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Section III proposes a multidimensional taxonomy of the development of Latin American and the Caribbean countries, which includes the four dimensions of sustainable development considered in this article. Cluster analysis is used to classify and characterize three groups of countries, in which the development profiles are similar within each group but dissimilar between them, while also identifying three other countries with unique development challenges that do not resemble those of any of the three groups.

The article concludes by summarizing the main results of the classification and the analysis of its relevance for international development policies in this region. Secondly, the fact that the socioeconomic realities of the different countries are highly diverse and changing makes it difficult to perform universally valid and stable analyses over time. As Nielsen points out, there is no generally accepted classification criterion —whether based on development theory or based on an objective benchmark.

Despite these difficulties, development classifications have important analytical and operational potentials Tezanos and Sumner, In terms of analytics, development taxonomies serve to simplify a complex and diverse world by identifying groups of countries that share similar development features.

Classifications of this type are common in different domains of knowledge such as biology, medicine, philosophy, international relations and economics. In the case of development studies, which is a multidisciplinary knowledge domain, country classifications serve both to establish the main differences and similarities between countries in terms of development outcomes, and to study the dynamics of progress through time.

For example, as discussed below, the eligibility of countries for official development assistance ODA is based on a development taxonomy. Nonetheless, there are several international development classifications that use different criteria to define a type of global development threshold that distinguishes between developed and developing countries.

Since , the World Bank has published a classification of countries according to their per capita income estimated by per capita gross national product GNP calculated using the Atlas method. Although the World Bank itself recognizes that development is more than just income, it does consider that GNP per capita has proven a useful and simple indicator that is highly correlated with other non-monetary measures of the quality of life, such as life expectancy at birth, and the infant mortality and school enrolment rates World Bank, undated.

These are developed countries which generally correspond to the high-income countries in the World Bank classification and developing countries low, lower-middle, and upper-middle income according to the World Bank. Lastly, UNDP classifies countries according to their human development levels by computing the synthetic Human Development Index HDI , which partially reflects the multidimensional nature of the human development concept. Specifically, HDI summarizes three dimensions of development: longevity, education and purchasing power.

To what extent do these three development classifications coincide in the context of Latin America and the Caribbean? The latter group contains just one low-income country Haiti , along with six lower-middle and 17 upper-middle-income ones. In HDI terms, the region has two countries rated very high, 23 high, seven medium and one low UNDP does not classify the remaining eight countries.

In short, Latin America and the Caribbean is a region dominated by countries of upper-middle income and high human development. Although the lists are broadly consistent with each other, there are several discrepancies between the World Bank and DAC classifications by per capita income and that based on human development UNDP. Only two of the 17 high-income countries Argentina and Chile and none of the 17 uppermiddle-income countries are rated at the very high human development level.

High High.. According to DAC regulations, these three countries ceased to be recipients of official development assistance in Therefore, they will cease to be recipients of official development assistance as from , provided they are still high-income countries at that time. An alternative classification for Latin America and the Caribbean: taxonomy of the Sustainable Development Goals Once the indicative variable —or the various indicative variables— of development levels have been chosen, different procedures are used to define the country groupings.

Nonetheless, this procedure does not make it possible to determine the appropriate number of groups, or where to place the thresholds that separate them. Starting with the main dimensions of sustainable human development, a small set of indicators of these dimensions is then chosen to classify the countries of the region through the statistical technique of cluster analysis.

A justification is then provided of the advantages of cluster analysis for establishing an international development taxonomy. Thirdly, the resulting clusters are analysed and the main development challenges characterizing each of the country groups are identified. Dimensions of the Sustainable Development Goals The process of producing an international classification of development starts by clearly defining the dimensions to be assessed in the classification.

This strategy of 17 major goals combines two convergent agendas: first, the human development agenda inherited from the Millennium Development Goals; and, second, the sustainable development agenda that emerged from the four conferences popularly known as Earth Summits: the United Nations Conference on the Human Environment, held in Stockholm in ; the United Nations Conference on Environment and Development and the United Nations Conference on Sustainable Development, held in Rio de Janeiro Brazil in and , respectively; and the World Summit on Sustainable Development, held in Johannesburg South Africa in The concept of sustainable development has evolved recently as a result of lively debate on the Agenda.

