rafmacalaba/datause-encoder-data huggingface dataset:
_9_

# IV. THE IMPORTANCE OF INDUSTRIAL PRODUCTS IN CEE-5 ACCESS TO EC MARKETS

Industrial products, as defined in Chapters 25 through 97 of the CN, consist not only of manufactures but also of some agricultiral materials, mineral fuels, and ores and metals. The equivalent of this group in the SITC is much broader than a standard description of manufactures. Accounting for products excluded from the provisions concerning industrial goods,'<sup>5</sup> this group includes the following SITC .Rev. 3 items: (2-21-22-29-24403-24404-2631-2632-2651-2652-26851) + 3 _+_ (559223)+6+7+8+(9610+971).

The provisions concerning industrial products are the most significant part of the lTAs for two reasons. First, these products account for more than three quarters of EC imports from the CEE-5 (see Table 1). With 93 percent of their exports to the EC fallig into this group, the FCSK and Romania had -he largest share among CEE-5 countries. Bulgaria and Hungary, with strong specilization in agricultural products, had the lowest shares.

The importance of the industrial product group is further underscored by the fact that the share of industriat produc in EC imports increased significantly for all CEE-5 economies in the 1988-92 period, except for Romania. Some portion of this increase in 1992 may be attibuted to a 5-8 percent fall in agricultural imports caused by adverse weather conditions." However, this decrease was more than offset by increased imports of industrial products triggered by the decline in domestic demand following the implementation of stabilization-cum-transformation programs and the collapse of previous intra-CMEA trade.'<sup>7</sup>

**15** Anmex 1 of the ITAs identifies 10 six digit CN items faling into chapters 25-97 but excluded from this group: albumins (CN 3502), natural cork (CN 4501), cotton, not carded or combed (CN 520100), flax, not spun (CN 5301), and hemp, not spun (C:{ 5302). In terms of SITC.Rev.3., these are: 0253, 59223, 24403, 24404, 2631, 2651, 2652.

16 The value of imports fell by 5 percent from Bulgaia, by 7 percent from Hungary, and by 8 percent from Poland.

**1'** The exception was Romania whose exports comtinued falling **(in** 1992 they fell by -0.5 percent). Other CEE5 economies recorded double digit growth rates. As a resut, between 1988 and 1992 the value of industrial imports from Bulgaria increased by 115 percent, from the FCSK by 183 percent, from Hungary by 127 percent, and from Poland by 152 percent.
Table 1: Calibration parameters

|Notation|Variable|Value|
|---|---|---|
|_β_|Discount factor|0.97|
|_δ_|Depreciation rate|0.1|
|_α_|Capital share in production|0.33|
|_Ad_|Productivity: developed economies|1|
|_Aem_|Productivity: emerging economies|0.4|
|_ρ_|Size of emerging market (relative to developed)|10|
|_l_|Labor supply|1|
|_θ_<sup>_d_</sup>|Public backing as a share of output: developed economies|0_._7|
|_θ_<sup>_em_</sup>|Public backing as a share of output: emerging economies|0_._35|
|_γ_|Private liquidity as a share of capital: developed economies|0_._078|
|_kd,_0|Initial capital level: developed economies|3_._97|
|_kem,_0|Initial capital level: emerging economies|0_._15|

economies, in particular China and India.<sup>11</sup> The value of _ρ_ was chosen to roughly match the population of BRIC countries (Brazil, Russia, India and China) relative to the US.

I use the following procedure to calibrate _γ_ . According to Gorton et al. [2012], privately backed claims (including MBS, ABS, corporate bonds and loans) accounted for about 25% of the supply of liquid claims in the US in 2006. I choose _γ_ to match this proportion in the integrated steady state. Importantly, this proportion approaches its steady state level quite quickly: at _t_ = 4 (the timing of the calibrated “shock”), this share is already 23%. Thus, the calibration of _γ_ would be roughly the same if I were to choose _γ_ to match this proportion at _t_ = 4.

> 11For the US, the average value of M3 as a percent of nominal GDP is about 70% between 1960-2011. It is worth noting that _P Y_<sup>_<u>M</u>_</sup><sup><u>3</u>intheUSisessentiallytrend-lessinthis</sup> period, despite the fact that the US has experienced high GDP growth. This feature of the data is consistent with the assumption that _θ_ is a time invariant parameter that does not change with GDP. Unfortunately, data for China is available only for the years 1977-1982. For these years, M3 was 30% of nominal GDP on average. More data is available for India. Between 1977 and 1992, the average M3 over nominal GDP in India was 38%. Source: World Development Indicators.

