PDF Tunisia: Rural Labour and Structural Transformation

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Summary. After two decades of spectacular growth, the Tunisian economy is in crisis. The authors identify the reasons for this, and look closely.
Table of contents

Subjects Labor market -- Tunisia. Agricultural laborers -- Tunisia. Agriculture -- Economic aspects -- Tunisia. Income distribution -- Tunisia.

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Food supply -- Tunisia. Notes Includes bibliographical references p. View online Borrow Buy Freely available Show 0 more links Set up My libraries How do I set up "My libraries"? Deakin University Library. La Trobe University Library. Borchardt Library, Melbourne Bundoora Campus.

Open to the public ; The University of Melbourne Library. University of Western Australia Library. Open to the public Book; Illustrated English Show 0 more libraries None of your libraries hold this item. Found at these bookshops Searching - please wait We were unable to find this edition in any bookshop we are able to search. These online bookshops told us they have this item:. Tags What are tags? Add a tag. Public Private login e. Other services accounted for most of GVA per capita growth in —, mainly owing to changes in the employment rate.

It also seems that the faster labor moves out of agriculture, the larger is the increase in output per capita, suggesting that faster economic development depends on the rate at which production resources are reallocated to more efficient uses. In Africa, the share of agriculture in total employment only declined by about 5 percentage points between and compared to 22 percent in Asia , but still delivered 0.

Hence, similar changes in employment structures will lead to greater gains in countries with larger productivity gaps. In the first case, the contributions add up to the annual compound growth rate of output per worker, while in the second they add up to percent. There are some discrepancies in terms of the contribution of structural change to output per worker growth.

Despite this, our results for Africa are very similar to those reported by McMillan and Harttgen The findings are broadly similar to the main results.

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The estimates for Asia suggest a stronger contribution from structural change than that reported in other studies. Nevertheless, UNCTAD suggest that structural change accounted for about 33 percent of GVA per worker growth in developing countries, which is comparable to what is obtained when aggregating Africa, Asia and Latin America and the Caribbean into a single region. There are also some differences in terms of the relative contribution of each sector.

Our results suggest that services were the key driver of economic performance, while manufacturing had a limited impact. However, Roncolato and Kucera argue that, on the whole, industry has been as important as services. A range of factors might explain some of these discrepancies, such as differences in country samples, time frames, level of sectoral aggregation, data sources, and empirical methodologies.

The initial sample for the econometric exercise comprised countries. Equatorial Guinea GNQ had an extremely large structural change component in — These trends generate an extreme outlier that undermines inference. Oman OMN had the second best performance in — 3. These values were also found to significantly affect model behavior. Therefore, basic descriptive statistics are scrutinized for each variable, while bivariate FE regressions are run to identify unusual observations that may unduly affect the results.

In this context, it is important to distinguish between the following statistical concepts: outlier, leverage, and influence. An outlier is an observation with a very large residual and is typically associated with an unusual value on the dependent variable. This was precisely the case of Equatorial Guinea and Oman, mentioned above. Leverage relates to an unusual value on an independent variable. Finally, influence can be thought of as a combination of the previous two — outlierness and leverage.

The Cook's distance D is calculated to uncover influential observations. The initial share of employment in agriculture, the real interest rate and access to water are statistically significant at the 1 percent level, while terms of trade, tertiary education, life expectancy, years of schooling, and political regime are statistically significant at the 5 percent level.

When this observation is dropped from the sample, the independent variable is no longer statistically significant.

The Cook's distance statistic suggests that DR Congo exerts significant influence on inflation, while Venezuela VEN does the same for political regime. These tests are also performed in the fully specified model. Bivariate regressions can also provide useful information on the unconditional determinants of structural change, especially given the small sample in statistical terms and likely collinearity between independent variables.

The initial share of employment in agriculture is probably the strongest candidate for inclusion. The positive sign suggests that the higher the initial labor share in agriculture, the greater the scope there is to engender structural change. This is unsurprising, since agriculture has the lowest sectoral labor productivity across most countries, and thus any move out of the sector is likely to induce positive structural change.

The positive signs on the education variables suggest that improved skills and knowledge enable workers to move to more productive jobs or even create these jobs through enhanced entrepreneurial skills , while the coefficient on life expectancy suggests that a healthier workforce may also contribute to structural change. With regard to physical capital, the positive signs suggest that infrastructure development can accelerate structural change.

In all cases, the eigenvalue of the first component is very large, while the eigenvalue of the second component is considerably below 1. Moreover, the first components explain most of the variation in the variables above 80 percent in all cases while the eigenvector of the first component shows similar values across variables. These results provide strong support for the use of common components as proxies for their respective dimensions.

