rafmacalaba/data-use-mentions-extended huggingface dataset:
materials in the control function.

Distinct production functions are estimated for each treatment industry and all control industries in each case. When estimating the production function for control industries, fixed effects for each six-digit control industry are included in the control function. Total factor revenue productivity (TFPR) for each establishment _k_ in case _j_ in each year is measured by subtracting from (the log of) sales, (the log of) employment, (the log of) fixed capital assets, and (the log of) materials consumption where the three inputs are multiplied by their output elasticities in a Cobb-Douglas production function (with unrestricted returns to scale).

**Effects of Antitrust on Revenue Productivity and Measured Misallocation** Table 7 reports the results of the meta regression in Equation (2) where outcomes are changes in alternative measures of productivity and misallocation. In Column (1) the outcome is the annualized change in the average of the log of TFPR _jk_ within an industry, where the average is weighted by the market (sales) share of firm _j_ . The effect of sanctions here is

> 17A match is not available between firms in the Economic Censuses of calendar years 1994 and 1999. Therefore the production function estimation only makes use of the censuses between 2003 and 2018.

30
during the period 2008-2015, on the average rate of change between 2004 and 2007 of four industry-level performance measures, i.e. total output, employment, TFPQ and markups. We use them as separate regressors and all at once.<sup>18</sup> As a further robustness test, we use the NTM probability only in the first year of the NTMs database (i.e., 2008). The results of these tests are presented in the Appendix Table A1. Odd columns show the simple correlation between dependent and independent variables, while even columns add 2-digit industry fixed effects. Virtually, all the coefficients of the performance measures are statistically indistinguishable from zero, thus providing further evidence against the possible violation of our identifying assumption.<sup>19</sup> Finally, after presenting our baseline estimates in the following sections, we check that the results still hold whenever we exclude large plants - defined as those producing more than 10 percent of a 5-digit industry’s output - or politically connected plants, as they might affect government’s decision on the application of NTMs.<sup>20</sup>

# **4. Impact of NTMs** **_vs_ Import Tariffs on Performance**

Table 2 presents the estimated coefficients of Equation 1 for three dependent variables: TFPQ, markup and TFPR. We do not find any evidence of a statistically significant impact of the aggregate NTM variables on productivity and markups.

The muted impact of NTMs contrasts with the effect of import tariffs, which in line with previous studies have a negative effect on firms’ performance. The coefficient of output tariffs on TFPQ (column 1) is negative, although not statistically significant at conventional levels. On the contrary, output tariffs have a positive and significant impact on markups (column 2). This can be interpreted as a competition effect: by lowering competitive pressure from imports, output tariffs allow plants to charge higher markups, while allowing their inefficiencies to increase. The combination of
in a society and affect public attitudes toward other social objectives such as growth and poverty reduction (Atkinson, 1980; Piketty, 1995; Corak, 2013). Because of this, the extent to which socioeconomic outcomes are transmitted from one generation to the next has long been of interest among development economists and policy makers.

Although understanding intergenerational mobility is important for policy, economic analysis of intergenerational mobility in developing countries is only in its infancy because of lack of appropriate data. In particular, intergenerational mobility studies on Sub-Saharan Africa are scarce.<sup>1</sup> The current study aims to fill this gap by taking advantage of recent nationally representative data that provide information on the social origin of adult individuals in nine Sub-Saharan Africa countries. We use education as an indicator of economic status. Using education as an indicator of economic status has four main advantages. First, the literature in both developed and developing countries identifies education as an important driver of labor market participation and, hence, income, more years of schooling is usually associated with higher income (see Chevalier et al., 2003; Blanden et al., 2005; Black and Devereux, 2011). Understanding the trends, levels and patterns of the persistence of the education attainment across generations therefore sheds light on overall mobility in economic status in a society. Second, schooling is an outcome on which it seems reasonable to assume respondents can reliably report on their parents. Third, data restrictions, especially in developing countries, are much less stringent; retrospective information on parents’ education has been more widely available recently than information on parental incomes or occupations. Finally, using income to proxy socioeconomic outcomes in developing countries is found to be problematic. There is a serious concern about persistence of measurement errors in consumption and income data from developing countries (see Deaton, 1997; Glewwe, 2005; Lee, 2009 for detailed discussion). Education is also considered a vital policy instrument in the creation of equal access to economic opportunity, leading to higher social
whole government accounts which consolidates more than 5,500 public sector entities although it does not have direct management responsibility for the assets and liabilities of all component organizations.<sup>19</sup> The budget process is often a key medium to report on management of assets and liabilities, sometimes with a specific parliamentary committee in charge of assessing the policy implemented and the results achieved.

