rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 3: Impact on Formalization**<br>_Dependent variables:_|(1)<br>Admin.<br>Data<br>(GUFE)|(2)<br>Declared<br>that the<br>business<br>is formal|(3)<br>Showed a<br>document|(4)<br>formality<br>or found in<br>admin.<br>data|(5)<br>Showed a<br>document<br>or found in<br>admin. data|
|---|---|---|---|---|---|
|Group 1|0.096***|0.066**|0.069***|0.107***|0.130***|
||(0.023)|(0.026)|(0.024)|(0.029)|(0.029)|
|Group 2|0.130***|0.108***|0.093***|0.143***|0.146***|
||(0.014)|(0.017)|(0.015)|(0.018)|(0.018)|
|Group 3|0.163***|0.128***|0.120***|0.176***|0.181***|
||(0.013)|(0.015)|(0.013)|(0.016)|(0.016)|
|Observations|3,596|3,061|2,929|3,061|2,929|
|R‐squared|0.392|0.436|0.453|0.446|0.464|
|Adjusted R‐squared|0.086|0.072|0.075|0.090|0.094|
|Mean dependent variable in Control|0.023|0.052|0.026|0.059|0.040|
|Pvalue Test Group1=Group2|0.175|0.153|0.353|0.257|0.602|
|Pvalue Test Group1=Group3|0.003|0.017|0.028|0.015|0.075|
|Pvalue Test Group2=Group3|0.022|0.211|0.066|0.068|0.057|
|Pvalue Test Group1=Group2=Group3|0.002|0.037|0.026|0.016|0.049|
|Pvalue Test Group1=Group2=Group3=0<br>Note: Administrative data from GUFE and<br>variable on treatment dummies, controll<br>* indicate statistical significance at 1, 5|0.000<br>survey da<br>ing for str<br>and 10%.|0.000<br>ta May 201<br>ata dummie|0.000<br>6. OLS regres<br>s (dummies|0.000<br>sion of the o<br>for each tri|0.000<br>utcome<br>plet). ***, **,|

35
putting each of these industries on substantially lower CO2 emissions trajectories. While these changes have not led to absolute declines in CO2 emissions, they have substantially weakened the link between industry growth and those emissions.

<!-- Start of picture text --> Figure 2<br>China<br>CO2 Intensity of Energy Consumption<br>(Kg of CO2 per Kg of Oil Equivalent)<br>3.6<br>3.4<br>3.2<br>3.0<br>2.8<br>2.6<br>2.4<br>2.2<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Source: WDI Online, 2012<br>KG CO2 per Kg Oil Equivalent<br><!-- End of picture text --> We focus on China because it is a major contributor to global CO2 emissions and because changes over time in its industrial and technology policies offer many opportunities to observe impacts on energy intensity and CO2 emissions.<sup>3</sup> We focus on the abovementioned four industries in China because they are the building blocks of China’s rapid urban<sup>4</sup> and industrial<sup>5</sup> transformation; because they currently account for nearly 60% of CO2 emissions from industry (figure 3); and because this trend is likely to continue as China works its way through further urban and industrial transformations.<sup>6</sup> The win-win improvements in CO2 intensity in these industries, illustrating the environmental technique effects of China’s high speed technological catch-up industrial development strategy, highlights opportunities to reduce emissions growth even in the absence of more explicit policies to reduce CO2 emissions.<sup>7</sup>

> 3 China’s share in global CO2 emissions from fossil fuels has risen from 8% in 1978 to 24% in 2009. Its incremental emissions accounted 65.7% of incremental global emissions between 1971 and 2000 and 71.8% between 2001 and 2009 (IEA Online Data Services, 2012).

> 4 China’s urban population increased 3.2 times between 1978 and 2009 as it grew from 178.9 million in 1978 to 585.8 million in 2009 (WDI 2012). At the same time the number of people living in cities with populations greater than 1 million increased from 71.3 million in 1978 to 233.9 million in 2009 (WDI 2012)

> 5 Industrial output grew by 9.8% per year between 1979 and 2009 such that real value added in industry in 2009 was 17 times larger than in 1979 (1.36 trillion RMB versus 79.9 billion RMB, WDI 2012).

