rafmacalaba/datause-encoder-data huggingface dataset:
the free entry condition ( _Vi =0_ ), the formal contract wage _wF (xF, y)_ for a worker of type _y_ producing at _xF_ is following.

The wage in sector _i_ is the weighted average of the worker’s productivity and the worker’s outside options. Evidently, regulations affect the formal wage bargaining process.

# **III. DATA AND CALIBRATION**

The model is calibrated using data from the 2015 Moroccan Labor Force Survey (MLFS), the latest publicly available to conduct this research. The survey have been conducted by the High Planning Commissariat of Morocco, covering all residents on the national territory. The MLFS is representative at the strata (urban/rural) and region levels. The MLFS provides information on wages and has large size (60,000 households of which 40,000 are urban, and about approximately 270,000 individuals every year).

Given the nature of the model, our sample excludes agricultural employment and the public sector. The agricultural sector (albeit large) is mainly composed of unpaid family workers, a group that is unlikely to be affected by changes in the labor policies assessed in the study. Also, the main features of public sector employment in Morocco (wages, size, hiring/firing decisions) are not necessarily driven by the model’s assumptions, despite the fact that this sector is quite compliant with labor regulation. Indeed, public sector decisions to set wages, hire and fire workers, etc., are not likely to follow the assumptions of the value functions proposed by the model but to be driven by other factors (e.g. politics / pork barrel / social peace). As a result, our sample choice is largely urban (75%) and accounts for 51% the national labor force (Table 1). While this sample choice leaves us with about half of the overall Moroccan labor market, we believe that it is a good representation of the share of the market that is somehow affected by the type of labor regulations assessed in the study. As such, the results of the study need to be interpreted with care and may not be generalized, as they are pertinent only to urban and non-agricultural settings.

6
campaign sustained over longer time generates greater impact on school outcomes and whether there are sustained differences in impacts across states.

# **Figure 1: Flow of study participants through the trial (Study design is similar for three states, except for few differences in Karnataka; see section 3)**

4 districts chosen purposively in Uttar Pradesh (UP) 2 blocks randomly selected per district and 25 _gram panchayat_ s (GPs) randomly selected per block = 200 GPs per state 100 GPs (half of sample GPs in each block) randomly assigned to intervention. One public primary school per GP randomly selected for baseline and follow up surveys 45 students per school – 15 each from grades 2, 3 and 4 randomly selected for baseline survey. Within each grade, 5 chosen randomly from each caste category- SC/ST, OBC and Others. Half chosen randomly to be girls. Baseline survey: Feb-April 2006 100 GPs (half of sample GPs in each block) randomly assigned to control. One public primary school per GP randomly selected for baseline and follow up surveys 45 students per school – 15 each from grades 2, 3 and 4 randomly selected for baseline survey.

Within each grade, 5 chosen randomly from each caste category- SC/ST, OBC and Others. Half chosen randomly to be girls.

Baseline survey: Feb-April 2006 Intervention: Information campaign took place from September to November 2006 Follow up survey: Feb-April 2007 No information campaign Follow up survey: Feb-April 2007 21
We use information on firm characteristics for 2003 and 2010 for all firms in the sample, and estimate mean difference-in-differences regressions on a constant, a dummy variable for issuing firms, a dummy variable for 2010 observations, and the interaction term of those two variables. To assess the evolution of firm size for issuers and non-issuers, we estimate the regression using the logs of assets, turnover, or the number of employees as dependent variables. The estimated coefficients from the difference-in-differences analysis are reported in Table 4 and should be interpreted as follows. The constant term reflects the average size of non-issuing firms in 2003. The issuer dummy coefficient measures the size of issuers relative to non-issuers in 2003. The 2010 dummy variable gauges the total growth of non-issuing firms between 2003 and 2010. The interaction term measures the additional growth of issuers, between 2003 and 2010, relative to non-issuers. That is, whether issuers grow on average more compared to non-issuers.