All the countries that responded to this question mentioned formalized reporting processes for government debt and, when mentioned, financial assets managed by SWF and public pension plans, both internally (e.g. quarterly monitoring report to the supervisory board) and externally (e.g. monthly /quarterly debt statistical bulletin, annual report on the debt management strategy).<sup>20</sup> In the case of Chile, the country publishes on a quarterly basis information on the

> 19 HM Treasury’s report for Whole Government Accounts is available at https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/525617/WEB _whole_of_gov_accounts_2015.pdf.

> 20 Only one respondent mentioned internal reports and no public reports.

18
**Table 9. Parameters of selected log-linear models. Youths from 18-35 years of age of ENCEL 2017.**

|(a)<br>Model 10.<br>diagonal|Corners (4p)|with diverse|(b)<br>Model 11. Cor<br>diagonal.|ners (5p)|with diverse|
|---|---|---|---|---|---|
|**Parameter**|OR|Value of P|**Parameter**|OR|Value of P|
|dp1<br>(NM inheritance)|2.61|<0.001|dp1<br>(NM inheritance)|2.27|0.003|
|dp23|||dp23|||
|(HM, LMM, LMS|<br>1.80|<0.001|(HM, LMM, LMS|1.80|<0.001|
|inheritance)|||inheritance)|||
|dp6|||dp6|||
|(Agriculture|2.74|<0.001|(Agriculture|2.92|<0.001|
|inheritance)|||inheritance)|||
|esq|1.21|0.019|esq1|0.68|0.174|
||||esq2|1.32|0.002|

Source: Own elaboration from ENCEL 2017 (n=3,084).

The results of the log-multiplicative version of the models 2, 4, 8 and 9, which present values of the φ coefficient different from 1 (between 0.74 and 0.79) for these theoretical models, provide additional elements to define whether or not there are differences between both sexes. This leads us to conclude that there is a difference in relative mobility between females and males, although it
# **1.2 Data**

As the main source of data for our investigation, we rely on household surveys, which have been systematically held in Central America over the past three decades.<sup>4</sup> As shown in Appendix Table A.1, the country with the most data is El Salvador, where 19 surveys are available between 1989 and 2013. Panama follows with 17 from 1991 to 2013. For Honduras and Costa Rica, 16 surveys are available for the periods 1989-2012 and 1987-2013, respectively, while for Guatemala and Nicaragua, a more limited set of 9 and 7 surveys exist between the 1990s and 2013.

One important advantage of using household survey data is that different education system structures can be accounted for in order to guarantee comparability in cross-country analysis. A crucial variable for comparability is the official age at which individuals are expected to attend each education level, and the number of grades comprising each level. As illustrated in Appendix Table A.2., most Central American education systems include 12 years of formal schooling between primary and graduating from upper secondary, with entry normally at age 6 and expected exit at age 17, in line with the rest of Latin America. Two exceptions are Guatemala and Nicaragua, where both entry and exit are set one year later (as in Brazil). Having access to the original micro data in household surveys allows adjusting the data accordingly, in order to make accurate comparisons in attendance by level and official schooling ages.

The data also allow internalizing differences in the definition of types of secondary schooling and relating dropout to important individual and household characteristics. In all six countries in Central America, the post-primary and pre-higher education cycle—which we generally label as secondary education (secondary) here—is split into two segments. The first segment of lower secondary (LS) has a typical duration of 3 years, followed by another 3-year segment of US. Apart from allowing for consistent comparisons, relating ages with schooling levels is particularly important
vehicle occupants. However, the results also suggest that improvements in pedestrian safety were significantly dampened by urban and elderly population growth over the sample period.

# **6. Conclusions**

In order to examine fatalities per distance traveled we must use data from countries with reliable information on VKTs. This limits our study to high-income countries. We believe, however, that the experience of the IRTAD countries over the period 1963-2002 is relevant to the current situation faced by developing countries. The levels of per capita income of the poorest IRTAD countries in 1963 (e.g., Korea, Greece, Ireland and Portugal) were the same as (sometimes below) the income levels of many low- to middle-income countries today. Moreover, the pattern of traffic fatalities in many IRTAD countries at the beginning of our panel was similar to those recently observed in developing countries: in 1970, the ratio of pedestrian to total traffic fatalities exceeded 40% in nine IRTAD countries. We therefore believe that our findings have implications for developing countries today.

