rafmacalaba/datause-encoder-data huggingface dataset:
The argument that the rise of deep trade agreements and the increasing importance of GVCs are related is not new and has been informally made in influential studies by Lawrence (1996), Baldwin (2010) and WTO (2011), among others. Intuitively, the unbundling of stages of production across borders creates new forms of cross‐border policy spillovers and time‐ consistency problems. Deeper forms of integration may allow to solve these coordination and commitment problems, because they discipline those national policies that are needed for the smooth operation of GVCs. Formal models of the relationship between GVCs and trade agreements are presented in Antràs and Staiger (2012) and Bickwit, Ornelas and Turner (2017). Recent studies have looked at related questions from an empirical point of view: Orefice and Rocha (2011), Johnson and Noguera (2016), Osnago, Rocha and Ruta (2015, 2016).<sup>3</sup> Differently from the current study, these papers either abstract from the depth of trade agreements (Johnson and Noguera, 2016), are based on a smaller database developed by the WTO (WTO, 2011) covering only 100 agreements and use different measures for GVC related trade (Orefice and Rocha, 2011; Osnago, Rocha and Ruta, 2015, 2016).<sup>4</sup> In the econometric analysis, we use a structural gravity model at the aggregate and sectoral levels to estimate the relationship between cross‐border production linkages and the depth of PTAs. To control for selection bias deriving from the presence of zero trade flows, our estimations are preformed using a Poisson Pseudo Maximum Likelihood (PPML) model. PTA depth measures are based on the new World Bank data set on the content of PTAs which covers 260 agreements signed by around 180 countries between 1958 and 2015. This is the entire realm of PTAs in force and notified to the WTO as of December 2015.<sup>5</sup> We build several indicators of PTA depth that capture the scope and legal enforceability of trade agreements. Bilateral GVC integration is measured in two ways, using value‐added trade and trade in parts and components.

> 3 For a survey of the literature, see Limão (2016). A companion paper by Mattoo, Mulabdic and Ruta (2017) uses the new World Bank database on the content of PTAs to revisit the classic Vinerian question of trade creation and trade diversion.

> 4 Contemporaneous work by Rubinova (2017) and Boffa, Jansen and Solleder (2017) also uses the new World Bank database on the content of PTAs to analyze various aspects of the relationship between deep agreements and GVCs.

> 5 In our analysis we exclude partial scope agreements.

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Pr[ext]t+n = α + β1γt + β<sup>′</sup> F<sup>′</sup> it + β<sup>′</sup> L<sup>′</sup> mt + β<sup>′</sup> C<sup>′</sup> t + β<sup>′</sup> C<sup>′</sup> (t,t+n) + β<sup>′</sup> R<sup>′</sup> + εh (3) Where the probability of firm exit ( _ext_ )<sup>12</sup> at the time of the second survey round ( _t_ + _n_ ) is determined by firm productivity (γ), a vector of characteristics of firm _i_ at time _t_ (F′it), vector of market-level indicators (L<sup>′</sup> mt<sup>) at a firm’s market (</sup><sup>_m),_and a vector of country-level characteristics (C′)</sup> including indicators at both time _t_ and averaged over the ensuing period ( _t_ to _t+n_ ); additionally, we include a vector of country, year, and sector fixed effects (R’), removing country fixed effects to adjust for perfect collinearity with elements of C’.

The list of variables included in the regression is guided by the discussion in section II<sup>13</sup> . We control for nine firm-level variables. Productivity is the key firm-level variable controlled for in the regression. Besides being an important determinant of exit by itself, this variable serves as an indication of the existence of creative destruction. As noted, inferring the intensity of creative destruction based on the rates of firm exits rests on the assumption that only less productive firms exit the market. If exit is not related to firm productivity, exit rate may not necessarily indicate whether the process of creative destruction is at work or not.

