rafmacalaba/datause-encoder-data huggingface dataset:
-19-

**Table 6: Composition of Personal Income According to Tax Returns, 1994**

||Amount<br>(HUF billion)|%|
|---|---|---|
|Income from work (wages, business income, small<br>farm production, individual activities)|1,385.4|80.8|
|Transfers|87.6|5.1|
|Other income, Income from abroad|54.4|3.1|
|Revenues accounted as expenses|147.9|8.6|
|Capital income|39.3|2.3|
|SUM: Total declared income|1,714.6|100.0|

Source: TARKI, 1995.

Capital income only makes up 2.3% of total declared income. This is partly explained by the fact that ,,capital income", as it appears in tax retums, comprises only interest and dividends on bonds and shares (domestic and foreign) plus the proceeds from the sale of bonds and shares. Interest from ordinary saving accounts does not need to be declared in tax files. It should also be mentioned that tax returns are a poor source for assessing capital income, since the reporting propensity is low. The survey results may therefore not reflect actual capital income in Hungary. For our purposes, however, these results are useful because they indicate the willingness of taxpayers (not) to declare their capital income in tax returns.

The more it is concentrated on wealthy households, the more pronounced is the distributional impact of capital income tax. Table 7 shows how strong the concentration of capital income is in high income households in Hungary. The highest household decile (the richest 10%) earns 81% of total declared capital income, compared to a share of 36% in total income (including capital income). The unevenness of the distribution is highlighted by a Gini coefficient of 0.84, as compared to 0.49 for tota income.2 26~~~~~~~~~~2

> 26 Note that the second column consists of total declared income, i.e., _includes_ capital income. The Gini coefficient excluding capital income would therefore be lower.
of Supply and Textiles, 1985, and Coleman and Thigpen, 1991), there have been no comprehensive studies to cover all the twenty elasticities which are needed in the model. Most of the past studies focused on the income and price elasticities of demand for cotton textiles by consumers (disaggregated by income levels), and what we can learn from them is that price elasticities in the cotton sector are generally very small.

In the multi-market model, the consistency among different elasticities is important. Considering that all the three markers are related with each other, it is not appropriate to mix the different elasticities from different sources into one elasticity set in this simulation model. Therefore, rather than relying on the results of past studies, this model computes most of the elasticities endogenously by assuming that the ratios among the elasticities depend on the cost structure for producers. In other words, the absolute value of the elasticity of demand of one input with respect to the price of that input divided by the supply elasticity of their final product with respect to the price of that product is equal to the cost share of the particular input in the long-run analysis (See **Appendix A** for proof). For example, the ratio between the supply elasticity of yarn with respect to cotton price and the supply elasticity of yarn with respect to yam price is - 0.6 (-0.12/0.2) because 60 per cent of the total cost of yarn production is considered to be cotton. In the short-nm, however, elasticities tend to be smaller as capital costs are fixed. For example, assuming that the capital cost for a spinning mill is 20 per cent, the elasticity of cotton demand with respect for the yarn price in the short-run is 20 per cent smaller than in the long-run. All the elasticities for India are shown in **Table 12.**

**Table 12:** **_Price Elasticities_**

|(A) Short-t|erm Ela|sticities|(India)||(B) Long-te|rm Elas|ticities (|India)||
|---|---|---|---|---|---|---|---|---|---|
|**Elasticitvfer**|**r**|**I**<br>p;|PY<br>|PT<br>|Elasticity for|\<br>w.r.t.|pC|Py|PT|
|Farmer|Sc|0.200|l<br>||Farmer|Sc|**0.200**|||
|Spinning|DC|-0.072|0.096 l||Spinning|Dc|-0.072|0.120||
|Mill|Sy|-0.120|0.160||Mill|Sy|-0.120|0.200||
|Textile|Dy||-0.032|0.072|Textile|Dy||-0.032|0.080|
|Weaver|ST<br>_<br>_-0.0|80||0.180|Weaver|ST||-0.080|0.200|
|Consumer|DT|-|-|-0.100|Consumer|DT|||-0.100|

where S= Cotton Supply, DC = Cotton Demand, Sy = Yarn Supply Dy = Yarn Demand, ST Textile Supply, DT Textile Demand Pc = Cotton Price, Py = Yarn Price, PT =Textile Price

**bold** (e.g. 0.02 for the supply elasticity of cotton with respect to cotton farmer in India) numbers are given exogenously, and the other numbers are computed endogenously.