The results in Table 4 show that, on average, firms that issue equity, bonds, or syndicated loans are larger ex ante than non-issuing firms. The differences are also economically significant. The estimates suggest that non-issuers in the Arab region had, on average, about $73 million in total assets in 2003, while firms that issued equity had $150 million in assets―about 105 percent more. Bond and loan issuers were even larger in 2003, holding $3.5 billion and $2.1 billion in assets, respectively. Similar qualitative results are found for turnover. In particular, while non-issuers had a turnover of $20 million in 2003, the values were almost $27 million for equity issuers and around $250 million for debt issuers. Looking at the number of employees, the estimates are only statistically significant for bond and syndicated loan regressions. Compared to non-issuers in 2003, bond and loan issuers had around 286 percent and 479 percent more employees, respectively.

Issuing firms not only start larger than non-issuing firms, but they also grow much faster. The coefficients for the 2010 dummy are positive and statistically significant in all the specifications. The estimates imply that, on average, non-issuers have expanded their total assets, turnover, and number 15
presence in period _t-k_ associated with greater unorganized sector presence in period _t_ within a state-industry cell?

This specification is particularly important since the issue of jobless growth is an important one facing Indian policy makers. The standard wisdom of encouraging large scale organized industry (which by itself may be highly capital-intensive and may not generate a lot of jobs) to locate in a particular state is that ancillary enterprises (which supply important inputs to the organized sector) will follow. A large proportion of these ancillaries will be in the unorganized sector. Thus we should see a positive relationship between current unorganized output and employment and lagged organized output and employment. However if there is truly jobless growth there will be no (or even a negative) relationship. Our framework allows us to test for this.

Note that we have only four years of unorganized sector data but many more years of organized sector data. Thus we can link unorganized data in the years (1989, 1994, 2000, 2005) with organized sector data in (1989-k, 1994-k, 2000-k, 2005-k), k<=9.

**Equation (6)** Y<sup>U</sup> jst<sup>=</sup>

β1′HORIZONTAL YOjst−k<sup>+ β</sup> 2′ BACKWARD YOjst−k<sup>+ β</sup> 3′ FORWARD YOjst−k<sup>+</sup> δ1′TFPjst−kO + δ2′ BACKWARD TFPjst−kO + δ3′ FORWARD TFPjst−kO + γ1′BACKWARD Yjst−kU + γ2′ FORWARD Yjst−kU + α+ I<sup>js</sup> + I<sup>jt</sup> + I<sup>st</sup> + I<sup>t</sup> + εijt Note that we do not include current other-type spillovers in the estimation equation. That is because these are likely to be correlated with lagged other-type spillovers. But we do control for time-varying state and industry fixed effects which will capture the strength of contemporaneous spillovers.

Table 15 and Table 16 show the results from estimating Equation (6) for lags of 2, 4 and 9 years. Columns 2, 4 and 6 contain state-year and industry-year fixed effects while Columns 1, 3, and 5 do not. We find that backward spillovers from organized sector employment are significant at lags of 4 years but not for lags of 2 or 9 years. Backward linkages in real output exist only for lags of 9 years. We can compare this weak and inconsistent evidence on the presence of _othersector_ backward spillovers to the evidence for _own-sector_ backward spillovers which are strong and positive for 2 and 4 year lags of employment and for 2 and 9 year lags of real output.

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|**Table 16: M**|**odel 2 -Single-Sh**<br>(1)|**ift & IDEB Middl**<br>(2)|**e Schools**<br>(3)|(4)|
|---|---|---|---|---|
||IDEB Mid (SD)|IDEB Mid (SD)|IDEB Mid (SD)|IDEB Mid (SD)|
|Single-Shift 2011*Post 2011|1.204|0.566|0.004|0.834|
||[0.186]***|[0.130]***|[0.147]|[0.156]***|
|Single-Shift 2012/13*Post 2012|1.011|0.566|0.009|0.656|
||[0.182]***|[0.114]***|[0.154]|[0.335]*|
|School of Tomorrow|0.064|-0.017|-0.599|0.081|
||[0.099]|[0.095]|[0.123]***|[0.137]|
|More Education|0.095|0.088|0.169|0.12|
||[0.055]*|[0.054]|[0.082]**|[0.074]|
|Mean items in the house||0.18|0.296|0.113|
|||[0.032]***|[0.060]***|[0.052]**|
|% Mothers w/ HE||0.243|-0.401|0.036|
|||[0.240]|[0.493]|[0.383]|
|Participation rate-Prova Brasil Mid||0.004|-0.053|-0.02|
|||[0.034]|[0.052]|[0.051]|
|Age-grade distortion||-0.025|-0.03|-0.025|
|||[0.003]***|[0.005]***|[0.004]***|
|Constant|-0.544|-0.287|-1.051|-0.053|
||[0.032]***|[0.285]|[0.517]**|[0.482]|
|Observations|1926|1610|164|800|
|Number of schools|321|322|41|160|
|R-squared|0.46|0.49|0.77|0.43|
|School FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Restricted Sample<br>Robust standard errors in brackets|No|No|Yes|Yes|

