rafmacalaba/datause-encoder-data huggingface dataset:
- Bourgiugnon, F and M Browning, P-A Chiappori and V Lechene (1994), “Intrahousehold Allocation of Consumption: A Model and Some Evidence from French Data”, _Journal of Political Economy_ , 102, 1067-1096.

- Brown, LR and L Haddad (1995), “Time Allocation Patterns and Time Burdens: A Gendered Analysis of Seven Countries”, International Food Policy Research Institute, Washington, DC.

- Browning, M, F Bourgiugnon, P-A Chiappori and V Lechene (1994), “Incomes and Outcomes: A Structural Model of Intrahousehold Allocation”, _Journal of Political Economy_ , 102, 1067-1096.

- Browning, M (1992), “Children and Household Economic Behavior”, _Journal of Economic Literature_ , 30, 1434-1475.

- Browning, M and A Lusardi (1996), “Household Saving: Micro Theories and Micro Facts”, _Journal of Economic Literature_ , 34, 1797-1855.

Chiappori, P-A (1997), Introducing Household Production in Models of Family Labor Supply”, _Journal of Political Economy_ 105, 191-209.

Chiappori, P-A (1992), “Collective Labor Supply and Welfare”, _Journal of Political Economy_ , 100, 437-467.

Chiappori, P-A (1988), “Rational Household Labor Supply”, _Econometrica_ , 56, 63-90.

Deaton, A (1997), _The Analysis of Household Surveys,_ John Hopkins University Press, Baltimore and London.

Deaton, A (1992), _Understanding Consumption_ , Clarendon Press, Oxford.

- Deaton, A and Muellbauer (1980), _Economics of Consumer Behaviour_ , Cambridge University Press, Cambridge and New York.

Duncan, A and J MacCrae (1999), “Household Labour supply, Childcare Costs and In-Work Benefits: Modelling the Impact of the Working Families Tax Credit in the UK”, paper presented at the Econometrics Society European Meetings.

Folbre, N (1997), “Gender Coalitions: Extrafamily Influences and Intrafamily Inequality in Haddad, L, J Haddinott and H Alderman, eds, _Intrahousehold Resource Allocation in Developing Countries: Methods, Models and Policy_ , Ch 16, 263274, Baltimore, The Johns Hopkins University Press.

- Fong Y-f and J Zhang (2001), “The Identification of Unobservable Independent and Spousal Leisure”, _Journal of Political Economy_ , 109, 191-202.

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agreement favors the relative expansion of dirty industries. Two scenarios—no technical effect and a positive technical effect—are considered for the analysis.

Results show that the environmental developments from DR-CAFTA vary significantly across member countries. Most results are consistent with the findings in the literature. Scale effects are positive and dominate the composition effects for all countries. Composition effects vary significantly across member countries. While Costa Rica and El Salvador, countries with stronger environmental regulations, experience a small but positive increase in pollution as a result of changes in the composition of production, Nicaragua and Honduras (as well as the regional average) experience negative composition effects. The results indicate that factors other than a lax regulatory framework play an important role in determining the patterns of production and trade. Results change after allowing for adjustments in pollution intensity. Under this alternative scenario, levels of emissions from production seem to have decreased after the agreement. The share of pollution in exports continuously expands in the alternative scenario as well.

The findings do not suggest the existence of pollution havens in Central America. Nonetheless, countries should continue strengthening and homogenizing environmental rules in the region. The environmental agenda should be combined with an effort to improve competitiveness that helps to sustain trade in the medium term as regulatory costs rise.

# **_References_**

Akbostanci, Elif, G. Ipek Tunc, and Serap Türüt-Asik. 2004. ―Pollution Haven Hypothesis and the Role of Dirty Industries in Turkey’s Exports.‖ ERC Working Paper 0403, Middle East Technical University, Economic Research Center, February.

Antweiler,Werner, Brian R. Copeland, and M. Scott Taylor. 2001. ―Is Free Trade Good for the Environment?‖ _American Economic Review_ 91 (4, September): 877–908.

Aslanidis, Nektarios. 2009.―Environmental Kuznets Curves for Carbon Emissions: A Critical Survey.‖ Working Paper 2009.75, Fondazione Eni Enrico Mattei, Milan.

