rafmacalaba/datause-encoder-data huggingface dataset:
<u>www.worldbank.org/content/dam/Worldbank/.../China-2030-complete.pdf, last accessed</u> on May 18th, 2013.

- Wu, F. (2013) Environmental Activism in Provincial China, _Journal of Environmental Policy and Planning_ , 15(1), 89–108.

- Wu, Jing, Yongheng Deng, Jun Huang, Randall Morck, and Bernard Yeung, 2013. Incentives and Outcomes: China’s Environmental Policy, NBER Working Paper No. 18754, February 2013

Xinhua, 2013. China monitors air pollution with "eyes" in sky.

- <u>http://news.xinhuanet.com/english/china/2013-04/26/c_124637946.htm, last accessed</u> on May 18th, 2013.

- Zhao, Y., 2010. Public participation in China’s EIA regime: Rhetoric or reality? _Journal of Environmental Law_ , 22(1), 89–123.

- Zhong, L. and Mol, A. P. J., 2008, Participatory environmental governance in China: Public hearings on urban water tariff setting, _Journal of Environmental Management_ , 88(4), 899–913.

49
- bility. Stanford Center on Poverty and Inequality.

- Torche, F. (2015). Intergenerational Mobility and Equality of Opportunity. European Journal of Sociology, 56(3):343371.

- Torche, F. (2019). Educational mobility in developing countries. WIDER Working Paper Series 2019-88.

- UIS (2022). Out-of-school rate (1 year before primary, primary education, lower secondary education, upper secondary education). Technical report, Montreal: UNESCO Institute for Statistics.

- Vella, F. and Karmel, T. (1999). Evaluating the Impact of Educational Expansion on the Occupational Status of Youth. Australian Economic Papers, 38(3):310327.

- World Bank (2006). World development report 2006: Equity and development. The World Bank.

- World Bank (2018). World Development Report 2018: Learning to Realize Education's Promise. The World Bank Group.

- Yu, Y., Fan, Y., and Yi, J. (2020). The One-Child Policy Amplies Economic Inequality across Generations in China. IZA Discussion Papers 13617, Institute of Labor Economics (IZA).

37
26 volumes rather than export value, - the unit price of processed wood is higher than that of unprocessed logs) where it is evident that China shows little inclination to import either veneers or plywood.

**Figure 18: Log and Wood Product Demand Distribution for China, France, and the EU-27 countries 1997-2006 (volume in 1,000 m**<sup>**3**</sup> **)**

<!-- Start of picture text --> 2000<br>1750<br>1500<br>1250<br>1000<br>750<br>500<br>250<br>0<br>logs sawnw. veneers plyw. logs sawnw. veneers plyw. logs sawnw. veneers plyw.<br>China France EU-27<br><!-- End of picture text --> _Source:_ Calculated from FAOSTAT Forest Trade Flows data, accessed November 2009

# <u>The development impact: factor utilization</u>

Since one of the major consequences of a shift in the final market from the EU to China is that the degree of processing falls, what are the consequences of this for factor utilization in the Gabonese economy? Table 5 simulates factor utilization if the same quantity of wood is exported in the form of logs, sawnwood, veneer sheets and plywood. It calculates the resultant earnings of foreign exchange, and the derived utilization of labor and capital, taking into account processing loss in the conversion of logs, as well as the unit-prices in global markets in 2006. It is necessarily a crude exercise, but nevertheless, the exercise does throw some light on the developmental consequences of alternative uses of forest resources, as reflected in different destinations of final markets.

It is evident from this that there is a substantial gain in employment to be achieved by the downstream processing of logs into sawnwood, veneer and plywood. Employment is more than doubled if the logs are converted into sawnwood, trebled with the extension to producing veneer sheets, and quadrupled if these veneer sheets are then incorporated in the production of plywood. Foreign exchange earnings are also enhanced with further processing, by 25 percent in the case of veneer and 12 percent in the case of plywood. However, this is not the case with regard to sawnwood. Here there is a value and foreign exchange loss in processing since the Gabon sawmill industry is inefficient and has low conversion rates. Veneer firms also lose money in processing. The reason why firms invested in sawmilling and veneer production is purely a function of the Forestry Code where firms, who were unable to afford the high capital entry costs into the production of plywood and/or veneer, deepened value-added in the expectation of meeting the 2012 Forestry Code target. These lossmaking firms manage to stay in production, due to the profits earned through their log exports (Table 5). This augmentation of value, employment, and foreign exchange earnings through processing come at a very considerable cost of capital, particularly
# About AICD and its country reports