25
**Table 1. Variables and sources**

|**Variable**|**Source**|
|---|---|
|_United States_||
|Unit of observations|Metropolitan statistical areas,2005–08|
|Population|Bureau of Economic Analysis|
|Income|Bureau of Economic Analysis|
|Consumption|Bureau of Economic Analysis, American<br>Community Survey (constructed, Desmet and Rossi-<br>Hansberg,2012)|
|Hours worked|Current Population Survey (constructed, Desmet and<br>Rossi-Hansberg,2012)|
|Capital|Bureau of Economic Analysis, American<br>Community Survey (constructed, Desmet and Rossi-<br>Hansberg,2012)|
|_China_||
|Unit of observations|Districts underprefecture-level cities 2005|
|Population|China citystatistics|
|Income|China citystatistics|
|Consumption|China city statistics (constructed, Desmet and Rossi-<br>Hansberg,2012)|
|Hours worked|2005 1 Percent Population Survey (constructed,<br>Desmet and Rossi-Hansberg,2012)|
|_Mexico_||
|Unit of observations|Metropolitan areas,1989,1994,2000,and 2005|
|Population|Mexican census|
|Income|Encuesta Nacional de Ingresos y Gastos de los<br>Hogares(microdata,constructed)|
|Consumption|Encuesta Nacional de Ingresos y Gastos de los<br>Hogares(microdata,constructed)|
|Hours worked|Encuesta Nacional de Ingresos y Gastos de los<br>Hogares(microdata,constructed)|

_Source:_ Authors.

To make reasonable cross-country comparisons, another relevant point is to use comparable geographic units. In the United States, metropolitan statistical areas should capture meaningful economic geographies, making them preferable to using counties or other localities. In China, prefecture-level cities cover the entire country and should thus be understood as metropolitan areas with their rural hinterlands, making them hard to compare with metropolitan statistical areas in the United States. That is why we rely instead on the districts under prefecture-level cities, which capture the urban part of the country and thus make them comparable to metropolitan statistical areas. In Mexico, we also use metropolitan areas, as defined by the country’s national statistical institute.

Once we have measures of these three determinants for each city in a country, we can do counterfactual policy exercises. For example, we ask what would happen if efficiency differences across Chinese cities fell to the U.S. level. Would this shrink China’s megacities or expand them? Would this improve the well-being of Chinese citizens? Which cities would gain and which would lose? Given that technology diffusion is likely to mitigate
deeply rooted in Rajasthan’s cultural heritage, have emerged as significant employers, contributing approximately 12 percent to rural non-farm employment (Vyas and Mehta, 2018). Construction has shown substantial growth, particularly in regions surrounding urban centers like Jaipur, Udaipur, and Jodhpur, with its share in rural employment increasing from 4.5 percent in 1993-94 to over 15 percent by 2011-12 (Kumar and Sharma, 2020). Tourismrelated activities have created significant non-farm opportunities in rural areas near heritage sites and wildlife sanctuaries, though these opportunities often exhibit seasonal variations (Rathore and Singh, 2019).

However, access to non-farm opportunities remains highly unequal. Education plays a crucial role, with workers having secondary education being three times more likely to secure regular non-farm employment compared to those with primary education only (Sharma et al., 2018). Gender disparities are particularly pronounced in Rajasthan, with female labor force participation in rural areas declining from 35 percent in 1993-94 to 28 percent in 2011-12, despite male workers’ increasing movement into non-farm employment (Gaur and Joshi, 2021). This gendered trend reflects both cultural norms and limited suitable employment opportunities for women. Regional variations within Rajasthan are stark. Eastern Rajasthan, benefiting from better connectivity to Delhi-NCR and relatively better rainfall, has seen faster growth in rural non-farm employment compared to western regions (Singh and Mathur, 2022). Districts with better infrastructure and proximity to urban centers show significantly higher non-farm employment growth, with rural areas within 50 km of major cities having 40 percent higher non-farm employment rates compared to more remote regions (Sharma and Kumar, 2021). State-specific initiatives like the Rajasthan Rural NonFarm Development Programme (RNFDP) and various tourism-based livelihood programs have attempted to address these disparities, though significant challenges remain in ensuring equitable access to quality non-farm employment opportunities.