Bovenberg, A. L., and Sjak Smulders. 1995. ―Environmental Quality and Pollution-Augmenting Technological Change in a Two-Sector Endogenous Growth Model.‖ _Journal of Public Economics_ 57 (3, July): 369–91.

Brock,William A., and M. Scott Taylor. 2004. ―The Green Solow Model.‖ NBER Working Paper 10557, National Bureau of Economic Research, Cambridge, MA.

Chichilnisky, Graciela. 1994. ―North-South Trade, Property Rights, and the Dynamics of Environmental Resources.‖ MPRA Paper 8415, University Library of Munich, Germany.

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5 intervention measures and across industries) by using a theoretically sound aggregation procedure to answer precise questions regarding the trade or welfare reductions imposed by each country’s agricultural or trade policies. The goal of the literature is to generate a single indicator that captures the overall trade or welfare effect of an individual country’s regime of price distortions in place at any time, and to trace its path over time and make cross-country comparisons.

The pioneering work in the literature is by Anderson and Neary (summarized in their 2005 book). Feenstra (1995) simplified the methodology to a partialequilibrium framework. These two authors define a Trade Restrictiveness Index as the ad valorem trade tax rate which, if applied uniformly across all tradable agricultural commodities in a country, would generate the same reduction in welfare as the actual cross-product structure of distortions. They also define a Mercantilist Trade Restrictiveness Index (MTRI) as the ad valorem trade tax rate which, if applied uniformly across all tradable agricultural commodities in a country, would generate the same reduction in international trade as the actual cross-product structure of distortions.

In recent years, several empirical papers have provided various series of partial-equilibrium estimates of scalar index numbers for individual countries. Irwin (2010) uses detailed tariff data to calculate the Trade Restrictiveness Index for the United States in 1859 and annually from 1867 to 1961. Kee, Nicita and Olarreaga (2009) estimate a series of indices for trade policies of 78 developing and developed countries for a single point in time (mid-2000s). Lloyd, Croser and Anderson (2010) modify the Anderson/Neary TRI and MTRI methodology to make it more applicable to agricultural policies, and show how it can be greatly simplified if certain assumptions about elasticities are adopted. Croser and Anderson (2010) build on that to develop a methodology for computing scalar index measures for individual policy instruments, which can be compared across instruments to see the relative contributions of different policy instruments to overall reductions in trade and welfare. In addition to being useful to summarize policy in an individual country, the Anderson-Neary scalar index measures has been adapted to measure the trade- and welfare-reducing effects of policy in a regional or global commodity market (Croser, Lloyd and Anderson 2010). In this paper we utilize the methodology in those latter three studies to generate a series of indicators of the trade- and welfare-reducing effects of agricultural policies in OECD countries over the past half century.
conflicts in Liberia mirrored divisions in Côte d’Ivoire. Because recent events proceed from these deeply structural factors that operate on a regional level and because the particulars of the conflict that began in Côte d’Ivoire in 2002 involved specific violent interventions by neighboring countries in each other’s affairs, we designate the Ivorian experience as part of the Mano River complex.

PRIO/Uppsala, however, do not code for the many interactions among countries evident in this history. Therefore, an MGB-type coding also misses them. PRIO/Uppsala only code Côte d’Ivoire as experiencing conflict at the “minor” intensity level and only beginning in 2002, thereby ignoring its participation in the violence of the Mano River complex before 2002. This coding also neglects the important roles of outsiders in the violent Ivorian experience beginning in 2002: there is no recognition in the coding of the role of Burkina Faso in hosting soldiers from northern Côte d’Ivoire who engaged in a coup and the subsequent partition of the country. The distant French and the contiguous neighbor Liberia were implicated in the post-2002 Ivorian conflict, but this involvement is not coded either.

