rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: Returns to Education from IV: Venezuela, 2000

||(1) IV:<br>Compulsory education|(2) IV:<br>Compulsory education|(3) IV:<br>Father’s years<br>of education|
|---|---|---|---|
|Variable||||
|Years of Schooling|0.119|0.125|0.095|
||(3.0)|(2.4)|(13.0)|
|Age|0.035|0.033|0.047|
||(1.7)|(1.3)|(5.7)|
|Age<sup>2</sup>|-0.0003|-0.0003|-0.0005|
||(1.0)|(0.7)|(3.5)|
|Log hours worked|0.442|0.442|0.439|
||(14.7)|(14.4)|(15.4)|
|Years of education of head|-|0.009<br>(0.6)|-|
|Constant|8.460|8.501|8.490|
||(53.4)|(55.2)|(58.4)|
|Centered R<sup>2</sup>|0.152|0.132|0.213|
|N<br>|3,103|3,103|3,103|
|Partial R<sup>2</sup>for excluded<br>Instruments in 1<sup>st</sup>stage|0.002|0.002|0.137|
|F-test|8.3|8.3|703.2|
|[p-value]|[0.04]|[0.000]|[0.000]|
|Pagan-Hall Test for|76.6|98.53|100.5|
|Heteroskedasticity||||
|[p-value]|[0.000]|[0.000]|[0.000]|
|Heteroskedasticity robust|<br>Exactly|Exactly|Exactly|
|Over-identification|Identified|Identified|Identified|
|Statistic||||
|[p-value]||||
|Hausman Endogeneity|2.62|2.44|30.72|
|Test|[0.105]|[0.118]|[0.000]|
|[p-value]||||

Source: _Encuesta de Hogares por Muestro_ Notes: z-values in parentheses At least part of the difference in the rate of return between OLS and IV estimates using father’s education as instrument may be attributed to the bias from using family background instruments. The true estimate of the return to an additional year of schooling using father’s education as instrument may, therefore, be closer to the OLS estimate.

17
|Non-exporters<br>Table 3: Training activity in manufacturing firms by expo|Exporters<br>rt status|All firms|
|---|---|---|
|Share of workers that received training by course category|||
|Technical upgrading<br>0.006|0.021|0.006|
|(0.039)|(0.051)|(0.040)|
|Other categories<br>0.009|0.022|0.010|
|(0.050)|(0.045)|(0.050)|
|Any category<br>0.015|0.043|0.016|
|(0.081)|(0.093)|(0.086)|
|Share of workers that received training by occupation group|||
|Share of workers in skill level 4<br>0.006|0.011|0.007|
|(0.067)|(0.068)|(0.067)|
|Share of workers in skill level 3<br>0.017|0.031|0.0197|
|(0.103)|(0.099)|(0.103)|
|Share of workers in skill level 2<br>0.005|0.022|0.007|
|(0.044)|(0.061)|(0.046)|
|Share of workers in skill level 1<br>0.005|0.018|0.006|
|(0.049)|(0.076)|(0.052)|
|Observations<br>907,694<br>Notes: Standard deviations in parenthesis. Data refer to firms operating in t<br>over the period 2009-2012. A firm-year observation is classified as exporte<br>revenue in that year. Skill levels 1 to 4 are based on the 1988 International St<br>Occupations (see Appendix Table A1 for a detailed description).|45,751<br>he manufact<br>r if the firm <br>andard Cla|953,445<br>uring sector<br> had export<br>ssification of|
greater than 2.<sup>21</sup> Our analysis focuses on the contribution of new firms to the total number of firms and to employment creation, measured as (1) the percentage of new firms and (2) the percentage of employment at new firms. Our employment ratio should be used cautiously, however, since 38% of total firms do not report employment.<sup>22</sup> We calculate entry rates at the 2- digit NACE industry level averaged over the years 1998 and 1999.<sup>23</sup> We refer to this variable as Entry.<sup>24</sup> We require firms to survive at least one year and exclude firms in year 0. We exclude firms less than 1 year to avoid frivolous filings and because of the difference in initial filing requirements across countries.<sup>25</sup> In particular, in some countries firms in their first year do not have to file accounting information until after the end of their first year of operation, while in others they have up to 1 year to file. We check that the results are not qualitatively affected by including firms of age less than 1 as new firms.

