rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10316

# **Abstract**

Addressing climate change requires individual behavior change and voter support for pro-climate policies, yet surprisingly little is known about how to achieve these outcomes. This paper estimates causal effects of additional education on pro-climate outcomes using new compulsory schooling law data across 16 European countries. It analyzes effects on pro-climate beliefs, behaviors, policy preferences, and novel data on voting for green parties—a particularly consequential outcome to combat climate change. Results show a year of education increases pro-climate beliefs, behaviors, most policy preferences, and green voting, with voting gains equivalent to a substantial 35% increase.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. 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/prwp. The authors may be contacted at hpatrinos@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
I POLICY RESEARCH WORKING PAPER 3124

# Abstract

Structural reforms are often designed to change the prices of key goods and services. Since the overall intention of such reforms is the reduction of poverty, it is important to understand how the resulting price changes affect the poor. However, organizations seeking to provide timely advice to policymakers in developing countries often do not have the data and resources needed to undertake the most sophisticated approaches to such analysis. McCulloch outlines a simple methodology based on the analysis of household survey data to estimate the first-order impact of a variety of structural reforms. He also elaborates on the ways in which this methodology may be extended in a flexible way to account for particular features of a country in question. Finally, he outlines the direction of some extensions on the approach to tackle dynamics, risk, and qualitative poverty analysis.

> This paper-a product of the International Trade Department, Poverty Reduction and Economic Management Vice Presidency-is part of a larger effort in the Bank to develop better methodologies for understanding the impact of structural reforms such as trade liberalization, privatization, and tax and subsidy reforms on poverty. Copies of the paper are available

> free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Melanie Faltas, room MC2-402,

> telephone 202-458-2323, fax 202-522-7551, email address mfaltas@worldbank.org. Policy Research Working Papers are

> also posted on theWeb at http://econ.worldbank.org. The author may be contacted at nmcculloch@worldbank.org. August 2003. (41 pages)

> _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 view of the World Bank, its Executive Directors, or the countries they represent._

Produced by the Research Support Team
Cross-sectional estimates in cols. 1-3 of table 5 suggest that posting of SLMs led to significant increases in the number of registered sales but not non-monetary transfers of residential (panel A) or agricultural (panel B) land. There is no evidence of a separate effect of land week events in specific sectors, beyond that of the national campaigns. Higher urban population shares or numbers of residential or agricultural parcels are associated with higher levels of registered transfers especially for residential land. Distance to the DLO, a proxy for transport cost, is insignificant for sales but significant and large for non-monetary transfers where estimated coefficients suggest that beyond 10 km from the DLO, the likelihood of registration of nonmonetary land transfers decreases by about half.

To support causal inference, we report results from fixed effects specifications (table 5, cols. 4-6). Presence of a SLM is estimated to raise numbers of registered sales per quarter and cell by 0.88 for residential and 1.23 for agricultural land, an annual increase of 71% or 146%. On the other hand, the estimated coefficient on non-monetary transactions of about—0.3 in both cases is marginally significant.

# **4. Exploring informality and transactions in rural areas**

Administrative data, although suitable to provide information on formal transactions at low cost and high levels of temporal and spatial disaggregation, need to be combined with household survey data to obtain evidence on informality. Using a nationally representative rural household survey to do so suggests that, for a number of reasons, including lack of information, high fees, and difficulties in accessing officials, and although those who transferred their land are willing to spend resources on registration.

# **4.1 Assessing levels of informality in rural Rwanda**

While the registry provides data on registered transactions, assessing levels and determinants of informality requires linking household to registry data at the parcel level. To do so in a way that is representative for rural areas in Rwanda, we use the 2015 round of a three-round panel of 3,600 households in 300 rural villages covering 23 districts that were interviewed in 2011, 2012, and early 2015.<sup>12</sup> This survey was administered using tablets and the questionnaire was pre-populated with a list of all parcels owned or cultivated by a household in 2012, requiring the enumerator to in each case check for changes in status (beyond asking for new acquisitions). The unique parcel identifier (UPI) was used to match with LAIS data, in particular the name of the registered owner, to ascertain that households who claimed to have completed the process of registering a transaction did actually do so.

