rafmacalaba/datause-encoder-data huggingface dataset:
industrial sectors are interlinked in such a way that they are all pulled down or up together or there are macroeconomic factors that affect all of them. Alternatively, if a few sectors are continually in flux, while others grow at a steady, even pace throughout the years, this suggests that there are comparatively few spillovers and relatively little linkage between sectors.

The analysis of variances presented in the table below indicates that sector-level growth rates in Russia are highly correlated to each other, year to year. This conclusion is based on the relatively higher coefficient for the year variable as compared to other economies and as compared to the sector variable coefficient as well. These results imply that nearly the entire set of Russian industrial sectors experience fluctuations in growth rates in tandem. This lends support to the spillover hypothesis; namely, that the relatively high levels of concentration of economic output, both across firms and sectors, contributes to volatility.

**Table 1: ANOVA Partial Sum of Squares**

||<br>|**ANOVA**<br>|**FOR 199**<br>|**3-1999**<br>|<br>||**ANOVA**<br>|**FOR 200**<br>|**0-2009**<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||**Russia**|**Brazil**|**India**|**China**|**Korea**|**Russia**|**Brazil**|**India**|**China**|**Korea**|
|**Model**|28.35|1.27|14.02|NA|29.24|16.32|4.96|6.68|3.86|7.88|
|**Sector**|4.72|0.21|8.15|NA|7.62|2.25|0.54|1.75|1.27|3.33|
|**Year**|23.63|1.05|5.86|NA|21.62|13.70|4.44|4.92|2.58|4.53|
|**Residual**|21.35|0.95|44.85|NA|37.80|23.15|3.99|25.12|2.54|16.31|
|**Total**|49.70|2.22|58.87|NA|67.03|39.47|8.95|31.80|6.40|24.19|

Source: Author’s calculation from UNIDO 2011 Industrial Output Data (4-digit NACE) The reader will note that the empirical results for the analysis of variances are presented for two separate periods: 1993-1999 and 2000-2009. The first represents the period following the economic collapse of the Soviet Union, between 1993 and 1999. The second covers the years of economic recovery where relatively higher growth (2000-2009) took hold. While these are two dramatically different periods for recent Russian economic history, the empirical results on the possible explanation for the patterns of economic output volatility is remarkably similar for both. In both, the year-to-year fluctuations in sector-level annual industrial output explain more of the variation in growth rates than the composition of sectors that contribute to output growth. This similarity in results demonstrates the persistence in the nature and sources of volatility of the Russian economy. While this temporal effect is seemingly less prominent in the latter period, the data indicate that in Russia, changes in sectors output generally move in tandem across the years.

# **THE NATURE OF VOLATILITY COMPARED WITH OTHER ECONOMIES**

Recent sector-level growth rates in Russia exhibit more volatility than in other economies. All volatility is made up of booms, referred to here as surges, and busts, referred to here as slumps. These two can be examined separately since they are quite different—surges foster firm entry 9
Besides growth monitoring, the program provides other activities. Eligible children are given micronutrient supplementation and de-worming. The promotion of behavioral change includes direct counseling to the lactating and pregnant women, nutritional education sessions and cooking demonstrations by the community nutrition worker who heavily emphasizes the proper weaning practices and prepares recipes that make use of available the food sources that are available in the local community. The community nutrition worker also carries out an annual census of all of children under the age of three and mobilizes the mothers to participate in the weighing and education sessions.

Seecaline is a large-scale, community-based nutrition program that was gradually phased-in over time. The rest of this section will describe in detail the phase-in process. This description provides a crucial guidance regarding the variables used to model whether a community belongs to a longer vs. shorter duration.