Some variables were discarded from the physical capital component due to the lack of commonality with the remaining variables: internet users, generation capacity per capita , and road density. It was not possible to obtain a good component for the remaining dimensions. The second specification ii adds human capital to the first specification. The declining magnitude of the coefficient on physical capital is due to some correlation between the physical capital and the human capital components.

Tunisia : Rural labour and structural transformation - EconBiz

In fact, a principal component analysis of the seven variables supports a single component, which is equally significant when inserted in the regression. Nonetheless, it is useful to know which variables are particularly relevant in the context of structural change.

Hence, the third specification iii replaces human capital by years of schooling, while the fourth specification iv uses tertiary education. The variables are strongly significant, while the change in coefficient magnitude can be explained by the different measurement units. For instance, years of schooling ranges from 1 to 13, while tertiary education ranges from 0 to Moreover, the common components have small values. Sequentially replacing physical capital by access to water, access to sanitation, and access to electricity leads to lower statistical significance at 5 or 10 percent see v.

This might be because the individual variables capture specific aspects from a household perspective, while the common component of the three variables is a better proxy for a country's broader infrastructure development. Adding other variables, such as inflation, trade openness, real exchange rate, terms of trade, credit to private sector, and governance variables, does not change the results. Some variables appear to be significant, but that is due to the presence of influential observations such as DR Congo inflation and real interest rate , Libya real exchange rate , and Hong Kong trade openness.

Once removed, the coefficients are no longer statistically significant. Using logarithms or adding squared values to account for possible nonlinearity does not improve the results. In order to assess the robustness of these results, the main specification ii was tested on selected subsamples. For instance, countries that had at least one value for the structural change component outside the 0—2 range were dropped. This led to the exclusion of 72 countries: eight countries had values between 2 and 3, while the remainder had negative values.

Despite this dramatic sample reduction, the conclusions remain valid even if the coefficient on human capital is smaller and only statistically significant at the 5 percent level see vi. Additional estimators were also used to test the robustness of the FE estimator. For instance, robust regressions RR assign different weights to each observation through iterations based on their absolute residuals. The results corroborate the key findings, although the coefficient on human capital declines somewhat see vii.

As before, using tertiary education and access to water also yields coefficients statistically significant at 1 percent, with similar magnitudes. Quantile regressions QR express the quantiles of the conditional distribution of the dependent variables as linear functions of the independent conditioning variables. The results presented here correspond to the median. The median, unlike the mean, is not affected by large outliers. Once again, the results corroborate the robustness of the main findings see viii.

As a further robustness check, the original dataset was divided into three equally sized time periods —, —, and — and the entire analysis was repeated i. Nonetheless, the results suggest that the original findings are robust to these changes. In this case, the results are significantly weaker, with generally lower estimated coefficients and lower statistical significance.

Since sample sizes are significantly smaller, inference needs to proceed carefully. The first point to make is that the initial finding regarding the share of employment in agriculture is robust to all subsamples: the variable remains strongly significant at 1 percent and the coefficient is broadly similar across all regions.

In Africa, the initial share of mining and utilities in total GVA is strongly significant, suggesting that the abundance of mineral resources in some African countries acts as a deterrent to structural change. This could be due to disincentives to invest in other sectors. Physical capital is also significant at 1 percent. However, including human capital variables in the specification produces clear signs of collinearity, since human capital and physical capital are highly correlated in the Africa subsample.

In the second specification, physical capital is replaced by secondary education, which is significant at 5 percent.

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Replacing it with years of schooling also leads to a positive coefficient at 5 percent, although R 2 drops somewhat. On the other hand, replacing it with human capital provides a slightly stronger specification, with the coefficient significant at 1 percent. In the third specification, the principal component that combines the seven variables relating to both human and physical capital is used to confirm the importance of both dimensions. In Asia, physical capital and tertiary education are statistically significant at 5 percent.

The finding that tertiary education matters more in Asia, while secondary education matters more in Africa, probably reflects the different education levels and skill needs across the two regions. Once again, human and physical capital are highly correlated. The joint component is predictably strong. In Latin America and the Caribbean, tertiary education is significant at 5 percent, while the only other variable that seems relevant is the real exchange rate.

The positive sign suggests that real exchange rate appreciations may actually promote structural change. Despite the highly parsimonious specification, the value of R 2 is considerably higher than in Africa and Asia. Finally, tertiary education is strongly significant in developed countries, while the political regime and the real exchange rate are significant at the 5 and 10 percent level, respectively.

