rafmacalaba/datause-encoder-data huggingface dataset:
# **Notes:**

1 For example the four-fold increase in the cost of redundancy imposed on employers through the new constitution of 1988 might be taken in this regard.

2 For example, Green, F., Arbache, J.S. and Dickerson, A., “A Picture of Wage inequality and the Allocation of Labor Through a Period of Trade Liberalization: the Case of Brazil.”, _World Development_ , 29(11) (2001), 1923-1940.

3 Source: International Labour Office.

> 4 Oaxaca, R., “Male-Female Wage Differentials in Urban Labour Markets”, _International Economic Review_ , 14 (1973): 693-709; Blinder, A. “Wage Discrimination: Reduced Form and Structural Estimates”, _Journal of Human Resources_ , 8 (1973): 436-455.

5 Juhn, C., Murphy, K. and Pierce, B. “Accounting for the Slowdown in Black-White Wage Convergence”, in M. H. Kosters. (ed.) _Workers and their Wages_ (Washington: AEI Press), (1991): 107-43.

> 6 See, _inter alia_ , Camargo, J.M. and Serrano, F. (1983) “Os Dois Mercados: Homens e Mulheres na Indústria Brasileira”, _Revista Brasileira de Economia_ , 34 (1983); Birdsall, J. and Fox, L. M. “Why Males Earn More: Location and Training of Brazilian School Teachers”, _Economic Development and Cultural Change_ , 33 (1985): 55-72; Birdsall, N. and Behrman, J. R. “Why Do Males Earn More Than Females in Urban Brazil: Earnings Discrimination or Job Discrimination?” in Birdsall, N. and Sabot, R. (eds.) _Unfair Advantage: Labor Market Discrimination in Developing Countries_ , (Washington: World Bank) (1991): 147-70. A recent survey of a number of studies of the Brazilian gender

17
This paper offers four main contributions to the study of global income inequality. First, we compile a new and improved database of national household surveys in response to criticism of earlier data sets (for example by Anand and Segal, 2008). Second, this allows us to present more credible results on global interpersonal income inequality between 1988 and 2008. Third, we create balanced and unbalanced panels of country-deciles for five benchmark years. This allows us to go further than the statements about which countries (and by how much) affected global inequality, by looking into a more disaggregated distribution of country-deciles. We can thus identify those country-deciles that have gained and lost most over this twenty year period. Fourth, we present one of the first comprehensive adjustments for missing top incomes in the study of global inequality.<sup>5</sup> We offer valuable new empirical insights because the effect on global inequality of non-response at the top within individual countries is unclear on a priori basis (Deaton, 2005).<sup>6</sup> We estimate the Gini index to be around 70%. The global Gini index has fallen over this twenty year period, with the decline being strongest between 2003 and 2008. However, these observed changes are probably not robust to plausible standard errors. The time-series pattern is robust to alternative measures of inequality, such as the Theil indices. Most of global inequality is accounted for by differences between countries, although this contribution has declined over time, suggesting that countries have become more similar. The within-country component of global inequality, however, has increased continuously over this twenty-year period.

> 5 Pinkovskiy (2013) estimates nonparametric bounds on the global Atkinson index allowing for any country-level income distribution given fractile shares and a Gini index. With a sufficiently high non-response at the top, the direction of change of global _welfare_ between 1970 and 2006 becomes ambiguous. He does not present bounds for global _inequality_ measures which allow for non-response at the top.

> 6 It might appear intuitive that stretching out the top tail would increase within-country inequality. However, this is not true in general and depends on the particular pattern in which underreporting occurs. As Deaton (2005) shows, if the probability of compliance at the top decreases following a Pareto distribution and the true distribution of incomes is lognormal, the “true” variance will not be different from the one obtained from a truncated distribution. The effect on global inequality depends on the extent of top income misreporting across countries, where in the global income distribution countries and their top income groups are, how far in the national income distribution non-participation begins (e.g. top 10% or top 5%), and how populous the countries and their top recipients are. If top income misreporting is particularly strong in poor countries, global inequality might actually fall once we allow for underreporting at the top.

3
Policy Research Working Paper 6102

# **Abstract**

Clean, safe energy for rural areas is an important component of green growth and sustainable development. Biogas could be an important contributor, if its record in reality lives up to its expected potential. This paper provides a preliminary assessment of biogas use by smallholder farmers in rural China, using data collected from 2,700 households in five provinces. The authors find that user satisfaction is high, and environmental and economic benefits appear tangible. There are strong indications of reduced use of wood and crop residues for fuel. Less time is spent on collecting fuel wood and cooking, which is especially beneficial to women. Adopters also save on fertilizers, because of the use of biogas residues. Moreover, problems with suspension of biogas use, whether due to technical or human factors, remained limited. However, few tangible benefits to respiratory health were detected. Overall, these findings are grounds for optimism about the potential for of smallholder biogas to contribute to more sustainable development, in China and beyond.