The four conferences gave rise to a three-dimensional definition of sustainable development, which includes the economic, social and environmental dimensions. Nonetheless, this four-dimensional definition did not gain the consensus of the General Assembly; so the Sustainable Development Goals as adopted finally recognize three dimensions and one essential element for sustainable development specifically, democracy, good governance and the rule of law United Nations, In particular, the assumption of exogeneity of the dummy variable for exporting firms looks problematic.

Economic intuition suggests that productivity itself affects the likelihood of export, and empirical papers evaluating the impact of exporter status on firm performance take into account this source of reverse-causality bias see, for example, Costa et al.

As a consequence, we also treat the dummy variable for exporting firms as an endogenous regressor in the IV estimates we present, in order to address the possible inconsistency of the OLS ones. Footnote 13 Computing estimates at the sectoral level means that we work with a simpler specification that excludes the interaction terms of the regressions in Tables 4 , 5 , 6.

Clearly, doing so allows a substantial reduction in both the number of parameters to estimate and the number of instrumental variables required to perform IV estimation. For the sake of space, in the following we will report the OLS and IV results for the urbanization variables only, but we will briefly comment on the estimates concerning the other regressors as well.

Footnote 14 Sectoral OLS Estimates OLS estimation of specification 2 does not obviously imply any additional technical difficulty with respect to those of Tables 4 , 5 , 6. The main results of interest here are presented in Table 7 , which again reports—along with estimated coefficients—Conley standard errors to account for possible cross-sectional dependence.

Table 7 The impact of urbanization on productivity, wages, and the productivity—wage gap: OLS sectoral estimates Full size table Let us first look at the manufacturing sector, starting from the upper part of the table where labour productivity is the dependent variable. It can be seen that being located in a highly urbanized area exerts a significant and positive impact on productivity only for firms whose production processes are characterized by a high level of knowledge intensity HT group , while the coefficients of the other are non-significant.

In contrast, the coefficient of the Rural regressor is systematically negative and significantly so in the MHT and MLT sectors , suggesting that firms in areas of low urbanization experience a productivity loss compared to their competitors located elsewhere. The coefficients reported in the middle part of Table 7 refer to the wage regressions and are generally estimated with greater precision than the productivity equations.

Indeed, most coefficients are significant at conventional levels and, not surprisingly, outline a positive monotonic association between the degree of urbanization and the wage rate. Most importantly, the upward pressure exerted by urbanization on the wage rate turns out to be higher than that on productivity. As a result, firms located in highly urbanized areas are characterized by a lower productivity—wage gap, as can be seen in the bottom part of Table 7.

Footnote 15 We now turn to the OLS estimates concerning services. Sectoral specificities related to technological complexity emerge more clearly in this case. Indeed, in the productivity regressions for the two sub-sectors where scientific knowledge and advanced technologies play a more relevant role HITS and KWNMS , the estimated coefficients point to a significant, positive, and monotonic relationship between the degree of urbanization and productivity.

In contrast, urbanization does not seem to affect productivity in the sectors characterized by lower technological intensity 'Other Services' and 'Household Services'. Footnote 16 Similarly to what is observed for industrial sectors, the OLS coefficients of the wage regressions indicate a positive monotonic association between the degree of urbanization and the wage rate.

This is observed in all sectors, regardless of the degree of technological complexity although the coefficients of the Rural regressor are again estimated with less precision. The differences between the role played by technological intensity when evaluating the marginal impact of urbanization on productivity and wages entail interesting implications in the productivity—wage-gap regressions. Here, the interest of adopting the technology-based sector classification is apparent.

In contrast, the point estimate is negative and significant for the two remaining sectors. An intuitive interpretation of this set of results is that whereas locating in urban agglomerations exerts an upward pressure on both productivity and wages, the positive externalities related to agglomerations overcome the increase in wages which, in turn, reflects an increase in congestion costs only in more technologically sophisticated sectors.

Sectoral IV Estimates A particular difficulty in tackling endogeneity in our estimation framework is that we have to employ adequate instrumental variables for categorical variables urbanization and exporter status. Following and extending suggestions in Wooldridge and Angrist and Pischke for the case of a single endogenous dummy variable, we adopt the following procedure. As far as urbanization is concerned, we actually estimate an ordered response model ordinal probit , which provides us with the estimated probability that a firm is located in one of the three possible DegUrba categories.

