rafmacalaba/datause-encoder-data huggingface dataset:
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picture that can be obtained from the simple head-count ratio: at least in the case of Mexico there are sharp differences In the level of poverty among the extremely-poor.

It is important to note that for four reasons the above measures overestimat **e** xtreme-poverty. One, the estimate of X took as a basis the CNA recommended by Coplamar. But as already remarked, this CNA, denoted CNA(C), is not the least cost diet to give<sup>2,082 calories</sup> and 35.1 grams of protein per day. If instead the 'true' minimum cost CNA, denoted CNA(M), is used as the basis for computing E I obtain, P[O,I(M)] - 0.1008, with 73.5% of this poverty total accounted for by the rural population; see panel (b) of table 7. More generally, given a value for a, as E falls the share<sup>of extreme-poverty</sup> accounted for by tbe rural population increases. This suggests that: first, P[a,y(C)] and P[a,g(M)] should be interpreted as upper and lower bounds on the indices of extreme-poverty, respectively, with the 'quality' (in terms of food variety and perhaps palatability) of the diet that satisfies the same 42 nutritional requirement decreasing as we move from CNA(C) to CNA(M) . Second, that while there is some uncertainty determining the exact proportion of the population that is extremely-poor, the proposition that extreme-poverty is fundamentally a rural phenomenon is very robust.

Two, the above measures of individual poverty are based on per capita household income, and while as seen above poorer households are larger, they also have a greater proportion of children, requiring a conversion into adult equivalent income levels. Three, the existence of economies of scale in consumption reduces income requirements for larger households (cf. Behrman and Wolfe (1984)). Four, finally, only sample data was used for the estimates of P(a,y). But since the total value of<sup>consumption</sup> expenditures implicit in the IES is lowar than the corresponding figure of the national accounts, this could bias the results upwards (assuming the national accounts are closer to the 'true' expenditure totals). No correction was introduced for this factor since I had no information to distribute the discrepancy across income

> 42The CNA(C) costs 6,193.00 pesos per capita per trimester,<sup>so that i(C)</sup> equals 7,742 pesos; the CNA(M) costs only 4,554.10, so that z(M) equals<sup>5,692</sup> pesos. On the other hand, if we assume<sup>that for the lowest deciles</sup> of the population all own-consumption consists of food, the implied share of expenditures allocated to food would be 70X. Multiplying CNA(M) by 1/0.70 gives a line of extreme-poverty of 6,505.85, which is still 19X below z(C).
corridor, so only aggregate numbers can be compared. For this comparison, mid-year estimates of the world migrant stock for 1990–2000 are taken from the 2008 edition and estimates for the earlier censuses, 1960–1980, are taken from the 2005 edition (table 8). The analysis subtracts the estimated number of refugees from the total mid-year estimates of the world migrant stock from the Trends in International Migrant Stock database to yield the net number of migrants in each decade. These numbers are then compared with the decadal estimates generated through this project, both the total and the net, after subtracting estimates of migrants within the Soviet Union for 1960–1980 (data for 1990 and 2000 should be directly comparable) and the number of ethnic German migrants added to the German censuses.

# **{Table 8 here}**

The aggregate estimates are remarkably close (the two net totals), differing at most by around 1 million migrants, except in 1990. There are several possible explanations for these differences. First, the census totals from the current work may not match because censuses do not always make allowances for temporary workers. For example, Singapore‘s official 2000 census records 563,430 foreign-born migrants. The United Nations, however, reports 1,351,806 foreign-born migrants for 2000. Second, there are cases where the current study reports data by nationality, but the corresponding figure in the Trends in International Migrant Stock refers to the foreign born. This situation generally arises when a census does not report the number of foreign-born migrants on a bilateral basis. Examples include Austria and Côte d‘Ivoire. Third, differences in the years to which the data refer can generate large disparities. For example, this study uses the 1966 data for Australia, whereas Trends in International Migrant Stock reports data for 1970. Overall, however, the fact that the totals are remarkably close in every decade adds credence to the estimates here.

# _IV. THE EVOLUTION OF GLOBAL BILATERAL MIGRATION_

The greatest strengths of the global migration matrices are their bilateral coverage, the number of decades covered, and the disaggregation by gender. These data are too rich for a full analysis of all movements between all pairs of countries. Instead, this section summarizes the major trends in the evolution of bilateral migrant stocks, based primarily on World Bank regions.<sup>25</sup>

# _Global Trends_

The migration matrix for the 1960 census round reflects a realigning world in the postcolonial era. Over the 1960-2000 period, the composition of world migration 25 Appendix 1 details the World Bank regions: South Asia, East Asia and Pacific, Sub-Saharan Africa, Latin America and the Caribbean, Europe and Central Asia, and Middle East and North Africa. High-income Middle East and North Africa refers to the predominantly oil producing countries in the Persian Gulf (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) and to Israel.

