rafmacalaba/datause-encoder-data huggingface dataset:
43 studied or taken care of a child under three years of age for 12 months during the previous three years.

After the introduction of this new benefit scheme the ratio of benefit receivers to all unemployed fell from 72% to 58% in January and to about 35% in the first half of 1993. The unemployment rate fell gradually from 4.4% in January 1992 to below 3%, but since August 1993 it has been constantly above 3%.

Active labour market policies were introduced in June 1991 in the Czech and Slovak Republics. They are administered by the district employment offices, implying that they have considerable discretion over the amounts to allocate to active labour market policies as a share of total spending and over how to implement the different programmes (in 1994 jan-sept active labour market polices shares of total spending ranged from 6,3% to 46,7% in the different districts and averaged 24,8%).

Since 1991 unemployment has been considerably reduced by active employment measures in the Czech Republic. In 1991 the number of people who found employment through such a measure amounted on average to a share of 48% of the unemployed, when unemployment reached its highest level so far of over 4%. In the following year after the tightening of unemployment benefits in January 1992, the share was up to 77%, but unemployment dropped back below 3%. In 1993 and the number of people employed through active labour market policies was reduced to less than a third of its 1992 level or 28% of unemployment. In 1994 the share of active labour market policies has been markedly reduced so far, even though unemployment is picking up again. The share of employment through active labour market policies to unemployment was 15% on average from January to September 1994.

Under the assumption that entry into an ALMP scheme implies a one-for-one outflow from the unemployment register the unemployment rate in the Czech Republic would have been 1.5-2% points higher in 1991 and 1992: in 1993 and 1994 these employment policies reduced directly the unemployment rate by, however, less than 1 % point with a decreasing tendency. Looking at the net effect of ALMPs (taking into consideration substitution effects and dead weight loss) the differentials in the unemployment rate implied by these policies should be, however, smaller.

In 1991 and in 1992 emphasis was put on long-term employment creation policies called "Socially Purposeful Jobs (SPJ)". The created jobs in this scheme were a major source of oufflow out of unemployment (SPJs accounted for 35% of
Collins (2008) and Perkins and Rawski (2008) obtain similar results, while in Chow and Lin (2002), Feenstra et al. (2015), Woo (1997), Wu (2011), and Young (2003) the TFP share of growth is somewhat lower. Tian and Yu (2012) conduct a meta-analysis of 150 papers and find that the mean aggregate TFP growth is about 2 percent a year after 1978, accounting for about a fifth of GDP growth.

In the three decades before the global financial crisis, half of the increase in output per worker was due to capital deepening. Since 2008, growth has relied to a significant extent on capital accumulation which accounted for over 80 percent of growth in output per worker. High investment growth was in part driven by the large fiscal stimulus packages introduced first in in response to the 2009 global recession and later to cushion the 2015-16 growth slowdown (see below). In contrast, TFP growth decreased sharply to 0.7 percent a year in the same period. Other studies also estimate that aggregate TFP has declined in recent years (Wei et al., 2017; Wu, 2017). The TFP growth slowdown is explored further in the following sections.

# **Figure 1 Decomposition of growth in China's output per worker**

(contributions to the average annual change in real output per worker, percent) <!-- Start of picture text --> TFP Physical capital per worker<br>Human capital per worker<br>10<br>8<br>6<br>4<br>2<br>0<br>1979-2018 1979-1988 1989-1998 1999-2008 2009-2018<br><!-- End of picture text --> Source: Herd (2020); NBS; PWT 9.1; authors’ calculations.

Improvements within sectors, as well as gains Source: Herd (2020); NBS; PWT 9.1; authors’ calculations. from the reallocation of resources between sectors, were both important in raising China’s labor productivity. Following the methodology in Timmer et al. (2015), labor productivity growth can be decomposed into changes in productivity within sectors and movement of workers across sectors:

> response to higher TFP. In this alternative calculation, TFP growth is multiplied by 1⁄ (Barro and Salai-Martin, 2004, p. 457). Following this approach, TFP plays a much larger role, contributing 6.2 percentage points to growth (more than four-fifths of growth in output per worker) in 1979-2008. Using this alternative method of (1 −αα) growth decomposition, Zhu (2012) also finds that TFP accounted for 78 percent of per capita GDP growth in China over the same period.