This paper is a product of the Poverty and Inequality Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at lchristiaensen@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
including these prices in the structural participation probit. Our results are robust to this specification.

Finally, we explore the validity of the lunar calendar as an instrumental variable. As previously discussed, there is an extended literature that links lunar phases with fish availability. However, it is also possible that the lunar calendar affects the marginal dis-utility from working, thus violating the exclusion restriction. One reason is night brightness: boat owners might prefer to fish during nights with full moon because getting oriented and conducting all fishing-related activities might be easier when there is more light. The fact that we observe lower likelihood of participation in full moon nights mitigates this concern.<sup>34</sup> An additional link between the dis-utility to work and the lunar phase might be driven by tide variation. Every day, there are two high tides and two low tides. When there is full moon, high tides are very high and low tides are very low while this difference decreases during quarter moons. Therefore, sea conditions might also affect the dis-utility of going fishing if boat owners prefer not to sail under cycles with a higher sea tide variance. In order to test for this possibility, Column 4 of Table 7 explicitly incorporates dummies for the different lunar phases as regressors of the structural probit. As we can see, the results are unaffected.

# **6 Discussion and Conclusions**

Most of the previous literature on labor supply has studied the intensive margin of participation decisions—how many hours to work—rather than the extensive margin—whether to work on a given day. However, the studies that are able to measure inter-temporal work elasticities in both margins, find that they are much larger in the extensive margin.

In this paper we use a dataset on daily labor force participation of Indian boat owners that we observe for 7 years. We develop an empirical strategy that allows us to estimate extensive margin elasticities. We find that a 10 percent increase in expected wages increases the likelihood of participation by 8 percent. The magnitude of these elasticities are similar to those found in the previous literature.

The richness of our data allows us to go beyond the specifications in previous studies and test for the preference of across-days spillovers in the opportunity cost of working. We flexibly control by fatigue and find evidence that boat owners are substantially less likely to fish if they have been working during the previous consecutive days. We also find that, conditional on recent participation, higher cumulative earnings within previous days of the week, decreases the likelihood of participation. This last results is at odds with the neoclassical model with time-separable utility and is consistent with models of reference-dependent preferences with

> 34See the reduced-form probit estimates in Table 4 where full moon is the omitted category.

19
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Central Africa (CEMAC). Where relevant, separate aggregate results will be presented for the ECCAS and CEMAC countries.

For the period 2003–08, all countries for which data are available registered economic growth, albeit at varying rates. Average annual growth in purchasing power parity (PPP) terms was above 1 percent for all countries. Burundi, Cameroon, the Central African Republic, and Gabon grew at an average rate of 1 percent annually during this period. The Democratic Republic of Congo, the Republic of Congo, Chad, Rwanda, and São Tomé and Príncipe grew between 2 and 5 percent on average for the same period, and Equatorial Guinea had outstanding growth of over 10 percent annually. Infrastructure has contributed approximately 1 percentage point to the per capita growth rate in Central Africa (figure 1.1a). Almost the entirety of this was due to the growth of mobile telephony, as was the case in all other regions. Improvements in road infrastructure made a small contribution to overall growth. The lack of adequate power infrastructure eroded growth by 0.1 percentage point, more in Central Africa than in other parts.

**Figure 1.1a. Infrastructure’s historic contribution to economic growth, 1995–2005**

<!-- Start of picture text -->
1.4<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>Telecom Power Roads<br>Source:  Calderón 2009.<br>economic growth<br>North Africa West Africa East Africa Central Africa Africa<br>Additional percentage points of per capita<br>Southern Africa<br><!-- End of picture text -->

Infrastructure, however, could contribute much more to economic growth in the future than it has in the past (figure 1.1b). Simulations suggest that if Central Africa’s infrastructure could be upgraded to the level of the best-performing country in Africa (Mauritius), the impact on per capita economic growth would be on the order of 5 percent. All areas of infrastructure—ICT, power, and transport—need to be upgraded. Improvements in power can impact growth by around 2 percentage points. Roads and telecommunications infrastructure upgrades could contribute 1.5 percentage points each to growth.