We consider Mozambique’s conflict between Renamo and the Frelimo government (Vines 1991; Finnegan 1992; Hume 1994) part of a regional war complex rather than a civil war, as MGB’s econometric studies do (table 1).<sup>3</sup> To begin to understand this postindependence conflict, one must acknowledge its inextricable links to the internal and external politics of the wars to decolonize Mozambique and Rhodesia. During the struggle for independence in Mozambique, Frelimo invited Mugabe’s ZANLA army to establish military bases and refugee camps in Frelimo-occupied territory. Rhodesians retaliated by attacking the camps and bases, thus regionalizing the conflict. After Frelimo became the government in Mozambique, the Rhodesian government continued to attack ZANLA bases and attempted to destabilize the 25
26 banks' reserve requirements. Thus the liberalization actually contributed to an increase in some components of financial taxation, at least in its early stages. However, the abandonment of the reforms in early 1987, accompanied by the reimposition of interest rate ceilings and a worsening of inflation as government expenditure increased once more, led to much - higher rates of financial sector taxation reaching ovet 18 per cent of CDP at end-1988, or some 60 per cent of M2.

In _Nigeria_ too, the reforms of 1986, coinciding as they did with a weakening price of oil, were followed by a sharp acceleration of inflation (cf Appendix Figure Al). As interest rates remained low in nominal terms, presumably influenced by moral suasion, implicit taxation of the financial sector soared to record levels - over 8 per cent of GDP at end-1987.

In _Ghana,_ a gradual return of confidence has increased the base of financial sector taxation since the reform (Appendix Figures A2 and A3), but the tax rates have remained comparatively modest - less than one-seventh of their peak levels.

# 3.4 Crowding out.

One of the largest components of implicit financial sector taxation, as measured here, is the earmarked tax coming from interest rate ceilings. In those countries where most of the banking system's assets represent claims on non-government borrowers, the benefit of this tax is transferred directly to these borrowers. It is worth noting, therefore, that in each of the countries in our sample, the government's share in total domestic credit has shown a secular growth. Each of the governments hava thus not only increased financial sector taxation at times of crisis, but they have progressively increased the proportion of those taxes which do not simply return to non-government borrowers.

The most conspicuous example of this crowding out is _Ghana._ 'nf the early 1970s, non-government credit occasionally exceeded that to government. Thereafter, however, the government took an ever larger share. After 1977, the decline in real monetary holdings seriously limited the government's ability to generate financing through this route and real credit, both government and non-government, fell until the reforms of 1983. Since then real money demand has recovered somewhat (though remaining far below the 26 Because these developments were so varied during a short period of time, not all of these phases can be clearly seen in the end-year data of Table 1.

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- 66 -

# Table A.10: Variations hI Male AgrbuJtural Wage a' Across Regns, ida 1970/71-1984/85

|State|1970/71<br>1974/75|1984/85|
|---|---|---|
||(1970/71 Rs./day)||
|Hlgh Agricultural Productivity|||
|PunJab|6.5<br>4.9|6.3|
|Haryana|6.3<br>4.4|6.3|
|Medium Agricultural Productivity|||
|Gujarat|2.9<br>1.9|4.2|
|Karnataka|2.5<br>1.8|2.4|
|Low Agricultural Productivity|||
|Madhya Pradesh|2.2<br>1.5|3.0|
|Bihar|2.6<br>2.1|3.7|

a/ Simple average of all agricultural labor categories as publishec by Ministry of Agriculture's A ricultural Wages in India and reported by Acharya (1988). Deflated by agricultural labor price indiex (ALPI) using i970/7?Tas the base year.

Souce: Acharya (1988).
While high levels of shelter shortage persist across different levels of urbanization, there appears to be evidence of a negative relationship between higher levels of urbanization and housing shortages. Given the higher levels of urbanization in São Tomé and Principe, Cote d’Ivoire, and Nigeria, these countries have very high levels of shelter shortage given how urbanized they are. However, in terms of infrastructure shortage, the relationship is less clear but shows a slight positive relationship between infrastructure shortages and levels of urbanization. This is indicative of the challenges that SSA cities face: provision of basic services is not keeping up with the urbanization pressures.

