rafmacalaba/data-use-mentions-extended huggingface dataset:
insignificant, with t-statistics below 0.9 in absolute value. Migrants therefore do not appear to differ greatly from non-migrant lottery winners in terms of unobservable characteristics affecting labor market earnings.

These results therefore suggest that lottery applicants are positively selected in terms of unobservable characteristics which affect labor market earnings, but that there is little evidence for selective compliance to the treatment of winning the lottery, conditional on applying. This positive selection concurs with the differences seen in the comparison of the experimental and non-experimental estimates of the income gains from migration.

# **6. Cost-of-living Adjusted Income Gains**

The experimental estimate of an average gain in income of $274 per week from migrating is based on comparison of incomes earned in New Zealand to incomes earned in Tonga, converted into New Zealand dollars using the exchange rate prevailing at the time of the survey, of 1.372 Pa’anga per New Zealand Dollar (P/NZD). To investigate the sensitivity of the size of the income gain to adjustments for differences in the cost of living, we collected price data in both countries and formed several purchasing power parity exchange rates.

The first measure, inspired by the Big Mac index of _The Economist_ , is the PPP exchange rate based on the price of KFC takeaway chicken in New Zealand and a similar product

- 35 -
<!-- Start of picture text --> Figure 1:<br>EMIGRATION TO THE EAST AND WEST AND CHANGE IN COMMUNIST PARTY<br>VOTE SHARES BETWEEN 2005 AND 2009, BY DISTRICT<br>(based on census data and official election results)<br>0 .02 .04 .06 .08<br>prevalence of migration to russia 2004<br>0 .02 .04 .06 .08<br>prevalence of migration to western europe 2004<br>.3<br>.2<br>.1<br>0<br>change in communist votes' shares 2005-2009 -.1<br>.3<br>.2<br>.1<br>0<br>change in communist votes' shares 2005-2009 -.1<br><!-- End of picture text --> as well as return migrants signi...cantly increase participation rates, and more so for the latter. Interestingly, in the spirit of Spilimbergo’s ...ndings, they ...nd that only migrants to the US seem to make an impact, while migrants to Portugal, the other main destination, do not. In contrast, they do not ...nd evidence of additional e¤ects for skilled migrants. The other context is that of Moldova, a former Soviet Republic with virtually no emigration before 1990 and which has seen a recent surge in migration out‡ows, estimated at half-a-million for a population of 3.6 million in 2008. The evidence we present for Moldova, which is purely descriptive, comes from the analysis of election outcomes in 2005 and 2009 (Omar Mahmoud et al., 2010). It shows that higher votes for the communist party are associated at the district level with migration to Russia while a negative correlation obtains for migration to the EU. Moreover, changes in the share of votes gained by the communist party between 2005 and 2009
# **1. Introduction**

An intensive debate focuses on the relative merits of bank-based versus market-based financial systems. Many authors stress the advantages that banks have over markets in financing the expansion of existing firms and in promoting the establishment of new firms. Others, however, emphasize the comparative merits of markets.' Historically, empirical research on the relative merits of bank-based versus market-based financial systems has centered on Germany and Japan as bank-based systems and the United States and Great Britain as market-based financial systems. This work has produced illuminating insights concerning the operation of financial systems in these countries. Nevertheless, it is very difficult to draw broad conclusions about bank-based and market-based financial systems from only four countries. To ameliorate this shortcoming, we have compiled a broad cross-country dataset with measures of the degree to which countries have bank-based or market-based financial systems.

Using this new data, this is the first paper to examine the following questions: (1) Do industries that depend heavily on external finance grow faster in bank-based or market-based financial systems?

(2) Are new firms more likely to form in a bank-based or market-based financial system? Thus, this paper concentrates on specific mechanisms through which financial structure influences economic activity and industrial structure.

This paper empirically assesses the validity of four competing theoretical perspectives regarding bank-based and market-based financial systems in the process of economic development. The _bank-based view_ highlights the positive role of banks in providing external 1
Under this strand of literature, determining whether yield growth has encountered deceleration or stagnation has been a topic of substantial debate with most studies focusing on grain and oilseed crops. At the global level, Alston et al. (2009) conducted a comprehensive study documenting a discernible slowdown in the growth of grain yields. Their findings underscored the potentially far-reaching implications for food price trends should this deceleration persist. Cassman (1999) contributed to this discourse by highlighting that the rate of yield increase for cereal crops potentially fell significantly short of the anticipated rise in food demand. Examining the trends in yields for the Big-4 commodities (maize, soybeans, wheat, and rice), Ray et al. (2012) concluded that, while yields continue to ascend in many regions, a noteworthy proportion—ranging from 24% to 39% of the Big-4 growing areas—exhibits a concerning pattern of either negligible improvement, stagnation, or outright collapse. Subsequent research by Ray et al. (2013) revealed that the growth rates of yields in these commodities fall considerably short of the levels required to meet the projected demand for food commodities by 2050.

