rafmacalaba/data-use-mentions-extended huggingface dataset:
- Sala, E., O. Aburto-Oropeza, M. Reza, G. Paredes, and L. G. López-Lemus. 2004. “Fishing Down Coastal Food Webs in the Gulf of California.” _Fisheries_ 29 (3), 19–25.

Sanchez, A., J. J. C. Nieto, I. M. Osorio, B. Erisman, M. Moreno-Baez, and O. Aburto-Oropeza. 2015. Caracterizacion de las cadenas productivas pesqueras – baja california sur, mexico (primera fase la paz). Database, Centro para la Biodiversidad Marina y la Conservacion (CMBC) and Scripps Institution of Oceanography.

- Schaefer, K. M., D. W. Fuller, and B. A. Block. 2007. “Movements, Behavior, and Habitat Utilization of Yellowfin Tuna (Thunnus albacares) in the Northeastern Pacific Ocean, Ascertained through Archival Tag Data.” _Marine Biology_ 152 (3), 503–25.

- Scott, A. 1956. “The Fishery: The Objectives of Sole Ownership.” _The Journal of Political Economy_ 63 (2), 116–24.

- SEPESCA 1992. Legal Framework for Fisheries 1992, Technical report, Secretaria de Pesca, Mexico City.

- Sievanen, L. 2014. “How do small-scale fishers adapt to environmental variability? lessons from Baja California Sur, Mexico.” _Maritime Studies_ . 13:9:doi:10.1186/s40152-40014-4000940152.

- Velarde, E., E. Ezcurra, M. A. Cisneros-Mata, and M. F. Lavín, M. F. 2004. “Seabird Ecology, El Niño Anomalies, and Prediction of Sardine Fisheries in the Gulf of California.” _Ecological Applications_ 14 (2), 607–15.

- Villa, A. 1996. “A Review of Recent Changes in Mexico’s Fishing Policy,” thesis, College of Marine Studies at the University of Delaware.

- White, C. and C. Costello. 2011), “
percent, 52 percent, and 41 percent, respectively. These numbers suggest that domestic migrants typically come from the upper range of the education distribution, while international migrants tend to originate from the middle part of the distribution.

Within the same three migration categories, I primarily used the 1998 KMS data set to derive the household characteristics shown in Table 1. These can be viewed as baseline or pre-migration variables. The exceptions—which I obtained from the 2003 KMS data set—are the number of dependents (e.g., children and spouse) who moved overseas and within India between survey rounds, the number of returning migrants after 1998, and the change in the number of employed household members in Kerala between 1998 and 2003.<sup>20</sup> Over this five-year period, households with one or more overseas migrants saw a larger decline in the average number of working members living in Kerala than households with one or more domestic migrants. This suggests that these internationalmigrant households became increasingly reliant on remittance income. Households with one or more members who migrated overseas after 1998 were also more likely to have a member who was already working abroad in 1998: 40 percent of such households were recorded as having at least one member in a foreign workforce. Similarly, 27 percent of households with one or more individuals who departed after 1998 for work within India already had a family member in another state’s labor force as of 1998.

Overseas migrants tended to emerge from larger households (mean of 6.4 members), while domestic migrants came from households similar in size to non-migrant families (mean of 5.2). Households with members leaving to work abroad over the five-year interval also had more male members between the prime ages of 19 and 40 years (mean of 2.3) than households with domestic migrants (mean of 1.9) and non-migrant families (mean of 1.6). Nearly 50 percent of the migrants

> 20 Only 5 percent of households with members moving
papers/w11753 Rothstein, B., & Teorell, J. (2008). What Is Quality of Government? A Theory of Impartial Government Institutions. Governance, 21(2), 165–190.

Strand, I., Ramada, P., & Canton, E. (2011). Public procurement in Europe. Cost and effectiveness. PricewaterhouseCoopers, London Economics and Ecorys.

‐ Straub, S. (2014). Political Firms, Public Procurement, and the Democratization Process (TSE 461; Working Papers).

Thai, K. V (Ed.). (2009). International Handbook of Public Procurement. CRC Press.

World Bank. (2002). Bangladesh Country Procurement Assessment Report. World Bank. World Bank. (2009). Fraud and Corruption. Awareness Handbook. World Bank. World Bank. (2016). Benchmarking Public Procurement 2016. World Bank.

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Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" employ logistic regression models to examine the relative importance of these variables after statistically controlling for a number of other factors.

