rafmacalaba/datause-encoder-data huggingface dataset:
counterintuitive, yet may be explained by the fact that there are many other perturbations affecting consumption positively as well as negatively, and thus the loss alone might have little effect on future consumption.

<u>Table 5: Regression losses on consumption changes for different samples</u>

|<br>Model|<br>Whole sample<br>(N=99)|<br>Sudden hydrometeorological<br>events (N=62)|<br>Sudden hydrometeorological<br>events, low income group<br>(N=35)|
|---|---|---|---|
||PV dConsumpt|ion inper cent ofper capita income|of theyear before the event|
|Constant|0.188***|0.175***|0.152**|
||(7.028)|(4.582)|(2.526)|
|Loss|0.206|0.083|0.345|
||(0.677)|(0.150)|(0.365)|
|R Square|0.005|0.001|0.004|

Note: Significance at the * 10% level;** 5% level; *** 1% level Focusing on the savings measures in a next step, we first assess how the savings measures explain consumption changes irrespective of disaster adjustments (table 6).

<u>Table 6: Regression results for the whole sample (N=99)</u>

|<br>Model|<br>Gross savings<br>PV dCo|<br>Gross disaster<br>savings<br>nsumption inper c|<br>Net savings<br>ent ofper capit|Net disaster<br>savings<br>a income of the|Genuine<br>savings<br> year before th|Genuine<br>disaster<br>savings<br>e event|
|---|---|---|---|---|---|---|
|Constant|0.061|0.104***|0.124***|0.155***|0.137***|0.166***|
||(1.59)|(3.42)|(4.40)|(6.54)|(5.17)|(7.17)|
|Savings<br>measures|0.692***|0.585***|0.654***|0.560***|<br>0.621***|0.518***|
||(3.78)|(3.67)|(3.28)|(3.25)|(3.04)|(2.99)|
|R Square|0.129|0.122|0.100|0.098|0.087|0.085|

Note: Significance at the * 10% level;** 5% level; *** 1% level The savings variables are all significant and most of the constants similarly so. Further, for the whole sample, the size of these values is in line with findings from other studies with about 13%, 10%, and 9% of the consumption change explained by gross, net and genuine savings measures respectively in the baseline year. To provide some perspective, Hamilton and colleagues in World Bank (2006) find that gross savings and genuine savings explain about 15% resp. 24% of the variation in consumption using consecutive 20-year periods. Coefficients are of the right order of magnitude (ideally they should be 1) ranging from 0.7 to 0.6 compared to a range of 0.4 to 1.3 reported in World Bank 15
Depending on the choice of SWF, this expression tells us how to weight changes in inequality happening at different parts of the income distribution, i.e. changes in the income shares of different individuals/groups within the population. In particular, (i) for the “selected deciles” social welfare DiYi <u>(N+1−i)Yi</u> functions, the weights are εi = N ; (ii) for the Sen index the weights are εi = N ~~;~~ and ∑j=1 DjYj ∑j=1(N+1−j)Yj Yi<sup>1−θ</sup> (iii) for the Atkinson index the weights are εi = �∑Nj=1 Yj<sup>1−θ</sup> � ~~.~~ These weights on changes in income shares of different percentiles of the income distribution are exactly those shown in the bottom panel of Figure 1.

This additive decomposition of welfare changes into growth in average incomes and a weighted average of changes in income shares allows a natural interpretation of changes in inequality in terms of growth. In particular, the second term in Equation (6) is just the difference between growth in welfare and growth in average incomes, and it can be interpreted as the “growth cost” of the observed increase in inequality, i.e. the amount by which growth would have to be higher in order to compensate for the increase in inequality, in the sense of delivering the same welfare growth. In the remainder of this paper we implement and analyze this decomposition in a large dataset of cross-country dataset of income distributions over the past forty years.