At the regional level, Van Ittersum et al. (2016) asserted that the stagnation of yields in Sub-Saharan Africa presents a formidable food security risk, particularly as the population of the region is anticipated to reach 2.1 billion by 2050 (from 1.4 billion in 2020). Using grid-level data, Iizumi et al. (2014) found increased yield instability for the Big-4 commodities across a broad region of the Southern Hemisphere, corroborating previous findings of yield stagnation and collapses in these regions. Lin and Huybers (2012) examined wheat yield data across 47 regions, finding that approximately half of the production within their sample continued with a linear growth trend, while the remainder exhibited yield stagnation. Michel and Makowski (2013) estimated that wheat yield growth exceeded 0.06 ton per hectare per year in 1961-2010 in various countries across Europe, Asia, Africa, and the Americas
three consecutive years, respectively. Parameters are estimated ii,ii−1 ii,ii−2 ββ γγ γγ separately for 2017 and 2018. As data on crop cover are available only for 2016-2018, the effect of monocropping is estimated for two consecutive years in 2017 and for two and three years together in 2018. Indices for vegetative cover (EVI, LAI, FAPAR) and soil water content (LSWI) for the areas covered by relevant crops constructed as described above from remotely sensed data are used as alternative left-hand side variables. For both yields and these indices, (1) is estimated separately for maize, soybean, cereals (wheat and barley) and sunflower.

# **4. Results**

Results from yield regressions support the notion of continuous cultivation of the same crop having negative effects but, contrary to findings in the literature for (largely) US-based rotations, suggest that having sunflower as a predecessor crop leads to worse outcomes. For maize and sunflower, estimated marginal effects of three-year monocropping are significantly different from those of twoyear monocropping. Regressions with indices of vegetative development or soil water content (based on remotely sensed imagery) as dependent variables yield similar results, allaying concerns about our results being driven by shortcomings in statistical data and suggesting that exploration of such indices’ suitability for broader analysis may be warranted.

# **4.1 Yield effects of crop rotation**

10
Cross country studies show that ICT production and usage increase women’s access to economic opportunities by increasing access to information, reducing transaction costs, providing flexible arrangements, and overcoming distance barriers, especially in conservative cultures where women face mobility restrictions. A study using panel data on 209 countries for the years 1960 through 2002 found that an increase in ICT infrastructure and the use of ICT would reduce the gender gap in education and employment (Chen, 2004). The results confirm the positive role played by ICT in improving gender quality in employment, which in turn enhances economic development. A study in Africa used a panel of 48 countries for the period 1990 to 2014 (Efobi et al., 2018). It found that increasing mobile phone penetration, internet penetration, and fixed broadband penetration increased women’s participation in the labor market, while controlling for other factors such as economic level, openness, and democracy level of the economy. Similarly, Valberg (2020) found that ICT, measured by internet users (per 100 inhabitants) and mobile subscriptions (per 100 inhabitants), has a positive effect on female labor force participation using panel data for 156 developed and developing countries from 1991 to 2014. However, this positive effect differs with the country’s economic level. The results show that ICT is not the “ _leapfrogging_ ” strategy for a more gender-equal labor market in developing countries.

Research to date has always looked at access to the internet and its impact on the gender gap in the labor market. However, access to the internet is a necessary but not sufficient condition to ensure that women benefit, as they must consider using the internet to be safe and relevant to their lives and to have the skills and resources—such as a device with which to access it— to use it. An inclusive internet environment is one that offers availability, affordability, security, relevance, and inclusive policies (The Economist Intelligence Unit, 2020b).

This paper aims to fill the gap in the existing literature
also an insignificant but suggestive reduction in rental debt in the endline, equivalent to approximately a quarter of average rental debt in the control group, where the average household owes the equivalent of four months of rent. There is also a smaller but marginally significant reduction in rental debt in the follow-up 1.5 years after implementation. Additionally, we find some indication that monthly rent paid to landlords (including from the implementing organization) increases in the treatment group, consistent with more reliable rental payments through the program (see Appendix).

There were no statistically significant impacts on other outcome measures that were collected across multiple rounds, including for household composition, migration, and employment. However, there are some meaningful effects in particular survey rounds and we turn to them next.

# **5.2 Impacts in the midline round**

Recall that the midline phone survey was conducted while the HSP was ongoing and many households were still receiving assistance. The midline survey data indicates that HSP improved several dimensions of housing quality in the short run (Panel A, Table 5): households experienced a 21 percentage point increase in access to clean water (SE 0.06, control mean 17%) and a 0.51 room increase in dwelling size (SE 0.17), likely the result of door installation and other construction. Yet, as in the pooled results, there is not a statistically significant change in the overall housing quality index.

