rafmacalaba/datause-encoder-data huggingface dataset:
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Brülhart, 2009). There are almost as many studies which point towards a link between trade and spatial convergence as those pointing towards spatial divergence (Brülhart, 2009) and the direction and dimension of this relationship is far from uniform and varies from one country to another and according to the data and methods used.

Although the number of single-country case studies which have delved into this question has grown significantly in recent years, very scant cross-country evidence exists unveiling a general causal linkage between greater trade openness and market integration, on the one hand, and intra-national spatial inequality, on the other<sup>2</sup> . This may be because the literature on the evolution of within-country spatial inequalities has tended – following the path opened by Williamson (1965) in his account of the relationship between spatial disparities and the stage of economic development – to focus on the internal and not the external forces of agglomeration and dispersion. From this perspective economic development matters for the evolution of spatial inequalities, which tend to wane as a country develops. Hence, the factors that make a difference in explaining the evolution of regional inequality are considered to be internal to the country itself, while external factors are, at best, regarded as playing a supporting role in this process. And when they are taken into consideration, the outcome is rather inconclusive. As Milanovic puts it (2005: 428) “country experiences differ and [...] openness as such may not have the same discernable effects on countries regardless of their level of development, type of economic institutions, and other macroeconomic policies”. Moreover, a large percentage of the literature dealing with the relationship between trade and spatial inequality has concentrated on developed countries – and in particular with the spatial effects of EU integration (e.g. Niebuhr, 2006; Barrios and Strobl, 2009) – meaning that the findings, as inconclusive as they are, may be irrelevant in middle and lower income country environments.

Finally, it is far from certain that the temporality and benign implications of any potential growth in within country regional disparities resulting from changes in trade patterns will materialize. In particular, in cases where increasing polarization takes

> 2 Brülhart (2009) limits the number of cross-country analyses to 11, virtually all using urban primacy data, rather than regional data (e.g. Ades and Glaeser, 1995; Nitsch, 2006; Brülhart and Sbergami, 2008).

3
to us more plausible than inferring that Chinese firms are less competitive than Ghanaian ones.

Labor costs, and the supply of labor in general and specific skills in particular, are important issues for firm performance and especially for the link between performance and poverty reduction. One striking result that has emerged from the research on the firm data is how much larger are wages in large than in small firms. This is only partly explained by large firms employing more skilled workers than small ones. That is, there is a large proportion of the wage differential across firms of differing size that is not explained by a differential in skills. Söderbom, Teal and Wambugu (2005) show that, conditional on all observed and unobserved worker skills that are fixed over time, workers’ earnings in Ghana and Kenya vary positively with firm size. The estimated effect is economically large, compared to other regions. In the preferred specification for Ghana, the estimated elasticity of wages with respect to firm size is 0.15; in Kenya it is 0.08. These numbers imply that as a firm grows from having, say, 20 employees to 40, wages will increase by 11 percent in the case of Ghana and 6 percent in Kenya.<sup>8</sup> While this might seem like good news for the employees, increasing wages results in higher total labor costs which, everything else held constant, tend to erode firms’ profits. Improved performance in the form of higher productivity would help to countervail this effect. Söderbom and Teal (2004) investigate if there is any evidence of increasing returns to scale in Ghanaian manufacturing. Under increasing returns to scale, the proportional change in output is larger than an equiproportional increase in all inputs (i.e. a doubling of all inputs leads to more than a doubling of output, for instance), and so when firms grow higher average labor costs would be offset by higher input productivity. Söderbom and Teal find, however, only weak evidence for increasing returns and constant returns to scale is not rejected. They argue that the reason large firms can remain profitable is that they face lower capital costs than small firms. One possible reason for this is better access to formal credit.

Since rapidly rising labor costs are likely to constrain firm growth and exports, it is important to understand why this pattern is observed in the data. The standard explanation in the literature why wages are so closely connected to firm size is simply that large firms employ individuals that are more skilled than those employed by

> 8 Ghana: exp(0.15ln(40) – 0.15ln(20)) – 1 = 0.11. Kenya: exp(0.08ln(40) – 0.08ln(20)) – 1 = 0.06.

