (537 industry) input-output table for 1977 (see below).

The Bureau of Economic Analysis (BEA) was kind enough to provide detail by major branch which permitted a somewhat better reconciliation for 1975 than in the published sources. This information was used in table 2.6 to adjust the national accounts figures for the 21 branches to eliminate the impact of their inventory valuation adjustment' and the impact of indirect taxes and subsidies. After eliminating these items in column 5, we arrive at the
|0.208|0.181|0.153|
|Δ Individual controls|YES|YES|YES|YES|YES|YES|
|Dependent variable|||||||
|mean|0.158|0.158|0.185|0.185|0.112|0.112|

Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1 _Notes._ Each cell represents a coefficient estimate using OLS regression using a first difference model. The analysis is restricted to working age individuals in the survey years. Columns (1) and (2) report results on the full sample. Columns (3) and (4) report results on the sample of men. Columns (5) and (6) report results on the sample of women. In Panel A, the dependent variables in columns (1), (3) and (5) are the first difference in dummy variable indicators for participating in the labor force in the years 2012 and 2006, according to the market definition. The dependent variables in columns (2), (4) and (6) are the first difference in dummy variable indicators for participating in the labor force in the years 2012 and 2006, according to the extended definition. In Panel B, the dependent variables in columns (1), (3) and (5) are the first difference in dummy variable indicators for being employed in the years 2012 and 2006, according to the market definition of labor force participation. The dependent variables in columns (2), (4) and (6) are the first difference in dummy variable indicators for being employed in the years 2012 and 2006, according to the extended definition of labor force participation. The independent variable of interest Internet job search is the first difference in dummy variable indicators for Internet use for job search between the years 2018 and 2012
student achievement to individual, family, school, and institutional inputs. We then proceeded to decompose the over-time test score change into an explained component (accounting for student, family, school and institutional characteristics) and an ―unexplained‖ component (the efficiency by which the country is able to convert characteristics into student learning outcomes as measured by test scores), using the traditional Oaxaca-Blinder decomposition method (Oaxaca 1973; Blinder 1973).

The model specification that we used to estimate the production function for cognitive achievement is as follows: where _Tiaj_ is the observed test score (from the PISA math test) of student _i_ in household _j_ at time _a_ (the time of the test), _Aija_ is a vector of individual student characteristics, _Fija_ is a vector of parent inputs, _Sija_ is a vector of school-related inputs, _Iija_ is a vector of the school’s institutional characteristics, and _єija_ is an additive error, which includes all the omitted variables including those that relate to the history of past inputs, endowed mental capacity, and measurement error. Todd and Wolpin (2003) discuss in detail the assumptions that would satisfy the application of this specification, in which the achievement test score depends solely on contemporaneous measures of family, school, and other inputs. These assumptions state that: (i) current input measures capture the entire history of inputs or, alternatively, only contemporaneous inputs matter and (ii) contemporaneous inputs are unrelated to endowed mental capacity. Its linear specification (after dropping subscript _a_ ) is given by: 4
outcomes in the schools.<sup>85</sup> Finally, Awortwi and Nuvunga (2019) investigate the role that information disclosure regimes in the extractives sector—such as the Extractives Industry Transparency Initiative (EITI)—can play in improving accountability. Surveying 17 different empirical factors, the authors find that unless there is a risk of a ruling party losing power, information disclosures are unlikely to make governments more accountable on their own.<sup>86</sup> Relatedly, there is a relatively large literature that supports the proposition that the users of OGD—for instance the press, academia, and civil society—can use this information to hold governments to account. For instance, Mungiu-Pippidi (2014) investigates—among other variables—press freedom and corruption. Using a large-n cross-country analysis, as well as many of the same corruption indexes used by this present paper, the author finds a robust correlation between the variables.<sup>87</sup> Similarly, Ferraz and Finan (2008) find that in Brazil, the proactive disclosure of audit outcomes significantly impacted electoral performance of incumbents, particularly when corrupt practices were uncovered. Interestingly, these effects seem to be amplified in places where public radio was active in transmitting this information, which suggests the importance of the media in perpetuating the accountability-enhancing effects of OGD.<sup>88</sup> Similar results were obtained in Mexico by Larreguy, Marshall, and Snyder (2015), who studied the performance of infrastructure grants that targeted the poor. The authors found that when provided information on the use and outcomes of these funds, voters tend to punish poorly performing politicians, but only in areas covered by local media—again, suggesting the key role that the media can play in disseminating information to hold government to account.<sup>89</sup> In addition to investigating the role of OGD, the present research also seeks to build on previous efforts (Joshi 2013) investigating the efficacy of transparency and
China‘s incredible export surge in the last 30 years. China overtook the developed country average in the mid-1990s, and surpassed the developing country average around the same time that it entered the WTO, at the end of 2001.

