rafmacalaba/datause-encoder-data huggingface dataset:
To understand jobseekers’ behavior, it is useful to analyze how many applications they submit to what kinds of job postings. The regression reported in Figure 24 examines who submits more applications. It finds that female jobseekers apply to 13% fewer jobs than male jobseekers. This may be because females have a better chance of being selected with fewer applications (which is discussed in a later analysis) or they cannot afford to apply for as many jobs due to tighter resource constraints. One-year younger jobseekers submit 7% more applications. Jobseekers with undergraduate and postgraduate degrees submit approximately twice as many applications as jobseekers with secondary or less education. As seen earlier, labor market tightness is low for graduates with higher education degrees, so highly educated jobseekers may need to apply for more jobs. It may also be the case that highly educated jobseekers have more job openings they qualify for. Lastly, jobseekers who currently earn higher salaries submit more applications. (Jobseekers with double salaries submit 18% more applications.)

**Figure 24. Who submits more applications**

<!-- Start of picture text --> Log current salary 0.27<br>Age -0.07<br>Gender<br>Base category: Male 0.00<br>Female - 0.13<br>Education<br>Base category: SS or less 0.00<br>HSS -0.11<br>Diplo/Bache 0.87<br>Graduate 1.21<br>-0.5 0.0 0.5 1.0 1.5<br><!-- End of picture text --> _Source_ : Authors.

Note: The other controls are experience dummies, industries, marital status, and year-month dummies.

We compare jobseekers’ desired salaries and the salaries advertised in the job postings they apply for (Figure 25). Jobseekers express their desired salaries in their profiles on Rozee.pk. Jobseekers apply for the jobs that pay lower than their desired salaries in 45% of the cases. The difference between the advertised and desired salaries is often as large as 40%.<sup>21</sup> This can be accounted for by the fact, demonstrated in an earlier section, that highly educated jobseekers apply for jobs requiring lower education. Another possible reason why jobseekers often apply for lower paying jobs is their risk aversiveness.<sup>22</sup> It is also worth noting that in 14% of cases, workers apply for jobs that pay higher than their desired salaries. This shows that they still try getting higher paying jobs while they mainly target not-so-high paying jobs.<sup>23</sup>

> 21 In half of the cases where workers apply for jobs that pay less than their desired salaries, the maximum salaries posted are more than 40% lower than their desired salaries.

> 22 It may also be the case that self-declared desired salaries are higher than what they truly desire.

> 23 (a) We have a similar finding when we compare job postings’ salary ranges and applicants’ current salaries. (b) These statistics are not substantially different between applicants’ gender.

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- 16 -

Figure 9. Relative nonresidential electricity tariff to residential price <!-- Start of picture text -->
1.8 1999-00<br>1.6 1.55 2004 1/<br>2007Q3<br>1.4 1.28<br>1.2<br>h  (residential=1) 1.03 0.99 1.02<br>1 0.90<br>0.78<br>0.8<br>0.6<br>0.4<br>0.2<br>elative  tariff of nonreesidential to  R 0<br>r esidential price per kW<br>A lbania B ulgaria C roatia a cedonia,  F YR R omania S erbia<br>B osnia and  H erzegovina M<br>1/ 2006 for Serbia.<br>Source: ERRA database.<br>Figure 10. Average price for baseload power at European Energy Exchange<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Source: EEX.<br>Average price (euro/MWh)<br>2000 Q3 2001 Q1 2001 Q3 2002 Q1 2002 Q3 2003 Q1 2003 Q3 2004 Q1 2004 Q3 2005 Q1 2005 Q3 2006 Q1 2006 Q3 2007 Q1 2007 Q3 2008 Q1 2008 Q3<br><!-- End of picture text -->

Figure 10. Average price for baseload power at European Energy Exchange Through continued government efforts to rationalize energy prices at the retail level, the deficits of the electricity sector were largely removed in the early 2000s, though not eliminated completely (Figure 11). Although no comparable estimates are available after 2003, some countries seem to have continued passing high international energy prices to end users to a certain extent, meaning that the quasi-fiscal deficits might have declined further. As shown in Figure 8, Bulgaria and Macedonia increased electricity prices aggressively since around 2004. However, these may be a partial translation. Recall that the market electricity
<!-- Start of picture text --> 300,000 | - 92.0<br>250,000 | ; 90.0<br>&<br>=} + 88.0<br>S 200,000 —<br>= + 86.0 =<br># 150,000 - 3<br>woSs=] } || g4.0 @ —— GRP per capita,<br><!-- End of picture text -->