4
# Figure 6 Determinants of the sensitivity of inflation expectations to shocks

A. Impact of inflation targeting regime (dummy) on sensitivity of inflation expectations <!-- Start of picture text --> Percentage points<br>0.2<br>-0.2<br>-0.6<br>-1.0<br>All Advanced EMDEs<br>economies<br><!-- End of picture text --> C. Impact of fixed exchange rate regime (dummy) on sensitivity of inflation expectations <!-- Start of picture text --> Percentage points<br>1.2<br>0.8<br>0.4<br>0.0<br>-0.4<br>All Advanced EMDEs<br>economies<br><!-- End of picture text --> E. Impact of 10 percentage point increase in trade openness (import penetration) on sensitivity of inflation expectations <!-- Start of picture text --> Percentage points Group-mean FMOLS<br>0.2 Group-mean DOLS<br>0.0<br>-0.2<br>-0.4<br>All Advanced EMDEs<br>economies<br><!-- End of picture text --> B. Impact of one-unit increase in central bank transparency on sensitivity of inflation expectations <!-- Start of picture text --> Percentage points<br>0.4<br>0.0<br>-0.4<br>-0.8<br>-1.2<br>-1.6<br>All Advanced EMDEs<br>economies<br><!-- End of picture text --> D. Impact of one-unit increase in financial openness on sensitivity of inflation expectations <!-- Start of picture text --> Percentage points<br>1.8 Financial openness<br>1.4 Financial openness ×<br>1.0 exchange rate regime<br>0.6<br>0.2<br>-0.2<br>-0.6<br>All Advanced EMDEs<br>economies<br><!-- End of picture text --> F. Impact of 10 percentage point increase in public debt-to-GDP ratio on sensitivity of inflation expectations <!-- Start of picture text --> Percentage points<br>0.4 Group-mean FMOLS<br>Group-mean DOLS<br>0.2<br>0.0<br>-0.2<br>All Advanced EMDEs<br>economies<br><!-- End of picture text --> Sources: Chinn and Ito 2017; Dincer and Eichengreen 2014; International Monetary Fund; Shambaugh 2004; World Bank.

Note: DOLS = dynamic ordinary least squares; EMDEs = emerging market and developing economies; FMOLS = fully modified ordinary least squares; GDP = gross domestic product.

A.-D. Inflation expectations are five-year-ahead expectations of annual inflation. Bars denote coefficients of panel regressions of 24 advanced economies and 23 EMDEs using annual data for 1995-2016, as described in Appendix 3. Vertical lines denote 90 percent confidence intervals.

D. Financial openness x exchange rate regime is the interaction of these two explanatory variables.

E.F. Bars denote coefficients of group mean panel FMOLS and group mean DOLS regressions of 24 advanced economies and 23 EMDEs using annual data for 1995-2016, as described in Appendix 3. Vertical lines denote 90 percent confidence intervals.

23
We present the results of the effects of warmer temperature on contemporary poverty in Table 1. Our main focus is the headcount poverty rate at the subnational unit level, using the $3.65/day or $6.85/day poverty thresholds.<sup>14</sup> To further highlight the differences between estimates using country-level data versus using subnational unit level data, we aggregate the subnational unit level data to the country level.<sup>15</sup> We begin with an examination at the countrylevel (Panel A), followed by the localized analysis at the subnational level (Panel B). We employ two models: the fixed-effects model and the long differences model, as shown in Equations (2) and (4). In each panel, the results of the fixed-effects panel model are presented in Columns (1) and (3), with the results of the long differences model in Columns (2) and (4).<sup>16</sup> Across both panels, the findings reveal strong evidence of the negative effects of rising temperature on poverty, regardless of the poverty measures used.