# **3.  Data**

Our starting point is a large dataset of 963 irregularly-spaced country-year observations for which household surveys are available, covering a total of 151 countries between 1967 and 2011. This dataset is the merger of data available in two high-quality compilations of household survey data: the World Bank’s POVCALNET database, covering primarily developing countries, and the Luxembourg Income Study (LIS) database, covering primarily developed countries. The POVCALNET database is the dataset underlying the World Bank's widely known global poverty estimates. Its data on average index, as well as absolute poverty measures such as the headcount or the poverty gap. For such measures, decompositions such as the one suggested in Datt and Ravallion (1992) are more appropriate.

9
# **Table 1. Ten Largest Increases in Russian Imports by Product, HS2-digit Level, 2012–20**

|**Product**|**Product description**|**Change in**<br>**Import Value**<br>**(US$,**<br>**millions)**|**Increase in Total**<br>**Russian Imports,**<br>**(Percent)**|
|---|---|---|---|
|85|Electrical machinery and equipment and parts thereof; sound recorders and reproducers,<br>television image and sound recorders and reproducers, and parts and accessories of<br>such articles|592|1.7|
|30|Pharmaceuticalproducts|365|2.7|
|2|Meat and edible meat offal|248|3.4|
|39|Plastics and articles thereof|238|2.1|
|84|Nuclear reactors, boilers, machineryand mechanical appliances, andparts thereof|151|0.3|
|94|Furniture; bedding, mattresses, mattress supports, cushions, and similar stuffed<br>furnishings; lamps and lighting fittings, not elsewhere specified or included;<br>illuminated signs, illuminated nameplates and the like;prefabricated buildings|145|3.2|
|73|Articles of iron or steel|139|1.8|
|88|Aircraft, spacecraft, andparts thereof|132|3.8|
|64|Footwear,gaiters and the like;parts of such articles|116|2.7|
|33|Essential oils and resinoids;perfumery; cosmetic or toiletpreparations|112|3.1|

_Source:_ World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 28
3 borders. At the same time, the available data on international remittances do not include the large (and unknown) sum of remittance monies which are transmitted through private, unofficial channels. As a result of these data problems, a host of key policy questions remain unanswered. Exactly what is the impact of international migration and remittances on poverty in the developing world? How do international migration and remittances affect poverty in different regions of the developing world? What are the factors which cause people to migrate in the developing world?

This paper proposes to answer these, and similar, questions by using a new data set composed of 74 developing countries. This data set includes all those low- and middle-income developing countries for which reasonable information on poverty, inequality, international migration and remittances could be assembled. The data set includes countries drawn from each major region of the developing world: Latin America and the Caribbean, Middle East and North Africa, Europe and Central Asia, East Asia, South Asia and Sub-Saharan Africa.

The balance of this paper is organized as follows. Part 1 sets the stage by reviewing the findings of recent village- or country-level studies on the relationship between international migration, remittances and poverty. Part 2 then presents the new data set. Part 3 describes how this data set uses new sources of information to calculate the relevant migration, remittances and poverty variables. Part 4 describes the main econometric findings on the relationship between migration, remittances and poverty, and Part 5 discusses the determinants of international migration. The final section, Part 6, concludes.
- 13 -

# **Incremental costs of inputs**

In principle, detailed firm-level data would be required on costs of producing yam and fabric in the most efficient country (say China) and in the United States and Africa (AGOA-designated sources of inputs). Given the difficulty of this exercise, we use a theoretical insight due to Krueger (1993) that allows us to use as a proxy for the incremental costs, the tariff on inputs levied in the AGOA-designated source country (Africa or the United States). This implies that the costs of sourcing inputs from such a country (because of the rule of origin requirement) would be greater than from the most efficient source by an amount equal to the protection accorded to them. Thus, rules of origin are essentially a way for a country to export its protection on inputs.

# **Incremental transport costs**

Data for Mauritius and Madagascar indicated that the cheapest source of yarn and fabric was China. We obtained data from shipping companies on the costs of shipping from China to Mauritius; China to the United States; and from the United States to Mauritius.' This allowed us to compute the extra transport cost imposed by the rule of origin.