_<u>Selection and timing of participation of districts of intervention</u>_ <u>: Madagascar is administratively organized in</u> six provinces, 111 districts, around 1,400 communes and 16,000 villages. Seecaline’s activities started in 1999 in four provinces and expanded to all six provinces in 2000.<sup>10</sup> The program targets the most malnourished districts: based on a nationally representative anthropometric survey collected in 1997/98, only the districts (46 out of 111) that had an average malnutrition rates above the national average were selected for the intervention. The total number of sites to be opened in each district was chosen to reach a coverage rate of 50% of all children below three years of age. The 50% target was to be achieved gradually over time, with 10% coverage per year in the first two years of operation and 15% coverage per year in the third and forth years. In addition, ten rural districts affected by droughts and cyclones in the year 2000 were selected to participate.<sup>11</sup> The program expansion stopped at the end of 2001/beginning of 2002. The intervention currently includes about 3,600 project sites.<sup>12</sup> _<u>Selection and timing of participation of communities within the intervention districts</u>_ <u>: The program expansion</u> followed a sequential contractual engagement of NGOs across districts. NGO were selected based on a bidding

> 10 CNII is an extension of an earlier Bank pilot project originally targeted to two provinces (Antananarivo and Toliary).

> 11 Finally six additional urban districts that cover the province capitals were added in 2002, in the aftermath of a political crisis. Emergency sites were opened to alleviate the cost of the crisis on urban populations, and were subsequently made permanent. Our data is not available for such urban areas.

> 12 Note that we are using communities and program sites interchangeably. A site is identified by its geographical delimitation (of a radius of five kilometers, within the commune boundaries). A site generally comprises 1 to 3 villages.

9
SHRUG and fixed effects for zones (i.e., groups of districts) within Haryana. Our inference relies upon robust standard errors clustered by ITI district.

Table 5: Effects of exposure to COVID-19 using district-level COVID-19 death rate, all sample

||Wage<br>aspiration|Wage<br>expectation|Aspiration<br>gap|Unrealistic<br>high aspiration|Realistic<br>aspiration|Unrealistic<br>low aspiration|Willing to<br>migrate|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|COVID death rate|-264.476|-181.186|-83.290|-0.006|0.003|0.003|-0.002|
||[69.350]***|[57.614]***|[23.629]***|[0.001]***|[0.001]**|[0.002]|[0.001]**|
|Outcome mean|19,740.57|17,005.66|2,734.91|0.52|0.26|0.23|0.48|
|Observations|3,180|3,180|3,180|3,180|3,180|3,180|3,143|

_Notes:_ These regressions are based on a modified version of specification (1) where we replace the district fixed effects with the following district-level controls from the 2011 Census of India: population density, share of population that lives in urban areas, share of population that belongs to a Scheduled caste, share of population that is female, share of population that lives in slums, and total district population. We also control for the all-cause mortality rate in 2019 at the district level from SHRUG and fixed effects for zones (i.e., groups of districts) within Haryana. Robust standard errors clustered by ITI district are in brackets. Variable definitions are in the online Appendix.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

Additionally, Table 6 shows that our results are robust to conducting inference using wild-clustered bootstrapped standard errors.

# **5 Conclusion**

Young job-seekers often have incorrect beliefs about the labor market and unrealistic wage aspirations that can prevent them from making optimal labor market decisions. Our study suggests that exposure to the pandemic lowered the wage aspirations of affected young women, especially for rural women, and made them more realistic without affecting their high willingness to work. However, this “correction” may also have a negative consequence for women’s agency related to work decisions—this is because the decrease in their willingness to migrate is likely to decrease their expected income (and hence their agency) given that migration to urban areas is an important pathway to higher incomes for many rural households. Indeed, we find some suggestive evidence that COVID-19 reduced young women’s ability to make independent decisions about whether they want to work and what type of work they want to do after graduation, especially in rural areas. Future research should evaluate whether the negative effect of COVID-19 on women’s labor market aspirations influences their future LFP, employment, and income. Lastly, we note that our findings are drawn from a sample of female vocational trainees in Haryana who do not represent the average Indian woman.

9
and the providers’ joint agreement on what needs be done to improve health care delivery, how, when, and by whom.

In total, the process of reaching an agreement took five days. After the meetings, the communities themselves had the responsibility to monitor the implementation of the issues outlined in the joint action plan. In 2008, about a year after the initial intervention, there was a one-day repeat engagement on a smaller scale facilitated by the CBOs during which health workers and community members discussed suggestions for sustaining or improving progress on the issues outlined in the joint action plan.