**Figure 12.** **<u>Urban Housing Shortages by Level of Urbanization</u>**

<!-- Start of picture text --> Shelter Shortage by Level of Urbanization<br>MWIUGAETHNERRWA TZABFA MOZ SLE LBRNGA STP<br>KEN ZMB CIV<br>AGO<br>COM SEN<br>GHA<br>CMR<br>20 30 40 50 60<br>Percent urban<br>shelter Fitted values<br>Infrastructure Shortage by Level of Urbanization<br>ZMB<br>SLE<br>BFA<br>ETHNER TZA<br>NGA<br>LBR CIV STP<br>MWI AGO<br>KEN<br>MOZ CMR<br>COM<br>RWA<br>GHA<br>SEN<br>UGA<br>20 30 40 50 60<br>Percent urban<br>infrastructure Fitted values<br>100<br>80<br>60<br>% of households facing shelter shortage<br>40<br>100<br>80<br>60<br>% of households facing infrastructure shortage<br>40<br><!-- End of picture text --> Figure 11 and 12 begin to explore the national level housing shortages in SSA. Combined with the macro indicators of GDP per capita and urbanization levels these offer both better cross-country analysis as well 18
cash payments minus "new money" (whether from official or commercial sources) minus grants (or their equivalents, if any) received from third parties.<sup>2 6</sup> Several problems arise in applying this definition, however. Among these, two are most important. First, official debt is not traded in a secondary market, so the effect of DDSR operations on the value of both existing and new official debt requires making an assumption about its seniority status relative to "eligible" commercial-bank medium and long term debt. Second, the ex ante market price of bank debt cannot be unambiguously observed, since it has to be purged of the effects of the anticipations of the operations. The change<sup>in Vt</sup> as a result of the DDSR operations in the five programs negotiated so far has been estimated by Claessens, Diwan, and Fernandez-Arias (1992). Table 8 presents their estimates of the increase in V,<sup>for</sup> each of the five Brady plan countries on the alternative assumptions that official debt is senior and that commercial and official debt are of equal seniority: Table 8 Brady Plan Countries: Effects of DDSR Operations on V, (in US $ millions)

||Official<br>Debt Senior|Equal<br>Seniority|
|---|---|---|
|Costa Rica|193|907|
|Mexico|2,189|8,074<br>|
|Philippines|451<br>|3,112<br>|
|Uruguay|53|392<br>|
|Venezuela|2,444|5,345|

Source: Claessens, Diwan, and Fernandez-Arias (1992).

The first column of this table assumes that official debt is not subject to country risk, while the second column assumes that official and commercial debt are of equal seniority. Though the differences between the two columns are substancial, suggesting 26 Notice that, when measured in this way, an up-front payment made out of the country's own resources would have no effect on **Vt,** because the market value of the debt would fall by the amount of the payment. This reflects a reduction in the resources available to service debt in the future.

21
|**Accounting method**<br>exporting-country perspective|bilateral perspective<br>sectoral-bilateral perspective<br>importing-country perspective<br>sectoral-importer perspective<br>sectoral-exporter perspective<br>exporting-country<br>perspective,<br>source or sink approach|exporting-country<br>perspective,<br>sink approach<br>exporting-country<br>perspective,<br>source approach|
|---|---|---|
|**Trade fow to select**<br>total aggregate exports|bilateral aggregate exports<br>bilateral sectoral exports<br>total aggregate imports<br>total sectoral imports<br>total sectoral exports<br>sectoral/bilateral/sectoral-<br>bilateral exports|bilateral exports<br>any export fow|
|**estion**<br>d in the total exports of a country|ly exposed to:<br>bilateral trade relation (e.g. generic trade frictions<br>untries)<br> a specifc sectoral-bilateral trade relation (e.g. a<br>mposed by a trade partner in a given sector)<br> the imports of a country (e.g. trade restrictions<br>tners)<br>the imports of a country in a given sector (e.g. a<br>l tarif vis-`a-vis all partners)<br> the sectoral exports of a country (e.g.<br>negative<br>on the exports of a given country and sector)<br>eakdown in disaggregated export fows, consistent<br>egate measures|eakdown of bilateral trade balances<br>orts vs GVC-exports|
|**u**<br>e|l<br>a<br>o<br>n <br>i<br>n <br>ar<br> <br>a<br>n <br> <br>br<br>r|br<br>p|
|**q**<br>dd|tia<br>on<br>o c<br> o<br>if <br> o<br>l p<br> on<br>tor<br> o<br>ock<br>d <br>agg|d <br> ex|
|**Empirical**<br>GDP embe|GDP poten<br>— a shock<br>between tw<br>— a shock<br>specifc tar<br>— a shock<br>vis-`a-vis al<br>— a shock <br>specifc sec<br>— a shock<br>demand sh<br>Value-adde<br>with total|Value-adde<br>Traditional|