2
To estimate (29) we take _j_ as decile (percentile) group so that the data are comparable across countries. The PPPs are then estimated as TCLIs from equation (25) for a given reference level of utility u<sup>∗</sup> (taken to be the one corresponding to the median level income of the base country, India). It may be emphasized that ���<sup>�</sup> �, ���<sup>�</sup> �and ���<sup>�</sup> � are estimated as composite variables and no explicit algebraic forms for these functions are assumed. This confers the advantage that the estimated PPPs are not dependent on a priori specified particular functional forms such as, for example, the specification proposed by Banks, Blundell and Lewbel (1997).

# **Comparison with the ICP PPPs**

# [Table 5 about here]

Table 5 presents the counterfactual PPPs based on World Bank data on broad item groups by income categories for rural and urban sectors,<sup>22</sup> with India as base. The table also presents the 2011 ICP Consumption PPPs, where the base country has been shifted from the US to India by dividing all the PPPs (with respect to US $) by the PPP of India with US as base. In case of a few countries, for example, Chad, Ethiopia and Ghana, the two PPPs are nearly identical, but this is not always the case. The bias in the 2011 ICP PPPs in relation to the counterfactual PPP is not unidirectional. In general, the 2011 ICP PPPs in the African region exceed the counterfactual PPPs, and the reverse is the case in the Asia/Pacific region. While in case of several countries, for example, South Africa, China and Indonesia, there is a large difference between the 2011 ICP PPP and its 2011 counterfactual, overall, there is a positive correlation between the two. The overall correlation coefficient, over all countries taken together, turns out to be 0.9134. The region specific correlations are 0.9189, 0.9996, 0.9985 and 0.9706, respectively, for the ‘African region’, the ‘Asia and Pacific region’, the ‘Commonwealth of Independent States and Eurostat-OECD’ region and the remaining region consisting of Latin America, Western Asia and the Caribbean.<sup>23</sup>

[Figures 4, 5 and 6 about here]

> 22 See Appendix A for description of income category level.

> 23 All the correlations are significant at less than 1% level.

26
_Agricultural Households_ . , ed. Calogero Carletto, Benjamin Davis and Paul Winters, 114–139. Routledge.

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39
production sector, and parameters used in the LES functional form to model the households. The values for elasticity of substitution are not available for Ethiopia. The only sources of these parameters are existing studies for developing countries which provide values for the elasticities of substitution. We use the values from Chen et al. (2013) and Mosa (2018). The parameters used in the LES function are taken from Mosa (2018) and Diao et al (2012). Since the values of elasticity of substitution and other parameters are taken from the literature, sensitivity analyses on these values should be conducted to validate the finding of the study. Other parameters such as household saving rates, rates for transfers between the agents, and distribution of total government consumption to individual categories are calibrated from the SAM.

# **3. Design of Scenarios**

The study considers a hypothetical carbon tax with a rate US$20/tCO2. We have developed one baseline scenario and four main scenarios depending on how the carbon tax revenue is recycled into the economy. These scenarios are defined in Table 2. A scenario comes up with multiple subscenarios or cases depending upon the criteria used to recycle the revenue under a given scenario. The selection of a single tax rate is guided by the objective of the study. The objective is to compare the distributional impacts of a carbon tax policy under the alternative schemes for revenue recycling. The rate may not be relevant for this purpose because the comparison is expected to hold unless the tax rates are altered by its multiples (e.g., two times, three times). In practice, tax rates are increased gradually overtime so that the economy can adjust to it. Moreover, if we use multiple rates, we will have too many cases because of the large number of alternative revenue recycling schemes considered in the study. It will introduce complications in the interpretation of the results and articulation of the main message.

**Table 2: Definition of Scenarios**

|**Scenario**|**Sub-Scenario**|**Definition**|
|---|---|---|
|**1. Government**<br>**use**|**(a) Like in base**<br>**case**|Carbon tax with tax revenues allocated in the same way the government<br>allocates its total revenues in the baseline|
||**(a) Investment**|Carbon tax with tax revenues allocated for investment|
|**2. Cash**<br>**transfer to**<br>**households**|**(a) Income**|Carbon tax with revenue rebated to different household income groups in<br>proportion to their income. A household income group with the highest<br>income gets thehighestrebate|
||**(b) Equal**|Carbon tax with revenue rebated to all household incomegroups equally|
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Bibliography and references

This country report draws upon a wide range of papers, databases, models, and maps that were created as part of the Africa Infrastructure Country Diagnostic. All of these can be downloaded from the project website: www.infrastructureafrica.org. For papers go to the document page (http://www.infrastructureafrica.org/aicd/documents), for databases to the data page (http://www.infrastructureafrica.org/aicd/tools/data), for models go to the models page (http://www.infrastructureafrica.org/aicd/tools/models) and for maps to the map page (http://www.infrastructureafrica.org/aicd/tools/maps ). The references for the papers that were used to compile this country report are provided in the table below.