## **The participation & information intervention**

The _participation & information intervention_ mirrored the participation intervention with one important exception. Specifically, at the start of both the community and the health facility meetings, the facilitators provided the participants with quantitative data on the performance of the health provider. These data were collected from facility and household surveys implemented prior to the intervention. A unique report card was established for each facility summarizing information that was identified from the baseline data as key areas subject to improvement, including utilization, access, absenteeism, and patient-clinician interaction. The report cards also included comparisons vis-à-vis other health facilities and with the national standard for primary health care provision. The report cards were translated into the languages spoken in the community and posters were designed to help the non-literate process the provided information.

The _participation & information intervention_ was initiated at the beginning of 2005 and was followed by small scale repeat engagements in mid-2005, 2007 and 2008 as illustrated in figure 1. In between this fairly "minimal" intervention (a total of 10 days in 4 years), the communities were left to themselves to monitor the providers as outlined in the agreed upon action plan.

# **3.3 Data**

Data collection was governed by two objectives. The first objective was to create report cards for the _participation & information intervention_ on staff performance and how the community views the quality and efficacy of service delivery. The second objective was to rigorously evaluate the short- and long-run impacts.

To meet these objectives, two types of surveys were implemented: a survey of health care providers and a household survey in the catchment areas of the facilities. A quantitative service delivery survey was used to collect data from the providers. Because health providers may have a strong incentive to misreport key data, the data were obtained directly from the records kept by facilities for their own need (i.e., daily patient registers, stock cards, etc.) rather than from administrative records. The former, often available in a highly disaggregate format, were considered to 7
**Table S31. Engagement in educational activities over time**

|_Panel A: Ethiopia_|
|---|

||Education<br>Activity|Listened to<br>Education<br>Programs on<br>Radio|Used Mobile<br>LearningApps|Watched<br>Educational TV<br>Programs|Meeting with<br>Lesson Teacher|
|---|---|---|---|---|---|
|Round 2|0.130***|-0.110*|-0.043|-0.070**|0.119***|
||(0.027)|(0.064)|(0.028)|(0.036)|(0.037)|
|Round 3||-0.117*|0.037|-0.051|0.041|
|||(0.063)|(0.038)|(0.038)|(0.031)|
|Observations|3,742|2,228|2,228|2,228|2,228|
|R-Squared|0.023|0.009|0.012|0.006|0.017|

# _<mark>Panel B: Malawi</mark>_

||Education<br>Activity|Listened to<br>Education<br>Programs on<br>Radio|Used Mobile<br>Learning Apps|Watched<br>Educational TV<br>Programs|Meeting with<br>Lesson Teacher|
|---|---|---|---|---|---|
|Round 2|0.054*|-0.053|-0.058*|0.087**|0.343***|
||(0.030)|(0.077)|(0.030)|(0.043)|(0.057)|
|Observations|2,355|516|516|516|516|
|R-Squared|0.004|0.003|0.017|0.031|0.141|

||Education<br>Activity|_Panel C_<br>Listened to<br>Education<br>Programs on<br>Radio|_: Nigeria_<br>Used Mobile<br>LearningApps|Watched<br>Educational TV<br>Programs|Meeting with<br>Lesson Teacher|
|---|---|---|---|---|---|
|Round 2|-0.012|0.267***|0.123***|0.141***|0.168***|
||(0.033)|(0.038)|(0.028)|(0.032)|(0.034)|
|Round 3|-0.113***|0.291***|0.182***|0.242***|0.304***|
||(0.034)|(0.038)|(0.032)|(0.035)|(0.036)|
|Observations|4,045|2,515|2,515|2,515|2,515|
|R-Squared|0.010|0.076|0.042|0.049|0.072|

_Note_ : Each column in each panel reports results from a single regression of a binary variable equal to 1 if the schoolaged children in the household have engaged in the educational activity. The question is asked conditional on the household containing a school-aged child. Each panel reports results for a single country. Uganda is excluded as there is only one round of data. All rounds of all available data for a country are included in each regression. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