This study is a product of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the world’s knowledge of physical infrastructure in Africa. The AICD provides a baseline against which future improvements in infrastructure services can be measured, making it possible to monitor the results achieved from donor support. It also offers a solid empirical foundation for prioritizing investments and designing policy reforms in Africa’s infrastructure sectors.

The AICD is based on an unprecedented effort to collect detailed economic and technical data on African infrastructure. The project has produced a series of original reports on public expenditure, spending needs, and sector performance in each of the main infrastructure sectors, including energy, information and communication technologies, irrigation, transport, and water and sanitation. _Africa’s Infrastructure— A Time for Transformation_ , published by the World Bank and the Agence Française de Développement (AFD) in November 2009, synthesized the most significant findings of those reports.

The focus of the AICD country reports is on benchmarking sector performance and quantifying the main financing and efficiency gaps at the country level. These reports are particularly relevant to national policy makers and development partners working on specific countries.

The AICD was commissioned by the Infrastructure Consortium for Africa following the 2005 G8 (Group of Eight) summit at Gleneagles, Scotland, which flagged the importance of scaling up donor finance for infrastructure in support of Africa’s development.

The first phase of the AICD focused on 24 countries that together account for 85 percent of the gross domestic product, population, and infrastructure aid flows of Sub-Saharan Africa. The countries are: Benin, Burkina Faso, Cape Verde, Cameroon, Chad, Côte d’Ivoire, the Democratic Republic of Congo, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, South Africa, Sudan, Tanzania, Uganda, and Zambia. Under a second phase of the project, coverage was expanded to include as many of the remaining African countries as possible.

Consistent with the genesis of the project, the main focus is on the 48 countries south of the Sahara that face the most severe infrastructure challenges. Some components of the study also cover North African countries so as to provide a broader point of reference. Unless otherwise stated, therefore, the term _Africa_ is used throughout this report as shorthand for _Sub-Saharan Africa._ The World Bank has implemented the AICD with the guidance of a steering committee that represents the African Union (AU), the New Partnership for Africa’s Development (NEPAD), Africa’s regional
**9**

Table 6: Consumption poverty in eight African countries during in the 1990sl

||_Po_|_verty headcou_|_nt(PO)_|_Se_|_verityindex(_|_PJ_|
|---|---|---|---|---|---|---|
||_Yearl_|_Year2_|_Percentage_<br>_change_|_Year]_|_Year2_|_Percentage_<br>_change_|
||||_Perc_|_ent_|||
|Ethiopia2)|||||||
|1989-1995(rural)|61|51|-16|17|12|-29|
|<br>1994-1997(rural)|39|29|-26|8|6|-25|
|1994-1997(urban)|39|36|-8||||
|Ghana|||||||
|1992-1998|51|39|-24|9|7|-22|
|Madagascar|||||||
|1993-97|70|73|4|17|19|12|
|1997-99|73|71|-3|19|19|0|
|Mauritania|||||||
|1987-1995|58|35|-40|17|6|-65|
|Nigeria|||||||
|1985-92|46|43|-7|8|9|13|
|1992-96|43|67|56|9|17|89|
|Uganda|||||||
|1992-1997|56|44|-21|10|6|-40|
|1997-2000|44|35|-20|6|5|-16|
|Zambia|||||||
|1993-1996|74|69|-7|30|22|-27|
|1996-1998|69|72|4|22|26|18|
|Zimbabwe|||||||
|1991-1996|26|35|35|4|5|25|

1) Consumption measured as regionally deflated real household expenditure per adult equivalent. Poverty lines mostly calculated according to the cost of basic needs approach, which includes an adjustment for non-food needs (except for Zimbabwe). Poverty line for Mauritania based on US$1/day equivalent. Poverty lines are country specific, so that the data are not comparable across countries (only over time within countries).