We use the (log of) labor productivity, measured by the ratio of real sales to total workforce, as a proxy for productivity, expressed in 2005 USD. While several other efforts use additional measures for productivity, including single-stage and two-stage TFP as well as various methods to account for the endogeneity and lumpiness of capital purchases, full inputs are only available for manufacturing firms in the ES data. Additionally, we lack comparable data for industry (sector) or price deflators to fully explore several TFP measures. Rather than sacrifice the breadth of our coverage and omit the sample of service-sector firms, we use the simpler labor productivity measure.<sup>14</sup> This is consistent with approaches taken elsewhere analyzing developing economies, and we assume our labor productivity measure to be generally correlated with TFP (for a recent example, see Hallward-

> 12 Firm and country subscripts are omitted to avoid cluttering the notation.

> 13 The full list of right-hand-side variables is described in table 5. All data are firm-level and from the Enterprise Surveys unless otherwise indicated.

> 14 Such measures are not without their own problems, for a full discussion see Foster et al (2008).

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# **3. The Occupational Gender Wage Gap around the World**

The ILO October Inquiry data allow us to look at the _occupational_ gender wage gap, that is, the female-male wage difference within an occupation in a given country and year. Occupations have been narrowly defined, so in principle we have information on how much female engineers or clicker cutters earn in comparison to their male counterparts. It is important to point out that the ILO October Inquiry does _not_ contain information on employment within these occupations, so we are unable to measure the average gender wage gap across _workers_ . In principle it is even conceivable that the average gender wage gap across workers increases, while we observe a decline in gender wage gap in each of the occupations.<sup>12</sup> Does this mean that the occupational gender wage gap is non-informative? Quite the contrary. Because it gives information on the gender wage gap within narrowly defined occupations, it is, in a way, more informative than the usual measures of the gender wage gap. Usual measures of the gender wage gap are the ‘raw wage gap’ and the ‘unexplained wage gap’. The raw wage gap measures the female-male wage differentials for typically all employed workers or for broad occupational categories. Because female and male workers may be different from each other in terms of human capital (such as type of education, work experience), occupations, and hours worked, this measure will typically overstate the actual gender wage gap if one would control for these differences. The unexplained wage gap is the female-male wage differential that remains if gender differences in human capital are taken out (typically through a regression analysis). It can be thought of measuring gender discrimination, in the sense of indicating gender-specific prices for similar levels of human capital.

The occupational gender wage gap can be viewed as providing a direct measure of the unexplained wage gap, without relying on the availability of good human capital data and a regression method to control for gender differences in qualifications. Assuming that female and male workers in the narrowly defined occupations have similar skills, any

> 12 It has been suggested that sectoral employment weights from other data sources could be used to derive an aggregate gender wage gap. The problem is, however, that in many countries there are not enough observations with female-male wage data across sectors to get a good measure of the overall gender wage gap within a country (see section 2).

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# **1. Introduction**

Innovative start-ups and SMEs in developing and transition countries often have good ideas, but may not have these ideas fine-tuned to the stage where they can attract outside funding. This is the case in the Western Balkans, where there is a perceived lack of investment readiness of innovative start-ups to be in a position where they can compete for, and take on, outside equity ( <mark>Karajkov, 2009).</mark> The most common reasons for a lack of investment readiness include a reluctance of entrepreneurs to surrender partial ownership and control of their business, lack of knowledge about the availability of external sources of finance, low investability of business development propositions, a lack of understanding about the key factors investors look for in making investment decisions, and presentational failings such as deficiencies in business pitches (Mason and Kwok, 2010).

Investment Readiness Programs which provide individualized training, mentoring and coaching are designed to overcome these constraints, but the programs can be expensive to provide, and to date there is no rigorous evidence as to their effectiveness. We conduct a five-country randomized experiment in the Croatia, Kosovo, Macedonia, Montenegro and Serbia to test the effectiveness of such a program. A sample of 346 innovative SMEs were randomly divided into two groups: a treatment group that received a high-cost and intensive program that involved help developing their financial plans, product pitch, market strategy, and willingness to take equity financing, along with master classes, mentoring, and other assistance; and a control group which received access to an inexpensive online-only basic investment readiness course. After this program, both groups of firms competed in a pitch event, where they were scored by independent judges (blinded to treatment status) on their investment readiness, with the top 50 firms then going onto a finals stage where they pitched to investors.