In Table 3, we describe the country averages of the entry variables that we use in our analysis. We calculate entry and new firm employment rates for (1) all firms and (2) firms with more than 10 employees. Our data for firms with greater than 10 employees are less comprehensive since employment (which we need to classify firms) is missing for about 38% of observations in our sample. As shown in Column 1, the average entry rate across industries and countries is about 13.3% (or 9.2% when excluding small firms). Since we define new firms as 2 years or younger, this is calculated over two years, on average, and corresponds to an average annual entry rate of about 6.6% (or 4.6% when excluding small firms). We find large variations in the share of new firms across countries, varying from a high entry rate of 19.2% in Lithuania to a low entry rate of 3.5% in Italy. Overall, we find an average of about 15.7% of new firms in

> 21 Our empirical results are robust to defining new firms as age equal to one.

> 22 However, employment data in the UK is missing for over 85% of firms.  For this reason we check that all econometric tests that use employment cut-offs are robust to the exclusion of UK data.

> 23 Our empirical results are robust to using entry rates calculated for one year (1998 or 1999) only.

> 24 For a complete list of variable names and definitions, see Appendix 1.

> 25 However, this does not affect our results. The median share of firms with age 0 over the period 1998-99 is 2.5 percent.

13
27

# **D. In Summation**

In summarizing the findings of this paper, it is important to separate the lessons learned across time from the Tunisia experience and the lessons that can be learned from the cross-sectional analysis presented in this paper. As revealed in the discussion of the demograph.c history in Tunisia, there has been a strong program, good supportive social legislation a1id the expansion of female education. All of these have played an important role in reducing fertility across all educational and presumably social economic groups. A large part of the fertility decline is explainable by increases in the age of marriage which is probably directly attributable to increases in female education and social legislation that has contributed to greater rights of women. This legislation has ranged from the abolition of polygamy to increased rights in the work force. For guiding other countries on the way to stimulate fertility decline, more analysis is needed on the way that changes in marriage behavior has been brought about in Tunisia. While comparative analysis of the 1978 WFS, the 1988 DHS and earlier surveys would be useful here, the cross- sectional analysis of this paper could not address the issues of the determinants of the age of marriage.

A second major factor in fertility decline has been the increased use of contraception. Over time, this has resulted both from an increase in the number of women with education and also increases in the use of contraception among all educational groups. This latter effect has been the result of a strong program and contraceptive use has increased by the greatest percentage among the least educated over the last 10 years. Although there has been a substantial program to improve access of the rural, poor and least educated to family planning, these groups are still less well served than the more privileged. This is revealed by the fact that the uneducated women have on average one child more than they say they want.

The history of the fertility decline in Tunisia should be more thoroughly studied by other countries embarking on a program to stimulate their own demographic transition. Cross-sectional analysis of the determinan.J of contraceptive use and fertility as carried out in the paper can be used to guide Tunisia itself on where it might most profitably expand its activities to further increase contraceptive use and thus fertility decline. The results here show the very central role of mortality decline and access to contraception in this process. Health facilities, particularly clinics, and good water are important in reducing mortality which in turn increases the motivation to contracept and thus contraceptive use. These effects, however, are lagged. It seems to be access to these facilities at age 20 that matters more than current access. Thus, a long-term program of further reductions of mortality is important. Hospitals and doctors in rural areas appear to play a less clear role than clinics, but further analysis of the determinants of mortality, particularly in rural areas would be needed for designing a nealth strategy.
Annex 1

# Estimation of FSU Economies' GNPs in Ruble Terms

# A. GNP Series up to 1990

1. The ruble GNP for non-Baltic economies emerging from the FSU was esdmated in three steps. First "official" time series were obtained from Bank/Fund documents. Second, a set of 1987 benchmark estimates was extrapolated, based on the trends of the "official' series. Finally, the extrapolated series were adjusted to express the extra-Union trades in foreign prices, to be consistent with the concept of the System of National Accounts.

