rafmacalaba/datause-encoder-data huggingface dataset:
# **IV. Ukraine-EU FTA Outcomes: An Illustration of Stylized Results**

# **IV.1. Introduction**

The purpose of this chapter is to illustrate how the GTAP model introduced above can be employed in the course of a policy dialog Ukraine-EU trade liberalization in agriculture. It is intended to illustrate sorts of questions the model can be used to analyze, what sorts of results it can produce, and how these results can be interpreted.

It must be emphasized that the purpose of this paper is _not_ to present simulated results for specific, realistic Ukraine-EU FTA scenarios. Instead, two stylized Ukraine-EU FTA scenarios are simulated using the GTAP model. These scenarios and results are meant to serve as a starting point to familiarize decision makers and administrative officials – especially those who are not experts in trade modeling or related quantitative analysis – with the model and its capabilities and limitations. These simulations are preliminary because important experimental settings have not yet been incorporated into the model. In particular, the commitments that Ukraine has made in order to join the WTO are not yet reflected in the initial tariff levels (compare Figure 3-1). Work that is described in the next chapter addresses this issue with a view to formulating a baseline and scenarios that provide a more realistic basis for the analysis of FTA options. Even in its present preliminary form, however, the model is able to highlight important topics and generate insights into fundamental issues that are at stake in Ukraine-EU FTA negotiations.

In the following section, the aggregation of the GTAP model and the policy scenarios that are analyzed are specified. Section 3 presents key results from the simulation of these scenarios, and Section 4 concludes. The Appendix B contains 4 tables that provide basic information about the Ukrainian agri-food sector as well as Ukraine agricultural trade.

# **IV.2.** Specifying the GTAP model aggregation and the policy scenarios

## **_IV.2.1. Aggregation_**

Before a simulation can be carried out, the modeler must make several decisions about key aspects of the model. One of these aspects concerns the level of sector and country/region aggregation. The 105 country/region and 57 sector GTAP-7 database can be adjusted to whatever level of aggregation the modeler chooses by combining countries/regions and sectors into corresponding aggregates using software that is part of the modeling package. For the scenarios presented below, an aggregation of the GTAP database into the 26 sectors outlined in Table 4-1 and the 27 countries/regions outlined in Table 4-2 has been chosen. The country/region aggregation has been chosen to depict most of Ukraine’s important trade partners individually, and the sector aggregation is designed to take full advantage of all the agri-food products available in GTAP (only rice and processed rice are aggregated into one product) while simplifying the rest of the economy considerably. These aggregations can be modified in future applications of the model.

27
poverty reduction in Kagera seem to be representative for rural Tanzania as a whole: only some pockets in Tanzania, such as Dar es Salaam, have had substantial growth and poverty reduction, but this has not spread to other areas. This reflects the typical problem of land-locked, agriculture-based economies: how to deliver poverty reduction if the main engine of growth appears to be elsewhere.

In 2004 and 2010 follow-up surveys aimed to re-interview all individuals that were ever considered a household member in the baseline survey and were alive at the time of re-survey. A full household questionnaire was administered in a household where a panel respondent was found residing. Due to household dynamics, the sample size increased to more than 3,313 households by 2010.<sup>10</sup> Excluding households for which all previous members were deceased (17 households and 27 respondents), the KHDS 2010 field team managed to re-contact 92 percent of the initial households. Table 1 gives further details on the interview status at individual level. Starting from the baseline sample, 11.2% of the individuals were not found, while 0.5% refused to be interviewed. The attrition rates in KHDS are extremely low, even compared to much shorter term panels (Alderman et al., 2001). The average baseline household spawned 4.1 households by 2010, out of which 2.4 were non-migrant and 1.7 were migrant households.

There is occupational data for 1991/94 and 2010. Because the analysis is conducted at the household level, households are classified into farm<sup>11</sup> and non-farm based on the self-reported occupation of the head. In 2010, households were found in three cities: Dar es Salaam, Mwanza and Kampala. This is defining cities as locations with more than 500,000 inhabitants. There are a further 21 respondents who moved to areas that, while administratively recognized within Tanzania as cities, have a population below 500,000 (Arusha, Tanga and Mbeya). Results do not change if we change the definition from a population-based one to an administrative one.<sup>12</sup> The occupational and spatial data are

> 10 De Weerdt et al (2012) provide more details.

> 11 Farming includes both agriculture and livestock keeping and can be both in self-employment or working for others (typically as a casual labourer).

