rafmacalaba/datause-encoder-data huggingface dataset:
|**All contributions**|-21.04%|0.4581|0.0732|0.0070|-0.0410|
|---|---|---|---|---|---|
|**All direct taxes and contributions**|-25.72%|0.4648|0.0799|0.0071|-0.0591|
|**Consumable income**|75.1%|||||
|All indirect subsidies|0.03%|0.1238|0.2611|0.0001|0.0000|
|Total VAT|-12.30%|0.2650|-0.1199|-0.0171|-0.0459|
|Total Excises|-4.38%|0.2143|-0.1706|-0.0078|-0.0134|
|All indirect taxes|-16.68%|0.2517|-0.1332|-0.0272|-0.0685|
|All taxes|-21.36%|0.3049|-0.0800|-0.0228|-0.0782|
|Alltaxes and contributions|-42.40%|0.3809|-0.0040|-0.0344|-0.2100|
|**Final income**|81.6%|||||
|In-kind health benefits|4.24%|0.0000|0.3849|0.0157||
|In-kind educationbenefits: preschool|0.04%|-0.0991|0.4840|0.0002||
|In-kind education benefits: primary level|1.22%|-0.2043|0.5892|0.0068||
|In-kind educationbenefits: secondarylevel|0.51%|-0.1533|0.5382|0.0026||
|In-kind education benefits: tertiary level|0.50%|0.1275|0.2574|0.0012||
|Alleducationtransfers|2.27%|-0.1175|0.5024|0.0108||
|All in-kind transfers|6.51%|-0.0410|0.4259|0.0257||

# **Direct and indirect taxes**

Table 4 shows that direct taxes and social contributions in Serbia have a small contribution to reducing inequality. They are slightly progressive, as shown by the positive Kakwani coefficient. They are also inequality-reducing. However, the marginal contributions of direct taxes to redistributive effect of 0.005 is smaller than that observed in other countries, for example, 0.007 in Albania (2015) and 0.03 in Croatia (2014).<sup>15</sup> The flat personal income tax with a tax allowance gives little progressivity. Since taxes and contributions take away from market income, they are poverty increasing, as shown by the negative marginal contribution to poverty reduction (poverty is measured using the US$5.5/day in 2011 PPP poverty line).

On the other hand, with respect to current income, indirect taxes are regressive (negative Kakwani index). They contribute to an increase in both poverty and inequality. Both VAT and excise taxes are regressive. Excises have a smaller negative impact on poverty and inequality than VAT since they are smaller in size. Appendix 1 analyzes the incidence of indirect taxes with respect to consumption, i.e., looking at welfare over the life cycle. Indirect taxes taken together in Serbia are relatively unequalizing with their marginal contribution to reducing inequality of -0.027 Gini points. While the effect is smaller than that for Croatia, it is more unequalizing than many Latin American and developing countries.<sup>16</sup>

# **Social spending**

All contributory and non-contributory benefits are progressive. They contribute to reducing both poverty and inequality, as shown in the positive marginal contributions of these items (Table 4, which shows the results for the main social benefits as captured in the data). Among

> 15 Inchauste and Rubil (2017). “The Distributional Impact of Taxes and Social Spending in Croatia”. World Bank Policy Research Paper #8203.

> 16 Inchauste and Rubil (2017). “The Distributional Impact of Taxes and Social Spending in Croatia”. World Bank Policy Research Paper #8203.

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Migrants into the NE from SE tend to earn lower incomes relative to the NE population as a whole (R$136 versus R$179), and substantially lower incomes than the average person who stayed in the SE. Migrants into the SE, while earning lower incomes than the prevailing SE residents, are considerably better off than those who stayed in the NE. These findings do not control for educational attainment, and confirmation of wage premia from migration will be investigated in more detail below.

As expected, the bulk of the densities of 1999 wages and incomes from NE-SE migrants is found to the right of those of SE-NE migrants (Figures 3.3 and 3.4). These densities reflect, to some degree, the generally higher standards of living in the SE, but the shapes of the distributions are also notable. The fact that the wage and income distributions for SE-NE migrants are more dispersed (have a larger variance), gives reason to believe that SE-NE migrants are more heterogeneous. This heterogeneity is consistent with the evidence on age and educational attainment (section 3.1).