## General

Africa’s Infrastructure: A Time for Transformation (AICD Web site), http://www.infrastructureafrica.org Foster, Vivien, and Cecilia Briceño-Garmendia, eds. 2009. _Africa’s Infrastructure: A Time for_

- _Transformation._ Paris and Washington, DC: Agence Française de Développement and World Bank.

## Financing

- Briceño-Garmendia, Cecilia, Karlis Smits, and Vivien Foster. 2009. ―Financing Public Infrastructure in Sub-Saharan Africa: Patterns and Emerging Issues.‖ AICD Background Paper 15, Africa Region, World Bank, Washington, DC.

## Growth

Calderón, César. 2009. ―Infrastructure and Growth in Africa,‖ Policy Research Working Paper 4914, World Bank, Washington, DC.

- Escribano, Alvaro, J. Luis Guasch, and Jorge Pena. 2010. ―Assessing the Impact of Infrastructure Quality on Firm Productivity in Africa.‖ Policy Research Working Paper 5191, World Bank, Washington, DC.

- Yepes, Tito, Justin Pierce, and Vivien Foster. 2009. ―Making Sense of Africa’s Infrastructure Endowment: A Benchmarking Approach.‖ Policy Research Working Paper 4912, World Bank, Washington, DC.

## Information and communication technologies

Ampah, Mavis, Daniel Camos, Cecilia Briceño-Garmendia, Michael Minges, Maria Shkratan, and Mark Williams. 2009. ―Information and Communications Technology in Sub-Saharan Africa: A Sector Review.‖ AICD Background Paper 10, Africa Region, World Bank, Washington, DC.

38
2 KENYA AND CHINA’S TRADE RELATIONSHIP

|Poisson Q|uasi Maximum Likelihood|
|---|---|
|Importer GDP per capita|0.00(0.00)<sup>_∗∗∗_</sup>|
|Population Kenya|_−_0.01(0.01)|
|Population Importer|0.00(0.00)<sup>_∗∗∗_</sup><br>|
|Distance|_−_0.00(0.00)<sup>_∗∗∗_</sup>|
|Regional Trade Agreement|1.16(0.10)<sup>_∗∗∗_</sup>|
|Contiguous|2.30(0.09)<sup>_∗∗∗_</sup>|
|Common Language|0.19(0.06)<sup>_∗∗_</sup>|
|GATT Kenya|0.78(0.23)<sup>_∗∗∗_</sup>|
|GATT importer|1.50(0.09)<sup>_∗∗∗_</sup>|
|Colonial History|2.53(0.08)<sup>_∗∗∗_</sup>|
|China dummy|_−_1.74(0.42)<sup>_∗∗∗_</sup>|
|AIC||
|BIC||
|Log Likelihood||
|Deviance|98819.06|
|Num. obs.|6118|

> _∗∗∗ p <_ 0.001, _∗∗ p <_ 0.01, _∗ p <_ 0.05. Standard errors in parentheses.

**Kenya’s exports falls with distance. China is far** Figure C36 presents the relationship between distance and trade. The farther Kenya is from its destination, the less it exports. Kenya may under export to China because they are 9,201.3 kilometers apart, nearly in the third quartile of distances in the sample.

**Kenya exports more to richer countries** Figure C37 shows the relationship between the per capita GDP of the importing country and exports in 2014. We see that a higher importer GDP per capita is linked to higher trade. Given its already high per capita GDP, the UK stands out as a bigger trading partner, and it probably trades more with Kenya because of the former colonial link. Kenya’s exports to China are still low as figure 6 shows, but the large per capita GDP of China presents a potential market for exports.

**China’s exports to Kenya are “normal”** We estimate a similar Poisson regression taking China as an exporter to the world. Table 2 presents estimates of China’s exports from the Poisson model: the importer GDP per capita and distance are significant predictors of trade; the coefficient of distance is close to zero, meaning that a one kilometer increase in distance reduces China’s exports by a factor close to 1. The effect of sharing a border with its trading parter is insignificant for China, but it benefits Kenya. China exports more to Kenya by a factor of 1.08, so if China exports goods worth one million USD to a country comparable to Kenya, it exports 1,080,000 to Kenya. The coefficient, however, is statistically insignificant. Since the results from Poisson are less biased than the ordinary least squares model, we observe that China’s exports to Kenya are as expected when we hold the other explanatory variables fixed. Over time, China’s exports to Kenya are

15