2) Based on respectively six and fifteen purposively sampled rural villages for 1989-1995 and 1994-1997; urban figures are based on per capita household expenditures in seven large towns including Addis Ababa and Dire Dawa; not nationally representative _Source:_ World Bank data and country studies under _Dynamics of Poverty_ study The poverty measures we report here are derived from the familiar class of poverty indices after Foster, Greer and Thorbecke (1984). The general formula for these poverty measures is: <!-- Start of picture text --> Pa ) a 2 0(1<br>n j=1 z<br><!-- End of picture text --> where n is the total population, q the number of poor people, yi the income (consumption) of individual i, z the poverty line, and a a 'poverty aversion' parameter. The larger is _a,_ the greater weight is placed on the very poorest people. If a = 0, equation (1) becomes simply _qln,_ which is the _head-count ratio,_ or the incidence of poverty. Estimates of the _headcount_ (PO) are reported in the first data panel of Table 6. Setting a=2 involves taking the square of the proportionate poverty gap. This measure (P2) is given in the second panel in Table 6, and is sometimes known
# **References**

Asian Development Bank (ADB), Department for International Development (DFID), Japan

- International Cooperation Agency (JICA), and the World Bank (WB). 2018. _The WEB of Transport Corridors in South Asia._ Washington, DC: World Bank.

- Alder, S., 2015. Chinese Roads in India: “The Effect of Transport Infrastructure on Economic Development.” Working paper, University of North Carolina, Chapel Hill.

- Alder, S., M. Roberts, and M. Tewari. 2017. “The Effect of Transport Infrastructure on India’s Urban and Rural Development."

- Ali, R., A. F. Barra, C. N. Berg, R. Damania, J. D. Nash, and J. Russ. 2015. “Transport Infrastructure and Infrastructure and Welfare: An Application to Nigeria." Policy Research Working Paper 7271, World Bank, Washington, DC.

- Arman, S. A., and A. T. Izady. 2015. “Design of a CGE Model to Evaluate Investment in Transport Infrastructures: An Application for Iran." Asian Economic and Financial Review 5 (3): 532–45.

Aschauer, D.A., 1989. Is public expenditure productive? _Journal of Monetary Economics_ 23(2): 177-200.

- Asturias, J., M. Garca-Santana, and R. Ramos Magdaleno. 2017. “Competition and the Welfare Gains from Transportation Infrastructure: Evidence from the Golden Quadrilateral of India."

- Atack, J., and R. A. Margo. 2011. “The Impact of Access to Rail Transportation on Agricultural Improvement: The American Midwest as a Test Case, 1850–1860." _Journal of Transport and Land Use_ 4 (2).

- Banerjee, A., E. Duflo, and N. Qian. 2012. “On the Road: Access to Transportation Infrastructure and Economic Growth in China."

- Baum-Snow, N. 2007. “Did Highways Cause Suburbanization?" _The Quarterly Journal of Economics_ 122 (2): 775–805.

- Baum-Snow, N. 2016. “Urban Transport Expansions, Employment Decentralization, and the Spatial Scope of Agglomeration Economies."

- Banerjee, A., Duflo, E. and Qian, N., On the Road: Access to Transportation Infrastructure and Economic Growth in China. Ali, R., Barra, A.F., Berg, C.N., Damania, R., Nash, J.D. and Russ, J., 2015. Transport infrastructure and welfare: an application to Nigeria. The World Bank Policy Research Paper, (7271).

- Berg, C. N., U. Deichmann, Y. Liu, and H. Selod. 2017. “Transport Policies and Development." _The Journal of Development Studies_ 53 (4): 465–80.

- Bosker, E. M., U. Deichmann, and M. Roberts. 2015. “Hukou and Highways: The Impact of China’s Spatial Development Policies on Urbanization and Regional Inequality."

24
We use two institutional measures to capture the differences in investor rights across countries. In the regression specifications, we interact country level institutional variables with firm level governance measures to examine how the effect of governance on maturity varies across different institutional environments.