<mark>* significant at 10%; ** significant at 5%; *** significant at 1%</mark> Notes: IDEB Middle Schools 2005, 2007, 2009, 2011, 2013 and 2015. Columns (2) and (4) refer to years 2007 through 2015. Column (3) refers to only schools that eventually become Single-Shift until 2015, in years 2007 through 2013. Column (4) refers to only schools with low age-grade distortion in 2010 (prior to the program). Single-Shift includes CMS in this model. Clustered standard error in school level.

Finally, Table 17 summarizes all results. It specifically shows the results presented in columns (2) of Tables 9 through 16, with the complete sample including all controls. As presented in Table 17, the results for elementary and middle Single-Shift schools are overall positive and statistically significant across different specifications. They are consistent across different grades (IDEB uses _Prova Brasil_ that tests 5<sup>th</sup> and 9<sup>th</sup> grades, and IDERio uses _Prova Rio_ that tests 3<sup>rd</sup> and 7<sup>th</sup> grades), and come not only from the first cohort that participated in the program, but also from the program expansion. Moreover, the increases seem to be coming not only from better pass rates, but also from improvements in student performance.

36
World Bank. 2003d. _China: Promoting Growth with Equity_ , Country Economic Memorandum, Washington, DC.

Xue, J. & W. Zhong. 2003. “Unemployment, Poverty and Income Disparity in Urban China”, _Asian Economic Journal_ , V17, No. 4.

Yao, Shujie. 1994. _Economic Reforms and Grain Production in China_ , MacMillan, London.

Yao, Shujie. 2000. “Agriculture’s Role in Economic Development: The Case of China”, from Cook, Sarah, Shujie Yao and Juzhong Zhuang, ed., “ _The Chinese Economy Under Transition”,_ MacMillan Press (London) and St. Martin’s Press (New York).

Yao, Shujie. 2002. “China’s Rural Economy in the First Decade of the 21st Century: Problems and Growth Constraints”, _China Economic Review_ , V13, pp. 354-360.

Zhai, Fan, et al. 2002. “WTO Accession, Rural Labor Migration and Urban Unemployment in China”, _Urban Studies_ , V39, No. 12, Zhai, Fan, T. Hertel and Z. Wang. 2003 (May). “Labor Market Distortions, Rural-Urban Inequality and the Opening of China’s Economy”, GTAP working paper, No. 27.

Zhang, Anming, et al. 2003. “A Study of the R&D Efficiency and Productivity of Chinese Firms”, _Journal of Comparative Economics_ , V31, pp. 444-464.

Zhang, Juwei. 2003. “Urban Xiagang, Unemployment and Policy Support.” A background study for the World Bank, Washington, D.C.

Zhang, Linxiu, et al. 2002. “Employment, Emerging Labor Markets, and the Role of Education in Rural China”, _China Economic Review_ , V13, No. 2-3.

Zhao, Y. 1996 (June). “Labor Migration and Education in Rural Sichuan Province: A Special Pattern”, International Conference on the Flow of Rural Labor in China, Beijing.

Zhao, Y. 2002. “Causes and Consequences of Return Migration: Recent Evidence from China”, _Journal of Comparative Economics_ , V30, pp. 376-394.

35
4 inter-hous*hold distribution of the benefits nf public expenditures, one would ideally like to compar.m the distribution of living standards without government spending to the one which attains with publicly provided services.

