rafmacalaba/datause-encoder-data huggingface dataset:
versus congestion before and after the curfew for each hour of the day).

There are increases in speed both during the daytime hours and during the pre-curfew hours, though the increase is twice as large during pre-curfew hours (Table 4).<sup>7</sup> This demonstrates that the combined effect of changes in density and behavior is an increase in the speed that individuals drive. Additionally, given that we see no effect on crashes during the daytime hours (if anything the coefficient is negative, indicating a decrease in crashes), it seems that the increase in speed from the drop in congestion does not lead to increases in crashes. This is in line with the findings from Green et al. (2016).

When we control for vehicle density using the congestion delay data from Waze, “relative” speed decreases during the daytime hours the COVID-19 policies are implemented. During the pre-curfew hours there is still a significant increase in “relative” speed. This demonstrates that beyond the increase in speed that can occur when the number of vehicles decreases, individuals are driving at even higher speeds during the pre-curfew hours. We also look at this in a triple-difference framework, comparing the increases in speed between the pre-curfew hours and the daytime hours, before and after the curfew period in 2019 and in 2020, controlling for the level of congestion. We see a large positive increase in speed during the pre-curfew period as compared to the other daytime hours. This increase in speed that goes beyond the mechanical change in speed that occurs when congestion decreases could help to explain the increase in the probability of a crash during the pre-curfew time interval.

> 7Note that during the curfew hours, there seems to be a decrease in speed as measured by Google Maps (Figure 1b). The number of vehicles on the road at this time drops to close to 0; therefore, the measured decrease in speed may be a function of how Google calculates driving time in the absence of vehicles. To confirm the data, we compare the percent change in speeds from Google with the percent change in speeds from Uber. While the Uber and Google data are fully aligned for the daytime hours and the hours right before the curfew, there is a deviation in the percent change between Uber and the Google speed data for the curfew hours for the period where Uber is still reporting this statistic. Since we do not have data from Uber after March 2020, we do not consider the curfew hours when conducting any analysis related to speed (Appendix Figure A.1).

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|**Appendix 1: What is different aboutprivate courses?**|Public|Private|
|---|---|---|
||Provider|Provider|
|_Course Characteristics_|||
|Course length (days)|55|60***|
|Course length (hours)|328|353***|
|Accounting course|0.13|0.45***|
|Professional course|0.11|0.14***|
|Craftsman course|0.23|0.02***|
|Technical course|0.19|0.07***|
|Service course|0.24|0.14***|
|Course in Istanbul|0.27|0.12***|
|Average teacher experience greater than 12 months|0.45|0.34***|
|Percent of course teachers with tertiary education|71.9|51.2***|
|_Individual Characteristics (administrative data)_|||
|Female|0.61|0.66***|
|Age|27.0|26.5***|
|At least high school|0.66|0.85***|
|_Individual characteristics (baseline data)_|||
|Years of education|9.7|10.9***|
|Has done previous training course|0.25|0.20***|
|Household head|0.12|0.10**|
|Household size|3.86|3.64***|
|Married|0.30|0.27*|
|Ever employed|0.57|0.55|
|Total years working for pay|3.17|2.64***|
|Raven test score (out of 12)|6.10|5.93*|
|Numeracy score (out of 4)|3.31|3.51***|
|Tenacity|8.36|8.35|
|_Labor market characteristics (Turkstat)_|||
|Non-agricultural unemployment rate|12.5|12.6|
|Agricultural sector employment share|22.9|21.6***|
|Industrial sector employment share|29.0|27.5***|
|<br>Service sector employment share|48.1|50.9***|
|<br>Female labor force participation rate|29.5|28.4***|
|Male labor forceparticipation rate<br>Note: Sample means shown. Turkstat data are 2011 Labor Forc<br>www.turkstat.gov.tr [accessed August 2012]<br>*, **, and *** indicate statistically different from public provide<br>1 percent levels respectively.|73.0<br>e Statistics at the N<br>rs at the 10, 5, and|71.6***<br>UTS2 level,|

43
# **2. Literature Review**

A key implication of technology diffusion is its impact on the labor market. Several studies (Davis and Haltiwanger 1991; Krueger 1993, and Mincer 1991) find that technology has raised the relative demand for more skilled workers and, consequently, reduced the demand for manual labor. Katz and Murphy (1992) use a basic supply and demand approach to show that relative labor demand shifts come from intra-industry changes (such as factor non-neutral technological change, changes in prices of non-labor inputs, and outsourcing) and inter-industry changes (such as shifts in market-wide product demand, sector differences in factor-neutral total factor productivity (TFP) growth, and trade shifts that change the domestic share of output at fixed relative wages). Aw and Batra (1999) also provide evidence that technology (measured by R&D and by worker training) has an impact on firm performance (measured by wages). This finding coheres with the World Bank (1999) "Mexican Labor Markets: New Views on Integration and Flexibility," which relates wages to technology (measured in terms of R&D and technology acquisition).