14
# **<u>Table A3: Registration Modality and Parcel Characteristics</u>**

||<br>**Systematic**<br>**Total**|<br>**Sporadic**<br>**Total**|<br>**1981–2009**|**Sporadic**<br>**2010–14**|**2015–19**|
|---|---|---|---|---|---|
|CBD distance (m)|0.137*|**–**0.216***|**–**0.161**|**–**0.108**|0.132**|
||(0.0733)|(0.0771)|(0.0650)|(0.0544)|(0.0516)|
|Parcel area (m<sup>2</sup>)|**–**0.0138***|0.0251***|0.0515***|**–**0.00304|**–**0.0347***|
||(0.00297)|(0.00312)|(0.00263)|(0.00220)|(0.00209)|
|Mean dep. var.|0.604|0.397|0.194|0.0986|0.104|

_Note:_ Regression is at parcel level for the 53,432 parcels that passed quality control procedures for inclusion in the cadaster; the dependent variable is a dummy of whether the parcel was first registered by a systematic or a sporadic process. Block fixed effects and constant are included throughout. Standard errors are in parentheses; * p<0.10, ** p<0.05, *** p<0.010 33
of sick-day leaves or the number of missed-school days because of health-related causes could be better reflections of outcomes. Two of the selected health output indicators, DPT and measles immunization are delivered in vertical programs, that is, in campaigns that are relatively independent of basic health systems and therefore may not be good indicators of the actual quality of the health system. Finally, the fact that life expectancy is influenced by diet, lifestyle, and a clean environment, that to the extent that are not included as factors of production may bias the efficiency scores.

# **III.2. Single Input-output Results**

III.2.1. FDH and DEA analysis: Education Figures 7a-c show both FDH and DEA estimation of the efficiency frontier for three of the nine output indicators: gross primary school enrollment, first level complete and learning scores. Individual country efficiency scores for the three indicators are reported in Table B.1-3 of Appendix B<sup>10</sup> . The graphical efficiency frontiers for other output indicators can be found in Appendix B (Figure B.1).

Figure 7.d illustrates the efficiency frontier for the learning scores if the developed countries are included in the sample, demonstrating the sensitivity of the results to the sample definition. This fact is particularly acute in the case of learning scores which capture the quality of education dimension that no other indicator captures. While in the sample of developing countries Chile, Hungary and the Czech Republic are on the frontier; once the developed nations are included they appear as inefficient. The complete set of efficiency scores (including and excluding the set of developed countries can be found in Table B.2 and B.3 in Appendix B<sup>11</sup>

> 10 The efficiency scores of all the indicators can be found at the PRMED website indicated in footnote 1. 11 The frontier depicted in Figure 7.d. excludes Japan, Korea, Ireland and Belgium to facilitate comparisons with the frontier without developed nations.

12
subsidies in Malawi. _World Development_ , _40_ (1), 124–133.

- Chinsinga, B. (2012). _The Political Economy of Agricultural Policy Processes in Malawi:A case study of the Fertilizer Input Subsidy Programme_ (Future Agricultures Consortium Working Paper No. 039). Brighton, UK.

- Chirwa, E. W., & Dorward, A. (2013). _Agriculture Input Subsides; the recent Malawi experience_ (First Edit). Oxford: Oxford University Press.

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- De Weerdt, J., & Duchoslav, J. (2022). _The impact of the war in Ukraine on Malawi_ (Strategy Support Program Policy Note No. 44). Lilongwe, Malawi.

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- Fan, S., Gulati, A., & Thorat, S. (2008). Investment, subsidies, and pro-poor growth in rural India. _Agricultural Economics_ , _39_ (2), 163–170. https://doi.org/10.1111/j.15740862.2008.00328.x

- FAO, IFAD, UNICEF, WFP, & WHO. (2018). _The State of Food and Nutrition Security in the World 2018. Building climate resilience for food security and nutrition_ . Rome, Italy. Retrieved from ISBN 978-92-5-108785-5

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