A better understanding of the historical background of tax reforms in a representative African economy Mozambique along with an assessment of taxation performance in a comparative perspective. Insights into the practice and implications of tax policy, both from the perspective of the consumer and the firm level.
Discussion of the existing institutional set up in which tax policy and its enforcement operate and analyses of current tax practices. Taxation themes at the border and at domestic level, which are typical for low-income economies, characterized by a high degree of reliance on foreign trade taxes. This volume is meant as a guide for developing country government officials and professional aid practitioners as well as academics, researchers and tax policy analysts working in the development field.
It will also be of interest to students of development with a special interest in public finance issues in poor countries and how to improve policy-effectiveness, including tax policy, in a developing country setting. Introduction 1. Taxation in retrospect 2.
Adapting to climate change: an integrated biophysical and economic assessment for Mozambique
Household Survey Evidence 3. Enterprise Survey Evidence 4. Comparative Perspective 5. Institutional Design 6. VAT and External Aid 7. The E-mail Address es you entered is are not in a valid format. Please re-enter recipient e-mail address es. You may send this item to up to five recipients. The name field is required. Please enter your name. The E-mail message field is required.
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Biofuels, poverty, and growth: a computable general equilibrium analysis of Mozambique
Preview this item Preview this item. Series: Routledge studies in development economics , Find a copy online Links to this item Table of contents. Allow this favorite library to be seen by others Keep this favorite library private. Find a copy in the library Finding libraries that hold this item Reviews User-contributed reviews Add a review and share your thoughts with other readers.
Be the first. Add a review and share your thoughts with other readers. Taxation -- Econometric models. Tax evasion -- Mozambique. The corresponding model estimate is Model results are also very close to IOF estimated rates in rural zones for both the full survey period and by semester. The only rates that do not lie very close to one another between the model projection and IOF08 are the first semester in urban zones.
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The difference between these two rates is a bit less than six percentage points. A significant advantage of CGE models is that they allow the analyst to decompose complex phenomena, such as the evolution of poverty rates through time, in order to provide insights about the driving forces behind results. So far, we have considered only results from the final scenario, Weather, which includes all of the effects discussed in the preceding subsection. Recall that the scenarios are cumulative. Hence, the Agriculture scenario differs from the Baseline both in terms of rates of productivity growth in agriculture and the rate of growth of the labor force by skill class.
In this scenario, the labor force grows at plausible rates, agricultural productivity growth is relatively rapid, world prices are held constant, and no weather events occur. Under this scenario, a poverty rate of This is essentially the same as the level targeted by government and external partners. When the principal differences between the Baseline scenario and the actual evolution of key external variables are applied to the model, the estimated poverty rate increases in every case.
The Education scenario illustrates that, from to , the contraction in the growth of the labor force due to increased school enrolments increases poverty. Over time, the more rapid growth in skilled and semi-skilled labor that this expansion in enrolments implies is expected to reduce poverty. However, in the near term, education is an investment that implies a small contraction in consumption and hence a slight increase in poverty.
The principal impacts on poverty derive from: i the combination of low productivity growth in agriculture, particularly food crops, substantial increases in world food prices, and a weather shock in ; and ii the nearly continuous increase in fuel prices from to When at their peak in , fuel prices contributed most to the increases in poverty above the Baseline scenario. In , with the decline in fuel prices but relative firmness of food prices, the combination of low agricultural productivity growth and food price increases contributed most to the increase in poverty.
The strength of the fuel price effect merits further mention. This effect is consistent with earlier analysis Arndt et al.
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Because fuel use is difficult to economize on, particularly over relatively short time periods, fuel price increases imply a need to either increase exports or reduce imports for any given level of foreign exchange availability from external sources. This terms of trade loss amounts to a reduction in the quantity of goods available to the economy. Earlier analysis by Arndt et al. For example, fuel subsidies can reduce the poverty impact of fuel shocks. However, fuel subsidies only partially offset the impact and come at the cost of reducing investment and increasing future poverty rates, assuming the investment is effective.
In addition, fuel subsidies increase the burden of macroeconomic adjustment; they imply by definition lower fuel prices and hence greater fuel consumption. This means that exports must increase more or imports of other items must decline by more than in the no-subsidy case.
For these reasons, Mozambican authorities have largely, though not completely, allowed international fuel prices to pass through to higher domestic fuel prices. In the model simulations, the international fuel price increases are assumed to be passed through to the economy.
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Changes in fuel prices influence relative prices throughout the economy. In Mozambique, the existence of large differences between farm-gate agricultural prices and consumer prices is well established. Transport costs, of which fuel is a substantial component, account for a large share of this difference. Other things being equal, higher fuel prices simultaneously lower farm-gate prices and increase consumer prices because they expand the marketing wedge between producers and consumers Tarp et al. The costs of distributing imported products, especially food, which is bulky and relatively low value, increases.
Finally, direct transport costs, which are often particularly important for urban residents, tend to rise. To complete this section, we consider the links between consumption growth, poverty, and inequality set forth by Bourguignon In the model, consumption is growing and poverty is essentially unchanged up in and down in Therefore, one expects inequality to rise, though not dramatically, as the rate of consumption growth is not particularly rapid. This is indeed the case in the model.
The Gini coefficient, derived from micro-simulation results for and , rises to about This section considers whether these regional variations are consistent with other sources of data. The first subsection uses agricultural price data as a cross reference on the household survey. This is a strong form of cross—checking, as the price data and the household survey data were collected separately.
In addition, we consider the internal consistency of the household survey using food-based poverty indicators. The SIMA data provide price information for a range of core agricultural products in 25 urban markets cities and towns covering all provinces. It has a much wider geographical coverage than the price database used for constructing the consumer price index CPI , which is restricted to the three principal urban centers. While the spatial coverage is better, the SIMA data cover a relatively small number of agricultural goods.
Nevertheless, these products tend to be important elements in the consumption baskets of poor people. Two price series are calculated from the SIMA, from which inflation is then derived. The ratio of these two averages measures cumulative food price inflation between the two periods. The second SIMA price series is a weighted measure, where the weights are estimated so as to correspond to the weights of the same items in the food poverty baskets estimated from IAF Given that SIMA prices are calculated as month averages corresponding to the full periods of the household surveys , the food poverty lines do not include a temporal price adjustment; the final two columns provide the percentage difference between the survey-based inflation estimate the food poverty line ratio and the weighted and un-weighted inflation estimates from the SIMA database.
Maputo province is excluded as it is not covered by the SIMA series; overall prices are calculated as weighted averages, with weights based on corresponding survey-based provincial population shares also excluding Maputo province. The poverty line-based price indices and the SIMA-derived price indices are not perfect comparators.
Discrepancies exist due to the more restricted number of products in the SIMA dataset as well as differences in geographical coverage. Nevertheless, the trends in the SIMA-derived measures of inflation are highly consistent with those derived from the household surveys. This is almost exactly the same as that estimated from the household survey data. At the provincial level the trends are also very consistent—for example, the correlation between these sets of provincial inflation measures is 0. A score above below one indicates a price premium discount relative to the national average for a province during a given period.
Of particular interest are changes in relative prices over time. Using the preferred weighted price series, one notes a fall in the relative price premium of the Northern and Southern provinces e. Niassa from 1. This is indicative of more acute price increases in the Central provinces relative to the rest of the country. A strong relationship exists between these changes in relative prices and changes in the survey-based poverty headcount rates.
The correlation coefficient between these two series shown in the final row of the table is 0.
Related Taxation in a Low-Income Economy: The case of Mozambique (Routledge Studies in Development Economics)
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