rafmacalaba/datause-encoder-data huggingface dataset:
example, multinomial probability specifications can be derived from utility maximization across a set of discrete choices; models of utility maximization over a continuous outcome might lead to binary and ordered response models, etc. An explicitly specified model allows researchers to establish the causal relations between poverty and various household and/or individual characteristics and, in some cases, uncover the causal mechanism whereby such characteristics affect poverty.

No published research on poverty in Russia contains formal representation of a theoretical model. The majority of authors limit themselves to a description of the processes of interest. Many papers discuss the method of data aggregation or construction of a certain index (e.g., deprivation index) confusing this discussion with a proper description of theoretical models. Two percent of papers outline a certain hypothesis but we have found no papers in our sample that formally test alternative hypotheses.

# _Sources of empirical data_

The design for data collection, measurement instruments, and methods of data analysis are selected to directly address the research question. In turn, the research question must be formulated in ways that potentially allow for empirical investigation. Scientific beliefs must be evaluated and supported by empirical data. A hypothesis is rationally warranted if a body of evidence and inferences has been provided in support of it. Morgenstern (1963) emphasizes importance of data quality for empirical economic analysis. Price and national accounts statistics, as well as data from household and individual economic surveys are prone to substantial errors. Data for economic analysis are collected from agencies or individuals that have few incentives to provide accurate information. However reliable statistics are essential for scientific decision-making and, therefore, improved performance of the economy as a whole.

More than 40 percent of research articles published in Russia since 1992 are based on aggregated data from Household Budget Survey (HBS) collected by the Federal State Statistics Service of Russia (Goskomstat). HBS is the only official, nationally and regionally representative source of data about living conditions and poverty in Russia. Nevertheless, the HBS unit-record data are not available for public use. Russian 11
i = β W +β F F w<sup>+ εε</sup> i<sup>(3)</sup> WW ii FF ii<sup>+ β</sup> FFPP<sup>F</sup> ii<sup>∗PPho</sup> ii<sup>+ τ</sup> YY PPho oo WWo WW<sup>Foo WWoWWW</sup> Where FLOH is a dummy for working in a fixed location outside of the house in a permanent structure. The coefficient β measures how sensitivity to modality varies among the selfemployed. FFPP

# **5.1 Results**

Respondents assigned to weekly phone interviews reported significantly less employment, fewer hours, and fewer days worked than respondents interviewed in-person each week, as is shown in table 4 which presents the main survey mode results. The effects are both statistically and economically significant. Relative to weekly in-person interviews, weekly phone-based interviews are associated with a statistically significant 8 percentage point reduction in the likelihood of reporting work. This is a sizeable effect given that 86% of the respondents who took part in weekly in-person interviews and reported being employed at baseline (the omitted category) report working in any given week. The reported number of days worked per week is a statistically significant 0.57 fewer overall, some 12% lower than the average of 4.60 days reported by respondents in the in-person weekly group. Conditional on having reported working, respondents in the weekly phone group reported 0.15 fewer days, only some 3% less than the average of 5.37 reported by respondents that partook in weekly in-person interviews. This difference is not statistically significant. The number of hours worked reported is 6.9 fewer overall, a sizeable and significant effect; 17% less than the average number of hours worked reported by participants in in-person interviews (41.3 hours). Conditional on having reported any work, differences are smaller and no longer significant, but still present; phone interviews are associated with reporting 3.6 fewer hours than in -person interviews, which is only 7% less than the average of 48.3 hours worked reported by those who participated in weekly in-person interviews. Put differently, phone interviews are associated with reporting fewer work spells, days and hours. However, conditional on having reported any work, the number of days and hours are not significantly different.

# **5.2 Heterogeneity between the wage workers and the self-employed**

13
A question is whether donors would be willing to put up funds for such purposes, in adequate amounts to assure market actors that ITMO markets will be operative during the final stages of the PA. As of today, resources allocated globally to dedicated climate finance are small (of the order $3 billion per year) and could currently not play such a role.<sup>23</sup> On the other hand, presuming that donors have serious concerns for mitigation at a global level, one could easily envisage a big increase in such funding if necessary to make the ITMO markets operative and credible, when this is fully recognized by donors and effectively communicated to the relevant PA parties. Excess funds would then be used for purchasing and stimulating global mitigation in excess of NDC targets (also beyond the targets of these donors). The key issue for donors should then be the cost imposed on them per ton CO2 mitigated, which could be moderate, or even potentially negative if ITMO market efficiency (or even market existence) would otherwise be in peril.