# "Official" Time Series

2. For the period of 1987 - 1991, GNP data were taken from the Bank/Fund's documents. One exception is Russia, for which the missing 1987 and 1988 data were extrapolated backward from the 1989 numbers. Constant prices for some FSU economies were taken from the same source if they were available. If not, full series of NMP in constant prices were determined through extra- or interpolation, first; then the difference between GNP and NMP in the base year of 1987 was extrapolated via the capital stock series in constant prices for each FSU economy; finally these extrapolated GNP-NMP differences were added to the constant price NMPs as described above. GNP deflators were calculated from the GNP data in current and constant prices.

3. For the period of 1980-87 a different method was used. Some republics had current price data back to 1985. For others, 1980 data were available. For the missing data the following method was used. First, constant price series were prepared via backward extrapolation or interpolation, using the NMP trend at constant prices. Second, time series of GNP deflators were estimated, based on NMP deflator's trend. Current price data were then obtained by multiplying GNP series in constant prices by the corresponding deflator series.

4. For the Baltic republics, complete time series were available in current and constant prices for 1980-90; thus no estimation was necessary. All "official" GNP series as mentioned above are shown in Tables 4-6.

# Benchmark Data and Extrapolation

_5._ For 1987, the Intelligent Decision Systems (IDS) prepared a set of GNP data for each republic (Table 7.). IDS also supplied extra-Union trade data at both domestic and foreign prices for 1987-90 by republics. These GNP data, however, include both inter-Republic and extraUnion trade values in foreign prices. To make these data conceptually consistent with the 28
**Figure 3. Short-Term Income Mobility, RLMS 1994-2015**

<!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>1994-1998 1998-2004 2004-2009 2009-2015<br>Upward (unconditional) Downward (unconditional)<br>Upward (conditional) Downward (conditional)<br>Mobility(%)<br><!-- End of picture text -->

**Note:** Estimation results are obtained based on total household income per capita. The terciles are defined using the cross-sectional sample for each year. All numbers are deflated with December to December regional CPIs and weighted with population weights, where the first survey round in each period is used as the base year.

44
5 hypothesis of upward occupational mobility induced by past migration. Given the non-random nature of international migration and return, we use an instrumental variable (IV) approach to measure the impact of interest. The results show that past migration experience increases the likelihood of upward occupational mobility. Differentiating the impact by host country indicates that the positive effect of past migration experience on upward occupational mobility is driven by past migration experience in ITB, as past migration experience in Greece does not exert any significant impact on any mobility outcome.

The paper is organized as follows. Section 2 provides an empirical literature review on return migration. Section 3 describes the data. Section 4 explains our empirical approach to modeling occupational mobility. Section 5 reports the regression results and Section 6 concludes.

# **2. Literature Review**

The first strand of literature on return migration is composed of numerous studies from a number of international settings that use data _only on return migrants_ and analyze the determinants of occupational choice, particularly participation in self-employment, upon their return to the home countries. These studies emphasize the importance of human and financial capital accumulated abroad to start and successfully manage micro-enterprises back at home. Murillo Castaño (1988) highlights how in the case of Colombian return migrants from Venezuela once basic needs had been satisfied, savings were used to establish or expand self-employment activities. Arif and Irfan (1997) utilize survey data on Pakistani return migrants and study the factors affecting their occupational composition upon return from the Middle East. They estimate that 44 percent of those employed changed their pre-migration occupations upon return from employment in manufacturing or service jobs to self-employment in small family businesses. The highest level of occupational change was observed in non-irrigated areas. They add that occupational change was strongly related to duration of stay in the Middle East, age upon return, and the level of educational attainment.