**Figure 1 Export Shares in Developing and High-Income Countries**

<!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>High income Countries<br>15 Developing Countries<br>China<br>10<br>5<br>0<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br>Exports of good and services (% of GDP)<br><!-- End of picture text --> Source: World Development Indicators It is likely that this increasing globalization is one factor that can be used to explain the high growth rates that developing countries exhibited over the last decade. During the 2000s, developing countries grew on average at 5.6 percent and in 2010, developing countries are estimated to grow at 7.0 percent (World Bank (2011)). In comparison, developed countries grew at only 1.3 percent in the previous decade and in 2010 are estimated to grow at 2.8 percent (see Figure 2a). However, these high growth rates in developing countries mask the vastly different growth patterns of individual economies over the last several decades.

4
Figure 10. Percentage of People in Poor Households per Age Group before and after Pension Transfers.

2006 2016 Source: author elaboration based on ENEMDU In 2016, the incidence of poverty is significantly lower (15.6%) to that recorded throughout 2006 as observed for all age groups. Just like in 2006, income transfers coming from the pension system play a key role for reducing it, since without these programs, the percentage of individuals living in poor households would have climbed up to 18.2%. However, two different phenomena arise as compared to year 2006. On the one hand, the impact of transfers on poverty incidence is higher given that the reduction in theory is equal to 17%, while in 2006 it was just 6%. On the other, the importance of social pensions in these reductions increased significantly. That is the result of an increase in the benefit amount given. In this sense, while in 2006 the social pension was USD11.5, accounting for 20% of the poverty income threshold value, in 2016 this transfer was increased to USD50 up to 60% of the poverty income threshold value.

With respect to the distributive impact of the pension system, Figure 11 shows the Lorenz curve of total household income before and after transfers for years 2006 and 2016. The results are less encouraging from the distributive perspective. The Gini ratio in Ecuador is 0.41 in 2016 (similar to the one observed in 2006, 0.47). However, in a scenario that does not take into account the contributory pensions and/or social pensions, this indicator is not affected in any way. In other words, given the value of the benefits paid by the Ecuadorian pension system, both contributory as well as non-contributory, they do not affect income distribution.

18
days from tomorrow (“later”).

More precisely, each participant was presented with a small bowl containing 20 beans (tokens) and two empty dishes, dish _A_ and dish _B_ . One token allocated to dish _A_ corresponded to 100MK tomorrow. One token allocated to dish _B_ corresponded to100 _MK_  1(  _r_ ) 30 days from tomorrow, where _r_ is the rate of return for waiting 30 days. The rate of return took on five different values: 0.10, 0.25, 0.50, 0.75, and 1.00. The rates of return rose, in order, with each of

> 22 Based on authors’ calculations from the 2004 Malawi Integrated Household Survey (IHS), individuals in tobacco farming rural households in central Malawi live on PPP$1.48/day on average, while the average for central Malawian rural households overall is PPP$1.51/day.

> 23 At the time of the study, the relevant exchange rate was roughly 150 MK per US dollar.

10
)_|0.000|0.000|0.015<br>0.001|
|_AR(2)_|0.281|0.238|0.330<br>0.527|
|_Hansen_|0.072|0.198|0.477<br>0.386|
|_p_-values in parentheses<br>||||
|* _p_ <0.05,** _p_ <0.01,*** _p_ <|0.001|||