In the last decade, Mexican wage inequality has increased sharply. Three theories - the liberalization hypothesis, the labor institution hypothesis, and the SBTC hypothesis - can explain this increase in earnings inequality in Mexico. The liberalization hypothesis argues that reduced trade barriers put an economy under competitive pressure to specialize along its lines of comparative advantage. A developed country with high levels of human capital will shift to advanced industry and services as low-skilled industries feel competition from countries with abundant low-wage workers.

In a test of liberalization theory, Hanson and Harrison (1995) examine the impact of Mexican trade reform on wage structure using firm-level data. They test whether trade reform shifted employment toward skilled labor-intensive industries (the Stolper-Samuelson-Type [SST] effect). They conclude that changes within industries and firms caused the wage gap, and

> 2 See, for example, the "Symposium on Wage Inequality" (1997) and the "Symposium on How International Exchange, Technology and Institutions Affect Workers" (1997).

5
# **Box 1. Baseline Input Data Requirements**

A. **Baseline Input Data Requirements** Dt=Bt+etB*t-etNFA*t, public sector net debt, end of the period (eop), in local currency units (LCU) Bt : domestic net/gross debt in LCU (excludes net credit to the government of the Central bank), eop B*t : foreign debt in LCU (includes domestic debt issued in FX and any domestic debt linked to FX), eop NFA*t: stock of net foreign assets in dollar terms, in LCU, eop PRP<sup>*</sup> t<sup>: amortization on external MLT debt, in LCU</sup> PRPt: amortization on domestic MLT debt, in LCU Int<sup>*</sup> t<sup>: interest payment on public foreign debt, in LCU</sup> CBHt: Holding of government securities by the Central Bank, in LCU, eop. STDt: Short term debt, in LCU eop. Et: nominal exchange rate LCU/$, eop Et, av: nominal exchange rate LCU/$, average (av) Mt: monetary base at the eop, period t Pt,AV: average CPI level for period t Yt: real GDP in period t in LCU Tt: tax revenue, in LCU Gt: total primary expenditure, in LCU INTt: Total public interest expenditure, in LCU OBt: Overall balance of the public sector, in LCU itl is the dollar nominal interest rate earned over reserves over the period t itd is the nominal interest rate on domestic debt paid over the period t (average) itf is the dollar nominal interest rate paid on foreign debt and reserves over the period (average)

# B. **Variables Calculated by the Template**

π t,AV=(Pt,AV-Pt-1,AV)/Pt-1,AV is average consumer price inflation from year t-1 to year t π∗ t,AV=(P*t,AV-P*t-1,AV)/P*t-1,AV, where P*t is the average U.S consumer price index. gt: (Yt-Yt-1)/Yt-1, real GDP growth rate in the year t.

st=(Et-Et-1)/Et-1, rate of change of the nominal exchange rate, LCU/1US$, eop êt=(1+st)*(1+ π *t,AV)/(1+ π t,AV)-1 is the rate of change in bilateral real exchange rate, (LCU per 1 USD), where + denotes real depreciation, while – denotes real appreciation. PBt=( Tt - Gt)- primary balance as a share of GDP Maturity=(B<sup>*</sup> t<sup>+B</sup> t<sup>)/(PRP*</sup> t<sup>+PRP</sup> t<sup>)</sup> Real interest rate on public debt: α _t_ ⋅{[ 1( + _itf_ ) ⋅ 1( + _e_ ) _t_ ) /(1 + π _t_ * ) ]− 1 }+ 1( − α _t_ ) ⋅{[ 1( + _itd_ ) /(1 + π _t_ ) ]− 1 } Share of external public debt in total public debt, α t=B<sup>*</sup> t-1<sup>/(B*</sup> t-1<sup>+B</sup> t-1<sup>)</sup> Gross Financing Needs = -Pdt+PRP<sup>*</sup> t<sup>+PRP</sup> t<sup>+STD</sup> t σ t=(Mt-Mt-1)/(PtYt ) is defined as seigniorage revenue as ratio to GDP. OFt are other exogenous factors of public debt accumulation that may increase or decrease the outstanding stock of net public debt such as privatization receipt or recognition of contingent liabilities – percent of GDP.