# **Cost function**

For the purposes oi our anarysis, we assumed a simple Leontief technology, implying limited substitution between intermediates and primary factors of production. The assumed coefficient of yam for use in the production of apparel was 0.38, which is consistent with the assumptions for Africa in the Global Trade Analysis Project (GTAP) model used by the World Bank in its general equilibrium computations on the effect of trade liberalization. 12

# **Export supply elasticities**

> We assumed a range of values for the export supply elasticities, from 1 to 5. Countries such as Mauritius and South Africa that have higher wage costs and are running

> into capacity constraints are expected to fall at the lower end of the range of export supply elasticities. In contrast, Lesotho and Madagascar, which are witnessing large investments as

> firms try to exploit the lower wage costs, are likely to be able to expand output and exports more easily, suggesting that high export elasticities would be applicable to them.1<sup>3</sup>

l l These data are available from the authors on request.

12 The assumed coefficient is also broadly consistent with the figure obtained from inputoutput data for South Africa.

13 Faini (1994), one of the few studies on developing countries, reports an estimate of 3.4 for an aggregate export supply elasticity for Turkey. Thus a sectoral elasticity of 5 for small African countries need not be unrealistic. To a large extent, investment in the apparel
change in aggregate productivity from period _t −_ 1 to _t_ can be decomposed as follows: The first and second terms on the right-hand-side represent changes in within-firm productivity, and the covariance between productivity and market share, of firms that survive from _t −_ 1 to _t_ . The third term represents the contribution of firms that enter in period _t_ , weighted by the market share of entrants, _SE,t_ ; note that their contribution will only be positive if the productivity of entrants is higher than the productivity of surviving firms in period _t_ . Similarly, the last term represents the contribution of firms that exit in period _t −_ 1, weighted by the market share of exiters, _SX,t−_ 1, and will only be positive if the productivity of exiters is lower than the productivity of surviving firms in period _t −_ 1. It is also important to note that the productivity of a firm that enters in, for example, 1990, is associated with entrants when calculating TFP growth from 1989 to 1990, but with survivors when calculating TFP growth from 1990 to 1991. We use this method to calculate the change in TFP between period _t −_ 1 and period _t_ , then add the change in TFP to the existing level of TFP in period _t −_ 1. TFP is normalized to be zero in 1985. In this analysis, we do not use the sampling multipliers.

To use this method, we have to assign every firm in our panel to the category of survivor, entrant, or exiter in every year. Given the nature of our panel data, this requires two relatively strong assumptions. First, to address the fact that we do not directly calculate TFP for 1995-1997, we impute missing values for TFP and output for each series that bridges these years using linear interpolation. We perform a similar linear interpolation of TFP and output for individual firms for which we have bridged over another year.<sup>13</sup> Second, we have to make some assumptions regarding firm exit. When we observe a potential exiter, it is unclear whether the firm actually exited, whether it still existed but was not surveyed in the following year, or whether it was surveyed but we failed to match it.<sup>14</sup>

> 13For example, if a firm was surveyed in 1992 and 1994, and we are able to link that firms across those years, we use a linear interpolation to estimate TFP and output for that firm in 1993.

> 14In the pre-formed panel from 1998-2004, the third case is not a concern, though we are still unable to distinguish

17
3

**major focus of this paper, as suggested by the title, is** to measure the impact **of relative price changes** on **the growth of government as economies de'. ,p.**

**The paper measures the real growth of government by econometrically estimating price deflators for the public sector with the help of nominal government expenditure and GDP deflator series, using these deflators** to **create real government expenditure series for** 71 **countries, and estimating** the **income elasticity of** government **expenditure. Baumol's 'hypothesis** of increasing **costs in the public sector as well as Wagner's hypothesis of expanding** government size are tested. Although **these tests are** based on **time-series data, the difference in results** for **low-income,** middle-inlome and industrial **market economies permits some conjectures about the** long-tezm behaviour of **government size. This is** particularly **useful, since** analysis **of crosssectional data does not permit incorporation of relat:.ve price effects on** the size of governmente.