> 12 For Tanzania we use population projections per district based on the 2002 census data (URT, 2006b), which are available at the district level. These include the population living in the ‘green belts’ around the cities in question. Administratively Tanzania has the following cities (2002 population in brackets): Mbeya (358,939),

9
Table A10. Regional emissions intensity index of non-CO2 emissions, Southern Federal District, SUST-RUS sectoral classification (thousands tons of emission per trillion nominal RUB of output of 2006)

|**Southern**<br>**FD**|**Hydrocarbons**|**Carbon**<br>**monoxide**|**Nitrogen**<br>**oxide**|**Particulate**<br>**matter**|**Sulphur**<br>**dioxide**|**Volatile organic**<br>**components**|
|---|---|---|---|---|---|---|
||**CnHm**|**CO**|**NOx**|**PM**|**SO2**|**VOC**|
|CA_col|1 162,63||||||
|CA_gas||106,02||||32,62|
|CA_oil|306,23|2 205,15|36,52|||934,71|
|CB||546,32|59,23|||18,79|
|DA||13,77||||6,12|
|DB||95,91|20,20|||7,28|
|DD||347,81|15,15|||22,29|
|DE||122,54|64,86|||19,10|
|DF||95,31||||225,70|
|DG||275,68|56,28|||165,48|
|DH||||||105,11|
|DI||309,56|146,97|198,80||26,90|
|DJ||661,59|25,89||499,40||
|DK||88,71|14,33|||29,53|
|DL||47,79||||14,98|

106
not too large.

**Proposition 6.** _Starting from t_ = 0 _, a small increase in t will shift the low-skill labor-supply curve to the right if_

# _for each low-skilled household i._

# **Proof: See appendix.**

In this case, the introduction of the fine will lower the low-skill wage; lower the wage more for under-age workers; and increase the total labor supply of low-skill household members other than under-age workers (with the effect on the latter ambiguous).

The condition (4) is a very strong sufficient condition, and the rightward shift in the labor-supply curve will likely occur under much weaker conditions as well. Note that (4) is strictly stronger than (3) because _χ_ ( _i_ ) _<_ 1. It amounts to a requirement that _RRA∗χ_ ( _i_ ) _>_ 1, where _RRA_ is the coefficient of relative risk aversion. Estimates of _RRA_ in the developing world vary widely. Cardenas and Carpenter (2008) survey numerous studies and find values from 0.05 to 2.57. Yesuf and Bluffstone (2009) provide estimates from experiments in Ethiopia that range from close to 0 up to over 15, with an average around 4. Whether (4) would be satisfied or not in practice is not clear. On the other hand, Basu (2005) and Bharadwaj _et al._ (2020) show that, regardless of _RRA_ , imposing a fine on child labor can result in both an increase in child labor and in total unskilled labor in a model with a household subsistence consumption constraint, so the possibility of such an effect is much broader than (4) would suggest.<sup>7</sup> The contrast between the implications of the supply-side implementation and the demandside implementation of the child-labor ban is striking. The demand-side implementation implies much more adverse effects on low-skilled households. From Proposition 4, a supply-side ban, by withdrawing low-skilled labor from the market, tends to raise unskilled wages, but by Proposition 6, a demand-side ban tends to lower them, and lower them especially for the under-age child workers

> 7Indeed, the assumption that there is a minimum necessary consumption requirement, and that this constraint binds in households that use child labor, is equivalent to assuming a locally infinite _RRA_ . Under that interpretation, the estimates of average values of _RRA_ mentioned above are not relevant; only the value for child-labor-using households would matter, and that value may be much larger than the average.

12
# **Table 3**

# **Income and Transfers of Households in the Estimation Sample**

(Standard Deviation in Parentheses)

||1995 t<br>|o 1998<br>|2000 t<br>|o 2003<br>|
|---|---|---|---|---|
||No<br>Migrant|Has<br>Migrant|No<br>Migrant|Has<br>Migrant|
|Pre-Transfer Income Per Capita|1681|1342|1766|1419|
||(1679)|(967)|(2131)|(1539)|
|Net Transfer Per Capita Into Household|311|495|501|736|
||(739)|(759)|(1980)|(1315)|
|Share with Pre-Transfer Income...|||||
|Under 1/4 Poverty Line|0.046|0.020|0.083|0.059|
|Between 1/4 and 1/2 Poverty Line|0.062|0.069|0.092|0.098|
|Between 1/2 and Poverty Line|0.185|0.283|0.212|0.292|
|Between Pov and 2*Pov Line|0.362|0.393|0.273|0.301|
|Between 2*Pov and 4*Pov Line|0.272|0.190|0.223|0.184|
|Greater than 4*Poverty|0.074|0.045|0.118|0.066|
|Observations(HHs with Resident Over 60)|1,970|247|2,443|592|

Notes: In the 1995 Survey Round 494 of 3999 households had a resident over 60, by the 2003 survey round 816 had a resident over age 60.