Despite extensive research on rural non-farm employment (RNFE), significant knowledge gaps persist that limit our understanding and ability to design effective policies. These gaps span multiple dimensions of the rural non-farm sector and its role in development, particularly in how individual characteristics interact with local economic conditions. While research has separately examined the roles of education, gender, and social identity in determining access to non-farm employment (Lanjouw and Murgai, 2016), we have limited understanding of how these individual characteristics interact with district-level factors such as infrastructure, market access, and financial development. This interaction is crucial for understanding why similar individuals might experience different outcomes across locations. The operational dynamics of rural enterprises represent another critical area requiring further investigation. While we understand broad constraints facing rural enterprises, we lack 5
22 doubling of all nominal piece rates. Inflation helped (and it will continue to do so). However, the more important factor was that the available work on EGS shifted toward activities which are paid at lower piece rates. This allowed considerable "buffering" of the average EGS wage rate from the doubling in the piece rate schedule.

ii) There was a sharp fall in EGS employment after the increase in wage rates. Employment fell by about one-third. However, this alone does not imply that the authorities introduced rationing. There are other variables to consider. The two crop-years after the wage increase were good for agriculture, thanks to excellent and timely monsoons, particularly in 1988/89.

The comparison of aggregate statistics on EGS before and after mid-1988 is thus consistent with rationing, but it is not convincing evidence. Only by careful modelling of the determination of employment on EGS can we hope to answer the question of whether or not the imposition of higher minimum wage rates led to a significant and substantial change in the scheme, whereby an unfulfilled demand for relief work emerged on a large scale.

Our econometric investigations suggest that this is what happened. We have presented three empirical observations to support that conclusion; i) A model of EGS employment (measured by an attendance count) was estimated on monthly data over 13 years prior to the doubling of the minimum wage rate in mid-1988. The model behaves pretty much as one would expect over this period; EGS attendances respond positively to the EGS wage rate, negatively to foodgrain output, and have the expected response to unusual rainfall patterns ar.d the normal seasonality of agricultural operations. However, this changes markedly after mid-1988. The model predicts substantially higher EGS attendances than those actually observed after the
infrastructure, basically measuring whether a district has more than the sample median of land area with any evidence of ever having had irrigation (Siebert et al 2007). Irrigation can help farmers cope with rainfall deficits, but as mentioned earlier, the vast majority of African farmland is not irrigated. Besides the overall change in moisture between census years, the variability of conditions within and between years could also affect farmers’ decisions to migrate. High variability could be important in encouraging exit from the rural sector because of higher uncertainty. Noise in climate trends could delay migration responses because signals are noisy. We therefore experiment with measures of rainfall uncertainty and noisiness. Finally, when rainfall is normally spread evenly over the year, as opposed to being highly concentrated in 1 or 2 months, as is typical in monsoon climates, this may accentuate or mute the effects of overall changes in moisture. To measure this degree of “inequality” in rainfall across the year, we use a Gini measure, where the Lorenz curve is the accumulation of annual rainfall across months of the year (using data for 1950-69) ranked from lowest to highest rainfall and the Gini, as usual, is the degree of deviation from the 45 degree line, which corresponds to equal rainfall in every month.

In Table 1 we present summary statistics on the estimating variables for all countries and for the more arid ones. The average annualized growth rate of moisture is negative, consistent with Figure 2, and the average growth rate in the urban share is positive. We are concerned that outliers in these variables could reflect measurement problems. For example, an extremely high urban share growth rate could be due to a low poorly measured base. An extremely high or low moisture growth rate could reflect intercensal changes in the density of weather stations, especially in arid regions. We thus trim 0.3% from the top and bottom of the distribution of growth in urban share and in moisture, a total of 8 observations for the whole sample. The reported arid sample in Table 1 is trimmed. We report the impact of trimming in footnotes.

# **5.3 Base specification results**

Table 2 presents basic panel results for the growth rate in urban share as a function of the growth rate of moisture between censuses. Column 1 shows that there is no effect on average. In columns 2-4 we allow the base effect for arid countries to be isolated from the effect for moist countries. Columns 3 and 4 introduce controls, first alone and then also interacted with the moist country dummy. The most notable and robust result is a large negative effect that is limited to arid countries. In columns 3 and 4 (with little difference between them), a one standard deviation increase in moisture growth in arid countries (0.016 from Table 1) leads to an increase in the annual growth of urbanization of 0.005, which 17
**Figure 4. Predicted consumption distribution, Model 3**