Export market concentration, measured by the Herfindahl-Hirschman index of exports from country _i_ to its trading partners ( _HHIM_ ), has a positive and significant coefficient in all specifications reported in Table 2. The coefficient estimate of _HHIM_ fluctuates between 0.006 to 0.020. A positive relationship between growth per worker and _HHIM_ can be rationalized as a country having fewer trading partners whose economic activities are highly synchronized (in the medium term). On the other hand, export product concentration, measured by the Herfindahl-Hirschman index of export products ( _HHIP_ ), has the expected negative sign and it has a robust relationship with growth per worker. The estimated coefficient of _HHIP_ ranges from -0.019 to -0.038, and this coefficient decreases in size as more control 12
there has been an increasing demand for Indian engineers in the software and information technology-enabled service (ITES) sectors that are provide services in English to customers in the United States and the United Kingdom (Ferrari and Dhingra, 2009). A one-way analysis of variance (ANOVA) was conducted to test whether the mean importance scores of Communication in English are equal between firm sizes, firms with/without foreign capital, and firms in difference economic sectors. Large firms consider Communication in English more important than medium and small firms. Firms with foreign capital have higher importance mean scores than those without foreign capital. The IT sector has relatively higher scores than other sectors. However, these results are not statistically significant. The results of analysis are presented in Table A2:4 - 6 of Annex 2.<sup>8</sup> A similar employer survey undertaken in the US in 2004 equally asked for the importance level of skills of engineering bachelor graduates (Lattuca, Terenzini, Volkwein, 2006 study for ABET). The formulation of the skills is very similar if not identical for 10 _Professional Skills_ .<sup>9</sup> The two surveys allow us to examine whether US employers demands the same skills as the Indian employers. In particular, we focus on whether the employers share the same prioritization of skills; i.e. is the ranking of the importance similar among the employers of the two countries? Table 5 ranks the importance of skills in each country based upon the percentage of employers responding ―Very or Extremely Important‖ (the two highest responses on a five point scale). The picture is mixed. In general, employers in both countries rank team-work, applying math/science/engineering knowledge and communication skills high, while raking skills related to knowledge of contemporary issues, system design, and design of experiments low. However, some skills are ranked differently, such as lifelong learning (valued by Indian employers and less so by US employers). We cannot test whether the rankings
well as their college, degree, and major. We also observe a rich set of background characteristics, including parental income and education, as well as individual scores in the mandatory national high school exit exam, Saber 11, which measures academic readiness for higher education (or ‘ability,’ for short.). Importantly, we observe the location of an individual’s high school, college, and work sites.

Existing evidence for developed countries shows an important role for the pre-labor market sorting of more educated and skilled individuals into big cities. Ahlin, Andersson, and Thulin (2017) find that individuals with higher grades in high school and more educated parents sort into bigger cities in Sweden, largely as a result of attending college there.<sup>1</sup> Winters (2011) finds that us cities with a high share of college graduates tend to attract more undergraduates who end up developing local human capital and remaining there after graduation. In the closest study to ours, Suhonen (2013) shows that the positive earnings gap between Helsinki and smaller cities in Finland among young college graduates can be fully explained by students’ pre-university characteristics such as high school grades and parental education. Meanwhile, Bosquet and Overman (2019) show the significant role of birthplace on adult wages in the uk, which accounts for 4.2% or two thirds of the 6.8% raw elasticity of wages with respect to city size. They conclude that lifetime immobility explains much of the correlation between birthplace and current city size: in their sample of Britons, 43.7% of workers live in the same place where they were born.

> 1Ahlin, Andersson, and Thulin (2017) focus on urban vs. rural differences in Sweden and do not investigate the consequences of such sorting on wages.

1
crazy and that I am old. They said I threw myself on the floor on purpose.” Rude, demeaning treatment and long service times left women feeling humiliated and frustrated.

Women who sought help in women’s police stations (DEAMs) generally had more favorable experiences, except for one stand-out case. In most cases, women reported that police in the DEAMs attended them and listened calmly and explained the ways they could continue the process. Negative experiences of women seeking justice services serve as powerful reminders of the need for consistent and repetitive training of police personnel, even within specialized services. For example, one woman recounted an exchange with the officer in the DEAM in Nova Iguaçú: “He said he would kill me, and I went [to the DEAM]. When I arrived at the police station willing to report there was a policeman who said, ‘Does he drink?’ I said, ‘He does.’ ‘Does he have a firearm?’ ‘No.’ ‘Ma’am, can’t you see that this man needs help? This man needs to be forwarded to Alcoholics Anonymous, not that you file a report against him.’ This was in the women’s police station! I was already humiliated, I felt even more humiliated and left without reporting the case. I never returned but it is still something that stays with me because I think the women’s police station should only have women – it shouldn’t have men.” Data from Brazil show public perception of the effectiveness of Maria da Penha Act in protecting women against domestic and family violence: 26 percent believe the law protects women, 56 percent that the law protects only partially, and 20 percent thought that the law did not protect women (DataSenado 2017). Among women who said they had not suffered violence, only 17 percent said that MdP did not protect women; however, among those who reported having been survivors of some form of domestic or family violence, the
including three MNOs – Airtel, Africell, and Uganda Telecom through M-Sente – and a few non-MNO mobile payment providers, such as M-Cash, Ezee Money, and Micro-pay.

Via a network of mobile money agents, Uganda’s mobile money platforms offer a variety of services including cash withdrawals, cash deposits, purchase of airtime, sending and receiving money, bill payment (such as school fees and utilities), and payment for goods and services.