> 6 China’s incremental emissions accounted 65.7% of incremental global emissions between 1971 and 2000 and

> 71.8% between 2001 and 2009 (IEA Online Data Services, 2012).

> 7 However, we are not suggesting that industrial and technology policies can be an alternative to more explicit emission limitation measures for reducing longer-term GHG emissions.

3
-

54 -

# Figure 4

PoDulation Density and Nonfarm Employment in Seven Districts, Anambra State, Nigeria <!-- Start of picture text --> 80<br>4-<br>0 *<br>* b { 0<br>a. ~~~~~~~~~~~*<br>60k<br>0~~~~~~~~~~~~~~<br>0<br>0 40 * 0+<br>.4-<br>0 I ~~~~~~~~~~++<br>0<br>2 0 - + +<br>0~~~~~~~~~~~<br>200 400 600 800 1,000<br>Rural Population Density per Square Kilometer<br><!-- End of picture text --> <!-- Start of picture text --> Men.<br>o Women.<br>+ Children.<br><!-- End of picture text --> Source: Okafor (1983).
# **Figures**

Figure 1: Number of Syndicated Loans and Share with MDB Support by Country, 1993-2017 <!-- Start of picture text --> IBRD 44692 | OCTOBER 2019<br><!-- End of picture text -->

_Notes_ : The bubbles in the map represent the number of syndicated loans by country in 1993-2017. The area represents the number of total syndicated loans in the period: the larger the bubble, the more syndicated loans. The color represents the share of syndicated loans supported by at least one MDB in the period: the darker the blue, the more the syndicated loans receive MDB support.

24
|**Applicant**|**Number of applications**|
|---|---|
|<br>**Council of Scientific and Industrial Research (CSIR) New Delhi**|<br>**2,760**|
|Procter& Gamble|1,865|
|Siemens|956|
|Hindustan Lever(amajority-owned subsidiary ofUnilever)|892|
|Honda|631|
|BASF|599|
|Bayer|523|
|Daewoo|465|
|General Engineering Services|465|
|Sony|437|
|Samsung|427|
|Matsushita|411|
|IBM|407|
|Lucent|405|
|Motorola|403|
|Novo Nordisk|361|
|Pfizer|359|
|Praxair|329|
|Glaxo SmithKline|320|
|Qualcomm|288|
|Maschinenfabrik Rieter|262|
|Mitsubishi|251|
|Ericsson|251|
|**Indian Institute of Technology (IIT)**|**245**|
|<br>Hoechst|241|
|British Telecom|234|
|Dow Chemical|213|
|Shell|206|
|SocieteD’exploitationdesProcedesMarechal(SEPM)|201|
|Kabushiki|199|
|Hoffman La-Roche|196|
|KoninklijkePTT|190|
|Johnson& Johnson|188|
|**Ranbaxy Laboratories**|**185**|
|American Tool|183|
|**Steel Authority of India**|**178**|
|Bosch|177|
|Kimberly-Clark|175|
|Merck|171|
|LGElectronics|161|
|**Ministry Of Defense, India**|**161**|
|AstraAktiebolag|158|
|<br>Ciba Specialty Chemicals|155|
|Corning|152|
|UniversityTechnologiesInternational(University OfCalgary)|149|
|AseaBrown Boveri|148|
|**Dr Reddy’s Research Foundation New Delhi**|**148**|
|Eaton|145|
|Eli Lilly|143|

27
- **Krauser, Mario** , “In the Eye of the Storm: Rebel Taxation of Artisanal Mines and Strategies of Violence,” _Journal of Conflict Resolution_ , 2020, _64_ (10), 1968–1993.

- **la Sierra, Raul Sanchez De** , “On the Origins of the State: Stationary Bandits and Taxation in Eastern Congo,” _Journal of Political Economy_ , January 2020, _128_ (1), 32–74.

