rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> First child a girl<br>(a) 1972-1984 (N=2,791) (b) 1985–1994 (N=5,011) (c) 1995–2006 (N=3,827)<br>Prob. boy (%) Prob. boy (%) Prob. boy (%)<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>Prob. no birth yet Prob. no birth yet Prob. no birth yet<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>First child a boy<br>(d) 1972-1984 (N=2,877) (e) 1985–1994 (N=5,421) (f) 1995–2006 (N=4,015)<br>Prob. boy (%) Prob. boy (%) Prob. boy (%)<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>Prob. no birth yet Prob. no birth yet Prob. no birth yet<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>75 75 75<br>70 70 70<br>65 65 65<br>60 60 60<br>55 55 55<br>50 50 50<br>45 45 45<br>40 40 40<br>35 35 35<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>75 75 75<br>70 70 70<br>65 65 65<br>60 60 60<br>55 55 55<br>50 50 50<br>45 45 45<br>40 40 40<br>35 35 35<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br><!-- End of picture text --> Figure 7: Predicted probability of having a boy and probability of no birth yet from nine months after first birth for urban women with 8 or more years of education by quarter. Predictions based on age 22 at first birth. Left column shows results prior to sex selection available, middle column before sex selection illegal and right column after sex selection illegal. N indicates the number of women in the relevant group in the underlying samples.

results show the pattern with a mature and readily available technology. That sex selection for the 1995–2006 period is used earlier in the spell compared to later also shows that not taking account of timing of births and the potential censoring of birth spell can lead to an upward bias in the final 31
6 days over a two week period. Enumerators administered a short “intake” interview to all consenting patients seeking care for an acute illness. After the patient’s consultation with the provider was complete, the enumerator recorded details of all blood tests performed, medications prescribed, medications purchased, and fees paid. A randomly chosen subset of these patients were selected for a more detailed home-based follow up survey on the day after their clinic visit. As part of this visit, a trained enumerator performed an RDT on the patient to confirm his/her malaria status.<sup>11</sup> Table 1 uses the 628 patient observations in the Control group to give an overview of how acute illnesses are treated in clinics in Bamako. Panel A shows average indicators regarding malaria beliefs and treatment outcomes. Panel B reports treatments that often accompany malaria medications: antibiotics, which are often given for fevers but are not indicated for malaria, as well as injections/IV, which are used in severe malaria treatment. Panel B also shows how often patients were referred to a hospital, the recommended course of action for severe malaria. Finally, Panel C shows average costs in West African CFA (at the time of the experiment CFA 610 was approximately USD 1), split by the type of treatment received.

Looking at means in the whole sample of 628 (column 2), treatment for malaria is very common, with 31 percent of patients prescribed treatment for simple malaria and 30 percent for severe malaria. Twenty-one and 26 percent of patients purchase medication for simple and severe malaria, respectively (Panel A).<sup>12</sup> Malaria treatment is expensive: while the average patient not treated for malaria reported spending CFA 2,793 ($4.58 at the average exchange rate of CFA 610 per USD that prevailed during the study period), simple malaria patients paid CFA 4,475 ($7.34) and severe malaria 11RDTs detect antigens for malaria, which remain in the bloodstream after the infection is cured. We used CareStart HRP2(Pf) tests, which detect an antigen that typically takes a couple of weeks to become undetectable in blood samples (Humar et al., 1997; Kyabayinze et al., 2008). This test detects the _P. falciparum_ malaria parasite, which accounts for 92 percent of all malaria infections in Mali (PMI, 2016). In WHO quality assurance testing, these RDTs correctly identified malaria infection in 91 percent of low parasite density blood samples and 100 percent of high parasite density samples. False positives occurred less than 1 percent of the time, and the invalid test rate was 0 percent (WHO, 2017).

> 12We classify treatment as indicating severe malaria treatment if the patient received artemisinin monotherapy or quinine, (1) as an injectable or (2) recorded in the survey as tablets, _and_ received an ACT. These cases are consistent with guidelines for severe malaria treament which require administering monotherapy/quinine into the bloodstream or rectally.