Furthermore, the estimates derived from the subnational-level analysis (Panel B) exhibit stronger magnitudes compared to the country-level analysis (Panel A). Results of the equality tests confirm that these variations between the two levels of analysis are significant, indicating that an analysis based on country-level data may underestimate the effects of increased temperatures. This finding is consistent with that in another study using different poverty data (Dang _et al._ , 2023a) and aligns with earlier research that has identified stronger effects of temperature on economic growth at the subnational level (e.g., Damania _et al._ , 2020; Kalkuhl and Wenz, 2020).

We then turn our focus to the subnational analysis to interpret the estimation results. Specifically, Column (1) of Panel B illustrates that a 1<sup>◦</sup> C increase in temperature corresponds to a 7.2 percentage point increase in poverty, translating to a 61.5% increase in poverty given

> 14 We also check the robustness of our findings using alternative measures of poverty in the Appendix.

> 15 Alternatively, we also use country-level data from the World Development Indicators (WDI) database. Our findings remain consistent (see Table A4 in the Appendix).

> 16 For the long differences model, we measure poverty and weather variables as the differences between 2010 and 2019.

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16 investing across borders: do fdi-specific regulations matter?

rates are measured by the weighted average of applied tariffs on manufactured imports. The effect of high tariffs on FDI, a priori, is ambiguous: it may encourage tariff-jumping horizontal FDI, but discourage vertical FDI that relies on repeated flows of parts and components across borders. The general skill level in the country is assessed by the average number of years of schooling undertaken by adults aged 25 and over. Other determinants of FDI that are not included as regressors are subsumed under the unobserved term, e. The description, source, and summary statistics of the variables are in Tables 13 and 14.

# 1.4 data

I average outward FDI position (stock) in US dollars for 2007 and 2008 from 30 OECD countries into 87 OECD and non-OECD countries belonging to the IAB sample (Table 16). The years 2007 and 2008 represent the latest and most stable number of observations in the OECD’s FDI database, prior to the plunge in crossborder flows in 2009 because of the global financial crisis. Two-year averages are used to smooth out annual fluctuations. For robustness, I also use un-averaged annual data for 2006, 2007, and 2008. Bilateral FDI from each source country, instead of aggregate FDI from all source countries, is used to cast the relationship in a gravity framework yielding a richer set of observations than would be the case for a cross-country regression with aggregate FDI. Source countries include only members of the OECD because of data constraints; however, they have historically accounted for the majority of global FDI outflows.<sup>18</sup> The choice of FDI stock as the dependent variable, preferred to FDI flows, needs elaboration. The model in Melitz (2003) which inspired Helpman et al. (2008) is inherently cross-section because it assumes steady state productivity levels for

> 18 FDI outflows from non-OECD countries are increasing. For example, in 2003, 12.3 percent of total FDI inflow into the 10 member countries of the Association of Southeast Asian Nations (ASEAN) was from China, India and other ASEAN countries; by 2008, the corresponding share had reached 24.8 percent (ASEAN 2010).
<!-- Start of picture text --> 3<br>S<br>Q<br>So<br>a<br>Ae?<br>o.2<br>N -_<br>ae<br>= 5 2.3<br>s Jo 1 2.0 16<br>ge ' °<br>& =I<br>Eo<br>ge= ° -0.7 -0.8<br>= 7 -1.2<br>Oo<br>-]<br>Global Without Without Without<br>China India China & India<br>HMMM Due to changes to between-country inequality<br>MOE Due to changes to within-country inequality<br>SoQ l —<br>S<br>a U3<br>ron<br>==<br>SZ<br>A28 5 1.05<br>SS 0.87<br>> Sb<br>ES 2 0.54<br>os 0.43<br>Zo<br>ae<br>22 0<br>on2, -0.20 -0.26<br>S<br>3S<br>=<br>Oo<br>-.5<br>Global Without Without Without<br>China India China & India<br><!-- End of picture text --> MH Due to country-level negative growth ME Due to changes in within-country inequality
_Page 3_ _Bidding for Concessions_