> 12 The survey originally aimed to evaluate the impact of the LTR program’s national roll-out. 100 sectors, 4 in each of the country’s 25 districts, were randomly chosen. In each selected sector, three enumeration areas with 12 households to be interviewed in each were then selected randomly. In 2015, 3,348 of the original households could be re-interviewed.

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with more efficient allocation of industry for districts between urban and rural settings. The spatial mismatch index compares the observed industry distribution of district employment across urban and rural locations to a counterfactual where plants or employment in the district are allocated (in some sense optimally) to urban and rural locations according to national propensities by industry to be in urban settings. As elaborated further below, higher mismatch values for a district indicate that plants that would have been expected to be in urban areas are in rural areas, and vice versa. Figures 2a-2b plot the trends in our spatial mismatch indices. Encouragingly, there has been an aggregate decline in spatial mismatch since 1989, primarily driven by improved allocation of the unorganized sector. Additional regressions identify that the urbanization shifts associated with better education and infrastructure have improved the spatial allocation of industry.

The plan of this paper is as follows. Section 2 discusses our data and the broad patterns of urbanization for India’s manufacturing sector. We also discuss in depth how urbanization is defined in India and issues for longitudinal consistency. Section 3 presents the decomposition of urbanization changes across districts. Section 4 considers the district-level traits associated with urbanization changes; this section also presents our interaction analysis of industry traits. Section 5 analyses spatial mismatch in industry allocations. The final section concludes and discusses implications from this work.

# **Section 2: Surveys of Indian Manufacturing--Organized and Unorganized Sectors**

This paper employs cross-sectional establishment-level surveys of manufacturing enterprises carried out by the Government of India. Our work studies the manufacturing data from surveys conducted in fiscal years 1989, 1994, 2000, and 2005. In all four cases, the survey was undertaken over two fiscal years (e.g., the 1994 survey was conducted during 1994-1995), but we will only refer to the initial year for simplicity. This section describes some key features of these data for our study.<sup>3</sup> It is important to first define and characterize the distinction between the organized and unorganized sectors in the Indian economy. These distinctions in the Indian context relate to establishment size. In manufacturing, the organized sector includes establishments with more than 10 workers if the establishment uses electricity. If the establishment does not use electricity, the threshold is 20 workers or more. These establishments are required to register under the India Factories Act of 1948. The unorganized manufacturing sector is, by default, composed of establishments which fall outside the scope of the Factories Act.

> 3 For additional detail on the survey data, we refer the reader to Nataraj (2011), Kathuria et al. (2010), Fernandes and Pakes (2008), Hasan and Jandoc (2010), Dehejia and Panagariya (2010), and Ghani et al. (2011b).

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institutions (e.g., lack of government corruption) documented in most of the existing literature, have supported the growth of non-government firms in these two countries. As of 2005, they have a combined population of 40% of the entire world, and their combined GDPs (in PPP terms) are almost as large as that of the U.S. and equal to 19% of the world total. Given the status of these two countries, our findings call for more research in order to better understand whether similar “substitute” mechanisms that have worked well in China and India have also supported the growth of firms in other economies where formal mechanisms are not available.