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# **7. The Value of Preferences**

Previously we have looked at the importance of products subject to preferences in each country’s total exports to the United States. The current economic value of these preferences will be determined by the amount of preferential trade and the margin of preference that is granted. Figure 3 shows the estimated value of preferences requested in 2002 as a proportion of total exports to the United States for each country grouped according to the preferential treatment that they receive. This shows the tariff payment that is foregone by the U.S. due to the preferences that each country is able to request.<sup>15</sup> In what follows we implicitly assume that this amount of tariff revenue avoided is captured by the exporter in the African country, although in practice these rents may accrue to importers in the United States. Indeed, Ozden and Olarreaga (2003), suggest that two-thirds of the transfer under AGOA may actually go to U.S. importers.

The left-hand section of Figure 3 shows that for AGOA beneficiaries that are not eligible for preferences on clothing the value of U.S. preferences under current trade structures is very small. For none of the countries in this group does the value of preferences exceed 1 percent of the value of exports to the United States. For 13 of these countries the value of preferences is less than 0.25 percent of the value of exports to the United States. Thus, the preferences available to these countries under AGOA and the GSP are having little apparent economic impact at present.

The value of preferences are more substantial and more widespread for countries in the second group of AGOA beneficiaries; those which are eligible for duty-free access for clothing products. In this group there are 10 countries for which the value of current preferences in the United States exceeds 1 percent of the value of exports to the U.S.

> 15 This is derived by multiplying the value of exports to the US requesting preferences by the preferential margin, the MFN tariff for each product, at the tariff line level. For agricultural products which are subject to tariff quotas we apply the out-of-quota duty rate. However, for certain products subject to what are called technical duties (comprising a duty related to the unreported content of the product, such as the sugar content) the value of preferences is obtained using duties calculated by USITC for AGOA countries in total (or when not available for all exporters) to derive an ad valorem equivalent tax. This means that for products subject to tariff quotas the average within and out of quota duty is used as the margin of preference. In practice these technical duties affected only 8 tariff codes of products currently exported by AGOA countries and do not have a significant impact on the results presented here.

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term representing the export intensity in 2000 since the pass-through from tariffs to prices in each sector is likely to be influenced by the sector’s export orientation.<sup>40</sup> Whereas Vietnam’s provinces all face the same tariffs abroad, the impacts of tariff cuts differ across provinces due to differences in initial industrial composition. The provincial measure of the impacts from the U.S. tariff cuts is the tariff drop rate aggregated at the province level using the structure of employment prior to the BTA ( _t_ = 2000). Specifically, where _∆ιj_ is the U.S. tariff drop at the two-digit VSIC level, which is taken from McCaig (2011, Figure 2); _XSj,2000_ is the export intensity which in turn is computed as the share of exports in gross output for industry _j_ in 2000 at the national level; _Employmentjp,2000_ is the number of workers employed by industry _j_ in province _p_ in 2000; and _Employmentp,2000_ is the number of economically active population in province _p_ in 2000.<sup>41</sup> The rationale of using the provincial measure of the U.S. tariff cut as an instrument is that the U.S. tariff reduction ( _∆ιj_ ) is likely to influence the change in Exports Index, but the change in wages does not influence the U.S. tariff reduction. The U.S. tariff reduction is also unlikely to affect the change in wages other than via trade. In addition, the lagged level of industry mix is likely to affect the change in Export Index by influencing the impacts of the U.S. tariff cut at the province level, but the change in wages cannot affect the past level of industry composition.

> 40 The inclusion of the export intensity is consistent with Gonzaga, Filho, and Terra’s (2006) study which shows that the pass-through from tariffs to prices in each sector depends on the sector’s import penetration. For instance, the pass-through, measured by the share of trade relative to production, is obviously zero in sectors with no trade whereas the pass-through coefficient is one in sectors where all goods are traded. Investigating the impacts of trade liberalization implemented in Brazil from 1988 to 1995, Gonzaga _et al_ . (2006) show that only after an adjustment of tariff changes for import penetration was the trade liberalization pattern with respect to skill intensity consistent with that of relative price changes.