Commonly used indicators of living standards, such as household per capita expenditures, which exclude the monetary value derived from publicly provided goods, only provide a rough approximation of the distribution that would be obtained prior to government intervention, however. There are several reasons for this. Household per capita expenditures on private goods are influenced by what governments spend on public services. Public services may displace private spending: for example, when outpatient care in a public hospital is provided at a subsidized rate, people will spend less on private doctors. Public services may also augment private spending: for example, subsidized schooling may encourage households (who might not othcrwise send their children to a private school) to spend income on their children's clothing -- such as by providing sandals and a school uniform. Furthermore, the distribution of living standards is influenced by the outcomes (such as good health and education) of past publ,c spending. These are very difficult effects to quantify. Here, I follow common practice in assuming that household consumption expenditures on privately supplied goods ("consumption" for short) are an adequate proxy for living standards in the absence of publicly provided goods. Thus, by looking at how the benefits of the latter are distributed across households ranked by consumption one can assess the impact of public provisioning on living standards.

The SUSENAS surveys are large national representative samples. The 1987 survey consists of about 55,000 households.2 The SUSENAS is a consumption survey of considerable detail, which provides us with the best source of household level data for Indonesia. Although it also records incomes, there is some evidence that the total expenditure variable is the 2 The sampling frame is stratified so that it is necessary to expand each sampling point by the corresponding expansion factor in forming population estimates.
_Figure 2: Decomposition of economic growth (Q12014 – Q42017)_ <!-- Start of picture text --> 20<br>15 household consumption<br>10 public consumption<br>5<br>gross fixed capital formation<br>0<br>export<br>-5<br>-10 import<br>-15 other, incl. statistical discrepancy<br>-20<br>GDP<br>2014Q1 2014Q2 2014Q3 2014Q4 2015Q1 2015Q2 2015Q3 2015Q4 2016Q1 2016Q2 2016Q3 2016Q4 2017Q1 2017Q2 2017Q3 2017Q4<br><!-- End of picture text --> Source: Shymanovich, Shcherbina, Chubrik (2018).

The fall in household consumption in 2015–2016 was related to the developments in the labor market. One characteristic of the recession was falling wages – a dynamic that was observed for more than two years starting with the second half of 2014. In addition, the economic crisis was accompanied by falling employment, which was related, in part, to demographics, as the share of working age population in Belarus was shrinking. Prior to the crisis this trend was offset by the growing economic activity of the population of pensionable age. The economic recession revised this trend and accelerated the employment contraction over and above the demographic factors. In particular, a sharp contraction of employment occurred in 2016, as the influence of demographic factors was exacerbated by the reduced employment rate of the working‐ age population. In 2017, the demographic factor was offset by the pension age increase. The flipside of the pension age increase was a fall in economic activity of the pension age population. At the same time, the employment rate of the working‐age population remained rather stable during the recent crisis. As a consequence, the unemployment rate was only 5.8 percent<sup>3</sup> at the peak of the crisis in 2016, and it declined to 5.6 percent in 2017.

_Figure 3: Employment dynamics of the working age and above working age population_ <!-- Start of picture text --> 100 thsd pers. 78.0 % 19.0<br>77.5 18.5<br>77.0 18.0<br>0<br>76.5 17.5<br>76.0 17.0<br>75.5 16.5<br>-100<br>75.0 16.0<br>74.5 15.5<br>74.0 15.0<br>-200<br>73.5 14.5<br>2011 2012 2013 2014 2015 2016 2017<br>73.0 14.0<br>change in employment, working age population<br>2010 2011 2012 2013 2014 2015 2016 2017<br>change in employment, above working age population<br>change in the working-age population (employable) working-age population (employable)<br>total change in employment population above working age (right axis)<br>(a) Change in employment, thsd pers. (b) employment rate, %<br><!-- End of picture text --> 3 Unemployment estimates from the labor force survey according to ILO methodology are only available since 2016 in Belarus, and as such we are not able to compare with the pre‐crisis levels.