We hypothesize that the likelihood that a household will have a connection depends on six variables: 1) the monthly household income proxy, 2) whether or not the household lived in a rural area (RURAL), 3) whether or not the household lived in a low-income country, with GNP per capita below US$760 (LOWINCY), 4) whether or not the household is among the poorest 30% of the population in its own country (POOR), 5) whether or not the household was a homeowner (HOMEOWNER), 5) the size of the household (HHSIZE), and 6) whether or not the household lived in an Eastem European or Central Asian country (EEUROPE). Table 4 presents results of logistic regressions for five different binary dependent variables: whether or not the household has electricity, in-house tap, house/yard tap, sewer connection, and telephone. All models are estimated with the pooled cross-country data set.<sup>26</sup> Among the six independent variables, three measure how income affects the likelihood that households will be connected to these services: the household income proxy, LOWINCY and POOR. The household income proxy measures household wealth across countries. The model results show that it has a significant and positive influence in all five models, and the magnitude of its effect is largest in the model for electricity, and smallest in the model for sewers. While the household income proxy measures the differences in wealth for households _across_ countries, the second income-related variable, POOR, measures such differences _within_ each country. The coefficients on POOR are very consistent for the five models. Being poor in one's own country thus dampens the chance of being connected to these services at all income levels.<sup>27</sup> The third
budget reform outcomes. The level of technical assistance seems to be less important for a successful budget reform than the overall fragmentation of aid flows and the ways in which technical assistance is delivered. Thus donor hopes of “buying” better budget governance are more likely to be enhanced by better behavior rather than by additional aid resources, which implies that this is likely to work only in countries with enough capacity and interest in reforms.

The use of governance indicators by the World Bank came under increasing criticism from experts, academics, and partner countries. Composite indices, such as the Worldwide Governance Indicators and Doing Business, are based on empirical research on the links between institutional quality and development outcomes and used by the Bank to benchmark governance performance across countries and allocate IDA funds (see above). By the mid-2000s, several papers—including an internal review by IEG (2008)—highlighted the limitations of these indicators. Several issues were raised. Composite scores such as the Worldwide Governance Indicators were relative rankings of countries within a given period and were not meant for timeseries analysis. Some indices sought to integrate multiple source indicators, each of which

> 21 The Paris Declaration rests on five principles: aid is more likely to promote development when (a) _ownership_ : developing countries exercise leadership over their development policies and plans; (b) _alignment_ : donors base their support on countries’ development strategies and systems; (c) _harmonization_ : donors coordinate their activities and minimize the cost of delivering aid; (d) _managing for results_ : developing countries and donors orient their activities to achieve the desired results; and (e) _mutual accountability_ : donors as well as developing countries are accountable to each other for progress in managing aid better and in achieving development results.  In a parallel development, the European Union issued a Joint Statement in 2006 and approved a Code of Conduct for its Member States in May 2007. See OECD, 2006 _Aid Effectiveness Survey on Monitoring the Paris Declaration_ .

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|28.4|

**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 household weights, where the first survey round in each period is used as the base year. We use panel of household heads and define heads according to RLMS recommendations as: (1) the oldest working-age male in the household, (2) if there is no working-age male, then the oldest working-age female, (3) if there is no working-age female, then the youngest retirement-age male, (4) if there is no retirement-age male, then the youngest retirement-age female, (5) if there is no retirement-age female, then the oldest child. We use two types of panel: with all households and with households that have 50% or more members in other wave. Estimation sample size is 1926 and 1055 panel households from the 5th and 13th rounds of the RLMS, 1410 and 569 panel households from the 13th and 24th rounds of the RLMS and 753 and 173 panel households from the 5th and 24th round of the RLMS respectively.

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farm size classes (<20 ha; 20-50 ha; 50-120 ha, and > 120 ha). As there were no PSG applicants with more than 120 ha, this yielded a total of 7 strata. See Deininger _et al._ (2024) for a more detailed discussion of the sampling procedure, including a comparison to the official statistical database.