<!-- Start of picture text --> N<br>50,000 — | g0.0<br>—M— 1- poverty rate, per<br>= nt + 78.0 cone<br>BPBEEE RB ETEDERB<br>5Ss FS% 3e8SSoony?s° 2%&§= &<br>$338 7<br>$2 °% &<br>=r =<br>= =So<br><!-- End of picture text -->
# d. Bilateral trade with the U.S. and Western Europe

Exports to the U.S. and Western Europe (% of gross exports) Imports from the U.S. and Western Europe (% of gross imports) <!-- Start of picture text --> 100 90<br>90 80<br>80<br>70<br>70<br>60<br>KHM<br>60<br>50<br>50<br>40<br>40 VNM<br>CHN 30<br>30 CHN<br>THA PHL PHL<br>20 THA<br>20 MYS MYS<br>IDN<br>MNG IDN<br>10 MNG 10 VNM<br>LAO MMR<br>MMR K...<br>LAO<br>0 0<br>0 20 40 60 80 100 0 20 40 60 80<br>1995 1995<br>2015<br>2015<br><!-- End of picture text --> Source: IMF DOTS and authors’ calculations. Notes: 45‐degree line shown as dotted line. Labels indicate developing East Asian countries and other selected countries.

Much of the rise of China as a trade partner of developing East Asian countries has to do with its larger role in GVCs. Figure 10 depicts the Chinese foreign value added embodied in the other countries’ gross exports of goods and services, as well as the domestic value added produced in various countries that is re‐exported by China.<sup>7</sup> Developing East Asian countries collaborate more closely with China on these two aspects of GVC participation relative to most other countries in the world, but different countries have different roles. Vietnam and Cambodia have a high content of Chinese value added in gross exports (Vietnam in its exports of electrical and optical equipment and Cambodia in its exports of garments), an indication of downstream positioning relative to China in a triangular trade to destinations overseas. Malaysia, Philippines and Thailand, which are not highly dependent on China in the bilateral gross trade charts, have more than 40 percent of their forward participation in the electrical equipment industry accounted for by China’s re‐exports. This suggests that the three countries are heavily integrated in global value chains, upstream of China, as its suppliers of parts and components.

> 7 Note that country availability is restricted to the coverage in the OECD TiVA Database. For example, Mongolia, a country with high gross trade exposure to China, is not included.

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pothole creation and is otherwise uncorrelated with electoral outcomes. This strategy allows for advancing a causal argument that stands a number of robustness tests. We rely on the additional data on the effectiveness of road maintenance across different municipalities in Moscow to propose a causal mechanism driving the observed outcomes.

The findings from this study stand in sharp contrast to similar studies undertaken in other locales that have found a reverse pattern from the public engagement with local authorities on the issue of road quality. In one of the largest cities in the United States, more pothole complaints have a significant negative effect on the incumbent vote share (Burnett and Kogan 2016). Local democracy in the United States works by holding politicians accountable for the quality of public goods delivery, as demonstrated by bad roads. On the other hand, local authorities in Moscow rely on the crowd-sourced information on road quality to demonstrate government responsiveness, repair city roads in a timely manner and win more votes with better roads. In the discussion section, we present empirical tests pointing to effective performance demonstrated in response to resident complaints on road quality as the most likely causal mechanism driving these results.

This study contributes to different strands of literature on governance and institutions. First, we contribute to the growing literature in public administration and public management on the effects of digital technologies on the co-production of public services (Joshi and Moore 2004; Linders 2012). This literature is mostly focused on the countries where co-production practices are frequently motivated by the opportunities the new technologies bring to relieve financial pressures on the government (Falco and Kleinhans 2018; Nam 2012). By investigating the political effects of digitally-driven co-production of public services, we highlight additional reasons behind these governance technologies outside the advanced industrialized world. Second, we engage with the literature on local participation and government performance in developing and post-communist countries (Besley and Burgess 2001; Sjoberg, Mellon, and Peixoto 2015; Trucco 2017). While most of this literature examines the effects of government performance on participation levels, our study takes a step further and explores the electoral effects of government responsiveness in the postcommunist context of local governance. Third, we also add to the emerging literature on political effects of participatory governance in developing countries that has been dominated by the studies of China and the Chinese Communist Party’s (CCP) efforts to maintain regime legitimacy (Dickson et al. 2016; He and Thøgersen 2010; Truex 2016, 2017). We bring attention to other country contexts where similar dynamics are played out in electoral politics, even if heavily managed. Finally, we add to the emerging literature on ‘informational autocracies’ by demonstrating that the reliance 3
generate positive economic impacts. The shift to solar reduces electricity prices, enhancing affordability and leading to an increase in both electricity demand and supply. An exception occurs in the short term after the regulation is introduced in 2025, causing an immediate rise in electricity prices and resulting in short-term negative GDP impacts. This increase is attributed to the higher capital cost associated with oil power generation due to the shorter project lifetime. The augmented capital cost also applies to existing oil power plants, and this expense is passed on to the average electricity price before the transition to alternative sources of electricity can take place.