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be differential stock endowments across the two areas. This could explain our results for differential catch, but such an alternative explanation would have to take a particular form to capture both of our empirical results: specifically, the stock advantage of the Pacific side would have to be relatively larger for small scale species than for large scale species. Existing work suggests the presence of differences in ocean productivity in the Pacific versus Gulf sides of the Baja Peninsula; unfortunately, however, not much is known (as far as we are aware) about how these differences manifest differentially across small scale and large scale species. Therefore, this alternative explanation for our catch results may be plausible, and we do not have evidence to support or refute it at this time. Nevertheless, we believe our model is the most plausible explanation that captures all of the empirical patterns we have seen for price and catch in one unified framework.

# V CONCLUSION

In this paper, we set out to understand how cooperatives use property rights to control fishing effort, and how this use is shaped by key characteristics of the targeted fish populations: scale of individual movement and responses to large environmental fluctuations. Using rich logbook data from three cooperatives, including one that enjoys strong property rights, we find support for the implications of our theoretical model. The magnitude of the differences between the cooperative with property rights and the ones without rights—as well as the shrinking of these differences when resources have a large scale or when growth rates are high—are economically significant.

These results highlight the value of linking theory with empirical analysis in order to examine the reciprocal interactions between natural resources and resource users. Our approach focuses on the economic mechanisms underlying these interactions and thereby complements more descriptive, existing analyses of fishery outcomes. Our results are also relevant beyond fisheries, as they illustrate how the decisions of resource users embedded in local institutions are mediated by characteristics of the resource and external dynamic factors. However, given that our logbook data are restricted to only three cooperatives, our conclusions are necessarily provisional. We hope that our analysis points the way to future investigation with more representative data.

More generally, integration of the connections between resources and resource users may well increase the effectiveness of state policies in coastal areas, whether focused on environmental stewardship, economic development, or both. Indeed, CONAPESCA’s recently developed National Program of Inspection and Vigilance ( _Programa Nacional de Inspección y Vigilancia_ ) 3
1.63 for a car and 0.47 for a two-wheeler.  These figures are higher than one would find in high income countries, but broadly consistent with findings in developing countries (Kopits and Cropper 2005), although the latter are usually based on country-level panel data.  Households with self-employed or more educated household heads are more likely to own a car or two-wheeler.  Other results are more puzzling—living in the suburbs, which implies a longer commute, ceteris paribus, is not associated with higher odds of vehicle ownership—nor is having more workers in the household.  The own and crossprice elasticities for mode choice are slightly lower than in Tables 4 and 5 for bus and slightly higher for car and two-wheeler.

# **IV.  Implications of Our Estimates for Pollution Control Policies in Mumbai**

What do the estimates in the preceding section imply about the impact of various policies to control pollution from passenger transport? Of the policies we consider, the most effective policy to reduce emissions from passenger vehicles—in terms of the total tons of PM10 reduced—is to convert diesel buses to CNG. Using emissions factors from NEERI (2004) the reduction in PM10 per km from converting a diesel bus to CNG would be 2.76 g/km. Applying this to a fleet of 3,391 diesel buses that travel approximately 240 million km/year would result in an emissions reduction of 662 tons of PM10 per year, 14% of total emissions from transport (ignoring emissions from taxis and threewheelers).

By how much would fares have to rise to cover the cost of these conversions, and to what extent would this erode the emissions reductions calculated in the preceding paragraph? According to NEERI (2004), the capital cost of converting a diesel bus to 17
across the sectors. Wage rates and capital prices (or user costs of capital) are different across the sectors. Similarly, the total supply of a good/service (imports plus domestic production) is equal to the total demand for that good/service (domestic consumption plus exports) – Walrasian condition. The total investment is equal to total savings, which is the sum of household, government, firms and foreign savings (macroeconomic balance).

The model is made dynamic through the population growth rate (i.e., labor supply growth rate) and investment. Total savings of the previous period (or year) is the investment of the current period (year). Demand for the total capital of the current period is determined by the previous period’s capital stock plus depreciation and interest payment and new-added investment (which is the previous period’s total savings). The total investment is allocated across sectors in proportion to each sector’s share in the aggregate capital account, and these proportions are adjusted by the ratio of each sector’s profit rate to the average profit rate for the whole economy. In addition, Autonomous Energy Efficiency Improvement (AEEI) in the CGE model is considered in this study, and is assumed to be 1% per year following the common assumptions in the CGE model. Since the available social accounting matrix (SAM) is for 2012, our base year is 2012. If the model has to adopt the projected growth rate of GDP (e.g., projected by the government), it is done through adjustments in total factor productivities (TFPs).