Data were obtained by phone in two waves in winter 2022/23, when information on the 2021 and 2022 agricultural seasons was collected, and in winter 2023/24, collecting data for the 2023 agricultural season, providing a three-year panel with data from before and during the war for the same farms. As might be expected in a setting where trust is low, the population has been traumatized by conflict, and mobile connectivity or electricity are frequently disrupted, levels of nonresponse and attrition were unfortunately very high: out of a total sample of more than 10,000 sample farms, only 2,500 valid responses were obtained in the 2022 round and only about half of these could be traced and provided answers in 2023.<sup>18</sup> To assess whether selective non-response may have resulted in bias, we can use data for all SAR registrants to check for variables that might increase the likelihood of a valid survey response by running a probit regression for whether a survey response was obtained. Results from doing so in appendix table A2 show that, with a marginal effect of 0.18 having applied for programs other than PSG is by far the most important predictor of a positive response to the survey, followed by being organized as a FOP (marginal effect of 0.13), having applied for PSG (0.10), and considering an application for credit (0.07).<sup>19</sup> Once these factors are controlled for, proximity to the front, as indicated by a dummy for being located within 50 km from the frontline, does significantly affect the likelihood of a positive survey response
Messen and Flaig, 2015). Instead, governments need to design and implement a variety of “soft” (Harrison and Rodriguez-Clare 2010) or “light-handed” (Taglioni and Winkler 2016) industrial policies, within the context of creating and facilitating open and liberalized economic environments. Our survey will identify the main areas that government policy making needs to focus on and the types of policies that can promote economic development by facilitating the extent and productivity impact of linkages between MNC affiliates and local suppliers.

The paper is constructed as follows. Section 2 discusses the concept of productivity spillovers and the various channels that transmit these effects. Section 3 presents the key findings from our survey of empirical studies on MNC sourcing linkages and their effects. This section focuses in particular on how characteristics of MNC affiliates and domestic firms and various host economy conditions and mediating factors influence the degree to which MNC affiliates use local suppliers, the nature and degree of technology dissemination, and the materialization of productivity spillovers
Applied to this study, the strategy assumes that firms within the same cell are likely to face similar levels of informal competition (confirmed below). Another assumption is that a given firms’ R&D activity has no or very little effect on the likelihood of other firms in the cell facing informal competition (reverse causality problem). Similarly, own firms’ characteristics that may affect its R&D activity are unlikely to be correlated with the proportion of other firms in the cell that compete against informal firms (omitted variable bias problem), although the possibility cannot rule out completely. Thus, using cell averages mitigates the reverse causality and the omitted variable bias problem. The use of cell average also helps to control for potential measurement error if some firms choose not to respond or misreport the variable of interest (Pounov 2016).

For our main regressions, we use cell averages at the country-level. This follows Distinguin et al. (2016) who also use the proportion of formal firms in the country that compete against informal firms as the instrument for the level of informal competition faced by the firm in question. Thus, we define our main explanatory variable, _Informal Competition_ (country-level cell average), as the proportion of all other firms (that is, other than the firm in question) in the country where the firm is located that report competing against informal firms. To ensure adequate thickness within the cells, all cells with fewer than 5 firms are excluded from the sample. In our baseline sample, the mean value of _Informal Competition_ equals 0.533 and the standard deviation is 0.209. Thus, on average, about 53.3 percent of the firms in the country-cell face competition from informal firms.

In the robustness section, we show that our main result continues to hold when we use cell averages at more disaggregated (sub-national) levels. These cell averages are at the country times industry times firm-size level and city times industry times firm-size level. Regressions using cell 11
expenditure, revenues, and primary balance moves somewhat in line with the energy price index in the post-GFC period.<sup>15</sup>

# **4.2 Determinants of fiscal policy volatility**

In the previous section, we established that EMDEs and commodity exporters display higher fiscal policy volatility than other countries. But what are the factors that make fiscal policy in these countries more volatile? Are there specific economic characteristics, such as GDP per capita, that can explain this? Advanced economies tend to have a significant presence of services and manufacturing sectors that dwarf their commodity sectors. As a result, being an EMDE and a commodity exporter is highly correlated. At the same time, the group of EMDEs masks substantial heterogeneity in income levels across countries. Likewise, commodity exporters are also characterized by important differences in the type of commodity exported and their degree of commodity dependence. In the empirical exercises that follow, we examine the role of these heterogeneities across countries in driving fiscal policy volatility.

We focus on indicators of fiscal policy that are related to the spending side of the budget. While each of the four fiscal policy indicators contains important information about fiscal policy, primary expenditures, and government consumption are likely to provide a more accurate perspective on the volatility of fiscal policy decisions. The reason is that capturing exogenous changes in fiscal policy using a specification like the one in equation (1) is more straightforward when using expenditures than when using revenues (or the primary balance). The automatic stabilizer component of expenditures tends to be small, so changes in expenditures tend to be driven by discretionary stimulus packages or by changes that are unrelated to the business cycle, and these latter ones are the changes we capture in our measure of volatility. On the other hand, when it comes to revenues, our linear specification might not appropriately capture the potential nonlinear nature of tax elasticities. Given that we are dealing with a large sample of diverse countries, it would be impossible to incorporate more precise specifications of tax
difference in means were performed to check balance around the cutoff using different bandwidth sizes. The McCrary test pointed to a perfect manipulation of the assignment variable around the cutoff point. This result usually suggests that ‘treated’ and ‘control’ firms can be systematically different. In fact, a joint test for difference in means suggested that eligible firms created before and after the program do not seem to look much alike on average. But these results seem to be highly influenced by spikes (heaps) in the number of firms created in October each year.