# BIDDING FOR CONCESSIONS

# TABLE OF CONTENTS

|**I.**|INTRODUCTION<br>.1||
|---|---|---|
|II.|WHAT TO AWARD- CONTRACT<br>DESIGN.|3|
|**III.**|To AUCTION OR NOT TO AUCTION?<sup>.</sup>|8|
|IV.|HoW TO AUCTION .15||
|V.|WHO AUCTIONS<br>.20||
|VI.|CONCLUSIONS<br>.21||
|BIBL|IOGRAPHY ..................................................................|23|
- 42 -

used to derive an estimated freight rate (Foi) for specific country exporters.4/ Third, the expected ratio (Rei) of the projected freight rates was derived from,

# (6) Rei = ((Fci/Foi) x 100

where Fci is the projected rate for the caribbean country and Foi is that for the major exporter in another region. Similarly, the ratios of the actual dollar freight rates were computed and the difference between the projected and actual freight ratios derived. A systematic tendency for the predicted ratios to be smaller than the actual ratios would indicate that some factor(s) were causing a systematic upward bias in Caribbean freight rates.

Table 8 compares the expected ocean freight rate differentials derived from equation (5) with actual rates. The key finding from Table 8 is that there is no systematic evidence of an upward bias in Caribbean exporters vis-a-vis their competitors in other regions. In only 10 (28 per cent) of the comparisons do the actual Caribbean-competitor transport cost ratios exceed the predicted ratios with four of these "reversals" accounted for by crude petroleum exports from Trinidad. As such, after differences in distance, unit values, stowage factors, shipment sizes and competitive factors have been

> 4/ Information on stowage factors and other independent variables in equation (5) were taken from Lipsey and Weiss (1974, pp. 185-188). Unit value information was derived for different groups of products directly from United States import statistics. In calculating the distance term for equation (5) the following liner routes were used: Near East - Port Said - to New York; South America (Atlantic Coast) Montevado to New York; South Central Asia - Bombay to New York; Southern Africa - Cape of Good Hope to New York; South Asia - Singapore to San Francisco; Northern Asia - Korea to Seattle. These distances had to be modified for some specific regional exporters (i.e. sugar from Peru).
where _e_ indicates the price of a dollar in terms of local currency.<sup>12</sup> In turn, our measure of intervention for country _s_ and year _j_ is defined as the annual average of the absolute value of monthly interventions (months are indexed by _t_ ), that is, the average absolute change in net international reserves relative to the monetary base in the previous month, both measured in US dollar: (2) where the monthly absolute change in reserves is normalized by the monetary aggregate (both measured in the same currency) to capture the monetary impact of the intervention.

Using this measure of intervention, together with data on the volatility of exchange rates, we updated the LYS dataset to cover the period 1974-2004 and, based on new information, completed the classification for a number of undisclosed basket pegs. As a result, the new dataset includes 179 countries and 4189 observations, covering 82% of all country-year observations for the period.<sup>13</sup> We can use the measure in (1) to benchmark actual interventions by type of regime against a “typical” intervention under a float, which can be proxied by the distribution of the intervention variable _R_ for the Australia, Japan and the US, three countries that are often considered the closest to textbook floating regimes ( **Figure 1** ).<sup>14</sup> As can be seen, while pegs generally exhibit heavier intervention than floats, there are still many pegs with limited intervention –relative to the benchmark floats–a fact that can reflect the success of pegs in preempting market pressure, or the fact that many countries choose to peg only when they do not anticipate that the peg will be subject to considerable shocks.

**Figure 2** shows the distribution of exchange rates over the recent years. The graph shows that regime choices remained remarkably stable, particularly since 1990. This evidence looks unkind to the bipolar view that forecast the disappearance of intermediate regimes, although it shows a very slight increasing

> 12 All variables correspond to the end of period for a specific month.

> 13 To our knowledge, the updated LYS regime classification offers the largest country and year coverage over the postBretton Woods period. The data is available online at the authors’ web pages.

> 14 The distributions are based on pooled observations of the variable _R_ averaged over the year.

7
psychological explanations which view individuals in isolation. In other words, sociologists are more inclined to ask questions about how cognition or perception may differ by social status, wealth or other demographic variables, while psychologists often treat these as biologically driven and/or subject to more universal laws.