**Figure 3.3: Log Wage Densities for NE and SE migrants in 1999**

<!-- Start of picture text --> o wages of NE to SE migrants - wages of SE to NE migrants<br>B wages of all migrants<br>.8<br>.6<br>.4.<br>0 5 10<br>W age<br><!-- End of picture text --> _Note:_ Distribution of log-transformed monthly wages for migrants over the last 10 years based on PNAD 1999. Population aged 18 and above. Estimates based on Epanechnikov kernel density estimates with a width of approximately 20.

_Source:_ Author's own calculations 12
||||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|---|---|
||||||A. Main|results|||
|Serv.|lib.||0.135<sup>_∗∗∗_</sup>|0.137<sup>_∗∗∗_</sup>|0.127<sup>_∗∗∗_</sup>||||
||||(0.014)|(0.014)|(0.016)||||
|Serv.|lib.|(FDI)||||0.082<sup>_∗∗∗_</sup>|0.083<sup>_∗∗∗_</sup>|0.065<sup>_∗∗_</sup>|
|||||||(0.019)|(0.019)|(0.020)|
|Expo|rter|||0.100<sup>_∗∗∗_</sup>|0.091<sup>_∗∗∗_</sup>||0.098<sup>_∗∗∗_</sup>|0.083<sup>_∗∗∗_</sup>|
|||||(0.011)|(0.012)||(0.011)|(0.013)|
|Serv.|Lib.|_×_ Exporter|||0.028<br>(0.025)||||
|Serv.|Lib.|(FDI) _×_ Exporter||||||0.049<br>(0.031)|
|Firms|||11057|11057|11057|11057|11057|11057|
|N|||40440|40440|40440|40440|40440|40440|
|_R_<sup>2</sup>|||0.65|0.66|0.66|0.64|0.65|0.65|
||||||B. Main r|esults. IV|||
|Serv.|lib.||0.153<sup>_∗∗∗_</sup>|0.155<sup>_∗∗∗_</sup>|0.144<sup>_∗∗∗_</sup>||||
||||(0.015)|(0.015)|(0.017)||||
|Serv.|lib.|(FDI)||||0.106<sup>_∗∗∗_</sup>|0.108<sup>_∗∗∗_</sup>|0.099<sup>_∗∗∗_</sup>|
|||||||(0.013)|(0.013)|(0.015)|
|Expo|rter|||0.100<sup>_∗∗∗_</sup>|0.089<sup>_∗∗∗_</sup>||0.100<sup>_∗∗∗_</sup>|0.091<sup>_∗∗∗_</sup>|
|||||(0.011)|(0.012)||(0.011)|(0.012)|
|Serv.|Lib.|_×_ Exporter|||0.033<br>(0.028)||||
|Serv.|Lib.|(FDI) _×_ Exporter||||||0.027<br>(0.024)|
|Firms|||11057|11057|11057|11057|11057|11057|
|N|||40440|40440|40440|40440|40440|40440|
|_R_<sup>2</sup>|||0.65|0.66|0.66|0.65|0.65|0.65|

_∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001 Notes: Dependent variable is ln( _TFP_ ) estimated by Olley-Pakes procedure. Robust standard errors are reported in parentheses. Each estimation is performed with industry-time cross-effects and firms’ fixed effects, which are not reported for brevity.

Table 4: Services liberalization and total factor productivity in Ukraine in 2001-2007.