Likewise, Ilahi (1999) uses cross-sectional data from Pakistan and finds that upon return, overseas savings become a critical determinant of occupational choice. Migrants with high savings choose self-employment while others opt for wage employment. McCormick and Wahba (2001) employ cross-sectional data from Egypt and explore the incidence of entrepreneurship among return migrants, and the effects of overseas savings, overseas work experience, and pre-
behavior of funds following these indexes, even though the upgrade was announced in advance and it occurred because Israel’s fundamentals had improved (Business Week, 2010). Similar discussions emerged with the upgrades of Portugal (1997), Greece (2001), Qatar (2014), and the United Arab Emirates (2014), the potential upgrades of the Republic of Korea (2013) and Taiwan, China (2013), and the downgrades of Venezuela (2006), Argentina (2009), and Greece (2013) (Financial Times, 2013a,b,c). One reason for the effect on capital flows is that a country’s inclusion (exclusion) in a benchmark index should drive managers with index-tracking strategies to rebalance their portfolios and direct capital flows into (out of) that country (The Economist, 2012, 2014).

In this paper, we systematically study different ways in which benchmarks affect the international asset allocations and capital flows of mutual funds. To understand their impact, we first focus on the cross-sectional and time-series determinants of the country composition of benchmark indexes (“benchmark weights”) as well as on characterizing their dispersion. Then, in the main section of the paper, we present thorough econometric evidence that movements in benchmark weights result in movements in the actual country weights (“weights”) of the funds that declare that benchmark, depending on their degree of activism. Last, we show the consequences that the relation between mutual fund weights and benchmark weights has for capital flows, and explain the various channels through which the benchmark effect impacts those flows.

To conduct the research we compile a novel dataset of detailed portfolio allocations across countries by a large number of international mutual funds that we match with the allocations of the benchmarks they follow. The dataset covers the period from January 1996 to July 2012 and contains international mutual funds based in major financial centers around the world investing in at least two countries (i.e., it excludes country funds). A total of 2,837 equity and 838 bond funds are in the sample. These equity and bond funds collectively had 1,052 and 293 billion U.S. dollars in assets under management as of December 2011, respectively.<sup>4</sup> One important advantage of our database is that it allows us to test how the use of benchmarks affects international capital allocation and capital flows. First, we measure the independent effect of these benchmarks on country allocations and capital flows after controlling for several factors often mentioned in the literature, most notably industry and macroeconomic effects. In particular, the fact that the weight of a given

> 4 Mutual funds are offered to investors in different ways, for example, in different currencies and with different costs. These funds have the same portfolios but many times are counted as separate funds. In our data, we just count them once to avoid repeating the portfolios, but we report their aggregated assets.

2
<!-- Start of picture text --> N~ ©<br>e =<br>eee<br>®8°°353 a)e<br>5_ e @ e ee<br>3 St<br>g ee e 2<br>3 e g eo @<br>aane}£-oee 2£S> e e O<br>2EaEaoe ee ZeEex N ee<br>in e e2E<br>ee id<br>e e e<br>©<br>-.05 0 .05 1 -.05 0 .05 A<br>Commodity Price Shock Commodity Price Shock<br><!-- End of picture text -->
are once more the developing regions, with the Middle East and North Africa decreasing up to 8.3 percent in a global reshoring war. The rest of the high-income countries would see a slight increase in real income, due mainly to the terms of trade effect.

Closer integration in the global economy would pay dividends for developing countries. Eliminating input tariffs and implementation of trade facilitation measures would strengthen the integration of developing countries in regional and global value chains. An alternative response by developing countries to the reshoring of production by the leading economies would be to alleviate its negative impacts with more GVC-friendly policies and support for more integration. The gains would be evident in all developing regions. In global terms, real income increases by 0.4 percent compared to the COVID L-shape recovery. East Asia and Pacific and South Asia are the regions which stand to benefit the most, with real income increasing 4.7 percent and 4.3 percent, respectively. In other words, GVC-friendly policies could reverse the real income losses inflicted by the reshoring efforts of the leading economies (Figure 5).

Figure 5. Real income compared with COVID L-shape recovery – 2030 (%) <!-- Start of picture text --> 6.0%<br>4.0%<br>2.0%<br>0.0%<br>-2.0%<br>-4.0%<br>-6.0%<br>-8.0%<br>-10.0%<br>Reshoring leading economies Reshoring all GVC friendly liberalization + TF imports<br><!-- End of picture text --> Source: ENVISAGE simulations.