# **4.7 Opening Up for Article 6 Rule Changes**

A main Article 6 rule change, that could improve on the possibilities of market existence and functioning, would be to allow for banking of ITMOs beyond 2030, and to the next implementation period of the PA. Revisions of Article 6 rules are however not planned to happen until 2028, which would be too late to have impact for the current implementation period which ends in 2030. Thus, unfortunately, the possibility for such rules changes to have impacts on execution of the PA agreement at the moment (May 2022) seems remote.

# **5. Conclusions and Final Comments**

We have in this paper discussed issues raised by uncertainty about NDC compliance among parties to the Paris Agreement (PA), and how these problems can be alleviated by alternative market modes of ITMO transactions. The traditional market form, used widely for the two trading mechanisms, the Clean Development Mechanism (CDM) and Joint Implementation (JI), under the KP, was (early-stage) forward contracting with later verified emissions reduction (VER) delivery (corresponding here to ITMO delivery). With high uncertainty about mitigation outcomes (and parties’ abilities to fulfill their NDC responsibilities based on own policies only), such forward contracting may under the PA however jeopardize NDC fulfillment. Parties may then need to have available additional strategies and market mechanisms to ensure NDC compliance. This paper

> 23 See World Bank 2020.

30
The results are also consistent when estimates of the combined contribution of growth-enhancing variables to change in aggregate GDP do not control for possible colonial effects. Figure 5 below shows the long-term trend of combined contribution of human capital, land and labor force participation to aggregate economic growth across gender and over the reference period for the region as a whole. Men enjoy a higher relative contribution to aggregate growth. Interestingly, the results hold even though the base line scenario assumes that there is no gender gap in returns to education.<sup>27</sup> The positive and excess contribution of men to aggregate growth over the reference period reflects the larger effects of land ownership over labor input and human capital (proxy for skilled workers). The much lower contribution of human capital is in line with mixed TFP effects and reflects the poor level of technological endowment and inability of countries to draw on increased technical efficiency and technological change to enhance the productivity of factor inputs. However, the outcome also reflects the modeling specifications. The base case scenario assumes gender bias in access to land, with limited female ownership.

**Figure 5 - Baseline case: Total contributions of males and females to growth in SSA**

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> 27 The much higher relative contribution of land owned by men to overall growth is the consequence of modeling specifications. The relative contribution of female and male land ownership to growth is exactly equal when the model assumes an equitable distribution of land across gender .

18
9 corresponding counterfactual income distributions, we report our estimates of the effects on poverty and inequality measures.

# **3. Data**

Three different data sources are used in this paper: individual price data from the National Consumer Price Index (INPC); the Household Budget Survey (POF); and the National Household Income Survey (PNAD). The INPC records percentage changes in consumer prices on a monthly basis. In total, information is collected for 472 consumption items, which are then classified into increasingly more aggregated commodity groupings. Ultimately, the information feeds into Brazil‟s overall consumer price index. Individual prices are recorded for a total of 156 food items in nine metropolitan regions (Belém, Fortaleza, Recife, Salvador, Belo Horizonte, Rio de Janeiro, São Paulo, Curitiba, and Porto Alegre), and for two municipalities (Brasilia and Goiânia). In what follows we refer to these eleven urban centers as Brazil‟s “large urban areas”. Monthly prices are available beginning in April 1979, and we use the series from January 2007 to December 2008.

To facilitate the concordance between food item classifications in the INPC and in the household budget survey, our analysis is conducted at the level of 16 food groups, which are listed in Table 1. The table also presents the average price increase for each food group during 2007-2008 and the maximum price increase (12 months to peak) observed during the two-year period. (The last column indicates the peak month for each price series.) This maximum 12-month price increase is the proportional price change variable we use throughout the analysis.