In 2017, there were 147,146 mobile money agents in Uganda, serving 22.9 million registered customers (56 percent of the population) who made 1,111 million transactions worth 52.8 trillion Ugandan shillings (about 14.6 billion USD) in one year (Bank of Uganda 2017). Less than 3 percent of agents were in Northern Uganda, the majority of which (76 percent) were MTN agents.

For this study, IFC collaborated with Airtel to roll out agents in Northern Uganda in locations not yet served by Airtel Money. Airtel Money was launched in January 2012, and despite its ambition to increase the delivery of financial services to the Ugandan population, growth in Northern Uganda has been slow. Administrative data shows that most existing Airtel Money agents in Northern Uganda had low profitability. IFC has had an interest in increasing Airtel’s mobile money market share by improving and strengthening its operations for two reasons: (i) to increase outreach into remote communities and (ii) to foster competition, avoiding a monopolistic environment to improve pricing, sharing of agents and the potential for interoperability of mobile money providers.

# **3. Study Design and Agent Rollout**

# **3.1 Study Design**

The Uganda Bureau of Statistics (UBOS) helped define the study sample. The starting sample included all rural enumeration areas (EAs) in all sub-counties in the 6
**|NS|S***|
|**Ghana**|S*|S**|NS|NS|
|**Kenya**|S***|S***|NS|NS|
|**Serbia**|NS|NS|NS|NS|
|**Ukraine**|NS|NS|S**|S***|
|**Vietnam**|S*|NS|S***|S***|

Note: These are based on the full model results using schooling splines as a measure of education. The complete results are contained in Appendix Table 4. Results for all countries pool the samples of Armenia, Bolivia, Colombia, Georgia, Ghana, Kenya, Ukraine and Vietnam and are estimated with country fixed effects. No data on time preference for Colombia; no data on hostile attribution bias, risk-taking and time preference for Serbia. Statistical significance is denoted as * p < 0.10, ** p < 0.05, *** p < 0.01. Data sources: STEP data for 9 countries, 2012-13 63 In an effort to understand the interrelationships among individual noncognitive skills and to find a more parsimonious measure of this aspect of human capital, we estimated a principal components model for noncognitive skills using the pooled sample of countries and repeated the analysis for individual countries. We find no individual noncognitive skill that explains a large enough share of the variation in the log-earnings of either men or women to justify a principal components approach, so we continue to include the full list of noncognitive skills in addition to schooling and cognitive skills as a reduced-form specification of human capital. Our results indicated the variables of nine distinct noncognitive skills were characterized more by “uniqueness” rather than being correlated. We also implemented the approach developed by Sarzosa and Urzua (2015); see footnote 16.

47
000 Bangladeshis that chose to go to India for treatment in 1999 went without the right visas: in most cases, patients chose touri **s** t visas to avoid the more arduous medical visa process.

> 32

> Another new Apollo Group hospital is being established in Chittagong.

> 33

> See Appendix 2 for explanation of the GTAP model. This study uses the Version 7 database, with 2004 as base. This has been adjusted with some pre-simulations to reflect the base year as close to 2010 as possible.

> 34 See Appendices 3 and 4.

13
# **3 SERVICE EXPORT SOPHISTICATION**

## **3.1 The Service Export Sophistication Methodology**

We build upon the Hausmann, Hwang and Rodrik framework, which uses a goods export sophistication measure _Goods EXPY_ as a proxy for the most productive set of goods the country can produce at a given time. They argue that export data is the best way to reveal this production frontier as we can expect countries to export those goods in which it is most productive. Along the same reasoning and as described below we will construct a _Service EXPY_ to proxy the service production frontier of a country, by and large following the methodology developed by HHR.

We start by constructing so-called _PRODY_ ‘s for each category of goods exports, reflecting the income/productivity level associated with each good, and we do the same for each category of services. _PRODYj_ is the income value associated with the service _j_ , and is constructed by using the service export ( _x_ ) share of a country _i_ in world‘s export of service _j_ , divided by the sum of shares of _j_ in world exports of _j_ across all countries exporting that service.<sup>5</sup> These ratios are multiplied by the exporting countries‘ respective per capita income level ( _Y_ ) and the result is summed up across all countries. In other words, the _PRODY_ becomes the weighted average of per capita GDPs, where the weights represent the revealed comparative advantage in service _j_ for each country.<sup>6</sup> _PRODY_ s are constructed for each service category, for each year of available data, and are by construction the same for all countries.

_EXPY_ is then the weighted income value of services exported by a country, computed as the sum of _PRODY_ s using as weights the share of the particular service in the country‘s total service export basket. _EXPY_ s are constructed for each country and for each year with available data.

The trade data come from the IMF Balance of Payments statistics