Reshoring by the leading economies would drastically reduce exports and imports for most regions (Table 1). Global exports and global imports both see a decrease of 17 percent in this scenario in 2030. The deepest declines in trade would be for the countries engaging in the reshoring policies, with declines in export for China, the United States, and Western Europe, ranging from 20 to 25 percent. Developing regions would suffer collateral damage, with exports declining in the range of 1 to 5 percent. But in the scenario in which developing countries also attempt to reshore, losses in trade for the developing countries are much larger. Compared to a scenario in which only leading economies reshore, losses in exports in the case where developing countries also reshore increase from 4.3 percent to 26.0 percent for South Asia, from 2.9 percent to 22.7 percent in Latin America, and from 1.3 percent to 12.1 percent in MENA.

However, if developing regions choose to follow the GVC-friendly path, the global exports and global imports reduction is less pronounced. As compared with doing nothing or reshoring, both of which would lead to declining trade for developing countries, a policy of GVC-friendly liberalization would actually lead 13
to compete with Costa Rica and Cote d’Ivoire, the two major players in the European market.

The adjacent graph provides an overview of the cost structure for Ghanaian fresh pineapple freighted by sea and by air compared to fresh pineapples from its main competitors Costa Rica and Cote d’Ivoire. In this graph the CIF prices per unit of product from the various countries subdivided for the various cost factors are compared.

It is immediately clear that the sea freight costs are much higher in Ghana than in the two other countries, thus contributing to its fragile market position in the European Union, which is primarily based on low cost for low quality.

<u>Figure 11: Pineapple Value Chain Analysis</u> <!-- Start of picture text --> 2.00 €<br>1,75<br>1.80 €<br>1.60 € 1,50<br>1.40 € Margin<br>Marketing<br>1.20 € 1,15<br>Freight<br>1.00 €<br>0,75 Packaging<br>0.80 €<br>Inland<br>0,65<br>0.60 € Farmgate<br>0.40 €<br>0.20 €<br>0.00 €<br>GhanaCote d'IvoireCosta RicaGh-Air IC-Air<br><!-- End of picture text --> <u>Source: Frank Joosten, Agribusiness VCA Ghana</u><sup>24</sup> The two primary objectives for the Ghanaian pineapple export sector to be more competitive should focus on **Price** and **Product Quality** . Measures that would lead to (1) increased production volume would reduce prices through economies of scale and (2) enhanced standards would lead to higher product quality and bigger profit margins. Elements of international “cost leadership” are achieved in the pineapple supply chain by a combination of qualitative improvement (higher standards through accreditation, ISO9001 and HACCP) and standardization of steps and procedures within the chain.

Given the increasing emphasis of European importers on safety and quality assurance systems there is a growing need for technical support and inspection services among exporting businesses. These services are currently available in Ghana from international service providers which are accredited, but are expensive under Ghanaian conditions. Local business service providers that can instruct and certify agri-food companies for the establishment of for example HACCP-systems, EurepGAP, BRC, etc. are not available. The possibilities of introducing a general quality standard for exportable fresh produce from Ghana (‘Ghana GAP’?) that is recognized by Eurep and several of the other main

> 24 Frank Joosten, IAC Wageningen UR and George S. Ayernor, University of Ghana,

> “Agribusiness Value Chain Analysis: Ghana MSME Project,” Final report for the _IFC – World Bank_ , December 2005

13
- 62 -

# Tal A.6: Urbn Popuation Dlatrbution Across State with **Differin AUrIcltural kcom: hdba** 1981

||U|rban Town Slze (In thou|sands)||
|---|---|---|---|---|
|State|<br>5-5|<br>0<br>50-200|<br>OvAr200|All Urban|
|||(Percentage State P|opulatlon)||
|High Agricultural Income|||||
|PunJab|11.1|5.6|11.0|27.7|
|Haryana|7.2|12.2|2.5|21.9|
|Medium Agricultural Income|||||
|Karnataka|10.4|5.3|13.2|28.9|
|Gujarat|8.4|6.3|14.1|30.0|
|Low Agricultural Income|||||
|Madhya Pradesh|7.1|5.3|8.0|20.3|
|Bihar|3.9|3.6|5.0|12.5|

Source: Economic Intailigence Srevice (1988) and India (1s9lb).