The absolute values of the elasticities for the rest of the world are set to be two thirds of the equivalent elasticities for India, because cotton, cotton yarn and textiles are produced not only in developing countries but also in developed countries, vvhere elasticities are generally lower than developing countries.

34
**Figure 1:  Probability of bearing a son, by sex composition of woman’s existing children, China 1990**

<!-- Start of picture text --> Woman’s first child:<br>(53% of births)<br>normal probability of having a boy<br>Woman has only boy(s): Woman has only girl(s):<br>(18% of births) (21% of births)<br>8% reduced probability of 56% excess probability of<br>next birth being a boy next birth being a boy<br>Woman has both boy(s) and girl(s):<br>(8% of births)<br>12% excess probability of next birth<br>being a boy<br><!-- End of picture text --> Source: Das Gupta (2006), calculated from Zeng et al (1993).

11
**Table 7. 2SLS Regressions: Factors affecting education outcomes**

(White heteroskedasticity-corrected t-statistics in parentheses)

|**_Dependentvariable-+_**|||Failurerate|(naturallog)|||
|---|---|---|---|---|---|---|
|**_Independentvariables_**|_Eq.(7.1)_|_Eq.(7.2)_|_Eq.(7.3)_|_Eq.(7.4)_|_Eq.(7.5)_|_Eq.(7.6)_|
|GDPpercapitainPPPadjusted1990S(In)|-1.79|-1.44|-1.12|-1.27|-1.42|-1.11|
||(-2.31)|(-3.66)|(-2.39)|(-2.74)|(-3.72)|(-2.32)|
|Primatyeducationspending(Inofshareof|-1.13|2.38|3.07|-1.80|2.60|2.99|
|GDP)|(-2.62)|(1.61)|(1.51)|(-2.30)|(1.61)|(1.58)|
|EastAsia dummy|-3.72|-3.73|-3.42|-3.82|-3.72|-3.41|
||(-3.58)|(-4.83)|(-4.17)|(-3.73)|(-4.72)|(-4.06)|
|Adultilliteracyrate||||-0.02|0.003|0.001|
|||||(-1.03)|(0.16)|(0.07)|
|IndexofCorruption(least corrupt=6)||0.04|||0.05||
|||(0.29)|||(0.35)||
|QualityofBureaucracy (highest=6)|||0.11|||0.10|
||||(0.50)|||(0.43)|
|Indexofcorruptionxprimary education||-0.89|||-0.94||
|spending(InofshareofGDP)||(-2.95)|||(-2.94)||
|Qualityofbureaucracyxprimaryeducation|||-1.11|||-1.08|
|spending(InofshareofGDP)|||(-2.67)|||(-2.76)|
|Incomeinequality|0.03|0.02|0.02|0.03|0.02|0.02|
||(1.36)|(0.98)|(1.08)|(1.36)|(0.93)|(1.08)|
|PredominantlyMuslim|0.008|0.005|0.006|0.01|0.004|0.006|
||(1.40)|(1.03)|(1.21)|(1.66)|(0.56)|(0.78)|
|Ethno-linguistic fractionalization|-0.08|-1.22|-0.51|0.07|-1.30|-0.52|
||(-0.10)|(-1.26)|(-0.48)|(0.07)|(-1.23)|(-0.50)|
|Degreeofurbanization|<br>0.004|-0.001|-0.003|-0.0002|-0.001|-0.002|
||(0.33)|(-0.10)|(-0.25)|(-0.01)|(-0.06)|(-0.21)|
|Percentageofpopulationaged6to12|-0.009|-0.05|-0.02|-0.003|_-0.05_|-0.02|
||(-0.11)|(-0.65)|(-0.22)|(-0.04)|(-0.66)|(-0.24)|
|Dummyforyear1997|0.12|0.03|-0.15|0.07|0.04|-0.15|
||(0.34)|(0.09)|(-0.42)|(0.19)|(0.11)|(-0.42)|
|Constant|10.59|14.71|11.21|12.14|14.57|11.3|
||(2.25)|(3.68)|(2.72)|(2.40)|(3.76)|(2.69)|
|R<sup>2</sup> **offirst-stageregressions**|||||||
|Primaryeducationspending||.30|.32||.33|.36|
|<br>Primary education spendingx(corruption<br>orbureaucracy)||.41|.50||.43|.53|
|**Otherstatistics**|||||||
|R-squared|.54|.66|.63|.55|.65|.64|
|Numberofobservations|72|72|72|72|72|72|