<!-- Start of picture text --> Bangladesh 2015-2018/19 Malawi 2016/17-2019/20<br>13.5<br>11.5<br>13<br>11<br>12.5<br>12<br>10.5<br>11.5<br>10<br>11<br>5 10 25 50 75 90 95 5 10 25 50 75 90 95<br>Percentile Percentile<br>Nigeria 2012/13-2018/19 Tanzania 2019/20-2020/21<br>15<br>12<br>14.5<br>11.5<br>14<br>11<br>13.5<br>10.5<br>13<br>10<br>12.5<br>5 10 25 50 75 90 95 5 10 25 50 75 90 95<br>Percentile Percentile<br>Vietnam 2014-2016<br>11<br>95% CIs of true value<br>10.5<br>True percentile value<br>10<br>95% CIs of imputed value<br>9.5<br>Imputed percentile value<br>9<br>8.5<br>5 10 25 50 75 90 95<br>Percentile<br>Log of consumption Log of consumption<br>Log of consumption Log of consumption<br>Log of consumption<br><!-- End of picture text --> 43
39 Figure 2

# TRADED GOODS BASED RER MEASURES

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# COMPOSITION OF GOVERNMENT EXPENDITURE

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Policy Research Working Paper 9001

# **Abstract**

Mexico’s conditional cash transfer program, PROSPERA, has demonstrated short- and medium-term positive effects on health and education, including: increased children’s height; decreased risky behaviors among adolescents, including the postponement of parenthood; and increased years of schooling. This paper explores whether these effects lasted in the long-term and translated into positive changes in outcomes across generations. This study uses the most recent PROSPERA Evaluation Survey (ENCEL 2017) and combines it with previous waves and with the 1997 Socioeconomic Characteristics Survey (ENCASEH). Using intergenerational mobility analysis and quasi-experimental methods, this study finds strong evidence of positive absolute intergenerational mobility in height and years of schooling. The findings show that, on average, male offspring are 2.8 centimeters taller and have 5.3 more years of schooling than their providers (usually their parents), while female offspring are 4.1 centimeters taller and have 5.7 more years of schooling than their providers. These intergenerational gains are relevant not only because they reflect improvements in human capital, but also because these improvements have a positive return to investment. The study finds that a 1 percent increase in height is associated with a 10.7 and 8.8 percent increases in hourly wages for men and women, respectively. The analysis finds that a one-year increase in schooling is associated with 3.4 and 4.8 percent increases in hourly wages for men and women, respectively. These results show that PROSPERA has been successful in helping children and youth build human capital through better health and education, which has led to positive returns in the labor market.

This paper is a product of the Social Protection and Jobs Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at jpgutierrez@unam.mx and the coordinators of the studies may be contacted at cavilaparra@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
# Corporate Tax Rates, Allocative Efficiency, and Aggregate Productivity<sup>_∗_</sup>

Marcos Dinerstein<sup>_†_</sup> and Fausto Patiño Peña<sup>_‡_</sup> _JEL Codes:_ D24, H25, H32, O23, O47.

> _∗_ For helpful comments, we thank Anmol Bhandari, David Bradley, Gabriela Cugat, Keyvan Eslami, Carlos Esquivel, Salomón García, Rafael R. Guthmann, Kyle Herkenhoff, Loukas Karabarbounis, Pete Klenow, Ben Malin, Ellen McGrattan, Jim Schmitz,Venky Venkateswaran, and participants of the Quantitative Macro Workshop at the University of Minnesota. The views expressed herein are those of the authors and not necessarily those of the Penn Wharton Budget Model or the World Bank Group. All mistakes are our own.

> _†_ Penn Wharton Budget Model. 3440 Market Street, Suite 300. Philadelphia, PA 19104 (email: mdiner@wharton.upenn.edu).

> _‡_ World Bank Group. 1850 I St NW. Washington DC, 20005 (email: fpatinopena@worldbank.org).

1
# **Table A-3 (continued)**

# **(ii) Brazil** **<u>Educational Multinomial</u>**

|_Variable_|_White_<br>_Men_<br>(N=43,303)|_Nonwhite_<br>_Men_<br>(N=36,820)|_White_<br>_Women_<br>(N=48,285)|_Nonwhite_<br>_Women_<br>(N=36,736)|
|---|---|---|---|---|
|Age|||||
|Age^2||See Table A|-2 (i);||
|Mother's years of schooling||full results|||
|Mother's years of schooling^2||for all|||
|North||18 categorie|s||
|Northeast||by race-gen|der group||
|South||available|||
|Center-West||upon|||
|Urban||request|||
|Constant|||||