Our first country level governance measure is investor protection ( _investor_ ). This variable measures the extent of minority shareholder protection against expropriation by the controlling shareholders. It ranges from zero to 10 and higher values imply better protection of minority shareholders. We use the “strength of minority investor protection” variable from “World Bank Doing Business Surveys” to construct this measure. The data for constructing this variable comes from surveys administered to corporate and securities lawyers and includes sections on countries’ disclosure requirements for related-party transactions, extent of director liability in lawsuits, shareholders’ ability to initiate lawsuits, shareholders’ rights in major corporate decisions, existence of governance mechanisms controlling excessive board control and the transparency of corporate decisions.<sup>6</sup> The survey for this measure begins in 2006. We use the value at year 2006 to back fill the missing values for each country. Within-country variation of this variable is highly persistent. Hence, filling missing values with most available data would not create a large bias. Moreover, our focus in this paper is on cross-country variation in the debt-maturity relationship rather than within-country variation.

Our second variable is the legal environment. We use a dummy variable ( _common law_ ) that takes a value of one for common-law countries (the United States; the United Kingdom; Australia; New Zealand; Canada; Hong Kong SAR, China; Ireland; and Singapore) and zero for civil law countries. This variable comes from Djankov, McLiesh, and Shleifer (2007) and is similar to the one described in La Porta et al. (1998). We use this variable to instrument investor protection.

# **2.4. Short-Term Debt, Leverage, and Control Variables**

We use Compustat-Global to measure short-term debt and to construct the control variables. _std_ is defined as the ratio of debt due in less than one year divided by total debt. Total debt is the sum of debt due in less than one year (Compustat item “dlc”) and long-term debt

> 6 Further details for constructing the _investor_ variable are available on the Doing Business website located at: - - <u>https://www.doingbusiness.org/content/dam/doingBusiness/media/Annual Reports/English/DB19 Chapters/DB2019-Data-Notes.pdf.</u>

12
show the road project stock that we estimate for North Kivu in Figure 11. The starting point here is the same as in Figure 10 –i.e. the peaks in the Figure represent the quality of roads just after the completion date of road rehabilitation projects–but now we treat the completed road projects as an ‘investment’ that adds to a ‘capital stock’ that decays with the road discount factor derived above, with the gentle slopes downward from top quality peaks representing the gradual degradation of the rehabilitated roads. The red line in the Figure shows the number of violent incidents, and these indeed rise each time the quality of roads nears its lowest point. The overarching pattern seems one of a cyclical dialectic between road rehabilitation and armed violence.

Figure 11: Road Stock and Armed Conflict in North Kivu <!-- Start of picture text --> 2000 2005 2010 2015 2020<br>year<br>road project stock events<br>15 400<br>300<br>10<br>200<br>road project stock 5<br>100 number of violence events<br>0 0<br><!-- End of picture text -->

**Notes** : The Figure shows the number of violent events sourced from UCDP and the road project Stock for North Kivu. A newly completed project takes a value of one. This value goes down as the road deteriorates.

# **4.2 Regression Analysis**

We address the issue of endogeneity of the completion of road projects with the overall violent environment at the province level through two strategies. First, we run the analysis and data collection at the smaller PRIO grid cell level (0.5×0.5 decimal degrees). It is unlikely that at that level, the completion of the road project is driven by violence in the cell. This level of analysis also allows us to control for province/year fixed effects. Second, we match grid cells on their violence risk outlook following the methodology proposed by Mueller and Rauh (2023). This allows us to compare cells with the same violence risk profile before and after the completion of a road project.

**OLS with cell-fixed effects** We test the link between conflicts and road investment by running the following regression controlling for both cell-fixed effects and year-fixed effects

where _yit_ represents fatalities and events and is expressed in natural logs. We start by using the aggregate number of events and fatalities and we then run the same regression 20
are taken as an exogenous transfer, which adds to the pre-existing home earnings, they are treated independently from home earnings. In other words, for a given household, with a given level of home earnings, an increase in remittances raises total income by the same amount. This could be true if the migration participation was to compensate a short term shock, such as a bad harvest or drought/flood. But, more often than not, participation in migration is a long-term alternative choice of participation in farm activity for households – migrants would contribute to their families in other ways if they had not migrated. Hence, this method does not address the interdependence of migration and home production. The results are hence biased if there is substitutability between the participation in migration and home productive activities (Escobal, 2001; Kimhi, 1994).