We find that the decline in the road death rate (fatalities/population) with income at high income levels (Kopits and Cropper 2005) is dominated by a decline in the pedestrian death rate. Both pedestrian fatalities per VKT and per capita have steadily declined with growth in per capita income in the IRTAD countries, at least over the range of incomes one can expect in low- and middle-income developing countries over the next 20 years. Our more detailed models fail to explain exactly why this decline occurred; 27
Uganda is another East African country whose structural change experience has been examined by Ahmed _et al_ . (2015). These authors document that Uganda’s economy underwent significant structural change in the 2000s, whereby the share of non-tradable services in aggregate employment rose by about 7 percentage points at the expense of the production of tradable goods. The process also involved a 12-percentage-point shift in employment away from small and medium enterprises and larger firms in manufacturing and commercial agriculture mainly to microenterprises in retail trade. In addition, the sectoral reallocation of labor on these two dimensions coincided with significant growth in aggregate labor productivity.

Nigeria’s structural change experience has been examined by Adeyinka _et al_ . (2013). This paper documents that structural change accounts for approximately one-fifth of the total change in labor productivity in Nigeria between 1996 and 2009. Labor moved out of the agricultural and wholesale and retail trade sectors into manufacturing, transportation and communications, business services, and general services. While structural change did occur in this period, significant gains to aggregate labor productivity are still available from further shifts of labor to higher-productivity sectors. The paper discusses the factors limiting structural change, which include poor agricultural productivity, insufficient infrastructure to support high productivity sectors, and a lack of appropriate skills in the labor force.

The World Bank 2018 Systematic Country Diagnostic (SCD) for Guinea comprises an analysis of labor productivity and structural change. According to the report, labor productivity growth was driven for the most part by structural change and, to a lesser extent, by within-sector productivity growth. More specifically, static structural change contributed approximately 12 percentage points of the 10 percent labor productivity growth during 1994-2015, and within-sector productivity changes accounted for -1.2 percentage points.

In Northern Africa, the experience of Tunisia has been examined by Marouani and Mouelhi (2015). These authors analyze the dynamics of sectoral productivity growth in Tunisia and assess the contribution of structural change to these dynamics. Using sectoral and firm data, the paper shows that productivity increased at a relatively
cooking fuel but had not purchased it presumably represents those who had bought large quantities of LPG but not in the month preceding the survey. Because of this large discrepancy, data on LPG as the primary cooking fuel are used in the selection equation. The quantities of LPG purchased were estimated for the survey period from the expenditure information using national average LPG prices (published by the energy ministry) for the month prior to that in which the survey was administered to the various households.

Quantities of LPG purchased in Mexico were markedly higher than in other countries. Household consumption was slightly larger in urban than in rural areas (Table A2.21). In each  quintile, household size was smaller in rural than in urban areas by as much as 20 percent in  the top quintile making rural per capita consumption of LPG larger.
. Therefore this is an optimistic assumption particularly in developing countries, where most, if not all, components of the disaster and climate risk management value chain lack capacity to varying degrees. WMO et al (2015) highlights that assumptions of perfect decisionmaking based on all available information are not realistic, advocating for simultaneous and complementary capacity building for both providers and users of weather and climate information to realize maximum benefits.

# 3.3.2 Attribution to global NWP

As providing global forecasting products alone is in most LICs not sufficient to improve forecasting quality to western European standards, the attributable benefits would only be a certain percentage of the total potential benefits estimated by Hallegatte (2012). However, defining this percentage is a difficult task and – to the authors’ knowledge – there are no publications that have determined such a percentage.

Using historical data on forecasting quality trends, combined with expert assessments, an attempt is made to estimate the percentage of potential LIC forecasting improvement due to global NWP. It must however be noted that forecasting is the value provided by the interpretation of guidance, including NWP, along with other expert knowledge and information. If the local forecasts are good, they will benefit greatly from improved guidance, in this case global NWP outputs. If local forecast capacity is limited, improved access to global NWP may not add any significant value. The analysis therefore 14
until 2007 and then decreasing in construction, hotels and restaurants and also for all sizes of firms. On the other hand, interesting things happen for some groups of workers. The probability of entering shadow employment has been decreasing for the entire period for the youngest cohort and increasing for those close to pension age. The former might be related to the changing demographic situation with decreasing number of young people entering the labor market both due to natural reasons and increasing participation in tertiary education. The latter may result from changes in the disability pension system limiting the access to these kinds of benefits and resulting in increasing number of older workers with relatively low employability looking for jobs.

At the same time, the estimated probability of shadow employment kept constantly increasing for those with secondary education only, indicating a weakening position of this group in the labor market.

Last but not least, one should also observe that the estimated share of shadow employment was practically stable throughout the analyzed period for groups of workers divided according to the type of contract. It is a consequence of the strong interdependence of contract and shadow variables and it proves that most of the changes in estimated shadow employment have been driven mainly by changing structure of employment contracts.