_Table 3: Summary of impacts in the regulation scenario, % difference from BAU unless otherwise stated._

||**2025**|**2030**|**2040**|**2050**|
|---|---|---|---|---|
|**Carbon tax**|||||
|GDP Impacts|0.0|-0.1|-0.9|-2.8|
|Changes in electricitysupply|-5.5|-4.8|-6.7|-9.4|
|Electricity price|12.8|17.2|30.5|49.2|
|Debt share of GDP(absolute differences)|0.0|-0.1|-0.9|-1.3|
|**Solar kick-start policies**|||||
|GDP Impacts|0.0|0.0|0.5|2.8|
|Changes in electricitysupply|1.9|3.6|7.3|10.2|
|Electricity price|-4.0|-9.7|-22.2|-32.5|
|Debt share of GDP(absolute differences)|0.0|0.0|-0.3|-0.8|
|**Oil plant life time regulations**|||||
|GDP Impacts|0.0|-0.2|0.3|3.4|
|Changes in electricity supply|-3.3|2.7|11.8|13.3|
|Electricity price|7.5|-4.8|-30.4|-40.8|
|Debt share of GDP (absolute differences)|0.0|0.0|0.0|-0.8|

_Source(s): FTT-FLEX_ Average electricity prices go down in the solar kick-start policies and oil plant lifetime regulation (in the long run) scenarios because the policies increase the share of solar relative to the baseline. In the baseline, the average cost of solar is already lower than oil and coming down. Solar’s share remains relatively low despite that cost advantage because of the incumbent advantage of oil. As oil capacity shuts and is replaced by solar, that incumbency advantage shifts toward solar and electricity prices start to come down rapidly and the share of solar rise even further. The decrease in prices stimulates demand for electricity, which is increasingly met by the lower cost and now more abundant solar. The capital associated with the increase in supply of electricity is then added to the country's capital stock and increases potential output.

The carbon tax scenario results in additional revenues for the government, contributing to debt reduction. As the solar kick-start policies assume international funding, there are no direct impacts on the country's public balance and the effects on debt are by design minimal in the regulatory scenario. This assumption is based on the regulation working effectively, having negligible implementation costs, and not requiring any fines. Instead, if we relax these assumptions then there will be direct impacts on the fiscal balance.

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Inequality, Economic Growth and Nonrenewable Resources magnitude of the coefficient generally increases after the feedback effect has been taken into account.

Table 7: Estimated Results from the Non-Recursive Model Specification (baseline model)

||**IV Fixe**<br>|**d Effect**<br>|**3S**<br>|**LS**<br>|
|---|---|---|---|---|
||**Gini index**|**GDPc**|**Gini index**|**GDPc**|
|Non Renewable resource dependence|-0.149***|-0.443***|-0.151***|-0.467***|
|High-low education ratio|-0.082|-|-1.110***|-|
|High-low education ratio square|0.008|-|0.025***|-|
|Consumer price index (2005)|0.005|-|0.027***|-|
|Services value added share|0.072|-|0.035|-|
|Manufacturing value added share|0.055|-|0.145**|-|
|Agriculture value added share|-0.124**|-|-0.105**|-|
|GDP|-0.040|-|-0.005|-|
|Log labor force|-|-11.564**|-|-9.069**|
|Log gross fixed capital formation|-|9.842***|-|7.658***|
|Log exports of goods and services|-|1.344|-|3.123**|
|Log REER|-|0.764|-|1.458|
|World oil price index|-|0.078***|-|0.055***|
|World metal price index|-|-0.015|-|0.003|
|Gini index|-|-1.877***|-|-2.073***|
|Observations|873|873|873|873|
|R2|0.113|0.245|0.901|0.799|

Note: * ** and *** denote significance at the 10% 5% and 1% levels, respectively.