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- **Moreno, Laura, Jorge Gallego, and Vargas Juan** , “More roads, more conflict? The effect of rural roads on armed conflict and illegal economies in Colombia,” _Universidad de Rosario Working Paper_ , 2019, _248_ , 1–49.

- **Mueller, Hannes and Christopher Rauh** , “The Hard Problem of Prediction for Conflict Prevention,” _Journal of the European Economic Association_ , 2022, _20_ (6), 2440–2467.

- **and** , “Building Bridges to Peace: A Quantitative Evaluation of Power-Sharing Agree-

- ments,” _Economic Policy_ , 2023. Accepted.

- **, Dominic Rohner, and David Sch ̈onholzer** , “Ethnic Violence Across Space,” _The Economic Journal_ , 2022, _132_ (642), 709–740.

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32
LFSs show a tendency for unemployment rates to increase 1982-85 while the auxiliary placement office indicators suggest declining excess supply 1982-86, accelerating after 1984, as also weakly indicated by the SPO estimates. The series for registered unemployed shows strong increase of unemployment throughout the 1980s but is suspect due to incentives to register from new government programmes.

Everything considered I am inclined to believe that the strong upward trend in unemployment since the 1960s is a fact of life, qualified, Lowever, by cyclical short term characteristics not apparent in the SPO estimates.

Our few indicators, LFSs, for rural unemployment suggest very low unemployment rates in 1973-74 and very high rates in 1985. Taken as they are in a slack agricultural season they may exaggerate rural unemployment. The 1973-74 LFSs clearly support the notion of labor market clearing in rural areas around the year.Insufficient differentiation of job-seeling activities of non-employed rural labor permits recoociliatii'i of the high measured rural unemployment rates for 1985 with the assumption of continued market clearing around the year, assuming job seeking mainly directed towards urban areas (and foreign countries). This would in particular apply to educated labor which even in rural areas supplies the bulk of unemployed. As we shall see now in the following section a reversal of the rural-urban migration flow at the end of the 1970s may help explaining the relatively strong

- 20 -
<!-- Start of picture text --> 400 5 , 120<br>5 + 100<br>sy |<br>a 80<br>g 250 + | ——Russian mineral fuel exports<br>¢ 200 - + 60<br>ra , 40 Europe— Brent spot price FOB<br>100 — | (USS/Barrel)<br>50 | r 20<br>2007) 2008 2009 2010 2011 2012 2013<br><!-- End of picture text -->
consumption may do a better job in this respect, but will still be an imperfect welfare indicator given that inter-temporal markets do not work perfectly. There are also uncertainties about how best to normalize for heterogeneity in consumption needs, such as stemming from demographic differences between households (Pollak, 1991).<sup>4</sup> And even if we had an ideal measure of average economic welfare for a household, health status is an individual attribute, which will depend directly on personal economic welfare. Individual income is a flawed metric of individual command over commodities given some degree of income pooling within households. And household expenditure may well be equally flawed as an indicator of individual economic welfare given that pooling is almost certainly incomplete.<sup>5</sup> In short, heterogeneity in relevant individual and household circumstances, intertemporal consumption smoothing and inter-personal income sharing may entail that neither measured current income nor consumption are particularly good proxies for economic welfare, as relevant to health. This too will lead one to underestimate the true economic gradient in health using the methods commonly found in the literature.

This paper proposes a method of estimating the economic gradient in health status that is likely to be more robust to non-ignorable errors in _SAH_ data and the measurement errors in data on incomes or expenditures. The essential idea is to use largely independent covariates of both _SAH_ and perceived economic welfare to attempt to purge the raw data of the non-ignorable errors, prior to testing for the economic gradient in health status. To calibrate a broader measure of economic welfare we draw on subjective data, though recognizing that this too contains measurement errors when used as an indicator of objective economic welfare. Our estimation

> 4 For example, the poverty lines used as deflators may not correctly weight differences in household size or demographic composition.