In the case of the export dummy, we estimate a standard probit model for binary variables, obtaining the same kind of information. Then, predicted probabilities obtained from probit estimates are used as instruments in the IV estimation of the productivity and wage regressions. Such a procedure implies that the second-stage equation is exactly identified.

Footnote 17 As for the choice of variables to be included in set Z, we take advantage of hints from the existing literature. To begin with, we draw upon Costa et al. Footnote 18 A further reason to include firm age in set Z is that it also seems to be a reasonable predictor of location. The geographical distribution of producers is not random but reflects optimization decisions that take into account the economic implications of being located in areas characterized by different degrees of urbanization.

To the extent that land space is a scarce resource, it can be argued that younger firms face tighter constraints in their location choices and are also more likely to end up with suboptimal decisions. Furthermore, we assume that a set of locality-specific factors influence the location decisions of producers across urban and nonurban areas. In particular, drawing on Di Giacinto et al. Footnote 19 The inclusion of these variables reflects the current practice of choosing geographical altitude or historical features old values of population density and of the schooling rate as instruments when estimating agglomeration economies on this, see also Ciccone and Hall , and Combes et al.

The implicit assumption is that while these features are useful to proxy the factors driving the location decisions of firms and workers, they do not exert a direct effect on current differences in productivity. Also note that for the sake of comparison, this set Z is held fixed across both sectors and equations. Thus, for each sector the predicted probabilities we use to compute the second-stage regression are the same, regardless of the dependent variable.

Let us now turn to the IV results reported in Table 8. To begin with, the instrumental variables we build for each sector do not appear to be weak. Indeed, the conventional F-statistics reported at the bottom of the table are systematically much larger than the conventional threshold value of 10, below which—since the contribution of Stock and Yogo —weak identification is usually considered a potential issue. Table 8 The impact of urbanization on productivity, wages, and the productivity—wage gap: IV sectoral estimates Full size table Some relevant differences emerge between the OLS and IV estimates, and our comment essentially focuses on these, starting again from the manufacturing sectors.

Rural locations yield either not significant or negative productivity effects. Coefficients in the wage regressions are estimated with much lower precision than in the OLS estimate; those that are significant are consistent with the idea that labour costs are greater at higher levels of urbanization. IV estimates concerning the productivity—wage gap—the main variable of interest in our paper—confirm that locating in urban agglomerations implies a lower cost of competitiveness for firms belonging to industries characterized by low technological intensity LOT.

A similar disadvantage emerges for producers who belong to the most technologically-intensive group HT and are located in rural areas. As for services, IV estimates concerning the effect of locating in highly urbanized areas on productivity and wages are consistent with the OLS ones.

In the most knowledge-intensive industries HITS and KWNMS , the positive productivity effects related to urban agglomerations persist also when controlling for possible selection bias or other sources of endogeneity. In addition, the upward pressure on labour compensation emerges regardless of any distinction based on knowledge intensity. As a consequence, the IV estimates of the effects of locating in highly urbanized areas on the productivity—wage gap are very similar to the OLS ones and again display significant heterogeneity according to the degree of knowledge intensity.

First, when controlling for selection bias producers in knowledge-intensive sectors located in rural areas do not necessarily experience a loss of productivity compared to the reference category of an intermediate level of urbanization.

Secondly, whereas in the OLS results of the wage regressions the estimated coefficients of Rural are always negative, in the IV computations they systematically always take on the opposite sign. Moreover, they turn out to be statistically significant in the 'Other services' sector and not far from statistical significance in the HITS and KWNMS sectors the unreported p values of the null hypothesis are 0. A possible interpretation of these differences involves, on the one hand, the failure of OLS to control for selection bias and, on the other hand, the lower supply of infrastructure, transport, and other amenities in rural areas.

In other words, the negative OLS coefficient is consistent with the fact that less urbanized areas host less efficient producers who pay lower wages, on average ; by controlling this selection effect, the IV estimator is able to highlight the need for firms located in rural areas to pay higher wages, ceteris paribus, to attract workers to a less desirable location. In the case of the HITS sector, this 'wage premium' entails a loss in terms of cost competitiveness, as witnessed by the negative and significant coefficient of Rural estimated in the productivity—wage-gap regression.

Finally, we briefly discuss the other covariates' estimated and unreported coefficients. To begin with, the results concerning the variable assumed to be exogenous are broadly consistent with those in Tables 5 and 6. Moreover, in both the OLS and IV sectoral estimates, the coefficient of the endogenous export dummy is generally -as expected- positive and significant, leaving out the case of the manufacturing MLT sector where it fails to achieve significance in the productivity-wage IV regression.