Overall, these results indicate that a positive variation in the dependencies is empirically associated with a negative variation in both GDP per capita and the Gini index. In other words, resource dependence is associated with lower levels of GDP per capita, but also with lower income inequality. These results are consistent with the literature on Dutch disease, which explains them as an effect of the diversion of labor and capital from the industrial sector to the natural resource sector, combined with the negative effect of real exchange-rate appreciation on the tradable sector. However, the model’s findings suggest that, on average, the inter-sectoral shift in labor patterns—which affects the distribution of income through rising wage differentials between sectors—is offset by the investment of additional fiscal revenue in pro-poor policy interventions. The literature highlights that the sign of the relationship between resource dependence and inequality is likely to depend on each country’s institutional framework and the quality of its policy interventions. These factors determine whether—and how—natural resource revenues support rising productivity and consumption among lower-income households. The model’s findings suggest that, on the whole, policies designed to promote shared prosperity more than offset the increase in inequality caused by the inter-sectoral wage differential.

However, pooling such a heterogeneous mix of countries may undermine the reliability of the model’s conclusions. The next section explores the issue of country-level heterogeneity by adding dummy variables to the baseline specification. The objective is to disentangle the influence of specific countries or groups of countries by disaggregating 11
random effects model do not differ much from the conclusions of the OLS. But it should be noted that, although the Breusch and Pagan LM-test indicates that there is unobserved heterogeneity in the model, the conclusion from the random effects model is not valid, due to inconsistency of parameters. This can be seen from the Hausman test statistic being significantly different from zero.

_Table 4. Estimation results for all individuals_

||||ln(C|A)|||||ln(rev|enue)|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||OLS||Rand|om effe|cts||OLS||Ran|dom eff|ects|
||Coef.|SE|P>|z||Coef.|SE|P>|z||Coef.|SE|P>|z||Coef.|SE|P>|z||
|woman|0.01|0.13|0.96|0.02|0.18|0.90|-0.17|0.13|0.17|-0.19|0.16|0.22|
|can read|0.37|0.17|0.02|0.33|0.22|0.14|0.03|0.16|0.86|0.03|0.19|0.90|
|education>2|0.57|0.18|0.00|0.52|0.24|0.03|0.10|0.17|0.54|0.08|0.20|0.71|
|age 30-39|0.04|0.11|0.72|0.01|0.14|0.93|-0.18|0.10|0.07|-0.19|0.12|0.13|
|age 40-49|-0.09|0.15|0.54|-0.12|0.20|0.54|-0.13|0.14|0.35|-0.14|0.17|0.41|
|age>49|-0.08|0.17|0.63|-0.12|0.24|0.60|-0.21|0.16|0.19|-0.26|0.20|0.20|
|age unknown|0.11|0.16|0.50|0.08|0.21|0.72|0.01|0.15|0.95|0.00|0.18|1.00|
|exp. 5-10|0.02|0.17|0.93|0.01|0.23|0.98|0.17|0.16|0.29|0.20|0.20|0.30|
|exp.>10|0.27|0.20|0.17|0.26|0.27|0.34|0.43|0.18|0.02|0.45|0.23|0.05|
|couture|1.55|0.21|0.00|1.56|0.29|0.00|1.72|0.20|0.00|1.66|0.25|0.00|
|electronics|1.13|0.24|0.00|1.22|0.32|0.00|1.01|0.23|0.00|0.98|0.28|0.00|
|Abidjan|0.21|0.18|0.24|0.16|0.24|0.51|0.28|0.17|0.09|0.26|0.21|0.20|
|PB|0.16|0.10|0.13|0.21|0.10|0.04|-0.01|0.10|0.89|0.03|0.10|0.77|
|PA|0.21|0.10|0.04|0.24|0.10|0.02|0.19|0.10|0.05|0.20|0.10|0.04|
|NPB|0.00|0.09|1.00|0.01|0.04|0.84|-0.12|0.09|0.15|-0.09|0.06|0.13|
|constant|9.45|0.28|0.00|9.45|0.37|0.00|9.45|0.26|0.00|9.45|0.32|0.00|
|PB-NPB=0|2.35||0.13|3.78||0.05|1.35||0.25|1.59||0.21|
|PA-PB=0|0.21||0.65|0.29||0.59|4.01||0.05|5.71||0.02|
|PA-PB=-NPB|0.12||0.73|0.30||0.53|0.37||0.54|0.72||0.40|
|B&P LM-test||||324.2||0.00||||97.17||0.00|
|Hausman-test||||38.20||0.00||||19.40||0.00|
|N|1073|||1073|||1073|||1073|||