Results are presented in Section 5 for the country as a whole, as well as separately for Brazil‟s large urban areas (which accounted for approximately 30 percent of the national population in 2008), and for the rural areas (16% of the population). Since the IBGE only collects prices in the 11 large urban areas, price changes were imputed to other localities on the basis of geographic proximity. For example, in the North, where IBGE has information of the price changes only in the metropolitan region of Belém (in the state of Pará) we assigned Belém‟s vector of price changes to the other seven states. In the Northeast, IBGE collects price information in Fortaleza (Ceará), Recife (Pernambuco), and Salvador (Bahia). For the five additional states in the Northeast we assigned the price variation based on proximity between states. Table 2 describes the assignment rules countrywide.
The following subsections describe the processes behind the estimation of future poverty, inequality, and shared prosperity measures. Subsection A explains our assumptions regarding the income distribution and the details of the econometric method used to project income growth. Subsection B provides a detailed description of our assumptions to model the Gini coefficient dynamics. We present our definitions of relative income inequality and shared prosperity gaps in Subsection C.

# **_A. Income Distribution and Mean Income Growth_**

In this study, denotes the income of the population in country at period . The income variable is transformed via natural logarithms, such that cc,tt . A conservative and common YY cc tt assumption is to consider that YYcc,tt is distributed as a normal random variable wwcc,tt ≡ln YYcc,tt 2 �. The previous consideration implies that the income of the people in country cc,tt at period ww should follow cc,tt wwcc,tt ww NN�μμ , σσ 2 a lognormal probability distribution function, such as �.<sup>11,12</sup> cc tt cc,tt wwcc,tt wwcc,tt YY ~ log NN�μμ , σσ To provide the dynamic framework for the income distribution, we focus on modeling the mean and variance parameters of the lognormal assumption. Then, we construct a model that predicts the growth of both the mean income and the Gini coefficient.<sup>13</sup> The combination of independent randomly generated patterns of average income growth and changes in inequality allows us to quantify the uncertainty associated with the overall change in the income distribution.

Growth in the mean of the income distribution is assumed to follow a basic linear econometric specification where global and idiosyncratic factors play a role. Growth in the real mean income in country , ,<sup>14,15</sup> follows the stochastic pattern shown in Equation (1). This stochastic process consists of four components, three of which involve random elements. The first cc cc gg component is an idiosyncratic fixed effect factor, , which measures the long-run trajectory of cc αα

> 11 Cowell and Flachaire (2015) discuss that the lognormal distribution is useful to fit few economic processes. These authors note that there are important theoretical weaknesses in a process that involves adjusting the upper tails of more broadly-based income distributions.

> 12 Lopez and Servén (2006) provide empirical evidence indicating that income can be effectively proxied by a lognormal distribution.

> 13 As mentioned in the introduction, our dynamic framework expands on World Bank (2014).

> 14 Both income and expenditure household survey information were retrieved from World Bank (2019). Consumption and GDP data were retrieved from PWT 9.0 (Feenstra, Robert Inklaar, and Marcel P. Timmer 2015).

> 15If household survey’s information of mean income or mean expenditure is not reported in a specific period, the missing observations are inputed using the growth rates of per capita consumption or GDP per capita.

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36 malnutrition increased in much of Africa, remained stable in South<sup>America and</sup> decreased in Asia and Central America.

# Proportion of Undernourished Population by Region

|IL2|1269171|931|
|---|---|---|
|Sub-Saharan Africa|24|26|
|South Asia|21|17|
|Southeast Asia|18|8|
|Central America and Caribbean|20|15|
|South America|9|8|
|Near East and North Africa|15|5|

Note: Based on UN estimates as presented in the First Report on the World Nutrition Situation. Country groups ueed in this le exclude China and also differ from those used throughout this report.

Although UN estimates for China on "undernourished population" are not available. other available indicators suggest improvements in nutritional status in China. For instance, per caplta food production in China increased by 75 percent over the past 25 years; and index of undernutrition improved continuously during the early eighties from 1.2 in 1980 to -11.4 in 1986.