# **3. Scenario simulated**

We considered the following three scenarios:

**BAU Scenario** :  The BAU scenario assumes that the increasing trend of the wholesale price continues in the near future.

**Case 1** : The second scenario assumes that China follows market-based rules to allocate energy resources to produce electricity (e.g., priority building of power plants based on their lower levelized costs). However, it still keeps its existing practice of balanced revenue– based load dispatching.

**Case 2** : In the third scenario, the dispatching constraint of the second scenario is relaxed by following the economic or merit-order load dispatching instead of the existing practice of load dispatching.

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and GR) combine the market and time series based forecasts. The results of the out-of-sample MSFE are presented in Table 7. With the exception of cocoa and silver, the GR forecast has the lowest out-of-sample mean squared forecast error, and for silver the COMB forecast has the lowest. Also, the COMB forecast has the second-lowest MSFE for four of the five commodities where GR has the lowest. The obvious conclusion is that the two combined forecasts perform better than either the time series forecasts or the market based forecasts. This suggests that much more precise interval forecasts can be made using the GR or COMB forecasts of variance. Furthermore, since the GR and COMB forecasts clearly dominate the ISD forecasts, we speculate that the difference between the combined forecasts and the ISD forecasts can be used to identify mispriced options. The idea is that since expectations of future volatility play such a critical role in the determination of options prices, better forecasts of volatility should lead to better pricing and should therefore help an investor identify over- or under-priced options contracts.

TABLE 7

# **VI.** Conclusions.

The results presented above are promiEing. In particular, the COMB and GR forecasts, which combine market-based information with time series information, yield better forecasts than can be obtained from market expectations or time series models alone. Several implications of this are immediately apparent. First, the time series contains information about future volatility that is not captured by market expectations, suggesting that options markets are inefficient (and/or the option pricing formula we used is incorrect). This implies that it is possible that our volatility forecast can be used to identify mispriced options, and a profitable trading rule could be established based on the difference between the ISD and the COMB or GR volatility forecast. Second, our forecasting method can be used to obtain interval forecasts of commodity prices, which should be beneficial to market participants who are concerned about the precision of a point forecast. One final note is that the accurate matching of the forecast horizon and the time to maturity of the futures contract is relatively unimportant. Our results indicate that near-maturity options tend to forecast long-run volatility about as well as options that are far from maturity.

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One possible way to address this issue is as in Acosta et al. (2007) who impute per capita household income for migrant households in the counterfactual scenario of no remittances and no migration. Clearly, this requires information about the income of the household before the migrant left, and this information is in general not available directly from household surveys. As an alternative, Acosta et al. (2007) infer the counterfactual per capita income levels for those households with remittances on the basis of a reduced-form specification for the determinants of income among households without remittances. More formally, this approach involves estimating a model like: where _Yi_ represents per capita non-remittances income, _Xi_ is a vector of household characteristics (demographic and location covariates), _Hi_ is a set of characteristics of the household head, and _μi_ represents random shocks and possible unobserved heterogeneity in income generation. Equation (1) can be estimated using the sub-sample of households that do not receive remittances. The estimated coefficients then allow predicting the counterfactual non-migration income for remittances-recipient households. In the absence of information on migrant characteristics – which is the case of all the countries considered in this paper except Haiti and Nicaragua – it is necessary to make some basic assumptions about the number and the demographics of migrants. Following Rodriguez (1998) and Acosta et al. (2007), we assume that, on average, remittances are sent by a single adult male family member. Another needed assumption has to do with the education level of the migrant, which we assume to be equal to the average years of education of other adults in the household.

One additional aspect that needs attention is that OLS estimates of equation (1) will be inconsistent if _μi_ is not independently identically distributed (i.i.d.). In other words, if migrants are not randomly selected from the pool of households, estimates of equation (1) based on the sample of households without migrants could suffer from selection bias. To control for this possibility, we add to (1) a variable that represents the household’s “propensity to not migrate or not receive remittances” ( _Mi*_ ). This new term is calculated in the context of the two-step estimation framework proposed by Heckman (1979). In particular, we adopt the following model specification: (no-remittances selection rule) (earnings equation for non-recipient households) In practice, one can only observe the sign of the variable _Mi*_ , which represents the selection rule for no migration or not receiving remittances i.e. whether the household receives remittances or not, which in turns is equivalent to a negative or positive value for _Mi*_ . Identification of this model requires an exclusion restriction: a set of variables _Zi_ that are 12
- Productivity growth in the leader country has a positive and significant impact of productivity growth in the follower countries.

