rafmacalaba/datause-encoder-data huggingface dataset:
city and year (Caulkins, 1994). Also, the spread in expected purity does not decrease as the quantities being transacted increases, and, as a result, the interpretation of simple averages should be handled with care (ONDCP, 2004). According to ONDCP, the average expected purity of cocaine<sup>20</sup> in the US market increased rapidly throughout the 1980s for all quantities being transacted, then decreased during the first few years of the 1990s and remained relatively stable during the 1990s. Finally, in the last few years, expected purity of powder cocaine has increased, reaching levels of about 70% for purchases of less than two grams, 67% for purchases of 2 to 10 grams, and 62% for purchases of 10 to 59 grams and of more than 50 grams (see Figure 7).<sup>21</sup> Although one would expect increasing purity levels as the quantities being transacted increases (as was in fact observed in the 1980s), purity differences across quantity levels had almost disappeared in the 1990s, mainly because purity levels at the highest quantities transacted fell. According to ONDCP (2004) this suggests that diluting cocaine was not as common a practice in the 1990s as it was in the 1980s. In fact, after 1998 one observes higher purity levels for the lowest quantity purchases, which may come from the compositional effects from the aggregation of different number of transactions from higher and lower purity transactions, markets, and cities, and not because distributors inside the US refine cocaine across the distribution chain (UNODC, 2004).

According to evidence cited by Caulkins and Reuter (1998), purity levels do not seem to affect transaction prices at the retail level in consumer countries. This apparent puzzle, however, is explained by the authors with the observation that illegal drugs are experience goods, where the price

> 20 Expected purity levels are based on observations obtained through purchases only and do not include observations from seizures and other enforcement activities (ONDCP, 2004). 21 The same patterns are observed when information on purity levels obtained through seizures and other enforcement activities are also included (see Figure 8).

16
# **Appendices**

# **Appendix A: Mapping from the GTAP Sectors and Regions to the Sectors and Regions of our East Africa-Global Model**

We employ the GTAP 8.1 data set as the basis of the data set for our multi-region trade model. This was the most recent release of the GTAP data set at the time we built the model. It is documented in Badri Narayanan et al. (2012).<sup>27</sup> GTAP 8 contains 57 sectors and 129 regions. We aggregate both the sectors and the regions of the GTAP 8 data set to an 18 sector and 8 region model that is most relevant for analyzing the impact on trade costs of Kenya and Tanzania.

# **Sector Aggregation**

Given that access to business services constitutes an important aspect of the analysis of trade costs, we retain all the business services sectors of the GTAP data set such as communications, financial services and various transportation services. We aggregate the goods sectors into their important and common aggregates. The mapping may be found in table A1.

> 27 See Badri Narayanan, G., Angel Aguiar and Robert McDougall, Eds. (2012), _Global Trade, Assistance, and Production: The GTAP 8 Data Base_ , Center for Global Trade Analysis: Purdue University. Available at: <u>https://www.gtap.agecon.purdue.edu/databases/v8/default.asp.</u>

83
# **1 Introduction**

Teacher incentive programs are a promising policy tool to improve student learning outcomes in developing countries. This hope stems from the fact that most developing countries currently face a “learning crisis” (World Bank, 2018), and there is evidence of limited teacher accountability (Mbiti, 2016). In fact, multiple studies have demonstrated the success of teacher pay-for-performance schemes in improving student learning outcomes in developing countries. Yet few governments have adopted them, either due to political reasons —teacher trade unions are often opposed to them— or ideological resistance to the idea. In contrast, governments frequently implement policies or have informal systems in place that reward high-performing civil servants through public recognition, such as award ceremonies or certificates. However there is little evidence on the effectiveness of these non-pecuniary incentive schemes. Such schemes activate different sources of motivation —reputational and intrinsic, rather than financial— and so could have very different impacts, depending on the teacher (B ́enabou and Tirole, 2006).

This paper presents experimental evidence that allows us to investigate the effects of both types of approaches. We have two main research questions. First, whether a performancebased reward can result in better student learning outcomes, when implemented at a national level by government in a low-income country. Second, whether there is a difference in the effect of in-kind rewards versus rewards that only provide “recognition” for performance. We further explore whether there is heterogeneity across teachers in the effects of these reward schemes.

We analyze a nationally representative sample of 420 public primary schools in Guinea, situated in West Africa. These schools were assigned to one of three treatment arms. In 140 schools, relatively high-performing teachers were rewarded in-kind, with the value of goods increasing with level of performance (rice sacks, radios, phones, televisions, and generators). In another 140 schools, relatively high-performing teachers received a certificate and public recognition from government. The remaining 140 schools were assigned to the control group. Performance was calculated using a weighted average of (i) the annual gain in the average score of the teacher’s students on standardized tests; and (ii) the teacher’s annual improvement in the quality of his/her lessons preparation and delivery, as measured during inspection visits.