> 41As a robustness test, I construct the alternative tariff measure considering only those employed in the industrial sector so that the variation in the tariff measure is independent of the size of the industrial sector within a province (Topalova, 2010). The results remain essentially unchanged.

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9 public expenditure on schooling, plus the forgone earnings of those who are in school rather than working.

Estimates of social returns to education, as commonly found in the literature, ignore nonincome benefits of education (for example, improved health) and the possibility of positive externalities from education, such as productivity spillovers, lower crime, reduced use of social services, increased civic participation, and so on.

The private rate of return to an investment in a given level of education in such a case can be estimated by finding the rate of discount ( _r_ ) that equalizes the stream of discounted benefits to the stream of costs at a given point in time. In the case of university education lasting five years, for example, the formula is: where ( _Wu_ - _Ws_ ) is the earnings differential between a university graduate (subscript _u_ ) and a secondary school graduate (subscript _s_ , the control group). _Cu_ represents the direct costs of university education (tuition, fees, books), and _Ws_ denotes the student's foregone earnings or indirect costs.

Typical results of rate of return applications are reported in Table 3 showing that the returns are higher when investment takes place at the lower level of education, especially in low income countries. This well documented pattern is due to diminishing returns as investment in human capital ascends by level of education, similar to the diminishing returns on investment in physical capital. In addition, the returns to education are higher in developing countries because of the scarcity of human capital in those countries relative to industrial countries.

**Table 3.  Social returns to investment in education by level and** **<u>per capita income group (%)</u>**

||E|ducational leve<br>|l|
|---|---|---|---|
|Per capita income group|Primary|Secondary|Higher|
|Low income|21.3|15.7|11.2|
|Middle income|18.8|12.9|11.3|
|High income|13.4|10.3|9.5|
|World average|18.9|13.1|10.8|

Source: Based on Psacharopoulos and Patrinos (2004: table 2) An alternative methodology is the so-called ―Mincerian earnings function‖ used mainly to estimate the private returns to education (Mincer 1974): (2) _Ln_ ( _Yi_ ) = _f_ ( _Si_ , _<u>Zi</u>_<sup>)</sup> where _Y_ refers to the earnings of person _i_ , _S_ to the number of years of schooling and _Z_ to a battery of other individual characteristics. The property of this semi-logarithmic expression is that the regression coefficient of the years of schooling can be interpreted as the rate of return to investment in education.
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denote th., poverty level that one would have found in 1987 if only the Lorenz curve had shifted since 1984, leaving the mean unchanged. The observed change in poverty between two dates can then be decomposed into "growth" and "distributional" effects as follows: 87 84 87* 84 87** 84 pa -Pa .(Pa _pa ) +(pa _ Pa) +residual (2) change in change in poverty due poverty due to change in to shift in the mean the Lorenz holding 1984 curve holding Lorenz curve 1984 mean constant constant However, one should be cautious in drawing policy implications from this decomposition. Distributionally neutral growth is not the same thing as growth with distributionally neutral policies. The laizze-faire growth path of an economy need not be distributionally neutral, and so policy interventions aimed at reducing relevant inequalities may well be essential to attaining even distributionally neutral growth. The "growth-equity" decomposition is a simple descriptive devise intended to throw light on the proximate causes of poverty alleviation; a deeper analysis of those causes would be needed to draw sound policy implications.

Turning to the Indonesian data we find that the period 1984-1987 saw a simultaneous increase in mean consumption and a reduction in the overall inequality of consumption, in both urban and rural sectors. The three year growth rate in urban consumption implied by the SUSENAS was 12.1 percent, while the rural rate was 14.6 percent. Table 3 gives the Lorenz curves. The 1987 Lorenz curves unambiguously dominate those for 1984 in both sectors and, of course, nationally. Thus all well-behaved inequality measures will indicate a reduction in inequality over the period.<sup>12</sup> '<sup>13</sup> The
yields similar results. The elasticity of the number of formal employees per commune with respect to the sum of lights is 0.563 and again measured with a small standard error. Again the nightlights explain only a rather small share of the between variation in employment ( _R_<sup>2</sup> = 0 _._ 103). The elasticity of per capita expenditure with nightlights is much smaller and estimated with a _β_ of 0.126. This indicates that (at least in the development country setting of Vietnam) nightlights are more responsive to differences in production but less so to consumption. The R-squared measure of expenditure is even lower at _R_<sup>2</sup> = 0 _._ 023, suggesting that nightlights are not very predictive of overall cross-section consumption patterns in the setting of Vietnam. This finding is in contrast to other studies that find higher prediction power for small geographic units of up to 40-50% in Africa (Jean et al., 2016) and of up to 60% in Sri Lanka (Engstrom et al., 2016).