pd- primary balance as a share of GDP Tt: total government revenues in LCU Gt: total non-interest expenditure in LCU 18
There are several estimation methods used in the literature to fit New Keynesian models to data. The recently most popular method has been the Bayesian estimation which overcomes some problems of the Full Information Maximum Likelihood method<sup>7</sup> by imposing priors on the structural coefficients’ distributions (see e.g. An and Schorfheide, 2005; or Buncic and Melecky, 2008). Given the relatively higher model uncertainly for transition economies, and the fact that system estimators can be inconsistent if one of the equations in the system is misspecified (see Johansen, 2005) our preferred estimation method is the Generalized Method of Moments (GMM) (see e.g. Gali and Gertler, 1999; among others). We used two lags of the variables in the system as instruments. The long-run heteroscedasticity and autocorrelation consistent (HAC) covariance matrix weighting the moment conditions in the GMM estimator is estimated using the Quadratic kernel with the variable New-West bandwidth selection. In addition, pre-whitening of the moment conditions was applied.

# **4. Discussion of Estimation Results**

The estimated model parameters, using GMM and the quarterly data for Macedonia from 1997Q1-2007Q3 and for the Slovak Republic from 1995Q1-2007Q3, are reported in Table 1.<sup>8</sup>

> 7 One of the drawbacks of using ML is that parameters can take on corner solutions or theoretically implausible values. Additionally, it is often the case that the log-likelihood function is flat in certain directions of the parameter space and extremely hilly overall, so that without careful constraints on the parameters space, it is difficult to numerically maximize the log-likelihood function (see the discussion in An and Schorfheide, 2005, for more details).

> 8 An explicit, microfounded derivation of the kind of model presented here could be found in e.g. Svensson (2000) or Monacelli (2005). Examples of the model’s estimations using data for Australia, Japan, the U.S., or the euro area could be found in e.g. Melecky (2008) or Buncic and Melecky (2008).

9
is that sampling frames were different in different regions (Table 3 shows information on the sampling frames in each country). In particular, surveys in SSA countries focused on household heads. We do a number of things to deal with this concern. First our weights are to some extent able to make our sample more representative at the individual level. Second, and more importantly, in Section 4.3 we showed that stopping work measures are consistent with household-level income loss measures. The subsequent panels of Figure 8 also demonstrate that these broader measures of income are related to the WEO change in the same way as stopping work in the LAC region, but also do not show a strong relationship with the WEO change in the SSA region.

Panels (c)-(h) show the same relationship between different income components (farming, non-farming, and wage income) and suggest a similar pattern of consistent relationships in the LAC region but weak and inconsistently signed relationships in the SSA region. This suggests that HFPS-data may be picking up economic impacts that are not typically incorporated into macroeconomic projections. However, the non-representative nature of the sample in SSA may also influence the comparison between the rate of work stoppage and the WEO GDP growth projections, meaning that further investigation is needed.

One piece of additional evidence comes from firm surveys that were fielded by the World Bank, which show that firm survey measures of labor demand also do not line up well with macroeconomic projections of GDP growth. To assess labor demand from the firm side, we draw on aggregate country estimates of the share of firms that laid off workers and the share of firms that reduced wages, hours, or granted leave to employees from a recent policy research working paper (Apedo-Amah et al 2020). Figure 9 shows that these measures of labor demand are not associated with changes in the WEO’s GDP projections for 2020. This suggests that labor market impacts and ultimately other measures of household welfare during the pandemic are not fully captured by GDP projections in the developing world. This could be because of the differences in time horizons, as the macroeconomic projections pertain to the entire year while the household and firm surveys measure initial impacts in the 22
Together, Beijing and Shanghai hold a large share of innovative industries in the country according to the figures in Table 9, whether in terms of firm numbers or employee numbers. However, the innovative sectors are still small in the overall urban economy in Beijing and Shanghai. For example, employment in the high- and new-tech enterprises amounts to about 4% of the total employment in Beijing and less than 1% in Shanghai.<sup>7</sup> The disparities between Beijing and Shanghai in knowledge intensive business services and creative content providers seem even larger. The employment in the selected categories together adds up to nearly 10% of the total employment in Beijing, much higher than that of the nation as a whole (3.3%), while this figure is less than 4% for Shanghai, only a little higher than the national level.

# **Innovative industries in Beijing**

Beijing has unique advantages as the national capital in the development of innovative industries.

# _National R&D center for high-tech development_

Beijing has an abundance of academic think-tanks, universities, research institutes and research and development (R&D) centers. More than 50 universities and colleges and over 200 research bodies are located in the capital, helping to spawn the largest number of high-tech enterprises in China, and some of the nation’s top high-tech companies such as Lenovo and Founder. Table 10 compares the R&D investment in Beijing and Shanghai in 2000. The large number of state-owned higher education and R&D institutes received the lion’s share of government funded R&D investment.