A possible criticism underlying these estimates is that firm age may not be a suitable instrumental variable for the export dummy as selection effects imply older producers are more productive. While we are aware of this but also lack valid alternatives to instrument exporter status , we have to stress that the main focus of our paper is the impact exerted by agglomeration upon the productivity-wage differential.

Thus, as a robustness check, we have re-run all OLS and IV estimates while excluding the export dummy from the regressors list as well as firm age from the IV set and have found results similar to those presented in Tables 7 and 8. Since the impact of urbanization does not depend on whether the export dummy is dropped from the specification or not, we have preferred to present the estimates obtained when it is included.

Theoretical and empirical results suggest that exporter status may yield higher productivity rather than being simply due to selection see, Fryges and Wagner on the "learning-by-exporting" hypothesis. In such a case, the choice itself of dropping the export index may entail an omitted variable bias. Footnote 20 To sum up, the evidence of the IV sectoral estimates strengthens the idea that once both productivity and wages are taken into account, the location advantages of urban agglomerations concern only firms producing services characterized by high technological intensity.

In contrast, for suppliers of services active in less technologically sophisticated sectors, the productivity benefits of locating in urban areas are likely to be overwhelmed by higher labour costs. Also, estimates imply that, on average, industrial establishments operating in high-density areas either enjoy no advantage or even face a competitiveness loss as the upward pressure on wages is higher than that on productivity.

Footnote 21 Conclusions We have analysed the productivity—wage relation using a novel and integrated database that considers establishment information for subregional areas in the Italian region of Lombardy. The classification of the degree of urbanization we have applied is established at the European level and is thus suitable for international comparisons. In particular, we have investigated whether an urban-nonurban agglomeration divide exists and have estimated the impact of industry- and firm-specific effects.

The crucial issue is whether density matters, as is generally acknowledged when analysing productivity or wage differentials in urban and nonurban areas. However, the answer to this question is controversial since, ultimately, the productivity—wage gap is the key indicator of an urban advantage. Density per se does not positively affect productivity, thus negatively impacting the productivity—wage gap.

However, agglomeration significantly impacts productivity and wage differentials at the local level when considering the technological and knowledge-based resources characterizing the industrial mix within urban and nonurban agglomerations. We adopt an industry classification that enables us to identify manufacturing and service activities according to technological and knowledge-intensity features, in order to better capture the potential interactions between geographical proximity and the transmission of knowledge spillovers.

Moreover, the introduction of firm-level variables allows us to control the moderating role of firm-related effects, and particularly those concerning job characteristics. When an interaction with dummy variables reflecting an industry's technological level is considered, a positive effect is operational in manufacturing. This pattern is also confirmed in services and is mainly driven by high-technology and knowledge-intensive manufacturing plants. As concerns wages, localization in high-density areas shows a positive and significant effect on compensation in both manufacturing and services.

Nevertheless, sectoral characteristics may further widen this effect when associated with agglomeration economies. When located in high-density areas, manufacturing plants in high-technology industries show an additional increase in compensation. Plants operating in high-technology services localized in high-density areas get a further wage increase, whereas the extra premium for knowledge-intensive services is not significant.

In other words, wages in the KWNMS sectors are higher overall, without any premium related to localization. The results regarding the productivity—wage gap complement this evidence. As a result of the productivity pattern, the wage gap is negative in manufacturing and services in high-density areas. Nonetheless, the gap reverts to positive when considering the interaction with high-tech industries, although this effect is more prominent in services.

This fact can be rationalized on the grounds that manufacturing plants do not show a substantial gain in productivity due to agglomeration economies. However, this gain is aligned with wages, thus affecting the wage gap only marginally. It is worth noting that these results are robust to specifications that take into account sorting issues, i.

All in all, our study suggests that density matters only if one considers specific sectors—mainly those providing technologically advanced services. By viewing the whole picture of the possible advantages of urbanization, i. Conversely, nonurban areas show a clear overall disadvantage in productivity and wages, which threatens to widen the gap with urban areas or, more generally, affect living conditions in the former. One should also note that this territorial pattern occurs in the context of a national productivity trajectory that is largely unsatisfactory and, therefore, underlines how tackling the productivity challenge as a whole is a primary policy task.

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