Food security outcomes at midline echo those in the pooled sample and also shed light on the possible mechanisms (Panel B, Table 5). As in the pooled results, respondents were more likely to report that they and others went to sleep hungry in the past 7 days, with a robust 23 percentage point (q-value = 0.01) increase in respondents’ hunger, and other adults and children in the household being 17 and 24 percentage points more likely, respectively, to go to sleep hungry (SE 0.06 and SE 0.07)
Against this background, this paper examines the main features of global recessions and the ensuing recoveries and expansions. Specifically, it addresses three questions. First, what happens during global recessions and recoveries? Second, how do global recessions and recoveries vary across different groups of countries, particularly advanced economies, EMDEs, and low-income countries (LICs)? Third, what happens during global expansions and how does the current global expansion compare with previous ones?

The paper builds on an extensive literature on various aspects of global and national business cycles.<sup>1</sup> A branch of this research documents the growing importance of global business cycles in explaining national cycles (e.g., Kose, Otrok, and Whiteman 2003, 2008; and Mumtaz, Simonelli, and Surico 2011). A second branch focuses on the roles played by trade and financial linkages in the cross-border transmission of business cycles.<sup>2</sup> A third branch studies the turning points of the global business cycle and its phases.<sup>3</sup> Our study is closely related to Kose and Terrones (2015; KT going forward), which presented the first detailed account of global recessions. KT mostly focused on global recessions and recoveries using annual data for 163 countries over 1960-2012. They presented a detailed review of the relevant literature, analyzed how financial crises lead to recessions, and examined the interactions between global and national cycles. KT’s work builds on Rogoff, Robinson, and Bayoumi (2002), which briefly examined whether the 2001 worldwide downturn was a global recession. Rogoff, Robinson, and Bayoumi (2002) focused on movements in per capita GDP growth to identify episodes that could be labeled as global recessions. They emphasized the importance of statistical and judgmental approaches to identify the turning points of the global business cycle.

This paper extends the literature in four dimensions. First, it covers a longer time span of annual series (1950-2019) and a larger set of economies (
emerging economies are largely absent. CAHF is unique in its effort to approach the house price estimation from the perspective of low-cost developers. Yet, CAHF takes it to the other extreme. It only collects the _cheapest_ price of a house built by a formal developer in African countries and does not factor in otherwise transacted housing units in the formal housing market. While insightful, this approach does not allow pricing the entire housing market, offering an understanding of the quantity of “affordable” houses available to different income segments.

Lastly, studies assessing the historical developments of private property prices are concerned with measuring financial (in)stability, which they attribute to distorted household mortgage debt to asset values ratios. A myriad of studies estimating house prices in developed economies were published after the collapse of the housing bubble and the resulting financial crisis in 2008/2009 mainly concerned with estimating how to identify housing bubbles in the first place. An examination of property prices from the perspective of affordability and demand-supply mismatches for different income segments is absent. In addition, studies assessing house price data usually draw on different methodologies to estimate property prices and rely on varying data sources. Hence property price estimations are not comparable across studies and scholars have only recently started to use big data approaches to collect actual property price data for a larger number of economies. Yet, their focus mostly remains on developed countries.

To fill these gaps, our paper extends the novel approaches in collecting house prices through a web scraping approach to emerging economies, thus addressing the substantial data gap in EMDEs. Further, this paper offers a methodology contributing to a more distributional understanding of private property prices in emerging economies. It also provides a comprehensive methodology to estimate a standard house price that 10
88 port of Colombo ( _pColombo,k_ ), in USD/MT (which we then convert to USD/kg).<sup>115</sup> Since data on port prices ( _pColombo,k_ ) is only available for five crops, we use equation (35) to compute _B_<sup>ˆ</sup> _k_ for each of these five crops, then impute the _B_<sup>ˆ</sup> _k_ of the other three crops (cinnamon, cloves, and groundnuts) with the average _B_<sup>ˆ</sup> _k_ among the five crops for which data is available.

## **D.C Production Function Parameters**

As explained in Section VI.A.3, we estimate Cobb-Douglas coefficients _γk_<sup>_f_by matching fertilizer</sup> cost shares (see equation 36). For that, we use data on national crop prices _p_<sup>_LKA_</sup> _k_ , national annual crop production _Q_<sup>_LKA_</sup> _k_ , and crop-level national annual fertilizer expenditure _Mk_<sup>_f,LKA_</sup> .

As shown in equation (37), we then apportion residual input shares to land and labor using data from IO tables on value added and labor compensation. To do that, we map our eight crops to the IO table’s agricultural sectors, which include “minor export crops”, “highland crops”, “tea growing” (separately for high, medium, and low elevations), “paddy”, and “potatoes” as sectors. We classify cinnamon and cloves as “minor export crops”, groundnuts, maize, and onions as “highland crops”, and rice as “paddy”. For tea, we aggregate high, medium, and low elevations into a single sector.