Because data heap could be confounding both the RD and DID estimates, a couple of robustness checks were performed to attest the impact of the program on formalization rates. First, RD regressions were rerun, controlling for data heaping. Second, placebos tests with different cutoff points were undertaken to check if there is some data seasonality by the end of each year that could be confounding the estimates.

It turned out that the available evidence of the impact of the program does not seem to be very robust, given the similar point estimates between what is supposed to be the effect of the program and those found in the placebo tests. The results seem to indicate the program was not effective in increasing the formalization rates of small firms; however, one could have a more cautious interpretation of the data and conclude that the microdata used in both Fajnzylber et al. (2011) and Monteiro and Assunção (2012) do not seem to be ideal to inform the impact of SIMPLES on firms’ formalization decision and performance.

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by contrast, only found significant productivity gains from materials offshoring, including Daveri and Jona-Lasinio (2008) for Italy between 1995 and 2003 and Ito and Tanaka (2010) for Japanese manufacturing over the period 1988–2004. Focusing on low-skilled workers, Egger and Egger (2006) found a U-shaped labor productivity effect from materials offshoring in the manufacturing sectors of 12 EU countries between 1993 and 1997.

A few studies use firm-level data. Görg and Hanley (2003) analyzed the impact of services offshoring on labor productivity for Ireland using plant-level data. The effect was positive in the electronics industry between 1990 and 1995. In a more recent plant-level study, Görg and others (2008) evaluated the productivity effects of materials and services offshoring for Irish manufacturing for the period 1990–1998, differentiating between exporting and non-exporting firms. They only found a significantly positive impact of services offshoring on TFP for exporting firms.

A recent strand of literature looked at the impact of trade liberalization on productivity through improved access to foreign inputs in developing countries. Amiti and Konings (2007) found, for Indonesia, that reducing tariffs on intermediate inputs leads to a productivity gain at least twice as high as gains from reducing output tariffs. Topalova and Khandelwal (2011) confirmed this result using firm-level data from India. In addition, Crino (2012) showed for 27 transition countries that importing inputs induces skills upgrading by facilitating the production of new goods, improvement of product quality, and, to a lesser extent, research and development (R&D) and technology adoption.

# **_FDI and Economic Upgrading within Sectors_**

Econometric studies of FDI spillovers usually investigate why the level or growth rate of productivity of domestic firms is higher in sectors where foreign firms are more prevalent. In the last decade, many researchers have econometrically examined the existence and direction of FDI-generated horizontal and vertical spillovers. Starting with the pioneering study by Caves (1974), early research on the topic examined the effects of horizontal spillovers using industry-level cross-sectional data, and generally
investment decisions (Baum 2022), as firms often train workers in firm-specific tasks, and workers themselves gain tacit and firm-specific knowledge the longer they are employed in a given job – which in turn makes them more productive. The relationship between tenure and productivity may not, however, be linear and, in fact, it may plateau at very long tenure values (Gagliardi, Grinza and <u>Rycx 2022). Finally, from a household perspective, changes in job tenure also impact wealth</u> accumulation. Households with more stable jobs -i.e., longer-tenured jobs- accumulate more wealth even when controlling for income (Kuhn and Ploj 2020). This effect grows with age, and workers who start their careers in unstable jobs see their whole life-cycle wealth accumulation affected. Analyzing the evolution of job tenure can thus be informative about changes in the labor market and also provide indications of the expected evolution of household welfare.

Motivated by these arguments, this paper first main objective is to provide an accurate assessment of the evolution of job stability, measured by changes in job tenure, across generations in Europe. The analysis is based on data from the Eurostat Labor Force Surveys (EU–LFS) on 29 European countries over the period 1995–2020 (Eurostat 2021)<sup>4</sup> .

The initial assessment is that job tenure has shrunk for workers from more recently born cohorts. For example, a worker born in 1980 has, on average, 3.6 years shorter tenure than a worker born in the 1940s. This result is obtained using an age-period-cohort decomposition of the group-level trends in job tenure, controlling for lifecycle changes and aggregate labor market shocks that are common across age cohorts.

This result is also confirmed when we model the duration of tenure at the individual level to account for changes in the labor market that might affect the composition of characteristics within the cohorts. We estimate the probability of having a job of three lengths of tenure—short, medium,