**Table 10: The R&D Investment in Beijing and Shanghai, 2000               (Unit: b** ¥ **)**

||Beijing|Shanghai|National in total|
|---|---|---|---|
|Total R&D investment|21.4|5.3|62.5|
|Government funds|13.6|3.1|39.9|
|Enterprises investment|1.7|0.6|5.8|
|Foreign investment|0.68|0.01|0.74|

_Source_ : National R&D Resources Survey 2000.

> 7 However, according to a more extensive definition of high-tech industries which not limited within high-tech industrial parks, the gross output value of high-tech industries in both Beijing and Shanghai has hold a large share in total industrial output, over 20%, in both cities in 2002 (See Beijing and Shanghai Statistic Yearbook 2003).

15
face value, these paint a substantially more positive picture of the evolution of outcomes for TASAF members. At baseline they had food and overall consumption roughly two dollars below the ENBs; by R2 the entire beneficiary group has closed that gap and it remains closed for the duration of the study. It appears that in R2 the ENBs were hit by a negative consumption shock that was largely avoided by the whole group of TASAF beneficiaries. By R3 this differential consumption relative to ENBs has risen to $2.21 overall and a significant $2.28 for food consumption.

This result is subject to two quite different interpretations. The more optimistic interpretation is that we have a permanent-income-driven story where even the Late treated group increased their consumption from the outset in anticipation of receiving the program in 12 months, and that by R3 the whole beneficiary group was experiencing a relative consumption increase of roughly 12% of the baseline mean, equivalent to moving from the median to the 59<sup>th</sup> percentile of consumption among all eligibles. The more pessimistic interpretation is that we simply have a violation of parallel trends; our prior work suggested that the selection of beneficiaries among the eligibles favors the wealthy, educated, informed, and politically active (Baird et al. 2013), possibly indicating the presence of superior counterfactual trends in consumption. Overall, we conclude that, even if one were to favor the optimistic scenario, the TASAF treatment confers modest effects at best on household welfare.

# **6.2. Impacts of Cash Transfers**

For the cash transfer study, we first randomly selected half of the villages to be included in the UCT experiment, blocked by TASAF treatment status (Early, Late, and ENB), and then individually randomized UCT treatment within selected clusters. Hence, we can consider the impact of standalone transfers among the ENBs as well as the impact of transfers on top of TASAF 27
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# 3.2.2 Bulgarian corruption risks from the broader European perspective

The observed trend toward lower competitiveness and higher risks in Bulgarian public procurement follows broader European trends, albeit the magnitude of deterioration is greater.

To better understand and interpret the above risk profiles and trends in Bulgaria, we compare selected risk factors to other European countries. These comparisons are based on a narrower dataset than the one used so far to make the composition of compared procurement markets more similar. We use the TED dataset which is the online version of the 'Supplement to the Official Journal’ of the EU, dedicated to European public procurement. All tenders and contracts announced on TED follow the EU procurement directives, which implies that the regulatory frameworks are largely comparable across countries. TED publishes about 700,000 procurement award notices a year, including 250,000 calls for tenders which are worth approximately €670 billion. Tenders published on TED are different from national sources in many aspects, most importantly they tend to be of higher value (publication on TED is mandatory for high-value contracts albeit thresholds differ by supply type, among others). In spite of these differences between TED and national Bulgarian public procurement data, the subsequent comparisons can help contextualize and interpret corruption risks in Bulgaria.

To further increase comparability of corruption risk trends between European countries (European Economic Area [EEA] hitherto) and Bulgaria, we narrow our focus to a standard risk indicator: single bidding. Single bidding reflects the competitiveness of tenders and therefore can be a proxy for measuring potential corruption risks due to deliberately excluding non-connected firms from tenders. Single bidding is one of the most universally applicable CRIs, as it does not require identifying risk thresholds or classifying categories into risk groups. When there is only one bid submitted in a tender, the single bidding variable takes the value of 1, otherwise it is 0. For the sake of comparability, average annual values for each country are used.

The share of single bidding contracts has increased steadily in Bulgaria since 2011 and it is higher than corresponding shares in the other EU countries where data are available. First, we compare Bulgaria to selected EU member states, both among low and high corruption risk countries. (Figure 11). In this comparison, Bulgaria has featured the highest single bidding rate since 2018 relative to Germany, France, Italy, Romania, and the Slovak Republic. While there has been a consistent upward trend for most of the other countries since 2011 (the Slovak Republic is the only country which managed to lower its single bidding rate over the period), Bulgaria experienced a steady and steep increase in the share of single bidding.

17