The findings of this exercise indicate that both Baumol's and Wagner's **hypotheses have to** be eit'.er reformulated or at least qualified in terms of the levsl of developmen' of the country under consideration. Statistical tests do not support Baumol's hypothesis. Still, one can detect a certain pattern in the evolution of public sector prices relative to those in the overall conomy by organizing the results according to per-capita income--level of government there should be no difference between a government distributing food stamps and one that runs soup kitchens--the former will be overlooked if one excludes transfers--or between a government that subsidizes education with education coupons and one that builds and operates schools and universities-the latter would be excluded if one would consider current expenditures onlv. The idea is to measure the extent to which a government chooses to incervene in the working of the market. By excluding a particular category of expenditure one would bias the analysis against goverrunents that prefer one kind of intervention instrument over the cther. Therefore, this study will use total government expenditure to measure the size of government.

r This inability of researchers to measure tre effects of changing relative prices on the changes in government size may well be the biggest problem of cross-sectional analyses. Nonetheless, like time-series studies, analysis of cross-sectional data is also fraught with problems. Gandhi [197l] reports that for developing countries income elasticities for various categories of government expenditures are less than unity--i.e., they do not support Wagner's hypothesis, while those for developed countries are greater than unity. He argues that this might be due to the choice of country groupings.
14 time effects m(t) is larger than before - 13 percent - but still negligible compared with the fraction explained by industry-specific effects c(i) and s(j,t) - 75 per cent.

These figures confirm that both aggregate and sectoral factors played a role in the 1992-93 output recovery of Polish manufacturing. However, the results suggest that structural, sector-specific factors largely outweighed aggregate, cyclical ones, in the period considered. We interpret our results as evidence that the transition reforns have started a significant process of structural adjustment, in both SOEs and private enterprises, and the strong output recovery has not simply been an across-the-board response of the economy to the new, favorable demand conditions, but has also reflected a permanent process of restructuring and resource reallocation 3.

The data allows us to address a further issue, i.e. the differential in perfornance between public and private enterprises. In order to gain insights into the extent of structural adjustment that has taken place in state enterprises, we compare the sectoral and temporal pattern of the productivity response of public enterprises with that of private ones. Our a _priori_ assessment, based on anecdotal evidence, aggregate data and the disaggregate statistics reported in Table 4, is that the private sector has been very responsive to the new system of microeconomic incentives brought by the reforms. Presumably. better management, easier availability of new technologies and capital inputs and the more flexible use of labor inputs have put private firms, by average, not too far from the production possibility frontier of Polish manufacturing. If this is true, we can use the private sector and its productive behavior as a benchmark. The closer is the observed pattern of productivity response of public enterprises to that of private ones, the greater the extent of structural adjustment and restructuring that we can infer is taking place in the public sector.

We therefore pooled public and private enterprises data, and tested if the same statistical model of equation (1) applied to both data sets. We tested for heteroschedasticity, Even allowing for some underestimation of cyclical effects deriving from our identification approach -- that we cannot avoid because of lack of data (see footnote 12) -- the substance of our results does not change.
# _(b) Food consumption score equation_

Tables 10 and 11 also show regression estimates for the impact of households’ characteristics on food consumption score. Both types of assets appear to be positively correlated with food consumption score, whereas agricultural assets losses have a negative effect, adding or not other controls. Interestingly enough, asset losses no longer have a significant effect on food consumption score in the June survey, while it was significantly negative in the February survey. Households thus seem to have been able to cope with the shock, some with their own means and others with the help of assistance programs.

Indeed, it is interesting to note that food assistance appears to have a positive effect on food consumption score in June, whereas it wasn’t significant in February. So it appears that five months after the disaster, assistance programs may have partially succeeded in responding to the shock, whereas the effects of food assistance on food security was not perceptible a few weeks after the seism.

Beside this, aid from abroad, savings and bank accounts all have a strong positive effect on food consumption score, although they are likely capturing the effect of other unobserved variables.

# _(c) Joint estimates of both equations_

There is a strong possibility that emergency assistance is actually endogeneous to food consumption score. Therefore, we try to use instrumental variables to estimate the probability of receiving assistance. Housing damage, location variables (i.e., where people sleep) and associations membership are potential instruments. However, when using multiple instruments in a single equation, it is not possible to reject the over-identification assumption with a standard Sargan test. Indeed, the instruments might be correlated with the error term in the food consumption score equation. Furthermore, it is not possible to reject the endogeneity of the assistance variable with a Hausman test when using any combination of instruments. So, this could also be an indication of the instrumental variables not being completely valid.