Improvements in the populous regions of Asia suggest an overall decline in the proportion of the world population suffering from undernutrition.<sup>As</sup> is the case with the use of undernutrition index, focusing only on the proportions masks the overwholming magnitude of the global nutritional problem at hand. The absolute number of the undernourished increased in sub-Saharan Africa from 60 million at the end of the sixties, to 80 million at the end of the seventies, and to 100 million by mig-eighties. In spite of the improvements mentioned above, about 170 million, or almost half of the world's
# **2.5 Domestic and International Labor Migration**

## **2.5.1 Rural-Urban Migration**

Significant rural-urban migration flows heightens the high pressures on the urban labor market. Available estimates indicate that, every year, around 200.000 migrants (in net terms) are entering urban areas, equivalent to around 40 percent of the change in the urban population. It however underlies important movements from and to cities, as well as international migrations. Among other factors, a low level of productivity and weak growth outcomes in the agriculture sector over the last decade is deemed to contribute to this dynamics. Overall, the rural population is roughly stable (in absolute numbers), whereas its natural growth rate around 2.6 percent. In fact, the urban population, expanding at around 3 percent a year, absorbs most of this expansion.

## **2.5.2 International Migration**

Immigrants from Morocco represent an important share of the foreigners residing in the European Union. Due to adverse domestic economic conditions and numerous family and community links, migration flows continue to be important as shown in recent OECD data. Data are available only for selected countries and official flows, namely Belgium, France, and Netherlands, which are traditional destinations for Moroccan international migrants. Over the 1990-1999 period, around 222,000 persons have migrated to these countries, equivalent to around 25,000 on average every year (see Figure 4). Hence, with between 6 and 13 percent of the change in the total domestic labor force (see Figure 5), yearly outflows to the three selected countries somewhat relieved pressure on the domestic labor market. In addition, the large number of workers abroad translates into a large amount of remittances, which represent a large fraction of GDP and income (see Figure 6) and provide an important source of foreign exchange.

# **2.6 Constraints and Challenges**

The main constraints operating on the labor market in Morocco, and by implication the challenges that policymakers would have to take up to substantially lessen unemployment, can be summarized as follows.

17
equals _H_ = β1 + β2 . Under perfect competition, an increase in input prices raises both marginal costs and total revenues by the same amount as the rise in costs. Under a monopoly, an increase in input prices will increase marginal costs, reduce equilibrium output, and (as a result) reduce total revenues.<sup>2</sup> The PR _H_ -statistic can thus be interpreted as follows: _H_ <0 indicates a monopoly; _H_ =1 indicates perfect competition; and 0< _H_ <1 indicates monopolistic competition.<sup>3</sup> A number of papers have applied the PR methodology to the issue of competition in the financial sector, although mostly to the banking system specifically.<sup>4</sup> Shaffer (1982) was one of the first to apply the PR model to banks. He estimated it for New York banks using data for 1979 and found monopolistic competition. Nathan and Neave (1989) study Canadian banks. Others have studied some non—North American and non-European banking systems. In Japan, for example, Molyneux et al. (1996) find evidence of a monopoly situation in 1986—1988. A number of papers have studied European banking systems.<sup>5</sup> Although the findings are somewhat varied, the papers generally reject both perfect collusion and perfect competition, finding mostly evidence of monopolistic competition (Bikker and Haaf (2001) summarize the results of ten such

> 2 This interpretation assumes that the test is undertaken on observations that are in long-run equilibrium.  Therefore, we also tested whether the observations were in long-run equilibrium and found the tests to be satisfied.  Given the short time period, we did not allow for any time variation in the _H_ -measure.  Arguably, however, liberalization and deregulation during this period has affected the degree of competition in many banking systems over time.  To the extent that these have been trends affecting banking systems globally in the same way and have preserved the ranking of competitiveness among countries, there will be no bias in our tests from using a constant _H_ -measure. Nevertheless, we did conduct tests regarding the assumption of stability in the competition measure over time (see footnote 11).

> 3 It can be shown that, if the bank faces a demand with constant elasticity and a Cobb—Douglas technology, then the magnitude of _H_ can be interpreted as an inverse measure of the degree of monopoly power or, alternatively (as in Claessens and Laeven 2004), as a measure of the degree of competition.

> 4 Cetorelli (1999) provides more detail on these formal tests and reviews some of the results of previous studies of empirical banking studies.

> 5 These papers include Molyneux et al. (1994), Vesala (1995), Coccorese (1998), De Bandt and Davis (2000), and Hempel (2002).  The countries covered, the time periods, and some of the assumptions used vary among the studies.

10