The results from the estimation using ln(revenue) as the dependent variable do not differ much from the estimations with ln(CA) as dependent variable. Again sector indicators come out highly significant, indicating that the revenue of the sectors of electronics and tailoring are higher than in the agricultural sector. However, the educational level does not play a role as determinants of revenue. Again the participants have a higher level of the outcome variable (CA/revenue) than the nonparticipants after the program participation. The test of PA-PB=0 rejects the null of no change in the revenue for the participants. This change, however, is not significantly different from the change of revenue of the nonparticipants, 15
atmospheric and land-soil variables such as temperature, winds, soil moisture and atmospheric ozone concentration.

Finally, the area of influence and intensity of the droughts in Mozambique during the period of analysis is measured using weather data from the African Flood and Drought Monitor (AFDM) system. This is an initiative developed by Princeton University that monitors and forecasts meteorological, agricultural and hydrological droughts for countries in Sub-Saharan Africa. AFDM data span the period 1950-2015 and are also available with a resolution of 0.25 by 0.25 degrees (Sheffield et al., 2014).

# _Cropping cycle data_

Agricultural production cycles are defined based on the temperature and moisture conditions suitable for crop growth across districts in Mozambique. The Harvest Choice Project developed by IFPRI provides information on the start and end dates of the growing cycle across countries in Sub-Saharan Africa with a 10km x 10km resolution. These data are derived from the Enhanced Vegetation Index (EVI) data set produced by the MODIS satellite images for the period 20012004. Since treatment is determined at the district level, we generate area-weighted averages of the start and end dates of the growing cycle for each of the districts in the sample.<sup>3</sup> Figure 1 shows the distribution in the number of days for the main cropping cycle across the country.

# **2.3 Weather Shocks**

After validating the occurrence and spatial position of the weather shocks listed in the EMDAT database using remote sensing data, we chose three extreme events based on their scale and timing with respect to the dates of the household surveys: 1) the floods that occurred between late 2006 and early 2007 along the Zambezi River Basin, 2) a category 3 cyclone (Jokwe) that struck northeastern Mozambique between March 8 and 18, 2008, and 3) a drought that affected parts of central and southern Mozambique between May 2005 to January 2006.

> 3 We use only the between-district variation in growing cycles, which is substantially larger than the variation within districts. The standard deviations for the start and end dates of the growing periods are 72.8 and 226.7 days across districts (between-district variation) and 39.6 and 36.7 within districts (within-district variation).

9
added per employee, log wage per employee, log sales per employee, log pro fi ts per employee, log capital per employee, log total employment and technology dummy). As expected (from our results in table 1) the OLS estimates are both negative, and (as expected from table 2) the IV estimates are positive and large. For example, an increase in the fraction of employed informal workers by 1 percentage point leads to an increase in productivity of 3.5% (an elasticity of 0.81) and an increase in wages of 1.7% (an elasticity of 0.39).

# 6. Conclusion

This paper has two main themes. First, we study the incentives fi rms face to employ informal workers. In particular, we study the role of enforcement of labor regulation (in the form of labor inspections and labor fi nes) on the behavior of fi rms. Second, we analyze how enforcement of formal employment affects fi rm performance. Stricter enforcement reduces the access of fi rms to unregulated labor and can damage their productivity by increasing labor costs. Using a combination of fi rm level data on illegal employment and fi rm performance, and administrative data on enforcement of regulation, we show that law enforcement reduces informal employment by fi rms. However, it also reduces the fi rm’s wages, productivity and investment. We argue that this is due to the fi rm’s limited access to fl exible labor.

Our paper shows that enforcement of labor regulation in the form of fi nes can be effectively used as a tool for fi ghting informality. However, our paper also shows that informal employment is an important source of unregulated labor for Brazilian fi rms, allowing them to operate more efficiently, and increasing their incentives to invest in new technology. Both of these conclusions have important implications for the design of labor regulation and of its enforcement.

# 7. Appendix A - Data

The data used in the paper comes from a variety of sources and covers thirteen regions of Brazil: Sao Paulo, Rio de Janeiro, Minas Gerais, Santa Catarina, Rio Grande do Sul, Parana, Goias, Mato Grosso, Ceara, Paraiba, Maranhao, Bahia and Amazonas.

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