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population and contributes to 34 percent of gross domestic product. Maize is one of the main sources of agricultural income in Burkina Faso, ranking second after cotton. As depicted by **Figure A1** in the Appendix, maize production has significantly increased in the last decade, rising at a faster pace than millet and sorghum. Production growth was most remarkable in Hauts-Bassins, Boucle du Mouhon and Cascades regions in western Burkina Faso ( **Appendix A2 and A3** ). In addition to these spatial disparities, maize is mainly traded within the country, flowing from maize-surplus to maize-deficit regions, but is also exported to Niger and Mali while imports originate from Côte d’Ivoire and Ghana. Our analysis relies on historical price data collected by the **_Société Nationale de Gestion du Stock Alimentaire_** _(_ **_SONAGESS_** _)._ SONAGESS manages its own market information system since 1992. Prices of main agricultural commodities are collected weekly on 48 markets, and price averages are computed monthly. In this study, we analyze 28 markets with available data over July 2004November 2013. We deliberately set aside markets for which price series present discontinuities. For each market, monthly maize price series are expressed in FCFA per kilogram and then deflated<sup>10</sup> by the Burkinabe Consumer Price Index (2008 base 100) calculated monthly by the Institut National des Statistiques et de la Démographie. Descriptive. Statistics of deflated maize price series in each market are presented in **table A4** .

**Figure 2: Evolution of maize real price**

<!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Batie Banfora Dedougou Diapaga Diebougou<br>Djibo Dori Douna Fada Fara<br>Faramana Faoua Gourcy Guelwongo Kaya<br>Kongoussi Koudougou Leo Manga Ouargaye<br>Pouytenga Sankaryare Sapouy Solenzo Tenkodogo<br>Tougan Yako Zabré<br>2004m7 2004m12 2005m5 2005m10 2006m3 2006m8 2007m1 2007m6 2007m11 2008m4 2008m9 2009m2 2009m7 2009m12 2010m5 2010m10 2011m3 2011m8 2012m1 2012m6 2012m11 2013m4 2013m9 2014m2 2014m7<br><!-- End of picture text -->

**_<u>Source: SONAGESS and INSD</u>_**

**Figure 2** displays the evolution of real prices on the 28 maize markets. The price series present similarities between markets, and notably important price spikes followed by price

> 10 Section 5.3 will make use of nominal price series in robustness checks.

10
MacKellar et al. (2002) explain the negative relationship between landlockedness and growth using a neoclassical theory. They highlight that crossing a border implies higher transaction costs due to customs and handling costs. Therefore, landlockedness can be thought as raising import prices and reducing export revenues. It is one reason why Radelet and Sachs (1998) advocate the idea that a re-export model is extremely difficult to achieve in landlocked developing countries due to higher cost of intermediate products. Amjadi and Yeats (1995) point out that the incidence of transport costs fall heavily on the landlocked African countries since they have to adjust their selling price to world prices.

Gallup, Sachs and Mellinger (1999) see two reasons why landlocked countries may be disadvantaged:

- coastal countries may have military or economic incentives to impose costs on landlocked countries.

- infrastructure development across national borders is more difficult to arrange than similar investments within a country,

The specificity of landlocked countries has long been recognized by the international community. The current policy framework, summarized in The Almaty Programme of Action (Box 1), is aimed at tackling the issues of dependence and accessibility, targeting three priorities: (1) ensure the recognition of freedom of transit in international agreements, (2) develop transport infrastructure, and (3) encourage transnational cooperation. Early efforts involved legal measures. Many bilateral, regional and multilateral treaties have been signed since World War II, following the recognition of the right to freedom of transit for landlocked countries by the GATT Art V<sup>9</sup> , and the 1958 Geneva Convention on High Seas (further developed in the 1982 Montego Bay Convention). A recent World Bank review of legal instrument transit trade in Africa found that the main problem today is not the inadequacy or lack of agreements and frameworks (Table 1) but their poor implementation stemming from a lack of capacity or

> 9Article V of the GATT states that (1947) “there shall be freedom of transit through the territory of each contracting party, via the routes most convenient for international transit, for traffic in transit to or from the territory of other contracting parties.”

6
- 38 -

data. The distributions were, however, detailed (51 groups) so the approximations should be quite good. In calculating the Gini indices we have used Gastwirth's formulae for the upper and lower boundc and then used the weighted mean of the two, with two-thirds weight to the upper bound; see Cowell (1977).

15. The P47* and pI7** mepsures are simulated from the parametized 1984 and 1987 Lorenz curves for urban and rural areas, as discussed in Appendix 2, which also outlines our estimation method for qo.