We first find that India, a long-time British colony, has the best investor protection on paper. India has a _perfect_ score on the Creditor Rights index (4 out of 4),<sup>5</sup> and scores 5 out of 6 for the Anti-Director Rights index, the _highest_ among more than 100 countries studied in Djankov, La Porta, Lopez-de-Silanes, and Shleifer (2005; hereafter DLLS). Moreover, India has had a Britishstyle judicial system and a democratic government for a long time. However, wide-spread government corruption and overburdened courts lead to poor legal protection in practice.<sup>6</sup> Perhaps not surprisingly, India’s stock market has not played a dominant role in resource allocation and providing external financing to firms. External financing through financial markets (equity and bond) has been dominated by banking and alternative sources, although the size and importance of the stock markets have grown significantly in recent years. Since the deregulation and financial liberalizations in the early 1990s, the entry and competition from non-state owned banks have stimulated the growth of the banking sector, which has maintained a low level of non-performing loans (NPLs) and a high level of efficiency in part due to stringent lending standards.

We next separately examine financing channels, corporate governance, and the growth of three groups of firms. First, the importance of the _state sector_ (public sector undertakings, or PSUs)

> 5 This score was revised from 4/4 in LLSV (1998), which was based on the Company’s Act (1956), to 2/4 in DMS (2005), which was based on the Sick Industrial Companies Act (1985).

> 6 DLLS (2005) construct the anti-self-dealing index (control of corporate insiders) for more than 100 countries.  India’s score of 0.55 (out of 1) is lower than the average (0.67) of English common-law countries.

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crippling state incapacity is a worthy achievement of CDD programs in and of itself. Despite this, we remain hesitant to claim that CDD is an effective strategy to obtain these results. Over its four year period, the Tuungane program spent $46 million. A large share of this funding was used for facilitation and indirect costs, with only around $16 million, 35% of total program costs, going directly towards infrastructure. On the one hand, CDD’s software-related activities may be essential to safeguard the initial financial investment over time. On the other hand, these same results may have obtained without these additional activities. The (short and longer term) studies that exist to date, however, only compare CDD programs to a control condition, and thus cannot directly test these claims. Future research could focus on disentangling the relative contribution of hardware-related activities from the contribution of software-related activities. Future studies could also attempt to directly compare CDD to other alternatives, such as more traditional, centrally-led programs or unconditional cash transfer programs. Finally, as is evident from the scarcity of longer term studies of CDD programs, external validity is limited, suggesting the need to accumulate more evidence from different contexts.

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In addition to negative health impact from smoke inhalation, burning cooking fuels, even when burned completely, emit CO2, methane and ozone precursors which are the primary source of GHG emissions. The health impact of climate change have also been extensively reviewed (IPCC, 2007; McMichael, 2012, Smith et al., 2012). Recently, Abdullashi et al. (2013) review typical styles of cooking reported in the literature and finds that different cooking styles emit different profiles of compounds influenced by factors such as cooking processes and ingredients. However, Ohimain (2012) finds that use of ethanol for cooking by replacing solid fuels in Nigeria may not reduce GHG emissions citing common argument of food versus fuel conflict. Chaudhuri and Pfaff (2003) find that relationship between air quality and household income is U-shaped implying that increases in income initially leads to deterioration in air quality, but later lead to increased air quality. Based on SEI (2013) report, the global potential for GHG emission reductions from ICS projects around the world is estimated at 1 Gt of CO2 per year.

# **6. Economics of Fuel and Technology Choices for Household Cooking**

A large number of empirical studies identify different costs and benefits associated with household's choice of cooking fuels and the ICS (Table 2). For example, from the viewpoint of users (demand-side), benefits include health benefit through reduction in indoor air pollutant emissions, economic benefit through time saved collecting fuels, and fuel and fuel cost savings, and other benefits such as aesthetic gains and improve social standings. While costs include cookestove-, fuel-, stove maintenance- and other- costs. Likewise, from the viewpoint of suppliers (supply-side), including INGOs and the government, benefits include environmental benefit such as preservation of forest reserves, GHG and black carbon emissions reduction, economic benefit through market development and other benefits such as job creation and local skill development, while costs include market intervention costs such as subsidies, fuel cost and program cost. The following section presents findings of selected studies from the literature.<sup>12</sup>

**Table 2** : Benefits and costs adopting ICS and modern fuel choice

||Demand-side(user)|Supply-side|
|---|---|---|
|Benefits|Health|Environment|

> 12 For a list of empirical studies associated with these issues by types of intervention, methodology and geographical coverage, see Appendix A.