# **_Four Existing Explanations_**

## **“If people only knew”**

Not surprisingly, given the extensive survey data discussed earlier on the public’s lack of knowledge regarding climate change, the dominant theme of research from fields as widespread as science and risk communication, environmental sociology and psychology, has emphasized the public’s lack of information and knowledge as a barrier to social action. Bostrom and coauthors write that, “To a significant degree the effectiveness with which society responds to this possibility depends on how well it is understood by individual citizens. As voters, citizens must decide which policies and politicians to support. As consumers, they must decide whether and how to consider environmental effects when making choices such as whether our resources are most efficiently deployed by using paper or polystyrene foam cups” (1994, 959). The sense that, “if people only knew,” they would act differently: i.e., drive less, “rise up” and put pressure on the government is widespread in popular discourse and environmental literature, and also underlies work from psychology, social psychology and sociology. Psychologists and social psychologists have described flawed cognitive and mental models that limit people’s ability to grasp what is going on, while sociologists have documented the manipulation of climate science (especially in the United States) and the media’s role in misinforming the public through magnifying the perception of uncertainty. Sociologists have also conducted opinion polls highlighting the lack of public understanding of climate science and espoused the need for greater awareness. Examples of each are reviewed below.

The “conceptual challenges” surrounding global warming have been primarily understood in terms of limitations of individual psychology (i.e., mental models, confirmation bias), or to media framing (see e.g. Bell 1994; Ungar 1992, Boykoff 2004, 2008). Psychologists have described the power of “faulty” decision making powers such as “confirmation bias” 17
30 any effects of COVID-19 on child labor as argued by ILO and UNICEF (2021).

In addition, even if child labor increases during a pandemic, the evidence suggests that child labor only marginally contributes to total earnings. More precisely, using the I2D2 data for the world, we first show that the employment rate is much lower for children than for adults (Web Appx. Fig. A.8). While more than two thirds of 1867 year-olds work across all countries regardless of the development level, only 3.9, 7.7 and 21.9% of 5-17 year-olds work in the HICs, MICs and LICs, respectively. Children who work work few hours and for low wages (Jedwab et al., 2021b). 5-17 year-olds thus only contribute 0.2, 3.1 and 1.9% of total wages in HICs, MICs and LICs, respectively. As such, ignoring the impact of child labor experience during a pandemic is likely to be inconsequential. Furthermore, allowing for increased work experience due to child labor during a pandemic to mitigate the negative effects of reduced schooling would if anything lower the losses for poorer nations, which would again reinforce our results.

Finally, children that attend school rarely work at the same time. Indeed, using I2D2 data for the world, we show in Web Appx. Fig. A.10 that children attending school on average work far fewer hours than children not attending school (note that these numbers include zeroes for children who do not work at all). Thus, we do not allow children to simultaneously accumulate human capital from education _and_ work.

**Cohort Effects, Measurement, and Selection.** We include decadal cohort FE or important events FE. We then exclude self-employed individuals. Furthermore, we use parameters estimated for men only. Lastly, estimates could be sensitive to the extent of non-employment, which varies across countries and by level of development. This could be because workers with certain characteristics might be more likely to be unemployed or out of the labor force. To deal with this, we repeat the estimation using country-year-samples with unemployment or non-employment rates below the 25th percentile (7% and 35%, respectively) or 50th percentile (10% and 35%, respectively) in the sample, and separately for country-year samples above each of these thresholds.<sup>53</sup>

Note that the returns to education are not causal. They are nonetheless consistent

> 53We cannot use historical information on unemployment/participation to adjust the estimates of potential returns to experience to equal actual returns. Since we focus on workers aged 18-67 in samples from 1990-2016, we study individuals who worked in 1941-2016. Unfortunately, data on unemployment and participation does not exist for _all_ countries in _all_ years. Unemployment data is typically only available from the 1990s. Also, since unemployment rates are higher in HICS and participation is lower in MICs (Table 1), we may under-estimate their returns, which should, if anything, reinforce our results.