4
Figure 24: Current and simulated CO2 emissions per expenditure quintile per country <!-- Start of picture text --> Afghanistan Burkina Faso Cameroon Malawi<br>.6 300 .6 300 .6 300 .6 300<br>.4 200 .4 200 .4 200 .4 200<br>.2 100 .2 100 .2 100 .2 100<br>0 0 0 0 0 0 0 0<br>Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5<br>Per-capita expenditure quintile Per-capita expenditure quintile Per-capita expenditure quintile Per-capita expenditure quintile<br>Mali Mongolia Nicaragua Niger<br>.6 300 .6 300 .6 300 .6 300<br>.4 200 .4 200 .4 200 .4 200<br>.2 100 .2 100 .2 100 .2 100<br>0 0 0 0 0 0 0 0<br>Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5<br>Per-capita expenditure quintile Per-capita expenditure quintile Per-capita expenditure quintile Per-capita expenditure quintile<br>Nigeria Pakistan Sierra Leone Tanzania<br>.6 300 .6 300 .6 300 .6 300<br>.4 200 .4 200 .4 200 .4 200<br>.2 100 .2 100 .2 100 .2 100<br>0 0 0 0 0 0 0 0<br>Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5<br>Per-capita expenditure quintile Per-capita expenditure quintile Per-capita expenditure quintile Per-capita expenditure quintile<br>Uganda<br>.6 300<br>.4 200<br>.2 100<br>0 0<br>Q1 Q2 Q3 Q4 Q5<br>Per-capita expenditure quintile<br>2035 current percentage change in CO2 emissions<br>Percentage change<br>CO2 emissions level (metric tons per capita)<br><!-- End of picture text -->

# **6 Conclusion**

This paper aims to understand how economic development impacts transport demand and the consequences for carbon emissions from the transport sector. Using household surveys data, we estimate the determinants of transport demand. We find that transport expenditures and vehicle ownership will increase significantly as households and countries get richer. As countries get richer, households will increase their relative spending on transport, be more likely to own a car, and switch from public to private transport mode. In poorer countries, a large share of households, currently not spending on transport, will start using transport services or want to own a vehicle. The demand for private vehicles is therefore expected to increase significantly as countries get richer. These results suggest a future steep growth of emissions as incomes expand. According to our simulations, higher predicted 2035 incomes will increase vehicle ownership and usage, leading to an average increase of 52% in transport CO2 emissions for our sample of 18 countries. Finally we show that the distribution of emissions across income groups is highly concentrated among the richest households. This concentration is expected to slightly decrease for the whole sample of countries, increase in some countries, but decrease in others.

41
|**Tabl**<br>**Code **<br>**Non-r**|**e B4:**<br> **Table**<br>**epres**|**Regulatory Quality (cont.)**<br>**Concept Measured**<br>**entative Sources**|
|---|---|---|
|**ADB**<br>**ASD**<br>**BPS**<br>**BTI**<br>**CPIA**<br>**EBRD**<br>**WCY**|A1<br>A3<br>A5<br> <br>A4<br>A8<br>A10<br>A18|Trade policy<br>Competitive environment<br>Labor Market Policies<br>Trade Policy and Forex Regime<br>Enabling Environment for Private Sector Development<br>Information on the laws and regulations is easy to obtain<br>How problematic are anti competitive practices for the growth of your business.<br>How problematic are unpredictable regulations for the growth of your business.<br>How problematic are labor regulations for the growth of your business.<br> How problematic are tax regulations for the growth of your business.<br>How problematic are custom and trade regulations for the growth of your business.<br>Competition<br>Price Stability<br>Competitive environment<br>Trade policy<br>Price liberalization<br>Trade & foreign exchange system<br>Competition policy<br>Access to capital markets (foreign and domestic) is easily available<br>Ease of Doing Business<br>Banking regulation does not hinder competitiveness<br>Competition legislation in your country does not prevent unfair competition<br>Customs' authorities do not facilitate the efficient transit of goods<br>Financial institutions' transparency is not widely developed in your country<br>Easy to start company<br>Foreign investors are free to acquire control in domestic companies<br>Price controls affect pricing of products in most industries<br>Public sector contracts are sufficiently open to foreign bidders<br>Real corporate taxes are non distortionary<br>Real personal taxes are non distortionary<br>The exchange rate policy of your country hinders the competitiveness of enterprises<br>The legal framework is detrimental to your country's competitiveness<br>Protectionism in your country negatively affects the conduct of business in your country<br>Labor regulations hinder business activities<br>Subsidies impair economic development|

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<!-- Start of picture text --> 100<br>HE Pane! estimation<br>[Fa individual estimation<br>80<br>ec 60<br>a<br>2<br>a<br>a 40<br>20<br>a =<br>EMDE: negative AE: negative EMDE: significant AE: significant<br>100<br>HE Pane! estimation<br>[Fa individual estimation<br>80<br>ec 60<br>a<br>2<br>a<br>a 40<br>: ' -<br>0<br>EMDE: negative AE: negative EMDE: significant AE: significant<br><!-- End of picture text -->