_Note:_ Instruments: Neighbor's primary education spending, population, neighbor's primary education spending x index of corruption, Neighbor's primary education spending x index of bureaucracy.

In Table 8 we present our overall results on the impact of primary education spending on the "failure" rate. Once again we find evidence in support of the hypothesis that in a country with good governance, education spending is efficacious in lowering education failure rates. The elasticity of primary education failure with respect to public education spending is consistently negative and increasing in absolute size when evaluated at a good level of governance.

_22_
related with the endogenous regressors and yet be orthogonal to the error term. I present the formal tests of instrument validity in Section 5.4 but I would like to provide some intuition here. Capital share is expected to be negatively related to the output/imports ratio (Qit/Mit) given that Colombia is more likely to produce products with smaller capital content and import those rich in capital based on a comparative advantage argument. The materials prices affect the domestic output prices, hence Qit/Mitεit but not the tariffs of a given sector i, conditional on Qit, Mit, and εit. Scale is positively correlated with productivity and it is an inherent characteristic of a sector. The productivity of a sector is also expected to be affected from the embodied upstream productivity which is likely to be independent of the sector’s own tariffs.

Since the model is quite parsimonious, it is also prone to an omitted variable bias. The use of the fixed industry effects in equation (18) and its different versions, and the first differencing in equation (19) and its different versions should alleviate this potential problem along with the Instrumental Variables (IV) estimation.

In the rest of this section I present and discuss the data, results, and robustness of my estimations. In Section 6, I test how accounting for the endogeneity of tariffs with respect to productivity affects the regressions that analyze the impact of trade reform on productivity.

# 5.2 Data

The base data for the estimations span 1982 through 1998 but given the lack of tariff and production information for certain years the sample reduces to 1983, 1985, and 1988-1998. The tariff and effective rate of protection (ERP) figures are obtained from DNP (National Planning Department) of Colombia at the 8-digit product level,<sup>39</sup> which are then aggregated to the 4-digit ISIC sectors by using simple averages.<sup>40</sup> The 4-digit ISIC level import data come from the COMTRADE dataset, United Nations Statistics Division and the industry production data at the same level are available through UNIDO’s Industrial Statistics Database.

The productivity estimates, value added, input, and materials prices data are obtained from Eslava, Haltiwanger, Kugler and Kugler (2004), where each variable (except the value added<sup>41</sup> ) is aggregated from the firm level to the 4-digit ISIC industry level with production shares used as

> 39The product classification code, called “Nabandina”, is due to the Andean Community of Nations. I thank Marcela Eslava at Universidad de Los Andes/CEDE, Colombia for generously sharing the data.

> 40I use simple averages to be consistent with the earlier literature. An alternative way would be to use the import or production shares of each product as weights but these data do not exist for all sample years at this level of disaggregation.

> 41The value added is used to compute a measure of scale economies where it is an unweighted total in each sector.