The independent judges scored the pitches on six aspects of investment readiness: team, technology, traction, market, progress, and presentation, with each firm scored by five judges. We find that firms that went through the investment readiness program receive an average of 0.3 standard deviations higher investment readiness scores at this event, and are more likely to get selected to proceed to pitch in front of investors. We then track firm outcomes over the next two years via a six-month and two-year follow-up survey, and through measuring their subsequent media mentions and social media attention. We find that treated firms do get significantly more 2
daughter is 12 percentage points more likely to choose a nonfarm occupation when at least one parent is in the non‐farm sector.

But the most striking evidence in Table 6 relates to the evolution of intergenerational persistence in occupation choices over 14 years from 1988 to 2002; the intergenerational link between parents and children in non‐farm participation has disappeared in 2002, both for daughters and sons. For daughters, the coefficient on the dummy for ‘at least one parent in non‐ farm’ is positive but small and not significant at the 10 percent level when we include only province fixed effects, and it turns negative and insignificant once county fixed effects are used. For sons, the strength of the intergenerational link in 2002 is a bit stronger, but it also does not survive when we include county fixed effects. The evidence thus shows that occupational mobility has dramatically improved in rural China. By 2002, a farmer’s children are no longer necessarily going to be farmers themselves. In terms of occupational mobility, the transition to more market oriented open economy has been nothing less than magical in rural China.

# **4.3 Sensitivity Analysis of Occupational Mobility**

The results from Probit regressions in Table 6 provides convincing evidence that occupational mobility has improved in rural China from 1988 to 2002. The observed change in the intergenerational occupational linkages cannot be due to genetic transmissions from parents to children, as the genetic traits do not change in a decade and a half. The evidence also indicates that the link between parental occupation choices and children’s occupation remained strong in 1988 even after controlling for a rich set of observable characteristics. However, the observed persistence in column (7) of Table 6 in 1988 can still be driven primarily by unobserved heterogeneity, such as positive selection on common genetic endowments and preferences. Since the treatment and the outcome variables are both binary, it is natural to take advantage of the AET (2005) sensitivity analysis to explore if low to moderate positive selection on common 34
# **Regionalism in Services:**

# **A Study of ASEAN**

Batshur Gootiiz and Aaditya Mattoo* _Keywords_ : Services trade policy, services trade barriers, regional trade agreements, multilateral trade agreements _JEL Classification_ : F13, F14, L80

*World Bank, 1818 H Street NW, Washington DC 20433.  Contact information: bgootiiz@worldbank.org, and <u>amattoo@worldbank.org.  This research was prepared as part of the ASEAN Services Integration Report, 2015, a</u> joint project of the ASEAN Secretariat and the World Bank.  The authors would like to thank other World Bank staff, Ahmad Ahsan, Martin Molinuevo, Sebastian Saez, Sudhir Shetty, Peter Walkenhorst, and Gianni Zanini, members of the ASEAN Secretariat and representatives of the ASEAN countries for helpful suggestions during the workshops in Jakarta in June 2012 and March 2013.  None of them are in any way responsible for the contents of this paper.  Research for this paper has been supported in part by the World Bank’s Multidonor Trust Fund for Trade and Development and the Strategic Research Partnership on Economic Development.
the average extreme poverty rate for countries with nominal GDP within 10 percent of PNG’s was approximately one-half of that reported in PNG- 21.2 percent.<sup>2</sup> Furthermore, many question the suitability of global well-being measures in PNG, and often refer to the term “subsistence affluence” to describe how community-level support and subsistence agriculture provide basic needs (e.g., Bourke 2000; Cammack 2007; etc.).