# **E Inversion Details**

In Section VI.B we describe our general approach to backing out unobserved exogenous variables (
<!-- Start of picture text --> Dashboard user inputs Mitigation— TM module energy consumption changes AirTM pollution. module<br>A chart-driven,interfaceuser-friendly ena reducedform A reduced form air pollution and health model<br>yy ©. Structure: uses national emissions factors (IIASA’s GAINs) by fuel-<br>+ carbon pricing policy — Structure: : 3, sector pairs, emissions-concentrations (TM5-FASST, EU JRC), and dose-<br>energy/carbon taxes, * 4sector groupings: ; , response functions (GBD 2019) to estimate health benefits of<br>* feebates,revenue useETSs — tax .* palinbuildings ~electricity — residential, generationservices %O° reductionsNMVOCs, SLCPs)in local air pollution (PM2.5, NOx, SO2, 03, CH4, BC,<br>reductions, transfers, * transport — road, rail, shipping & aviation , Outputs: reductions in mortality & morbidity from reductions in local<br>expenditures + industries — 11 sectors . Se, air pollutants — national totals by age group, fuel & sector source, and<br>+ complementaryfossil fuel & electricity policies — * etc.),15 N rucknuclear, aeereand renewables. fossil (coal natural gas) oil, inside vs. outside of the country, lost wages & health expenditures<br>subsidy reform + power sector has full techno-economic model<br>+ advanced module options: option; others use elasticity approach 3 Distributional module<br>o key projection sources Inputs (already in CPAT): VS<br>(IMF, WB, IEA etc.) + current domestic prices, taxes and subsidies \ = A cost-push microsimulation model for estimating effects on equity& industries<br>© elasticities (price, (from various sources) =. .<br>income, substitution) + international fuel price projections (IMF, WB, \ & Structure: uses harmonized HH surveys (WB & country), 1O
2

# **1 Introduction**

Securing market access and promoting exports are since long considered as primary drivers of economic growth and prosperity. The emergence of Global Value Chains (GVCs), which encompass production processes whose stages are spread across different countries, industries, and firms, complicate policy-making decisions. Global Value Chains introduce new opportunities and risks compared to traditional trade. On the one hand, GVC-led growth strategies feature additional gains from specializing in specific tasks or components and securing better access to inputs, technology, and skills through repeated firm-to-firm interactions (World Bank, 2019 and Antr`as, 2020). On the other hand, GVC participation presents countries with greater challenges, including more income inequality and heightened exposure to imported shocks and risks (due for example to dependencies on foreign partners, sourcing challenges, market concentration of critical inputs or stages of production, and new types of technology and intellectual property vulnerabilities).

Understanding how countries and sectors participate in global production networks is essential for analyzing the drivers of economic development, resilience to shocks, and the welfare implications of globalization. While trade in value-added and related GVC metrics have become mainstream in policy and academic circles, most rely on trade-based measures constructed from Inter-Country Input-Output (ICIO) tables, and bi-partite backward-forward classifications of linkages. These conventional measures, while insightful, fall short in some key respects: they often blur distinctions between participation modes, overlook indirect contributions of non-exporting sectors, and distort the true scale and nature of GVC integration.

This paper addresses these gaps by advancing both the methodology and interpretation of GVC participation metrics. It targets two fundamental questions: how much do countries and industries participate to traditional trade and to GVCs? And in what different ways? It argues that answering these questions requires a decomposition into three components: pure backward participation, pure forward participation, and a two-sided engagement strategically positioned between the extremes, involving elements of both backward and forward participation. Furthermore, the paper also advocates for extending the assessment of GVC participation to include both
66.7|0.0|33.3|
|ECU|0.1|10.9|2.0|87.0|
|GTM|0.0|62.9|0.0|37.1|
|HND|0.0|49.3|0.0|50.7|
|MEX|1.7|40.5|2.7|55.1|
|NIC|0.0|39.9|0.0|60.1|
|PAN|0.0|15.1|1.0|83.9|
|PER|0.0|38.6|0.0|61.4|
|PRY|0.0|56.3|0.0|43.7|
|SLV|0.0|61.6|0.0|38.4|
|URY|0.0|43.2|0.3|56.5|
|VEN|0.0|59.7|1.1|39.2|
|Total|0.3|38.1|1.4|60.3|

Source: Author’s calculation based on UNCTAD’s NTM data. Note: This table reports the turnover of NTMs for each of the 17 LAC countries. The row “Total” reports the average of the total NTM turnover in all 17 LAC countries.

Next, we study the margin with the greatest importance: regulations introduced between 2007 and 2017 and active in 2017. There the composition of NTMs is heavily skewed toward technical measures: SPS and TBTs make up 40% and 52%, respectively, of all new regulations introduced be13