35
# **Appendix Table 11. Heterogeneous effect of preschool availability on maternal employment by district’s population**

||||**Work par**|**ticipation**|||
|---|---|---|---|---|---|---|
|||DDD|||DDD-FE||
|Population:|< 0.5 mil|< 1 mil|< 1.5 mil|< 0.5 mil|< 1 mil|< 1.5 mil|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**Panel A: Publi**|**c**||||||
|Preschool *|0.026|0.038**|0.041**|0.031*|0.044***|0.054***|
|Eligible|(0.021)|(0.017)|(0.017)|(0.017)|(0.015)|(0.015)|
|<br>Preschool|<br>-0.020|<br>-0.012|<br>-0.010|<br>-0.018|<br>-0.013|<br>-0.011|
|density|(0.016)|(0.014)|(0.013)|(0.013)|(0.013)|(0.012)|
|<br>Eligible child|<br>-0.040***|<br>-0.038***|<br>-0.044***|<br>-0.025**|<br>-0.024***|<br>-0.030***|
||(0.012)|(0.008)|(0.007)|(0.010)|(0.006)|(0.005)|
|**Panel B: Privat**|**e**||||||
|Preschool *|0.002|0.000|0.000|0.003|0.001|0.001|
|Eligible|(0.003)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|Preschool|-0.001|-0.003|-0.002|-0.000|-0.002|-0.001|
|density|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|Eligible child|-0.041**|-0.032**|-0.038***|-0.028**|-0.022**|-0.026***|
||(0.016)|(0.012)|(0.012)|(0.012)|(0.010)|(0.009)|
|Observations|61,811|134,751|168,739|61,811|134,751|168,739|
|Number of|149|233|263|149|233|263|
|districts|||||||
|Mean|0.552|0.548|0.539|0.552|0.548|0.539|

Note: Sample includes all mothers with and without any preschool-aged eligible child (age 3-6). Comparison group includes non-mothers and mothers of children with the wrong ages. Sample is restricted to individuals living in districts with population as indicated in the column headings. Population count was based in 1996 because it was the earliest Susenas data covering all 290 districts. All columns regress work participation on preschool density, preschool eligibility dummy, and their interaction. All regressions include district, year, mother’s age fixed effects and an urban residence dummy. DDD excludes while DDD-FE includes individual fixed effects. Standard errors clustered at the district-level is shown in parentheses; *, **, and *** indicate statistical significance at 10, 5, and 1 percent, respectively. Number of observations, number of districts, and work participation means for each column are indicated at the last three rows.

78
The results reported in Table 9 show that the largest airports are the ones that registered faster productivity growth. In particular, those airports that handle between 7.5 and 10.0 million passengers per year posted an average annual growth rate of 5.4% for the whole period, and an even higher growth of 7.0% during the last sub period. Interestingly, the category made up by the three biggest airports in the region (CGH, GRU and MEX, which handle more than 10 million passengers per year), grew faster during the first sub period, but at a rather low rate over the two last sub periods.

Public airports appear to have performed better on average over the whole period compared to private airports (annual productivity changes of 2.9% and 0.7%, respectively). Nevertheless, if we focus on their evolution over the last two sub periods, for which the available information is more complete, both groups behaved quite similarly, registering negative productivity growth during the period 1999-2003 and positive growth between 2003 and 2007 (although with a slightly more favorable profile for public airports). These results are confirmed when we weight TFPC averages using work-load unites (WLU) as the weight variable.<sup>15</sup> Weighted averages give a better approximation of the productivity growth for the whole airport activity in the region. Since larger airports performed better than smaller ones, we see that the weighted average TFPC is higher than the non-weighted average (2.6% against 1.9%).

The results reported in Table 9 were evaluated applying the sensitivity analysis, bootstrapping method, introduced by Simar and Wilson (1999). Using FARE (Wilson, 2007), we compute 10% confidence intervals for each Malmquist TFP change observation in the sample. And using this information we distinguish observations with significant positive TFP change (>1.0), those with upper and lower confidence interval boundaries higher than 1.0, those with significant negative TFP change (<1.0), those having lower and upper boundaries lower than 1.0, and finally observations with no TFP change correspond to cases where the confidence interval includes the value of unity.

In all cases, the result of this sensitivity analysis showed a high correlation with the results presented in Table 9. For instance, among the observations corresponding to LAC public airports 57% and 22% showed, respectively, positive and negative significant TFP changes. While among private airports the shares of positive and negative changes were 43% and 29%, respectively. In the same vein, 73% of positive TFP changes were observed during the 2003 to 2007 period, while 39% during the period 1999 to 2003 and 57% during the first period. Finally, it is among the great size LAC airports that the proportion of positive annual TFP changes was the highest, 63% against 15% of negative changes, while the smallest airports, with less than 5 million passengers, positive and negative changes represented 42% and 34%, respectively.

> 15 One WLU is equivalent to one terminal passenger or 100 kg of freight or mail.