Controlling for potentially endogenous commune sizes confirms the strong positive relationship between nightlights and economic activity. Column (2) includes the log area (in square kilometers) as a regressor. The coefficient is strongly negative and indicates an elasticity of the number enterprise with respect to commune size of -0.631. The coefficient on log SOL increases to 0.589 and is still strongly significant. This is a natural consequence of the negative correlation between commune size and economic activity as outlined before. Similarly, in column (4) the employment elasticity increases to 0.806. For expenditure, the elasticity increases only slightly from 0.065 to 0.126. The considerable increase in the R-squared measure in all regressions indicates the predictive power of commune size independently of nightlights and confirms that commune boundaries are endogenous.

# **3.2.2 Landsat Spectral Indices**

The focus is now on the question whether Landsat band values have similar predictive power for ground truth data of economic activity as nightlights. Since Landsat bands measure the reflectance of light of a certain wavelength and are difficult to interpret numerically, the 12
Thus outside the general government sector, the ratio of the statutory minimum to the median wage appears relatively high.<sup>2</sup>

**Figure 2 –Statutory minimum wages relative to mean and median wages**

<!-- Start of picture text --> Minimum relative to mean wages Minimum relative to median wages<br>Philippines Philippines<br>Cambodia Cambodia<br>Indonesia Thailand<br>Thailand Indonesia<br>New Zealand Turkey<br>Vietnam non-government Chile<br>Slovenia France<br>France Slovenia<br>Ireland New Zealand<br>Australia Vietnam non-government<br>Mongolia Israel<br>Chile Portugal<br>Israel Australia<br>Belgium<br>Hungary<br>Netherlands<br>Lithuania<br>Lithuania<br>Belgium<br>Hungary Poland<br>Poland<br>Romania<br>Canada<br>Mongolia<br>United Kingdom Latvia<br>Turkey<br>Ireland<br>Portugal<br>Netherlands<br>Slovak Republic<br>Latvia United Kingdom<br>Korea Slovak Republic<br>Spain Greece<br>Romania Canada<br>Luxembourg Korea<br>Japan Luxembourg<br>Estonia Spain<br>Czech Republic Japan<br>Greece Estonia<br>Mexico United States<br>United States Mexico<br>Vietnam government Czech Republic<br>China Vietnam government<br>Lao Lao<br>0% 25% 50% 75% 100% 0% 25% 50% 75% 100%<br><!-- End of picture text -->

**Sources: OECD (2014); World Bank (2014a); World Bank staff calculations based on 2013 labor force survey.**

**Notes: Full time workers; data are for 2013 (2008 for Laos; 2009 for Cambodia, Mongolia and Thailand; 2010 for China, Indonesia and Philippines); average minimum wages for China and Vietnam. Non-government sector includes state-owned enterprises, domestic private firms and foreign-owned private firms.**

Figure 3 presents the growth of Vietnam’s inflation-adjusted, non-government, domestic sector minimum wages. It includes a projected path of minimum wages from 2016 to 2018 based on the

> 2 Cf. Appendix A for more detailed empirical evidence on the relationship between Vietnam’s statutory minimum wage and the country’s entire wage distribution.

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literature has largely relied on a small number of outcome measures that can be collected without the use of firm surveys – survival, whether or not they received venture funding, and web traffic measures. We have much more detailed data, including the use of judge scores and intermediate outcome indicators that allow us to focus on investment readiness, and not just investment outcomes. Finally, the majority of studies focus on the U.S. which has a well-developed venture capital market, whereas we focus on an area of the world where firms are only just starting to engage with outside investors, making investment readiness programs potentially more important.