# **4. Shadow Employment and Labor Market Transitions**

In order to run the analysis of labor market transitions between various kinds of labor market statuses using the data from the Polish LFS, one has to take advantage of its quasi-panel nature. Each interviewee in LFS takes part in the survey four times (on a quarterly basis) with two quarters of brake after the second interview. It means that there is exactly one year of break between the first and the third interview and between the second and the fourth interview. It is then possible to create a set of databases consisting of a series of biannual
tertiary education is relatively high. In 2023, 18.6 percent of people aged 25 to 34 had a tertiary education, compared to 11.5 percent of people aged 35 to 54, 9.1 percent of people aged 55 to 64, and 5.4 percent of people aged 65 and older (see Figure 5). It should be noted that there is a relatively low share of people aged 15 to 24 with a tertiary education because they are likely still in the process of obtaining formal education.

**Figure 4: Share of population by level of education, 2016-2023 (%)**

<!-- Start of picture text --> 100<br>9.4 9.5 10.1 10.0 10.2 10.5 10.2 10.7<br>80<br>60 49.3 49.7 50.4 50.9 52.2 52.7 52.1 53.5<br>40<br>20 41.3 40.8 39.6 39.1 37.6 36.9 37.8 35.9<br>0<br>2016 2017 2018 2019 2020 2021 2022 2023<br>Primary Secondary Tertiary<br>Share of population (%)<br><!-- End of picture text --> Source: Author’s calculations using Sakernas data

**Figure 6: Share of employment by sector, 2016-2023 (%)**

<!-- Start of picture text --> 100<br>80<br>46.7 48.1 47.9 49.1 48.7 49.3 49.2 49.6<br>60<br>40 21.4 22.3 23.1 23.3 21.6 22.3 22.2 22.2<br>20<br>31.9 29.7 29.1 27.
# 1. Introduction

Directly and comprehensively measuring non-durable consumption expenditures via large-scale household surveys such as the Living Standards Measurement Surveys (LSMS) is generally regarded as the best way to assess the prevalence of poverty in low income countries. But such surveys are complex and costly, and so are conducted in fewer places, with smaller samples, and with lower frequency than is desirable if our aim is to measure changes in poverty over time (Beegle et al. 2016).

In response, researchers have been looking for alternative approaches to track poverty at reduced cost and effort. One approach involves the construction of models that use observed characteristics that are easier to collect (or that have already been collected for other purposes) to predict consumption. The success of this approach hinges critically on the stability of the model parameters over time and across different survey designs. One way to mitigate this concern is to identify empirically the conditions under which this assumption is more likely to hold (Christiaensen et al. 2012; Mathiassen 2013; Kilic and Sohnesen 2019). Another is to combine the survey collection, with more complete consumption aggregates for some households, while only collecting proxies for consumption for others (Pape and Mistiaen 2018; Yoshida et al. 2015). Nonetheless, model parameter instability cannot be excluded. With more high-resolution earth observation data and more powerful machine learning expertise now readily available, another class of models predicts consumption and poverty by applying artificial intelligence techniques to a combination of satellite imagery (especially daytime imagery) and existing household data. Prediction accuracy often exceeds benchmarks from more standard regressionbased approaches, especially in estimating poverty levels at very disaggregated levels—so-called poverty mapping (Head et al. 2017; Jean et al. 2016; Watmough et al. 2019). However, whether these models calibrated on data for a certain year are better able to make accurate predictions on data for another year remains largely untested.<sup>1</sup> The issue of model stability remains.

This paper explores the potential of a
cyclicality has a positive and significant impact on labor productivity when industry is credit‐ constrained.<sup>54</sup> A recent report, Ball et al (2016), assesses past theories and recent empirical evidence on the tools available to central banks and their effectiveness, concluding that it remains effective in raising aggregate demand (even in the presence of deleveraging), and easing credit supply. Both the bank credit channel and the bond market/risk premia channels (portfolio balance channel) are important for output recovery. Romer and Romer (2013) demonstrate that it is dangerous to assume that monetary policy should not be used as it is not effective.

**Figure12.a: Official interest rates (%)                              Figure 12.b:  Balance sheets, FED and ECB** (Trillions of dollars or euros)

<!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>FED ECB BoJ BoE<br>2007‐01 2007‐07 2008‐01 2008‐07 2009‐01 2009‐07 2010‐01 2010‐07 2011‐01 2011‐07 2012‐01 2012‐07 2013‐01 2013‐07 2014‐01 2014‐07 2015‐01 2015‐07<br><!-- End of picture text --> <!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>ECB FED<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> Source: ECB and Federal Reserve Bank of St. Louis.

Weekly data. FED: FED, Federal funds (effective) rate ECB: ECB, Main refinancing operations interest rate BoJ: Bank of Japan, Basic discount rate and