# **<u>Demographic Multinomial</u>**

||_White Wom_|_en_|(N=38,989)|||_Nonwhite W_|_omen_|(N=28,443)|||
|---|---|---|---|---|---|---|---|---|---|---|
|_Variable_|1 children|2 children|3 children|4 children|> 4 children|1 children|2 children|3 children|4 children|> 4 children|
|Age|0.077**<br>(0.009)|0.301**<br>(0.015)|0.422**<br>(0.026)|0.491**<br>(0.040)|0.623**<br>(0.059)|0.048**<br>(0.008)|0.164**<br>(0.011)|0.254**<br>(0.016)|0.380**<br>(0.022)|0.551**<br>(0.031)|
|Age^2|-0.002**|-0.004**|-0.006**|-0.007**|-0.008**|-0.001**|-0.002**|-0.004**|-0.005**|-0.007**|
||(0.000)|(0.000)|(0.000)|(0.001)|(0.001)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Years of schooling|-0.034**|-0.048**|-0.097**|-0.192**|-0.278**|-0.015**|-0.026**|-0.065**|-0.142**|-0.222**|
||(0.004)|(0.005)|(0.006)|(0.011)|(0.017)|(0.006)|(0.006)|(0.007)|(0.010)|(0.012)|
|Mother's years of schooling|-0.056**|-0.054**|-0.078**|-0.188**|-0.241**|-0.044**|-0.073**|-0.111**|-0.124**|-0.253**|
||(0.011)|(0.011)|(0.015)|(0.034)|(0.055)|(0.017)|(0.017)|(0.022)|(0.034)|(0.046)|
|North|0.544**|0.846**|1.056**|1.624**|1.703**|0.188**|0.469**|0.825**|1.054**|1.792**|
||(0.103)|(0.108)|(0.126)|(0.169)|(0.221)|(0.075)|(0.076)|(0.084)|(0.107)|(0.108)|
|Northeast|0.329**|0.501**|0.745**|1.196**|1.541**|0.092*|0.272**|0.429**|0.607**|0.932**|
||(0.049)|(0.052)|(0.064)|(0.092)|(0.113)|(0.045)|(0.047)|(0.055)|(0.073)|(0.081)|
|South|0.083*|0.036|-0.014|0.019|-0.135|-0.098|-0.005|0.049|0.045|-0.213|
||(0.035)|(0.038)|(0.051)|(0.088)|(0.124)|(0.084)|(0.086)|(0.102)|(0.138)|(0.176)|
|Center-West|0.065|0.256**|0.394**|0.235*|0.100|-0.071|0.105|0.105|0.026|-0.269*|
||(0.052)|(0.053)|(0.067)|(0.120)|(0.172)|(0.061)|(0.061)|(0.072)|(0.100)|(0.126)|
|Urban|-0.020|-0.094|-0.191**|-0.351**|-0.696**|-0.013|-0.066|-0.243**|-0.409**|-0.636**|
||(0.048)|(0.050)|(0.061)|(0.086)|(0.099)|(0.052)|(0.053)|(0.059)|(0.072)|(0.073)|
|Constant|-0.199|-4.051**|-6.641**|-8.420**|-11.210**|-0.087|-1.979**|-3.801**|-6.637**|-9.861**|
||(0.178)|(0.273)|(0.464)|(0.719)|(1.079)|(0.189)|(0.225)|(0.310)|(0.420)|(0.574)|
<!-- Start of picture text --> Risk of layoff/reduced hours by income from work<br>(measured as probability from 0 to 1)<br>0.45<br>0.40,<br>0.35<br>0.30 —}—_}—5 ¢ +<br>0.25 - + - o<br>020-4 = o i bd e +<br>0.15 4 4 - +3, . a *<br>rs = , ets $<br>0.10 + > ° -$ - 2 ¢ +<br>0.05 + + F + o<br>0 Ceo lie oe SS eS een ee eS eS ae ae ee ee ee<br>2235 €s Sst #322 E52 8 2 § EF ¢s 2468<br>= 2 a § 2° 3% 5 & GG ee * 2 2 8 o a<br>5 2<br>*high —middle + low<br>Data not available for Germany<br>Source: Eurostat eurostat@l<br><!-- End of picture text --> <!-- Start of picture text --> Risk of losing job by income from work<br>(measured as probability from0 to 1)<br>0.14<br>o12 7<br>0.10 ,<br>0.08<br>0.08 $—I— 5-5 —F<br>0.04 j 1} fo» 1;<br>e T Ps 7 3 4 el a : 6 ecw «5<br>0.00 © moe oe i e : ° os - ;<br>s= . —v2 . a hae i are = = ore<br>SETTER~g = £3 2 EGEs e 2 & 5STEER3 REGFesaE#<br>a <** € 62 5ga 6 3 *eag8 88<br>5 5<br>*high -middie + low<br>Data not availadie for Germany<br>Source: Eurostat eurostat@TM<br><!-- End of picture text -->