# **2.3 Why an investment readiness program in the Balkans?**

Increasing innovation is a key regional priority in the Balkans region as a means to boost firm productivity and sustain economic growth. While it is generally accepted that debt finance is not the optimal source of funding for early-stage SMEs and start-ups, equity finance is only marginally used in the region. A regional report noted that there is a debate as to how much this lack of use of risk capital reflects a lack of supply of equity finance, versus a lack of readiness of entrepreneurs to attract and accept this financing (Karajkov, 2009). Based on the viewpoint that action was needed on both the supply and demand sides, the Enterprise Development and Innovation Facility (EDIF) initiative financed by the European Commission includes efforts to increase the supply of private equity to the region, improve the legislative frameworks to better encourage venture capital activity, and undertake efforts to increase investment readiness. This paper provides an evaluation of the investment readiness component of this initiative.

# **3. Experimental Design**

To implement this intervention, we ran a competitive procurement process where companies specializing in investment readiness programs provided bids. We shortlisted five companies, and together with evaluations of these proposals from Josh Lerner from Harvard University and his team at Bella Research Group, and with advice from experts in the national innovation agencies of the participating countries, chose as the winning firm the company _Pioneers JFDI GmbH_ (Pioneers henceforth). Founded in 2009 and based out of Vienna, they are one of Europe’s leading platforms for entrepreneurship, organizing an annual “Pioneers Festival” (with 3,000 attendees), as well as providing mentoring, pitch training, and opportunities for presentation and networking with European and international founders and investors. They launched a specific investment readiness program called _Pioneers of the Balkans_ for this project.

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Table 10: Employment, 2016-2020.

|||All sample||Com|petitive sect|ors|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|BOS|0.195***<br>(0.0118)|||0.160***<br>(0.0140)|||
|BOS * 2020|0.103***<br>(0.0329)|||0.124***<br>(0.0445)|||
|Public||0.0934***<br>(0.0182)|||0.0464**<br>(0.0191)||
|Mixed||0.197***<br>(0.0200)|||0.137***<br>(0.0226)||
|Participated||0.351***<br>(0.0231)|||0.485***<br>(0.0367)||
|Public * 2020||0.168**|||0.0760||
|||(0.0733)|||(0.0813)||
|Mixed * 2020||0.0962*|||0.111||
|||(0.0572)|||(0.0726)||
|Participated * 2020||-0.0204<br>(0.0479)|||-0.0252<br>(0.0791)||
|Ownership 10-_<_25%|||0.290***<br>(0.0463)|||0.457***<br>(0.0866)|
|Ownership 25-_<_50%|||0.266***<br>(0.0321)|||0.345***<br>(0.0572)|
|Ownership 50-_<_100%|||0.208***<br>(0.0185)|||0.170***<br>(0.0219)|
|Ownership 100%|||0.105***<br>(0.0181)|||0.0520***<br>(0.0189)|
|Ownership 10-_<_25% * 2020|||0.00797|||-0.0423|
||||(0.0931)|||(0.178)|
|Ownership 25-_<_50% * 2020|||-0.0748|||-0.0216|
||||(0.0651)|||(0.124)|
|Ownership 50-_<_100% * 2020|||0.0858*<br>(0.0496)|||0.101<br>(0.0653)|
|Ownership 100% * 2020|||0.167**<br>(0.0711)|||0.0747<br>(0.0785)|
|Firm size|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm age|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector2d|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Year|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|10762276|10762276|10762052|9827417|9827417|9827251|
|R-squared|0.625|0.625|0.625|0.613|0.613|0.613|

_Note:_ The table presents the results of firm-level regressions of employment on ownership status. All specifications include fixed effects to control for initial firm size, initial firm age, 2-digit industry and year. Data from RAIS, from 2016 to 2020. Robust standard errors in parentheses. Significance levels: * 10%, ** 5%, *** 1%.

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