# _(d) Impact of displacements_

Focusing on households in non-directly affected areas, Tables 12 and 13 (in Appendix) show the estimates of the impact of the presence of hosted/displaced people in the household on 21
remittances received from abroad. Unfortunately, we do not know of a similar natural experiment used to study the effect of remittances arising from internal migration.

# **Consequences for income distribution at the place of origin**

Several studies investigate whether remittances increase or decrease inequality in rural areas. Stark, Taylor and Yitzhaki (1986) compare two Mexican villages: one which has had much internal out-migration, and another one which has been subject to more international out-migration to Southern California. Using household data, the authors are able to simulate the effect of small changes in the different types of income (nonremittance income, remittances from internal migrants, and remittances from international migrants) and to provide a fine analysis of household inequality in each village. Obviously, the impact of marginal changes in remittances upon inequality depends upon where the recipients of remittances are located within the village income distribution, on the share of remittances in village incomes, and on the distribution of remittances themselves. The authors find that in the village where many households have links with internal migrants, remittances from internal migrants have an equalizing impact on the village income distribution, whereas remittances from international migrants have an unequalizing impact. Stark, Taylor and Yitzhaki (1988) show the robustness of these findings using extended Gini decompositions. Other studies on Pakistan (Adams, 1994) and the Philippines (Rodriguez, 1998) also find that internal migration is more likely to decrease inequality, in contrast to international migration. The postulated explanation is the selectivity of migrants among the two types of migration. Furthermore, it has also been argued that the effect could also depend on the phase of migration when considered over time. Studying international migration from Mexico to the United States, McKenzie (2005) finds that when the pool of migrants is small, the upper-middle range of the wealth distribution benefit more from migration and remittances. When the pool has grown bigger, it gradually benefits poorer individuals. As already mentioned, the same is likely to apply to internal migration in developing countries since landlord families usually migrate before peasant families during the 39
# **Table 1: Economic Significance Ben Ali Firms in 2010**

|**Aggregate Contrib**<br>Onlyfirms wi<br>|**utions Ben Ali Firms**<br>th Y>0 andL>0<br> <br>|
|---|---|
|**(1)**|**(2)**<br>**(3)**|
|**Entire Economy**|**Sectors Governed by the Investment Code**|
||**Highly Regulated**<br>**(De Jure)**<br>**Other**<br>**(Free Entry and**|
|**Restrictions?**|**Modestly Regulated)**|
|**_Ben A_**<br> <br>|**_li Share_**<br><br>|
|Firms<br>0.2%|0.9%<br>0.2%|
|L<br>1.7%|10.5%<br>1.0%|
|Y<br>5.3%|42.6%<br>1.2%|
|Net Profits<br>15.8%|54.5%<br>3.3%|
|Gross Profits<br>10.8%|55.6%<br>2.3%|
|Gross Losses<br>5.6%|58.2%<br>1.7%|
|**_Sector_**<br> <br>|**_Totals_**<br> <br>|
|L – wage jobs<br>907.387|41.813<br>603.253|
|% of economy|4.6%<br>66.5%|
|Y billions USD<br>56.938|2.816<br>24.930|
|% of economy|4.9%<br>43.8%|
|Net profits billions USD<br>2.239<br>|0.358<br>0.795|
|% ofeconomy|11.5%<br>47.4%|

Note: USD:TND exchange rate1:1.5146, data for 2010, L=wage workers, Y=output, Net profits=pre-tax profits declared to the tax authorities (all firms), gross profits=pre-tax profits declared to the tax-authorities only for firms for whom this is positive. Gross losses=tax profits declared to the tax authorities only for firms for whom this is negative. Highly Regulated sectors are those that are subject to both authorization and restrictions on FDI. Other (Free Entry and Modestly Regulated) includes sectors which are not subject to entry restrictions and sectors subject to either only authorization or restrictions on greenfield FDI. Sectors which are not governed by the investment code but by specific laws are included in column 1.

28