Given the substantial ambiguity regarding the extent of deprivation in the country and the degree to which it has changed in the past decade and a half, we aim to update key well-being estimates using the 2016-2018 Demographic and Health Survey (DHS). Specifically, we estimate the prevalence of monetary poverty in the country by tracking the change in close correlates of monetary poverty and imputing the poverty level using the relationship between those variables and poverty in the last official estimate. And we estimate the World Bank’s Multidimensional Poverty Measure (MPM), which is an index that combines information on access to critical and basic services and monetary poverty.

The data illustrate three clear patterns. First, despite growth in real per capita GDP of 29 percent and the other changes that have occurred between 2009 and 2016-2018, there was no change in the share estimated to be monetarily poor. The close correlates of poverty hardly changed over that time and the imputed share of the population living below the World Bank’s international poverty line of $ 2.15 (2017 PPP) in 2016-2018 was 39.3 percent, which is nearly identical to the estimate in 2009.<sup>3</sup> Additionally, poor access to essential services also changed very little from their initial low base. The changes were precisely estimated, and a significant improvement (or worsening) in any individual well-being measure analyzed here can be rejected at conventional significance levels.

Second, this lack of change in several key well-being measures is in contrast to the substantial monetary and non-monetary gains made in much of the rest of the world over the same time period, even in places with little economic growth. The World Bank’s global database illustrates that there were large improvements in four of the five indicators tracked here in countries with the most similar multidimensional poverty rates in the pre-

> 2Author’s calculations using data from the World Development Indicators.

> 3The high estimate of monetary poverty is corroborated using a variety of approaches, including estimates of severe food insecurity and projections based on non-resource GDP growth.

3
and Bradford Jensen, 1999), we find evidence of “self-selection” on innovation activities; innovative firms pay higher wages already in the three previous years to become innovative. In addition, we also find evidence of increases in the wage premium after becoming innovative, an effect that persists three years later.

The paper is structured as follows. The next section briefly describes the literature on wage premiums and the links to the innovation premium. The third section describes the data and methodology used. Section 4 shows the results for the estimated wage premium and its decomposition by type of firm and worker. Section 5 focuses on the causality between innovation and wages. The last section concludes.

# **2 Innovation Wage Premium**

The availability in the last two decades of microdata linking firms and workers characteristics has spurred a significant number of empirical studies looking at different wage premiums associated to key workers or firms characteristics. In a perfectly competitive labor market, sector and firms’ differences are expected to result in a different composition of workers, but should not influence wages. However, in imperfectly competitive labor markets, firms and workers have some ability to influence wages and capture some of the rents associated with improved performance. Evidence that firms and workers have some ability for wage-setting (Mortensen, 2003) has triggered a large literature trying to estimate what types of workers and firms have larger wage premiums, since these premiums can account for a significant share of wage inequality. For example, Alvarez et al. (2018) document a large decrease in earnings inequality in Brazil from 1992 to 2012 and find that firm premiums account for 40 percent of the total decrease in inequality. This section summarizes some of the most relevant studies that focus on premiums associated with firm size and age, international activity - exporters and FDI - and technology and innovation.

One of the first analyses of wage premium goes back to Moore (1911), who finds that the mean rate of wages in the textile industries increases with the establishments’ size. Over the decades, the large-firm wage premium (LFWP) passed the test of many empirical exercises (see Oi and Idson (1999) for a literature review and Bloom et al. (2018) for a recent description of the LFWP).<sup>2</sup> A more recent empirical strand has focused on the wage premium by age group, especially the young-firm pay premium. Initial findings argued that older firms pay more, even when controlling for size and industry (Dunne and Roberts, 1990). Heyman (2007), using matched employer-employee data for Sweden, also finds a positive premium

> 2Berlingieri et al. (2018) argue that, in the service sector, the stylized fact is a “productivity-wage premium” rather than “size-wage premium”.

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## **Figure 16  Women Survival Rates**

# **Survival Functions Women**

<!-- Start of picture text --> 1<br>0.8 Single<br>Married<br>0.6<br>Widowed<br>0.4<br>Divorced<br>0.2 EVK Pooled<br>0<br>60.00 70.00 80.00 90.00 100.00<br>age<br><!-- End of picture text --> Survival rates as a function of marital status for women (year 1990, data source SFSO). The dashed line corresponds to pooled annuitant survival rates provided by the FIF (1990).