We conducted three rounds of data collection: at baseline, prior to the start of the program (May 2012), at midline after the first year of implementation (May 2013), and at endline after two years (May 2014). During these visits, fieldworkers conducted school principal and teacher surveys, as well as student assessments in mathematics and French 2
Table 7 shows the findings from another interesting survey targeting executives in foreign companies which compares the investment environment in Beijing and Shanghai. This also favors Shanghai over Beijing with regard to the general business environment and living conditions.

Generally speaking, Shanghai leads Beijing both in overall economic strength measured by various indicators and in terms of living convenience. Our survey results support this conclusion. Shanghai is more attractive to talented people than Beijing. The municipal government invests heavily in the construction and modernization of urban infrastructure. Average payments for employees and stock buyers per million people are higher in Shanghai than in Beijing. The climate of Shanghai is milder compared to that of Beijing. However, although our interviewees in Beijing complained about the deterioration of air quality in the city, the traffic jams, and the bureaucratism of the government officials, they were appreciative of the cultural amenities available in Beijing, which tends to be more tolerant of diversity.

# **_IV. Emerging urban innovative industries_**

We define “innovative industry” to cover three kinds of knowledge-intensive enterprises, the high-tech manufacturers, the knowledge intensive business services, and the creative content providers, which are identified as the key business sectors deemed to have high innovative propensities. These were the subsectors from which we selected the firms for our survey. But this definition is not the standard industrial classification in official statistical yearbooks in China. In order to provide a picture of the development of these industries in Beijing and Shanghai, we, therefore, use alternative indicators, which might be subject to some overlap with each other, to show the number of firms and employees in these sectors in Beijing and Shanghai, as well as their proportions to the national total.

# **General comparison**

# _High tech industry_

China has been promoting high-tech industry as part of its national science and technology development policy for over 10 years, and established an authorization system to name high and new tech enterprises ( _Gaoxinjishu Qiye_ ) operating in the high-tech industrial parks in many cities. The authorized “high- and new-tech enterprises” in this system can enjoy preferential treatment. This category of high-tech industry covers various sectors, including 13
# 6. fhe Technological Support Systemnfor.SME Exporm

The relatively exacting demands of international markets pose a serious challenge to SMEs aspiring to export success. Technology acquisition can involve a costly search and the need to tailor information to the specific needs ofthe firn. Effective collective action may be impeded both by the firm-specific character of some needs and the risk of inter-firm spillover of information which is not firm-specificY' This section looks at how technology acquisition, adaptation, and use has proceeded in the three subsectors under discussion, and at the potential role of government and other support systems in contributing to a more effective process. Our survey data provide some insights into the sources of productivity gains in SMEs23, the contribution of public support agencies in that regard, and the challenges facing the firms in question. Several points stand out:

- In all three industries, firms report significant gains in technological capacity, especially in design and finishing, as well as in workplace organization and equipment.

- Finn founders report that they and their partners have played key roles in improving the firms' technological capacity, usually rating their own effcrts above those of external sources (private or public).

- Private channels have been the main source of the technical information on which these SMEs have relied for their productivity gains. Among these private channels, foreign buyers were especially important in the leather goods sector, equipment suppliers in garnents, and technical literature in non-electrical machinery.

2AIso of great importance, especially to SMEs because of their more limited in-house capabilities, is a stong training system with enough flexibility of deal effectively with the changing needs of private sector clients. SENA, one of Latin America's largest and oldest vocational training institutes, has been the country's main purveyor of worker training and also participates in provision of technical assistance. Its funding, mainly from a 2 percent payroU tax, gave it financial resources of 56 billion pesos (US $187 million) in 1983 and allowed its more than 8,000 employees to provide training to a large number of people. When the number of people who took any course, short or long, is added together, the 1988 total is 840,000. Since many people took several courses, the number of "beneficiaries" would be much less than this, but still impressive.

2 The aggregate data presented and discussed in Section 3 reveal strongly rising labour productivity among SME garment producers during the 1980s Trends are less clear in the other two sectors.

-19
gender and raion disaggregated employment data on employment in various industries.<sup>26</sup> It is likely that a comparison of pre-and post- war employment rates of males and females by industry would provide us with a distorted view anyway. Possible differences (if found) could not be entirely attributed to shifts in male and female employment due to conflict, but rather to the destruction of various industries during the war or changes in the industrial orientation due to transition processes. Further, if women entered “male” occupations, we should expect female wages to be higher in the more affected regions. However, the results presented above do not indicate that female wages differ across regions or birth cohorts.