The second method compares the observed income distribution with a counterfactual scenario in the absence of migration and remittances by including an imputation for home earnings of erstwhile migrants. Taking into account the substitutability of migration and home productive activities, Adams (1989) estimates a function of household income determination for non-migrant households, and applies the coefficients and the endowment bundles of migrant households (in the absence of migration and remittances) to impute their earnings under a non-migration scenario to study the impacts of remittance on inequality. Barham and Boucher (1998) correct the selection bias and improve the income simulation model. Using a bi-variate probit model of double selection, Lachaud (1999) moves a step forward to simulate household income obtained in the absence of remittance and migration, and examines the impacts of private transfers on poverty.

In the following sections, we take into account interactions between the participation in various productive activities and analyze the impacts of migration on poverty and inequality using data from a rural household survey in _Hubei_ province in China. We relax the assumption of the independence of migration and home production, and compare the observed household income distribution with a counterfactual income distribution in the absence of migration and remittances to identify the impacts of migration on inequality and poverty. Barham and Boucher (1998) imputed migrants’ home earnings using an income equation estimated from the nonmigrants’. Their results showed that the distribution of simulated income is more equal than that of observed income. However, their simulation was based on the conditional expected values, i.e. ˆ _yi_ = β<sup>ˆ</sup> _X i_ , and the effects of the error term,<sup>ε</sup> _i_<sup>, on income distribution were not appropriately</sup> taken into account. This might lead to an artificially low estimate of income inequality among 9
# **2. Literature on Stability of Preferences**

There are a small number of studies that examine the temporal stability of contingent valuation and choice experiment studies but the results on the stability of preferences are mixed. Brouwer and Bateman (2005) studied the temporal stability of WTP responses from two contingent valuation studies (on flood control and wetland conservation) administered five years apart. They find that when examining WTP values over an extended time period, there is a statistically significant decrease in how people value flood control and wetland conservation attributes over time. They attribute these results to the five-year gap in the survey administration, which is much longer than the test-retest period that most researchers use, and also to the utilization of expanded models to include more, ad hoc transitory factors.

Price et al. (2016) and Bliem et al. (2012) find differing results from Brouwer and Bateman (2005) in their analysis of the stability of WTP values obtained from stated preference surveys regarding the health effects of tap water consumption. Price et al. (2016) conducted their survey in 2004 and again in 2012 using multiple survey formats. Their findings show that there is no significant difference in WTP between the two survey formats, and they support the transfer of non-market benefits over extended time periods. Bliem et al. (2012) test for the temporal stability of individual preferences by using two identical surveys for a restoration project of a stretch of the Danube River in Austria repeated over a one-year period. The researchers find that the preferences are stable across the one-year time frame.

Mørkbak and Olsen (2014) test the reliability and stability using an incentivized test-retest choice experiment. In their study, they use a choice experiment for a market good with real economic incentives, and sample respondents using the exact same questionnaire twice within a two-week lag time. Analysis of the data shows that error variances between the two identical surveys do not differ significantly, but the larger the utility difference in a choice task, the larger the probability that the respondent will choose the same alternative in the retest. Morten and Olsen also determine that the longer the time a respondent takes to answer the choice sets in the retest, the lower the probability that they will choose the same alternatives in the retest as they did in the original test.

Our study assesses how households in Ethiopia value different attributes of improved cookstove technology, and if these values are stable over a period of three years. This work contributes to the existing body of work on the stability of WTP values elicited from CE surveys using a test4
- 18 -

aerial photography (which was contracted out). The use of interns permitted IPLAN to maintain relatively low wage costs--the program's total wage bill averaged US$200 per staff month--while maintaining the flexibility to release staff when the program was finish d.

# **Table 2:** STAFF ALLOCATION (Man-Years)

|Program Phase<br>IPLAN|Staff|Interns|
|---|---|---|
|Gather field data|7.5|52.5|
|Enter field data on computer|2.5|13.2|
|Digitize aerial photo data|2.4|11.5|
|Investigate, fix discrepancies|2.0|24.0|
|Produce final list|0.5|na|