Source: SFSO, FIF

**Figure 17  Men Survival Rates**

# **Survival Functions Men**

<!-- Start of picture text --> 1<br>0.8 Single<br>Married<br>0.6<br>Widowed<br>0.4<br>Divorced<br>0.2 EVK Pooled<br>0<br>60.00 70.00 80.00 90.00 100.00<br>age<br><!-- End of picture text --> Survival rates as a function of marital status for men (year 1990, data source SFSO). The dashed line corresponds to pooled annuitant survival rates provided by the FIF (1990).

Source: SFSO, FIF
# **Transit Migration: All Roads Lead to America**<sup>_∗_</sup>

Erhan Artuc<sup>_†_</sup> and Caglar Ozden<sup>_‡_</sup> JEL Codes: J61, F22, F16, J68 Keywords: International Migration, Transit Migration, Migration Policy

> _∗_ We thank two anonymous referees, Frederic Vermeulen, Michel Beine, Simone Bertoli, Michael Clemens, Hein de Haas, David McKenzie, Jesus Fernandez-Huertas Moraga, Chris Parsons, Hillel Rapoport, Moritz Ritter, Mathis Wagner and participants at the International Migration and Development Conference at Oxford University for comments and feedback. We are grateful to Zovanga Kone for his excellent assistance in constructing the data files and catching many of our errors. We also thank Varanya Chaubey and Maggie Liu for their editorial help. Research for this paper has been supported in part by the Knowledge for Change Program, by the Multidonor Trust Fund for Trade and Development and by the Strategic Research Program of the World Bank. All errors and omissions are the authors’ responsibility.

> _†_ World Bank, Development Research Group

> _‡_ World Bank, Development Research Group
modalities: seed recycling and supply, and forgetting.<sup>23</sup> We conclude these alternative mechanisms are unlikely to explain our results.

First, rather than learning about the new technology, farmers may have simply recycled seeds from demonstration year production; that is, demonstration acted as a supply shock, rather than triggering learning. In the absence of complementary seed purchase, this would mechanically lead to disadoption of the new seed variety over time. This is a possibility since the promoted varieties are open-pollinated. We leverage the fact that our survey records the sourcing method for each production input to document whether assignment to any demonstration or to different demonstration modalities predicts farmers’ reported use of recycled improved seeds (Appendix Table A27). We find no statistically significant effect of assigning demonstration on the use of recycled improved seed, and point estimates are too small to explain the stark dynamics we observe; instead, farmers predominantly source the improved seed from seed multipliers or local markets. Alternatively, while demonstration may have triggered learning, falling impacts over time may have been caused by falling supply rather than learning. In Appendix Table A14, we show there were no significant dynamics of adoption in the control group, suggesting the supply of the new seeds remained stable over time.

Second, while impacts on adoption one year post-demonstration may have been caused by learning, falling impacts two years post-demonstration may have been caused by farmers forgetting about the new seeds. We present three pieces of evidence that forgetting did not cause these dynamics. First, even though the impacts of demonstration fade two years post-demonstration, the level of adoption remains high, with over 31% of farmers cultivating promoted crops in demonstration villages adopting improved seeds two years post-demonstration; forgetting would be more plausible in a context with lower rates of adoption and without farmer groups that regularly discuss the promoted crops. Second, the fall in impacts two years postdemonstration is concentrated in decentralized demonstration villages, so forgetting would have to be concentrated in these villages. Instead, in Table 6, we presented evidence that impacts converged across demonstration modalities, which we attribute to convergence towards similar and high levels of learning facilitated by farmer groups.

> 23In our model in Section 5, we consider other mechanisms closely related to learning—nonBayesian learning, and forward-looking experimentation one year post-demonstration—and argue these do not affect our conclusions.

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