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# **4. Estimation**

The specification used for the empirical estimation is shown in Eq. (3).

The dependent variable used in the analysis is real annualized sales growth , which is computed for firm _i_ in cluster _k_ as the difference in log sales in 2009 and 2007 divided by two. Nominal sales values are deflated by GDP deflators obtained from World Development Indicators and converted to 2009 USD with exchange rates also obtained from World Development Indicators. The variable represents one of the bribery measures described earlier ( ). The vector _X_ of control variables include dummy variables for foreign ownership, if the firm is an exporter, sole proprietor, has external financing for investments, had at least one business transaction in the last two years, is medium (20-99 employees), or is large (over 100 employees). Continuous variables include log age and its squared term, log sales in 2007 (denoted as _Sik_ ) and its squared term (see Table 3 for description).

Lastly, a full set of industry, geographic location, and country dummy variables are included in all regressions. Industries are likely to vary in intrinsic volatility of demand and supply shocks due to differences in technological or market characteristics. These differences might create variation in how bribery affects sales growth. Industry dummies control for these differences. Error terms can be heteroskedastic and correlated across firms within the same country-locationsector group. This could bias the standard errors of the estimated coefficients downward. Therefore we present robust standard errors clustered by country-location-sector to allow for the possibility of this correlation. We also control for outliers in the data as they could bias the estimation results. In each country we performed an outlier test for log of age, sales in 2007 and 2009, and employment level in 2007 and 2009. By country, values that are further than three standard deviations from the mean are removed. A total of 860 firms are removed after outlier analysis on these five variables.

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38 Elbers, C. J. Lanjouw, and P. Lanjouw (2003), “Micro-Level Estimation of Poverty and Inequality," _Econometrica_ , pp355-364 Elbers, C. and Lanjouw P. (2001) Intersectoral Transfer, Growth and Inequality in Rural Ecuador , _World Development_ , 29/3, 481-496.

Estache, A. (2004), “Argentina Privatization: A Cure or a Disease?” in Hirschhausen, Christian, Thorsten Beckers and Kay Mitusch, **Trends in Infrastructure Regulation and Financing: International Experience and Case Studies from Germany** , Edward Elgar, Cheltenham, UK and Northampton, MA., USA Estache, A. V. Foster and Q. Wodon (2002 **), Accounting for Poverty in Infrastructure Reform – Learning from Latin America’s Experience** , World Bank Institute Publications, Studies in Development Series Foster, V., A. Gomez-Lobo, and J. Halpern (2000a), Better Household Surveys for better Design of Infrastructure Subsidies, Public Policy for the Private Sector, Note 213, available at <u>http://rru.worldbank.org/Documents/PublicPolicyJournal/213gomez.pdf</u> Foster, V., A. Gomez-Lobo, and J. Halpern (2000b), "Designing Direct Subsidies for Water and Sanitation Services. Panama: a Case Study," World Bank, Policy Research Papers 2344, Washington, DC Foster, V. and O. Irusta (2003), “Does Infrastructure Reform Work for the Poor? A Case Study on the Cities of La Paz and El Alo in Bolivia”, World Bank, Policy Research Papers, No3177, Washington, DC Foster, V. and M.C. Araujo (2004), “Does Infrastructure Reform Work for the Poor? A Case Study from Guatemala”, World Bank, Policy Research Papers, No3185, Washington, DC Galasso, E. and M. Ravallion (2004), Decentralized targeting of an antipoverty program **,** _Journal of Public Economics_ , forthcoming Gasmi, F. , M.D. Kennet, J.J. Laffont and W.W. Sharkey (2002), **Cost Proxy Models and Telecommunications Policy,** The MIT Press, Cambridge Gomez-Lobo and D. Contreras (2003), Water subsidy policies: a comparison of the Chilean and Colombian Schemes, _The World Bank Economic Review,_ Vol. 17(3), pp391 Kessides, I. (2004), **Reforming Infrastructure: Privatization, Regulation, and Competition,** Oxford University Press Laffont, J.J. and J. Tirole (1993), **A theory of Incentives in Regulation and Procurement** , MIT press, Cambridge Laffont, J.J. and J. Tirole (1999), **Competition in Telecommunications** , MIT Press, Cambridge Lanjouw, P. and M. Ravallion (1999), Benefit incidence and the timing of program capture”, _World Bank Economic Review_ , 257-274 Lanjouw, J. O. and Lanjouw, P. (2001a): How to Compare Apples and Oranges: Poverty Measurement Based on Different Definitions of Consumption, _Review of Income and Wealth_ , 47(1), 25-42.