> 5 It is instructive that, in the same setting, Ravallion and Loskhin (2001) find evidence that many “non-income” factors at the individual and household levels impinge on perceived economic welfare in Russia at given current incomes or expenditures on consumption deflated by standard poverty lines.

4
rural investment climate matter. Perceived constraints and constraints measured with objective data at the community-level are largely consistent, suggesting some robustness of the empirical results. Enterprise constraints in rural Tanzania mainly operate from the supply-side; suggesting that access to finance, road infrastructure and rural cell phone communication - by reducing transaction costs and facilitating market information - is correlated with employment growth.

The dominance of the agriculture sector indicates that most rural enterprises in Tanzania are highly dependent on the performance of agriculture in the long-run. This suggests that favorable policies and investment for agriculture play a big role. In the survey year 2005, however, supply-side constraints are more important than agricultural demand-side constraints. This may be because demand exists due to a relatively favorable agricultural environment. This suggests that in areas where a buoyant economic base exists, efforts focusing improving supply-side constraints could be successful to promote nonfarm activities. In addition, as 57% of rural nonfarm enterprises are trading enterprises, favorable internal trade policies seem to be of utmost importance in determining enterprise performance. The large share of informal rural nonfarm enterprises may be explained by the fact that being formal is costly - transaction costs and taxes for formal nonfarm enterprises seem to remain high.

It is important to emphasize that the analysis is based on a pilot data collection exercise. This analysis of rural enterprise dynamics is the first of its kind in Tanzania, and only a few of comparable surveys have been completed elsewhere. Therefore, the conclusions should be taken carefully. They aim to stimulate dialogue and future analysis of the rural private sector. Much remains to be learned about the investment climate and its impact on rural enterprise growth in Africa.

29
# Job Tenure and Structural Change in the Transition Economies of Europe

_Maurizio Bussolo, Michael M. Lokshin, Nicolás Oviedo, and Iván Torre*_

**Keywords:** Job tenure, employment, labor market, transition **JEL:** J44, J60, O33

> * The authors are at the World Bank. This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
Policy Research Working Paper 8442

# **Abstract**

Throughout many countries in the world, the measurement of food security currently includes accounting for the importance of perception and anxiety about meeting basic food needs. Using panel data from Malawi, this paper shows that worrying about food security is linked to self-reports of having experienced food insecurity, and the analysis provides evidence that rapidly rising food prices are a source of the anxiety and experiences of food insecurity. This finding controls for individual-level fixed effects and changes in the economic well-being of the individual. A particularly revealing finding of the importance of accounting for anxiety in assessing food insecurity is that individuals report a significant increase in experiences of food insecurity in the presence of rapidly rising food prices even when dietary diversity and caloric intake is stable.

This paper is a product of the Poverty and Equity Global Practice and the Development Data Goup. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at at djolliffe@worldbank.org and adelafuente@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
grade (October 2013 Annual School Census, School Education Department, Government of Punjab).

Available evidence indicates that teacher performance in public schools is poor. Using field survey data from rural Punjab, Andrabi, Das, Khwaja, Vishwanath, and Zajonc (2009) cast light on the performance of public school teachers, particularly when contrasted with teachers in low-cost, for-profit private schools operating in the same villages. They find that public school teachers tend to have higher levels of academic qualifications, teaching experience, and professional training than do private school teachers. Public school teacher salaries are, on average, three to four times higher than private school teacher salaries after accounting for differences in teacher credentials and other characteristics.

Despite the higher levels of credentials among public school teachers, Andrabi et al find that the levels of teacher presence and student academic achievement are lower in public schools than in private schools. It also appears that public school teacher salaries do not vary with teacher competency (measured by teacher test scores) or student competency (measured by student test scores). Higher public school teacher salaries also seem to be associated with lower teacher effort, as measured by teacher presence at school. This pattern is driven mainly by the relatively lower presence of more senior (and, therefore, better paid) teachers in public schools. In contrast, among private school teachers, higher salaries are associated with higher teacher presence, higher teacher test scores, and higher student test scores.