16. This interpretation isn only strictly valid if the Lorenz curve shifts such that L<sup>87</sup> (p) = L<sup>84</sup> (p) - X[p-L<sup>84</sup> (p)], where Lt(p) denotes the Lorenz curve for date t; then a decrease in the Gini coefficient will decrease poverty (for a broad class of additive measures) if ard only if the poverty line is less than the mean (Kakwani, 1989b). Noting that X is equal to the proportionate change in the Gini index, the above assumption implies the following national Lorenz curve for 1987 (where

- '= -.0302):

|p|10|20|30|40|50|60|70|80|90|
|---|---|---|---|---|---|---|---|---|---|
|L87 (p)|3.60|8.50|<br>14.31|21.01|28.64|37.33|47.28|59.05|73.81|

Clearly this is quite a good approximation to the actual Lorenz curve for that year (given in the last column if Tablc 3; the standard error of estimate is 0.23 percentage points), but it is still an approximation.

17. Thus we are considering J.he second-order effects of changes in distributional parameters on poverty: such effects are often of
resulting in a mean level of completed schooling of only two years.

## **4.4 Treatment-control Balance and Attrition**

**Balance** . Table 1 also tests for balance between the control and treatment groups at baseline, reporting _p_ -values of differences between groups in column (5). Both groups are consistently well-balanced, suggesting that the randomization worked well in practice. The one exception relates to the number of children in the household aged 3-5 where a small difference appears (control mean of 1.08 versus treatment mean of 1.06; _p_ = 0 _._ 022). However, it should be expected that 1 in 22 variables would display a difference by chance at the 5% level. Importantly, we test for joint orthogonality of baseline characteristics with treatment assignment using a linear probability model with all the variables listed in Table 1 as explanatory variables and the treatment status as the dependent variable. The _p_ -value of the joint F-test is 0.19, suggesting that baseline characteristics jointly fail to predict the treatment status of the target children.

**Attrition** . Figure 1 displays the level and causes of attrition across treatment and control groups between the baseline and the endline, three years later. In Table A3, we examine the effect of attrition at endline on the relative composition of the treatment and control attrition from the child test when a groups. We consider two types of attrition at endline: target child did not complete the assessment during the endline survey, and attrition for the household survey when a household could not be found at endline. The two types of attrition are analyzed in columns (1) and (2) of Table A3, respectively. We do not find evidence in support of attrition that is differential by treatment status. Thus, we conclude that the loss of children and households at endline does not affect the comparability of our samples and we consider our experimental setting valid for causal analysis.

# **5 Estimation Framework and Inference**

To study the impacts of the DICIPE program, we estimate the following specification for child _i_ in community _c_ and district _s_ : 17
21

- Anderson, K., B. Dimaranan, J. Francois, T. Hertel, B. Hoekman and W. Martin. “The Burden of Rich (and Poor) Country Protectionism on Developing Countries.” _Journal of African Economies_ 10(2001): 227-57.

- Anderson, K. and L.A. Jackson. “Some Implications of GM Food Technology Policies for Sub-Saharan Africa.” _Journal of African Economies_ 14(2005): 385-410.

- Anderson, K., L.A. Jackson and C.P. Nielsen. “GM Rice Adoption: Implications for Welfare and Poverty Alleviation.” _Journal of Economic Integration_ 20(2005): 771-88.

- Anderson, K. and Y. Hayami. _The Political Economy of Agricultural Protection: East Asia in International Perspective_ . London: Allen and Unwin.

- Anderson, K., W. Martin and D. van der Mensbrugghe. ‘Market and Welfare Implications of Doha Reform Scenarios’, Ch. 12 in _Agricultural Trade Reform and the Doha Development Agenda_ , edited by W. Martin and K. Anderson, New York: Palgrave Macmillan co-published with the World Bank, 2006a.

- Anderson, K., W. Martin and D. van der Mensbrugghe. “Distortions to World Trade: Impacts on Agricultural Markets and Farm Incomes.” _Review of Agricultural Economics_ 28(2006b): 168-94.