19
professionals or 8 percent among technicians. Low or inadequate skills are thus an important factor behind high unemployment in Lithuania.<sup>2 0</sup>

**Table 18 Unemployment by Educational Attainment and Occupation, 2000**

||Unemployment|Sharein|Sharein|
|---|---|---|---|
||rate|unemployment|employment|
|Total|15.4|100.0|100.0|
|_Education_||||
|University|5.9|7.2|20.9|
|College|13.6|21.7|25.0|
|Uppersecondarygeneral|17.3|23.1|20.0|
|Uppersecondaryvocational|18.6|22.2|17.7|
|Lowersecondarygeneral|26.2|14.9|7.6|
|Lowersecondary vocational|24.0|8.7|_5.0_|
|Primaiyorless|9.3|2.1|3.8|
|_Occupation_||||
|Managers|8.2|4.0|8.2|
|Professionals|3.6|3.0|14.3|
|Technicians|8.4|3.9|7.7|
|Officeclerks|16.2|_5.1_|4.8|
|Serviceandsales workers|15.2|12.3|12.5|
|Agriculturalworkers|2.0|1.8|16.1|
|Craftworkers|17.9|19.9|16.6|
|Machineoperators and assemblers|20.2|13.3|9.5|
|Elementary occupations|21.8|15.7|10.2|
|Armedforces|4.2|0.0|0.2|
|Unknown|x|6.2|0.0|
|Notpreviouslyemployed|x|14.9|x|

_Source: Laborforce, employment and unemployment (survey data), 1997-2000, Statistics Lithuania,_<sup>_Vilnius 2001;_</sup> _Bank staffcalculations._ In order to assess the magnitude of the skills gap, let's carry out the following thought experiment. Imagine that that the number of available jobs grows to the point where there is enough jobs for all the unemployed. Assume that jobs for each education

> 20 In prnciple, a flexible wage structure would make it possible<sup>toabsorbthe excesssupplyof low productivity</sup> worker. We examine wage flexibility and institutional constraints thereto in the proceeding sections.

_50_
Croke et al. (2016) studies]” (p.177-8). This suggests that just focusing on the weight outcome, deworming is highly cost effective relative to another widely implemented intervention. As noted by Galloway et al. (2009), school feeding is implemented in over 72 countries by the World Food Programme alone. To the extent that school feeding programs aim to produce child weight gain, deworming is likely to be a highly cost effective option for policymakers who already support school feeding.<sup>28</sup> Of course, a complete cost-effectiveness analysis of mass deworming treatment would also need to consider effects on later educational and labor market outcomes, in addition to child weight gains, and as noted above these are often substantial (Ahuja et al., 2015; Baird et al., 2016; Bleakley, 2007; Croke, 2014; Ozier, 2015).

# **6 Conclusion**

We began with the question of whether the expected benefits of mass drug administration according to WHO guidelines for deworming exceed the cost.

To summarize, the null hypothesis of a common effect of zero weight gain from multiple-dose mass drug administration is rejected at the 10% level using the TMSDG data. Employing either of two classification systems used in previous Cochrane Reviews would lead to rejection at p<0.01. Any one of five other updates to the data leads to rejection of the null hypothesis at the 5% level. Combining all updates leads to rejection with p<0.001.

> 28 Note that this comparison is imperfect for a number of reasons, including that the cost figures for deworming are for India whereas those for school feeding are based in three African countries. Moreover, both school feeding and deworming may affect other outcomes. However, it is unlikely that appropriate adjustments for these factors would overturn the conclusion that deworming is highly cost effective in increasing weight relative to school feeding programs, given that the cost per kg of weight gain is an order of magnitude higher for school feeding. Moreover, while school feeding may also promote school participation, deworming, too, has been found to be highly cost effective in increasing school participation (Miguel and Kremer, 2004).

27
better rewarded in the public sector. It may simply be a relic of the past policy _of allocating_ educated labor to public sector positions. Most of the workers in the private sector have primary education or less, while public sector workers usually have at least a primary education. However, the fact that in the public sector workers with no or very little education earned more, on average, than primary, secondary or vocational school graduates (Table 2) suggests that there are significant distortions in public sector pay. It is likely that factors other than education (perhaps, as in China, membership in the Communist Party) had an impact on public sector pay in Vietnam, as in other command economies.