The top industries where men and women found employment in 2007 demonstrate that women tend to work in “female” and men in “male” occupations (Appendix Table 3). On average, the top three industries, with about 60% of total male employment in Tajikistan, for men are “Agriculture, hunting, forestry” (29.4%); “Sale, maintenance and repair of motor vehicles and motorcycles, retail sales of automotive fuel” (16.5%) and “Construction” (14.4%). For women, the top three industries of employment are “Agriculture” (53.0%), “Education” (14.4%) and “Sale, maintenance and repair of motor vehicles and motorcycles, retail sales of automotive fuel” (8.1%), with about 76% of female employment. If we look at employment by industry and cohort, then we can see that women and men from the younger cohorts in the more affected areas are more likely to work in agriculture than older cohorts who live in the same areas (31.1% vs. 22.7% for men and 61.4% vs. 48.4% for women). This could be a reflection of the lower labor market skills of such individuals. This phenomenon could also indicate a structural shift in the economy as the proportion of population in Tajikistan employed in industries steadily declined from 13.0% in 1991 to 5.7% in 2005 and proportion employed in agriculture (including personal plots) increased from 44.7% in 1991 to 67.5% in 2005 (State Statistical Committee of Tajikistan, 2006).

Figure 3 shows us the ratio of female to male employment by region of residence to visualize industries with high level of female employment. The industries are ranked by overall share of total employment from highest to lowest. The total employment in the industries

> 26 Most of the employment data published by the State Statistical Committee of Tajikistan for earlier years, e.g. 1991, are available at the country or regional levels. The available raion-level employment time-series that are industry specific (with the industry being broadly defined into a handful of categories) start from 1991-1993 and the series are not disaggregated by gender.

29
# **The Burden of Water Shortages on Informal Firms**

## **1. Introduction**

Water scarcity, induced by climate change, may result in declines in economic growth of about 6 percent in some regions. More importantly the impacts of water mismanagement are likely to fall disproportionately on the poor (World Bank, 2016). Thus, the informal sector – also known as the sector of the poor – is likely to suffer productivity losses due to water scarcity. Water scarcity or poor water infrastructure may manifest itself in terms of water shortages experienced by informal firms. While a range of policies, from accelerating formalization to better education, have been considered to improve the productivity of informal firms (Aftab and Rahim, 1988; Amin and Islam, 2015; Djankov et al. 2002), very little attention has been afforded to the effect that poor infrastructure quality, specifically water infrastructure, may have on informal firms.

Informality is normal in developing economies. Estimates of the size of the informal sector have varied widely from 20% of net domestic product in India (Chaudhuri, Schneider, and Chattopadhyay, 2006) to 30–33% in Tanzania (Bagachwa and Naho, 1995) and 40–60% in Brazil (Henley,et al., 2009). Estimates from Schneider et al. (2010) indicate that the average size of the shadow economy as a percentage of official GDP is 25.1 percent in South Asia, 34.7 percent in Latin America and the Caribbean, and 38.4 percent in Sub-Saharan Africa. This is in contrast to the OECD, where the figure is 13.5%. In terms of informal employment, Golub and Hayat (2014) estimate that in low-income Sub-Saharan African economies, the informal sector accounts for between 80% and 97% of employment. In Bangladesh, 75% of firms are unregistered (Giorgi et 2
countries use income—for example, in SSA only those upper-middle-income countries located in the Southern Cone use income data.

When in high-income countries we compare consumption to income inequality, this latter tends to be higher (Heathcote et al. 2010; Aguiar and Bils 2015); in other countries, due to data limitations, this comparison is not always feasible (Devarajan 2013; De Magalhães and Santaeulàlia-Llopis 2018). Nonetheless, a quick overview of Povcalnet enables us to identify 71 cases where inequality measures are available for the same country and same year both for consumption and income.<sup>9</sup> Figure 2 plots the differences in the Gini index (panel a) and the 90th percentile over the 50th percentile ratio (P90/P50panel b) for these cases. In about 20 percent of the cases, the Gini based on income is slightly lower than that measured on consumption, but in the remaining 80 percent of the cases is higher. On average, income inequality is higher than consumption inequality by 10 percentage points, and in 40 percent of cases Gini measured on income exceeds that based on consumption by more than 10 percentage points. Interestingly, the gap between income and consumption slightly increases when looking at the P90/P50 ratio (panel b). In this latter case, the average gap between the two measures is 11 percentage points and about 30 percent of the observations have a gap higher than 20 percentage points. Indeed, the gap between the two measures tends to be more accentuated on the upper tail of the distribution rather than around the means.