Physical capital does not appear to be relevant in more developed countries, possibly suggesting that the existing variables are not capturing the type of infrastructure development that is required to accelerate structural change in these countries. However, the authors also conclude that human and physical capital are important for structural change — in addition to a set of initial conditions e. When including the share of raw materials in total exports which slightly reduces the country sample its coefficient is not statistically significant.

The rigidity of employment index is only available for the period —, thus not useful for this exercise. The undervaluation index was constructed by the authors and is not publicly available. There is a renewed interest in the study of structural change, mainly owing to concerns that recent growth patterns have not been inclusive nor sustainable. In fact, structural change has reemerged as a key policy priority for many developing countries.

There is little doubt that transforming economic structures is a necessary precondition for economic and social development. Not only does structural change stimulate economic growth, it can also lead to a more inclusive and sustained growth path. However, there is little research on how to accelerate its pace, especially for developing countries. Since agriculture has the lowest level of labor productivity, the reallocation of workers from agriculture to other sectors led to positive structural change, which helped boost aggregate productivity and thus economic growth.

Changes in the demographic structure had a positive impact on output per capita growth in developing regions, while the impact of employment rates varied considerably. With regard to sectoral dynamics, services were the main driver of economic performance and the key catalyst for structural change. Agriculture and manufacturing had a limited impact, but raising agricultural productivity remains crucial for eradicating poverty, while manufacturing can play a more important role if employment and labor productivity are simultaneously increased.

While our results do not directly contradict their hypothesis, they do suggest that physical and human capital play an important part in promoting structural change — namely, in the reallocation of employment across sectors. Whether these are less important than targeted measures, it is difficult to assess. In fact, it could be argued that even general policy measures are likely to induce differentiated effects across sectors e. Hence, this paper should not be seen as evidence that targeted policies do not matter.

While our results do suggest that initial conditions unconditionally influence structural change, they do not explain much of the variance. Physical and human capital do seem to play a vital role in boosting structural change. In sum, there is still much scope for accelerating structural change. Labor productivity gaps and employment shares in agriculture remain high in several parts of the world. While the period since has been unquestionably positive for developing countries, it is vital to improve the pace of structural change in order to fully seize its benefits.

The key message of this paper is that investments in education and economic infrastructure are critical to accelerating structural change. Volume 23 , Issue 1. The full text of this article hosted at iucr. If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account. If the address matches an existing account you will receive an email with instructions to retrieve your username.

Review of Development Economics Volume 23, Issue 1. Pedro M. Martins Corresponding Author E-mail address: pmartins worldbank. Martins, The World Bank. Disclaimer: The views expressed in this paper are those of the author and do not necessarily reflect the views of The World Bank or the United Nations where the author worked at the time of the writing. Tools Request permission Export citation Add to favorites Track citation. Share Give access Share full text access. Share full text access. Please review our Terms and Conditions of Use and check box below to share full-text version of article.

Abstract This paper provides a comprehensive assessment of structural change in the world economy. Hence, the starting point is output per capita y ,. ISIC Rev. Independent variables are computed as period averages, with the exception of initial conditions. The share of employment in agriculture and the share of mining and utilities in total GVA were calculated from the original data.

Total generation capacity GW and total road network km were used to compute generation capacity per capita and road density, respectively. Figure 1 Open in figure viewer PowerPoint. Structure of Output and Employment [Color figure can be viewed at wileyonlinelibrary. Figure 2 Open in figure viewer PowerPoint. Changes in Employment and Labor Productivity Gaps Note : Relative labor productivity is calculated as the natural logarithm of the ratio of sectoral productivity to aggregate productivity. Figure 3 Open in figure viewer PowerPoint.

Figure 4 Open in figure viewer PowerPoint. Trends in Agricultural Employment and Output per Capita, — [Color figure can be viewed at wileyonlinelibrary.

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Figure 5 Open in figure viewer PowerPoint. Variable short name Coef. Mining and utilities in total GVA —0. Current account balance —0. Government debt —0. Fiscal deficit 0. Inflation 0.

Trade openness 0. Real exchange rate 0. Credit to private sector 0. Real interest rate —0. Secondary education 0. Tertiary education 0.

Life expectancy 0. Years of schooling 0. Access to electricity 0. Access to sanitation 0. Access to water 0. Internet users 0. Generation capacity —0. Road density 0. Political regime 0. VEN Political rights —0. Voice and accountability 0. Political stability 0. Government effectiveness 0.