58
# **Introduction**

Poor governance in public construction is a considerable development issue. Construction is a major sector, responsible for the development of infrastructure capital that is central to growth and improved quality of life. Governance failures can lead to the construction of the wrong infrastructure, poor quality construction and excessively high prices for work. There is some evidence from both other sectors and the construction sector itself that improved transparency, especially when combined with oversight, can improve development outcomes through its impact on the quality of governance. This paper reviews that evidence, discusses costs and benefits of greater transparency in particular with regard to the contracting and delivery process in construction, and briefly discusses an initiative to improve governance in public construction –the Construction Sector Transparency Initiative.

# **Mismanagement, Corruption and Development Outcomes in Construction**

A justification for greater transparency and oversight in publicly-financed construction should start with an elaboration of the problem to be addressed. This problem is one of poor outcomes in a large sector that is central to development. Construction is a $1.7 trillion industry worldwide, much of which is linked to publicly financed projects. Government investment in road transport alone can account for between 2-3.5% of GDP.

Outcomes from this financing are frequently sub-optimal. In India, for example, nearly half of all roads projects see cost overruns greater than 25 percent, and more than half see delays adding 50 percent or more to completion times (see Figure One).<sup>2</sup> The quality of infrastructure constructed can also leave much to be desired. Press reports suggest that an Indian government survey of a recent rural roads project found one fifth of completed roads to be unsatisfactory.<sup>3</sup> Similar statistics can be found around the world – for example, a global survey suggests that “substantial cost escalation is the rule rather than the exception” in infrastructure projects estimating that for rail projects, average cost escalation is 45%, for fixed links (tunnels and bridges) it is 34% and for roads, 20%.<sup>4</sup> Some of this cost and time escalation, as well as poor quality, are linked to weak governance and corruption, which are endemic in the sector. Construction ranks as the most corrupt industry in global surveys. Construction firms represented in the Business Environment and Enterprise Performance Survey of Eastern Europe and Central Asia have significantly larger ‘bribe budgets’ than the average firm, and they bribe more often. Of their total bribe budget, a larger percentage goes to gain government contracts –an average of 23 percent for construction compared to 15 percent for all firms in the sample.

> 2 World Bank, 2007. 3 http://www.igovernment.in/site/condition-of-indias-rural-roads-found-unsatisfactory/ 4 Flyvbjerg, Holm and Buhl, 2003.

2
measure of the education level (percentage of the adult population that can read and write), _urbi_ is the urbanization rate of the district, and  _i_ is the error term for district _i_ .

The life expectancy regression will take the same form as the income regressions, except that we will not apply the natural logarithm to the dependent variable. All regressions are weighted OLS regressions, where the weights consist of the population size in each district.

The regression results for both income and life expectancy are reported in Table 3.

**Table 3: Estimated short-term relations between climate and income/life expectancy in Peru**

|**Explanatory variables**|**(1)**<br>(log percapitaincome)|**(2)**<br>(life expectancy)|
|---|---|---|
|Constant|2.5912|24.6883|
||(14.28)|(19.32)|
|Temperature|0.1837|3.1322|
||(11.37)|(27.53)|
|Temperature<sup>2</sup>|-0.0051|-0.0805|
||(-11.46)|(-25.64)|
|Precipitation|-0.5562|-1.8907|
||(-17.44)|(-8.42)|
|Precipitation<sup>2</sup>|0.1171|0.3730|
||(10.77)|(4.87)|
|Education level|0.0214|0.1874|
||(15.49)|(19.25)|
|Urbanization rate|-0.0095|-0.0339|
||(-8.17)|(-4.14)|
|Urbanization rate<sup>2</sup>|0.0001|0.0006|
||(10.67)|(8.30)|
|Number of obs.|1829|1829|
|R<sup>2</sup>|0.7449|0.8147|

_Source:_ Authors’ estimation based on assumptions explained in the text. Note: Numbers in parenthesis are t-values. When t-values are numerically larger than 2, we will consider the coefficient to be statistically significant, corresponding to a confidence level of 95%.