In order to deeply inquire into this aspect, we estimate a model following the general formulation taken from the literature ( <mark>Förster and Tóth 2015)</mark> that has enough explanatory variables but, at the same time, does not cover all the inequality determinants, because these are not easily available for many developing countries and for all the years we analyze.<sup>10</sup> Because the scope of this regression shows that—controlling for a set of socioeconomic variables and using different models—the use of consumption tends to underestimate inequality, our focus is mostly on the sign and significance of the consumption or income categorical variable.

> 9 The countries are Bulgaria, Croatia, Estonia, Haiti, Hungary, Latvia, Lithuania, Mexico, Montenegro, Nicaragua, Philippines, Poland, Romania, Serbia, and Slovak Republic in different years.

> 10 The data set used is the same constructed for plotting the figures but after eliminating the duplicates. Besides the mentioned cases where both income and consumption inequality measures are available—and we chose the one more used in the country in official publications—in some countries PovcalNet presents the inequality measures not only at the national level but also at the urban and rural levels and by subregions. In this case, we chose only the national measure. The final data set is an unbalanced panel covering 163 countries over almost four decades.

10
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P a g e <u>|</u> **50**

# **References**

American Institute of Planning.1997. Policy Guidance on Impact Fees. http://www.planning.org/policy/guides/adopted/impactfees.htm Balleney, Shirley and Bimal Patel. 2009. “Using the ‘Development Plan—Town Planning Scheme’ Mechanism to Appropriate Land and Build Urban Infrastructure” Chapter 24 in India Infrastructure Report 2009: Land: A Critical Input for Infrastructure. New Delhi: Oxford University Press Bertaud, Alain.2010 “Land Markets, Government Interventions and Housing Affordability” Wolfensohn Centre for Development at Brookings Working Paper No. 18 May 2010 Himanshu. 2010 “Towards a New Poverty Line for India” (Mimeograph based on background work for the Expert Group of the Planning Commission for Reviewing the Methodology for Estimation of Poverty) McKinsey Global Institute: 2010 India’s Urban Awakening: Building Inclusive Cities, Sustaining Economic Growth April 2010 Murgai, Rinku and N. Roberto Zagha. 2010. “Building Infrastructure for Accelerated Poverty Reduction in India” in India 2010 Mumbai: Business Standard Books.

National Council of Applied Economic Research (NCAER) 2010. The Next Urban Frontier: Twenty Cities To Watch Patel Bimal, Ballaney Shirley, Koshy C.K., & Nohn Matthias (2009), “Reforming Urban Land Management in Gujarat _”,_ Chapter 23 in India Infrastructure Report 2009: Land: A Critical Input for Infrastructure. New Delhi: Oxford University Press <u>http://www.iitk.ac.in/3inetwork/html/reports/IIR2009/IIR_2009_Final_July%2009. pdf</u> 50
We investigate in this paper patterns of welfare dynamics in India over the quarter-century between 1987 and 2012. In doing so, we build on an earlier study by Dang and Lanjouw (2018) that focuses on mobility during the 2000s. Similar to this study, we attempt to overcome a set of empirical and methodological challenges that bedevil analysis of this kind in the Indian setting. Recognizing that the debate about the evolution of welfare outcomes such as poverty in India has been overwhelmingly centred on consumption as the preferred indicator of economic well-being, we base our study on the nationally representative National Sample Survey (NSS) consumption surveys that are regularly fielded by the Indian National Sample Survey Office. This contrasts with most extant studies of mobility in India, which draw on income data from the Indian Human Development Surveys (IHDS) fielded by the National Council of Applied Economic Research (NCAER) and the University of Maryland.<sup>1</sup> We tackle the formidable analytical hurdle posed by the fact that India’s NSS surveys are cross-section surveys rather than panel surveys, by implementing a procedure to construct synthetic panels from the NSS surveys.

Yet, our study significantly extends the scope of analysis by Dang and Lanjouw (2018) in both breadth and depth. In terms of breadth, we expand the time window of analysis to the preceding two decades by comparing mobility in the 2000s against mobility that can be observed during the late 1980s and 1990s. In particular, we analyze five ‘thick’ (large-sample) rounds of the NSS for 1987/88, 1993/94, 2004/05, 2009/10, and 2011/12. In doing so, we comment not just on the extent of consumption mobility in India, but also on changes in mobility over time—a question of considerable interest, since this quarter-century has seen the introduction of a host of economic reforms, significant transformation of the Indian economy, and a dramatic acceleration of economic growth and poverty decline. This much longer time

> 1 We return to more discussion of the differences between the NSS and IHDS in the next section.

3