- Anderson, K., W. Martin and D. van der Mensbrugghe. “Would Multilateral Trade Reform Benefit Sub-Saharan Africa?” _Journal of African Economies_ 15 (forthcoming 2006c). Accessible at www.worldbank.org/trade/wto

- Anderson, K. and E. Valenzuela. “The World Trade Organization’s Doha Cotton Initiative: A Tale of Two Issues.” Policy Research Working Paper 3918, World Bank, Washington DC, May 2006. Accessible at www.worldbank.org/trade/wto
option, since it avoids introducing output subsidies that distort incentives to switch between energy sources as a means to reduce emissions. CAT also encourages efficient pass-through of emissions costs, meaning additional taxation of electricity is not needed. Thus, overlapping the stated RPS and embodied emissions pricing increases by about 10% the costs of a CAT system that would achieve the same emissions reductions as China’s actual TPS. Together, China’s stated policy overlaps reduce the cost differential between China’s TPS and an equivalently stringent CAT system by about two thirds.

These insights are relevant not only to China, but also to other countries considering or implementing a rate-based ETS, such as Indonesia, Kazakhstan, and India, as well as Canada.

In future work, we plan to consider how other pre-existing distortions or market failures might influence the outcomes from overlaps and suggest useful policy responses. For example, output-based allocations can have some benefits in the presence of distortionary taxes on labor, capital, or other factors of production, or investment dynamics under macroeconomic volatility. Clean energy policies are often needed to address technology market failures. Another area for extension is attention to the effects of a range of flexibility mechanisms often incorporated into ETSs to address uncertainty, including banking and borrowing, price corridors, or quantity adjustment mechanisms that make mass-based emissions targets more malleable. Incorporating more real-world policy settings and constraints would provide needed nuance to the recommendations for optimal ETS design and supplemental policy recommendations.

# **VI. References**

Aguiar, A., Chepeliev, M., Corong, E., McDougall, R., & van der Mensbrugghe, D. (2019). The GTAP Data Base: Version 10. Journal of Global Economic Analysis, 4(1), 1-27.

Böhringer, C., & Behrens, M. (2015). Interactions of Emission Caps and Renewable Electricity Support Schemes. _Journal of Regulatory Economics, 48_ , 74-96.

Böhringer, C., & Rosendahl, K. E. (2010). Green Promotes the Dirtiest: On the Interaction between Black and Green Quotas in Energy Markets. _Journal of Regulatory Economics, 37_ , 316325.

Boeters, S., & Koornneef, J. (2011). Supply of Renewable Energy Sources and the Cost of EU Climate Policy. _Energy Economics, 33_ (5), 1024-1034.

Braathen, N. A. (2007). Instrument Mixes for Environmental Policy: How Many Stones Should be Used to Kill a Bird? _International Review of Environmental and Resource Economics, 1_ (2), 185-236.

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However, in areas that are not covered by the network, and are responsible for the supply gap above, it is likely that even if supply were available, some households would not take up the service due to affordability issues. If one assumes that the take-up rate in non-served areas would be similar to the take-up rate in areas where there is service now, the additional coverage that we would obtain by providing access to these areas would be equal to the supply-side gap times the take-up rate where there is access. This is defined as the pure supply-side gap: _PSSG_ = _SSG_ × _U_ = 1( − _A_ ) × _U_ (4) The difference between the pure supply-side gap and the supply-side gap can then be deemed to represent a combined demand and supply-side gap, since first there is no access to the service, and second even if there were access, some households would not be connected. F&A defined this as the mixed demand and supply-side gap, defined as follows: _MDSSG_ = _SSG_ × 1( − _U_ ) (5) Given the above definitions, the proportion of the deficit in coverage that is attributed to demand-side factors is defined as the ratio of the pure demand-side gap to the unserved population. The proportion of deficit attributable to supply-side factors is the ratio of the pure supply-side gap divided by the unserved population. Finally, the proportion of deficit attributable to both demand and supply-side factors is the ratio of the mixed demand and supplyside gap divided by the unserved population. The sum of the three proportions is equal to one.

The results from the decomposition are presented in tables 1 and 2 for urban areas in subSaharan African countries (it is not as useful to do the same work for rural areas, because access is very limited there in most countries, so that lack of coverage is principally a supply-side issue). The data used are from the latest Demographic and Health Survey (DHS) completed in each of the countries. Most of the countries have data after the year 2000. A household is deemed to have access to piped water or electricity if the household lives in an area (which is the primary sampling unit of the survey to which the household belongs) where at least one household has access. We discuss here the Africa averages, leaving the discussion of countryspecific results for later. All Africa averages are provided both with population weights (in which case countries such as Nigeria play a larger role due to their larger population), and without weights.
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