# _B. Program_

The teacher performance pay program was designed and approved by the Punjab government in fiscal year (FY) 2009–10 for implementation on a pilot basis starting in FY2010– 11 (corresponding to the 2010–11 school year).<sup>5</sup> The main expressed aim of the program is to raise school performance by encouraging greater teacher effort.

# _Measuring school performance_

The government selected performance indicators which could be measured regularly, throughout the province, in a standardized way across schools, and for which the underlying data

> 5 The government’s fiscal year begins on July 1 and ends on June 30.  The public school system’s school year begins on April 1 and ends on March 15.

7
“downstream” matters. Instead, the lack of availability of frontier GPT can seclude firms into low productivity activities, limiting trade and economic growth. Further research on the interplay between the causes and consequences of lack of (government induced) GPT adoption would help understand the nature and consequences of upstream factors impeding productivity gains and growth.

From a policy perspective, one proposal put forward by Arezki et al (2018) to break with “business as usual” in the MENA region is for the authorities to embrace a “moonshot approach” to the adoption of information technology and communications. MENA countries could emulate President John F. Kennedy’s 1961 decision to unleash an extraordinary collective national effort that achieved its seemingly impossible goal: a manned lunar landing in mid-1969. A MENA moonshot would involve a collective regional commitment to achieve parity with advanced economies in information and communications technology by 2021. MENA countries would seek to equal or better OECD countries in terms of their level of access to the internet, capacity to transmit data (bandwidth) and the number of financial transactions carried out electronically. This would unleash the potential of the young and educated population—who have been subject to abnormally high levels of unemployment—and spur growth.<sup>16</sup>

> 16 World Bank (2019) shows that MENA has the highest youth unemployment rates in the world and these rates are highest among the educated.

21
In cement, figure 4 identifies year by year industry-level savings in CO2 from (1) improving energy efficiency in rotary and vertical shaft kilns, (2) shifting from vertical shaft kilns to rotary kilns, (3) shifting to blended cement, (4) using alternative waste fuels, and (5) recovering heat to co-generate electricity. Three aspects of figure 4 are important. First, the largest single savings in CO2 comes from the shift to blended cement—by 2010, 40% of cement production in China (745 million metric tons) was blended cement. This shift alone accounts for 608 million metric tons of CO2 saved or 65% of total CO2 savings. Roughly 20% of total savings (183 million metric tons) come from efficiency improvements in vertical shaft kilns and rotary Figure 4 <!-- Start of picture text --> China<br>CO2 Saved Cement<br>(Million Metric Tons)<br>1,000<br>800<br>Total CO2 Saved<br>600 Blended Cement<br>Cogeneration<br>Efficiency Improvements Rotary Kilns<br>400 Efficiency Improvements VSKs<br>Shift VSKs to Rotary Kilns<br>Alternative Fuels<br>200<br>0<br>80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10<br>Source: Rock and Cui (2011)<br>MMT of CO2 Saved<br><!-- End of picture text --> kilns, while 15% of total savings (136 million metric tons) come from the closing of vertical shaft kilns and the switch to rotary kilns. So far, very little savings have come from use of alternative fuels or cogeneration of electricity.

Taken together, CO2 savings (relative to the counterfactual) in 2010 from the ongoing advance of technology in this sector were 928 million metric tons. As a result, the CO2 intensity of cement production fell from roughly 0.9 metric tons of CO2 per metric ton of cement in 1985 to about 0.6 metric tons of CO2 per metric ton of cement in 2010 (figure 5). While the scale effect of growth has swamped the technique effect, it is clear that China has weakened the link between cement production and CO2 emissions (figure 6). As Rock and Cui (2011) argue, China has several significant further opportunities to reduce CO2 emissions from cement production. If adopted, they, along with a looming peak in demand for cement, might enable near-term CO2 emissions in the sector to peak and then decline.

22