The results at the bottom of the table show that just these four explanatory variables (temperature, rainfall, education, and urbanization rates) explain more than 74% of the variation in incomes between the districts in Peru. This is a very good fit, which suggests that we have included the most important explanatory variables, and that including addition variables would make little difference. The same four variables explain about 81% of the variation in life expectancy, which is even more impressive.

Education level, here measured as the percentage of the adult population that can read and write, is the most important variable, explaining about 48% of the variation in incomes and about 58% of the variation in life expectancy. The remaining variables are also all statistically significant, but in a non-linear way. As it is difficult to judge the effects directly by looking at the estimated coefficients, we have plotted the estimated relationships in Figure 2. The axes are scaled to represent the actual range of temperatures, rainfall, 7
data gives broadly similar results (see Annex 6, Table A6.1 – A6.4). The exception is when we restrict our attention to high-density clusters and use the _LandScan_ data, in which case the Latin American dummy remains significantly positive (indicating “over-urbanization”) in the regression of urbanization on the share of the agricultural sector in a country’s GDP. Finally, for the cluster method using the _WorldPop_ data, Caribbean countries continue to have significantly higher levels of GDP per capita than predicted given their levels of urbanization (see Annex 6, Table 6.5). However, again, the sample size for this regression is restricted to only 40 countries. As an additional observation, it will be noted that the basic stylized relationships between urbanization, development and structural transformation break down when the cluster method is used in conjunction with either the _GHS-Pop_ or _WorldPop_ data. Insofar as the basic stylized facts are more stable across different input population data sets, this suggests that the AI results are more robust than those for the cluster method. Given that the AI also relies on additional sources of data – in particular, travel time estimates – this should perhaps come as no surprise.

# **5. Conclusion**

When levels of urbanization are measured based on national definitions of urban areas, countries in the LAC region are seen to depart significantly from the basic stylized facts of urbanization, development and structural transformation. Most notably, Latin American countries appear to be “over-urbanized” relative to their levels of development and the importance of the agricultural sector to their economies. However, when levels of urbanization are instead measured using a consistent set of criteria across countries, this apparent “over-urbanization” is revealed to be an “urban myth.” This finding tends to hold irrespective of whether the Agglomeration Index or a cluster-based methodology is adopted as the approach for consistently defining urban areas across countries. It, furthermore, tends to hold irrespective of the choice of gridded population data used as input into these two methodologies.

16
# **1 Introduction**

Trade in green goods can be defined as the international imports/exports of eco-friendly and sustainable products and services. According to Kang (2020) and based on the Green Goods and Services - North American Industry Classification System (GGS-NAICS) 2012 classification, green industry products can be classified into five categories: i) energy from renewable resources; ii) energy efficiency; iii) pollution reduction and removal; greenhouse gas reduction, and recycling; iv) natural resources, and v) environmental compliance, education and training, and public awareness and reuse. In addition, they are products and technologies that do not consume many resources and are environmentally friendly in their production and utilization (Li et al., 2022). In recent decades, trade in green goods has become more important for international competitiveness since countries worldwide recognize the importance of addressing environmental challenges and transitioning to a more sustainable economy.

At the global level, the advantages of being engaged in green trade are numerous. First, it can boost economic growth by generating jobs and increasing the development of green industries (Bowen, 2012). Second, countries can gain a comparative advantage in their exports (Gong et al., 2020), for example, by focusing on one product where they have the advantage and reduce their dependence on non-renewable imports. Third, being involved in green trade could drive innovation, research, and development in the country to increase its competitiveness in the global market (Liu et al., 2022a,b). Overall, the promotion of green goods could contribute to mitigating the impacts of climate change, increase compliance with international commitments on climate change and reinforce a country’s reputation as a sustainable trading partner.

Regarding the relationship between trade in environmental goods and labor markets, green trade can indeed imply some trade policies that could affect workers. For instance, shifting towards green trade may require workers in traditional fossil fuel-based industries to re-skill or upgrade their skills to be employed in renewable energy projects, which are stringent regarding the skills and alignment with international reforms. Thus, although 1