Table 5: Vietnam: Earnings Functions by Sector of Employment (both sexes)

||Sector of E<br>Public|mployment<br>Private|
|---|---|---|
|Constant|0.265|1.168|
|Years of schooling|0.062|0.039|
||(6.3)|(4.0)|
|Years of experience|0.046|0.072|
||(4.4)|(8.4)|
|Experience-squared|-0.001|-0.001|
||(3.3)|(7.3)|
|Log hours-worked|0.878|0.617|
||_(10.5)_|(6.7)|
|R-squared|0.145|0.085|
|N|950|1,309|
|Source: VLSS 1992-93|||
|Note: t-statistics in parentheses|||

The earnings function is fitted using dummy variables for those who have completed a particular level of education (Table 6). The results are reasonable, except that for males the coefficient on primary education is not significant. Also it would appear that those with less than a complete primary education earn higher wages in the public sector than do workers with a 16
exporters over the whole 2002-2008 period and this proportion is even higher over the whole 2008-2011 period. The HS 6-digit product intensive margin accounts for the majority of long-run export growth of continuing exporters in their continuing destinations over the whole 2002-2008 period but the churning across new and dropped products played a meaningful role, accounting for close to a quarter of that growth and an even larger share over the whole 2008-2011 period. When the extensive margin refers to HS 12-digit products, the percentage of long-run growth of continuing exporters in their continuing destinations accounted for by net product entry is actually 60%, which reveals a substantial degree of experimentation and resource reallocation within Turkish exporters.

Third, the micro-dynamics of sectoral export growth reveal across all sectors a dominance of the firm intensive margin in accounting for both short-run and long-run export growth and a dominance of the destination intensive margin in accounting for both short-run and long-run export growth of continuing exporters. In all sectors net exporter entry accounts for a larger proportion of long-run growth than short-run growth, whereas net destination entry accounts for a larger proportion of continuing exporters’ long-run growth than short-run growth. The uniformity of patterns across sectors is a novel finding in the literature which is interesting and perhaps even curious in light of the heterogeneity during the 2000s in global demand, trade policies, technological developments, and other policies and shocks affecting different sectors.

Fourth, the micro-dynamics of bilateral export growth shows across all groups of destination markets a broad preeminence of the firm intensive margin in accounting for both short-run and long-run export growth and a preeminence of the HS 6-digit product intensive margin in accounting for both short-run and long-run export growth of continuing exporters. Noteworthy exceptions to the former are the importance of net exporter entry in accounting for long-run growth in exports to the Rest of the World (which covers for example China and India) over the whole 2002-2008 period and in exports to the EU and EFTA over the whole 2008-2011 period. In all groups of destinations, net exporter entry accounts for a larger proportion of longrun growth than short-run growth whereas net product entry accounts for a larger proportion of continuing exporters’ long-run growth than short-run growth between 2002 and 2008.

Fifth, our evidence shows a differential role of the intensive versus the extensive margin in explaining long-run export growth over the whole 2003-2011 period for different cohorts of firms: churning across destination markets and churning across HS 6-digit products (for 4
financial support from the World Bank, Canadian International Development Agency, and World Food Program. These surveys collect nationally representative data in a two-stage procedure where primary sampling units or enumeration areas (psu) are randomly drawn in the first stage with stratification, and households are randomly selected from each psu in the second stage. The survey questionnaires consist of a variety of modules on household composition and characteristics, education, health, migration, household expenses and consumption, household perception of economic status, unexpected shocks, and community activities. However, for costsaving reasons, in 2011 only a subsample (around one-third) of the sampled households were administered the full questionnaire with a household consumption module, while the rest were administered a more abbreviated questionnaire. We focus on this subsample of the survey in this round, and the samples available for our analysis thus hover around 13,329 and 5,904 households, respectively, in the first and second survey rounds. After restricting the sample to an appropriate age range for constructing the synthetic panels, our final samples are 8,584 and 4,030 households for the two survey rounds.

One caveat exists on the timing and thus comparability of the surveys. ESPS 1 was implemented between December 2005 and March 2006, while ESPS 2 was implemented between August and November 2011. Since the harvesting season takes place between October and December, the difference in the timing of the surveys may differently affect household reporting of consumption during the preceding 12 months prior to being interviewed. We believe our estimates are thus likely to provide somewhat conservative (downward biased) figures on poverty decline between 2006 and 2011, since ESPS 1 was implemented right after the harvest season.

7
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