Lanjouw, J. O. and Lanjouw, P. (2001b): The Rural Nonfarm Sector: Issues and Evidence from Developing Countries, _Agricultural Economics,_ 26, pg 1-23.

Lanjouw, P., Quizon, J. and Sparrow, R. (2001) Non-Agricultural Earnings in Peri-urban Areas of Tanzania: Evidence from Household Survey Data, _Food Policy,_ 26(4), August, pg 385404.
|Pan-Arab Free Trade Area<br>(PAFTA)|1998|Bahrain, Kingdom of; Egypt, Arab Rep.;<br>Iraq; Jordan; Kuwait, the State of;<br>Lebanese Republic; Libya; Morocco;<br>Oman; Qatar; Saudi Arabia, Kingdom of;<br>Sudan; Syrian Arab Republic; Tunisia;<br>United Arab Emirates; Yemen, Rep.|
|---|---|---|
|Pan-Arab Free Trade Area<br>(PAFTA)|1998|Bahrain, Kingdom of; Iraq; Jordan;<br>Kuwait, the State of; Lebanese Republic;<br>Libya; Morocco; Oman; Qatar; Saudi<br>Arabia, Kingdom of; Sudan; Syrian Arab<br>Republic; Tunisia; United Arab Emirates;<br>Yemen, Rep.|
|Protocol on Trade<br>Negotiations (PTN)|1973|Bangladesh; Brazil; Chile; Egypt, Arab<br>Rep.; Israel; Korea, Rep.; Mexico;<br>Pakistan; Paraguay; Peru; Philippines;<br>Serbia; Tunisia;Türkiye; Uruguay|
|Southern Common Market<br>(MERCOSUR)-Egypt|2017|Argentina; Brazil; Paraguay; and Uruguay|
|United Kingdom-Egypt|2021|United Kingdom|

Source: World Trade Organization Regional Trade Agreements Database

**Table 2. Mean values of main variables**

||2013|2016|2020|
|---|---|---|---|
|Employment (Number)|50.69|34.28|30.87|
|Export sales (EGP)|579,873|599,057|4,843,432|
|Exporter (yes=1)|0.05|0.07|0.08|
|Value Machinery (EGP)|1,066,584|11,700,000|2,619,007|
|Cost per worker (EGP/worker)|20,329|20,543|65,725|
|Age of firm (years)|11.71|15.48|18.97|
|Foreign ownership (yes =1)|0.10|0.10|0.08|
|Female ownership (yes=1)|0.05|0.03|0.09|
|Registered (yes=1)|0.98|0.97|0.84|
|Training (yes=1)|0.03|0.13|0.08|
|Foreign technology (yes=1)|0.01|0.02|0.06|
|Imported inputs (yes=1)|0.26|0.42|0.42|
|Indirect exporter(yes=1)|0.01|0.02|0.05|

Notes: Number of observations equals 198. Sample is restricted to the 2013-2016-2020 panel and excludes firms missing information on number of employees. Weighted using panel weights. Source: Author elaboration using Enterprise Survey for Egypt.

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