- The more distant a country is from the technological frontier, the greater is the scope for productivity improvements arising from the catching-up process.

- The coefficient of human capital (measured as education achievement but not necessarily representing the skills of the labor force) does not appear to be statistically significant, suggesting that the effect of quality of human capital -- at least in the measure used here --  is fully captured by the productivity gap term.

In order to shed light on the sectoral dimension of productivity growth, the same model was estimated at sector level over the period of 2003-08 for the same countries, except for Switzerland and Malta. In this sector level model, besides the regulatory quality indicator - from WGI database - a set of alternative indicators constructed through a principal component analysis (PCA) based on Doing Business (DB) data was used. The **sector level** results can be summarized as follows.

- Trade openness at sector level has a positive (and statistically significant) effect on productivity growth. The computation of the marginal effect of trade openness on productivity convergence by sector has pointed manufacture of basic metals as the activity to show the highest productivity dividend. As the average value of trade openness for this sector in Romania is largely inferior to the sector average of all countries considered in the analysis, this suggests that there is space for Romania to increase its trade integration to the benefit of productivity convergence.

- The total effect of regulation on productivity growth – when assuming both direct and indirect effects – was also shown to be positive: estimations of the marginal effect of the quality of the regulatory environment on productivity growth points to a positive (and statistically significant) effect for all proxies used. In particular, results suggest that productivity dividends are higher when the regulatory framework is measured by the indicator Institutional Environment_DB, which captures the quality of the legal and institutional framework regarding protecting investors, getting credit and enforcing contracts.  In order to identify the sectors in which the impact of regulation on productivity growth is higher, the average marginal effect of the regulatory environment variable was computed and results have shown that the marginal impact of the regulatory environment on productivity growth is positive and statistically significant for all sectors, regardless of the proxy used. Sectoral productivity dividends resulting from regulatory improvement range from 3.9 to 5.2 percent yearly.

- Consistent with the previous set of results at country level, labor productivity growth in less productive sectors is positively influenced by the speed at which productivity grows in the same sectors in the best performing countries. In addition, the coefficient for the productivity gap was, again, negative.

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However, there is not just one definition of multi-dimensional poverty or one set of multi-dimensional poverty indicators. Gordon et al. (2003a) distinguishes between eight severe deprivations related to food, health, education, water, sanitation, shelter, information and access to basic services. Others have only analyzed poverty among a subset of these deprivations, imposed stricter deprivation criteria, following the MDG and SDG indicators, requiring for instance a child to have access to improved sanitation, rather than just any sanitation facility in order to count it as non-deprived of sanitation or added other dimensions, such as energy deprivation (Poppola and Adetola, 2016; Ajakaiye et al., 2016). Over time, there has been a convergence to a core set of child poverty dimensions; namely, Health, Nutrition, Education, Information, Water, Sanitation, and Housing as discussed in Abdu and Delamonica (2018). These are also the dimensions, which were adopted in a 2006 resolution by the UN general assembly defining child poverty as multi-dimensional (UN, 2007).<sup>2</sup> While some studied one or several poverty dimensions in isolation, others have mapped the poverty incidence probability combining various dimensions using a first order dominance approach (Arndt et al., 2012), whereas others have constructed multi-dimensional poverty indices of several dimensions whereby the latter have sometimes attempted to attach weights to the different dimensions, such as Alkire and Foster (2011). Considering however that all dimensions are also one of the child’s rights as defined in the Convention of the Child, a child being deprived of any right is thus poor. Therefore, it is argued that the dimensions should not be weighted when constructing indexes (see Abdu and Delamonica (2018)).

# 2.2 Poverty in Nigeria

There exists limited evidence on aggregate poverty and child poverty in Nigeria. As in the literature in general, there is a lack of evidence on child poverty that connects the findings of studies focusing on specific poverty indicators and or in specific regions in isolation. Related to education Kazeem and Musalia (2016) highlight the historical colonial roots of the ethnic and socio-economic education discrepancy observed based on data from 2004. Western style education brought by Christian missionaries initially only spread in the Southern Igbo and Yoruba ethnic group dominated regions, whereas it was longer resisted by the Muslim Hausa and Fulani dominated ethnic groups with nomadic lifestyles in the North. Besides, poorer children needing to provide for their family face higher opportunity costs of completing

> 2 “Recognizes that children living in poverty are deprived of nutrition, water and sanitation facilities, access to basic health-care services, shelter, education, participation and protection, and that while a severe lack of goods and services hurts every human being, it is most threatening and harmful to children, leaving them unable to enjoy their rights, to reach their full potential and to participate as full members of society” (UN, 2007, paragraph 46).

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