21
# **Fixed capital**

The data series on fixed capital is based upon national accounts data on fixed investment, which includes structures, equipment, machinery, and the like. The mapping from fixed investment to fixed capital follows the methodology of Ball et al. (1993). The capital stock is represented as a weighted sum of past investments where the sequence of relative efficiencies of capital of different ages serves as the weights. The following function of physical depreciation is chosen to describe the relationship between the efficiency of an asset and its age. Let _L_ be the lifetime of the capital good, and  be a curvature parameter bounded from above by 1 in order to restrict productivity to be nonnegative. Then, The asset is discarded at age _L_ , at which time its relative productivity becomes zero. To analyze this expression, we note that _dsj/dj = L(_  _- 1)/(L -_  _j)_<sup>_2_</sup> _< 0_ , for _0_  _j < L_ , indicating that the productivity falls with age (use). The speed of the change in the depreciation with age depends on the sign of curvature parameter,  : When  is positive but less than unity _d_<sup>_2_</sup> _sj/dj_<sup>_2_</sup> _< 0_ , the physical depreciation accelerates with time (use), and the productivity curve is concave. Conversely, when  is negative, the productivity curve is convex. The lifetime of the asset is taken as a random variable with a normal distribution truncated at two standard deviations on both sides. Figure 1 illustrates the dependence of the productivity paths of buildings and agricultural machinery on the parameters in question.<sup>9</sup> The data sources that we use do not provide any information on the separate components of fixed capital, and so, we choose a single set of parameters.<sup>10</sup> Thus the capital stock in any given year is the sum of the relative efficiency for that year of all past investments.

Data on the value of gross fixed capital formation in agriculture (in local prices) were obtained from national accounts data. If the data series on investment is not sufficiently long,

> 9 The _s_ curvature parameters ( _β_ ) and lifetime parameters ( _L_ ) in Figure 1 are taken from Ball et al. (1993). For buildings, _β_ =0.75 and _L_ =38 years; for agricultural machinery, _β_ =0.50 and _L_ =9 years.

> 10 Judging the available evidence, we used the following parameters: curvature of 0.70, mean service life of 20 years, and standard deviation of 8 years.

5
Policy Research Working Paper 6373

# **Abstract**

The Living Standards Measurement Study—Integrated Surveys on Agriculture project collects agricultural and livelihood data in seven countries in Sub-Saharan Africa. In order to maintain representativeness as much as possible over multiple rounds of data collection, a subsample of households are selected to have members that have left the household tracked and interviewed in their new location with their new household members. Since the sub-sampling occurs at the level of the household but tracking occurs at the level of the individual, a number of issues arise with the correct calculation for the sub-sampling and attrition corrections. This paper is based on the panel weight calculations for the initial rounds of the Integrated Surveys on Agriculture surveys in Uganda and Tanzania, and describes the methodology used for calculating the weight components related to sub-sampling, tracking, and attrition, as well as the criteria used for trimming and post-stratification. It also addresses complications resulting from members previously classified as having attrited from the sample returning in later rounds.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at khimelein@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **I. Introduction**

Capital inflows are prevalent in emerging market countries. In 2016, foreign investors invested around 64 billion U.S. dollars into emerging countries in the form of portfolio equity, i.e., foreign investors’ purchases of stocks of publicly traded emerging market firms. Despite this large magnitude, one can question whether capital inflows are economically important in spurring economic development in emerging economies. It is conceivable that large flows of equity associated with significant cross-border diversification of investors’ equity holdings might not translate into large effects on firms raising capital in equity markets. A central question is whether capital inflows benefit recipient countries by encouraging firms to issue new equity to fund investment and growth. Most of the evidence that has been assembled to address that question has been indirect, based on aggregate relations rather than firm-level behavior (Kose et al., 2009).

In this paper, we contribute to the debate about the impact of equity capital inflows by providing direct micro evidence at the firm level on the channels of transmission. We study how equity capital inflows affect the economies that receive them by analyzing their connection to equity financing (through firm-level equity issuance) and to real economic activity (through changes in corporate balance sheets). We investigate whether publicly traded firms in emerging countries issue more equity when their countries experience increases in foreign equity purchases and what firms do with the equity capital they raise. In particular, we ask whether the firms that raise new equity use those funds to finance corporate investment.<sup>1</sup>

> 1 We focus on equity financing, which is especially important for expanding the growth opportunities of firms. Most of the literature on financial globalization and financial liberalization has focused on equity markets. Moreover, a larger array of firms from emerging economies issue equity than issue public debt, which allows us to consider heterogeneous effects across a wider range of firms. Furthermore, well-known indexes for equity markets exist, which guide foreign investors to invest in those economies. In addition, financial statements data on firms across countries are mostly available for those firms listed in equity markets. These data are important to understand the real effects of capital inflows.

1