21
**Table 3: Baseline estimation results and alternative estimators.**

||(1)<br>Poisson|(2)<br>Neg. Bin.|(3)<br>Gamma|(4)<br>OLS|(5)<br>Heck|(6)<br>man|
|---|---|---|---|---|---|---|
||||||Outcome|Selection|
|LPI (exp.)|0.693***|1.381***|1.350***|2.029***|2.199***|1.093***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|**LPI (exp.) * Parts**|**-0.116***<br>**(0.061)**|**-0.313***<br>**(0.066)**|**-0.227**<br>**(0.184)**|**-0.828*****<br>**(0.000)**|**-0.852*****<br>**(0.000)**|**-0.156*****<br>**(0.000)**|
|LPI (imp.)|0.328***<br>(0.001)|0.750***<br>(0.000)|0.721***<br>(0.000)|0.381***<br>(0.000)|0.471***<br>(0.000)|0.724***<br>(0.000)|
|**LPI (imp.) * Parts**|**0.148*****<br>**(0.003)**|**0.413*****<br>**(0.001)**|**0.462*****<br>**(0.000)**|**0.067****<br>**(0.016)**|**0.079*****<br>**(0.005)**|**0.029****<br>**(0.040)**|
|Parts|-2.990***<br>(0.000)|-2.270***<br>(0.001)|-2.651***<br>(0.000)|0.021<br>(0.885)|0.078<br>(0.601)|0.506***<br>(0.000)|
|Log(distance)|-0.347***|-0.755***|-0.776***|-0.906***|-0.951***|-0.397***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Contiguous|0.661***|0.738***|0.703***|0.994***|0.949***|-0.170|
||(0.003)|(0.000)|(0.001)|(0.000)|(0.000)|(0.229)|
|Language|0.293*<br>(0.070)|0.556***<br>(0.000)|0.574***<br>(0.001)|0.408***<br>(0.000)|0.449***<br>(0.000)|0.265***<br>(0.000)|
|Colony|-0.454**|0.682**|0.644**|0.583***|0.648***|0.828***|
||(0.033)|(0.021)|(0.026)|(0.000)|(0.000)|(0.000)|
|Colonizer|1.219**|-0.013|-0.042|0.641***|0.464***|-0.776***|
||(0.015)|(0.951)|(0.851)|(0.000)|(0.003)|(0.000)|
|Log(GDP exp.)|0.861***|0.863***|0.875***|0.845***|0.891***|0.288***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Log(GDP imp.)|0.745***|0.688***|0.709***|0.591***|0.626***|0.240***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Log(Doing Business)||||||-0.177***<br>(0.000)|
|Constant|-32.789***|-31.995***|-32.484***|-29.989***|-32.645***|<br>-14.040***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|R2|0.541|0.306|0.308|0.675|0.031||
|Observations|41760|41760|41760|19183|41472|41472|

_Note: The dependent variable in each case is exports. All trade costs variables are transformed as per Baier and Bergstrand (2009), as discussed in the main text. Estimation methods are indicated at the top of each column. Prob. values based on robust standard errors adjusted for clustering by country pair are in parentheses beneath the parameter estimates. Statistical significance is indicated by * (10%), ** (5%), and *** (1%)._ 26
<!-- Start of picture text --> Operating Expenses/Total Assets (Annualized)<br>0.2<br>0.18<br>Stanbic<br>P 0.16<br>E<br>0.14<br>R UCB<br>0.12<br>C<br>0.1<br>E<br>UCB+Stanbic<br>0.08<br>N<br>(Virtual)<br>T 0.06<br>Sector<br>0.04<br>0.02<br>0<br>1996 1998 2000 2002 2004<br><!-- End of picture text -->

**Figure 3**

Comparisons between Stanbic-UCB’s operating expenses with the rest of the banking sector are deceptive. Based on Figure 3, one might conclude that neither Stanbic nor UCB is, or was, a high-cost bank. However, Stanbic and, especially, UCB were large banks that presumably enjoyed scale economies that smaller banks did not. We would therefore expect their cost ratios to be lower than those for the smaller banks that comprise the sectoral average.

Regressions in Table 4 control for bank size, and thus help formalize the relationships suggested by Figure 3. Stanbic’s expense ratio was significantly lower than the typical Ugandan bank prior to privatization in Model 1, though the result is no longer significant when portfolio allocation variables are included in Model 2. This indicates that Stanbic’s costs were significantly lower in Model 1 because of the type of intermediation that it did. Controlling for portfolio allocation, Model 2 also indicates that UCB’s expenses were higher than average. Most important, the postprivatization coefficient is significant and positive in both models, indicating that Stanbic’s expenses have been higher than the typical Ugandan bank since the acquisition.<sup>22</sup> The results for

> 22 Beck and Hesse (2006) find that Stanbic’s postprivatization interest spreads and margins are significantly higher than those of other Ugandan banks. An increase in spreads and margins could indicate that Stanbic is passing on their high operating costs to borrowers, though Beck and Hesse point out that this also could be due to the riskier loan portfolio it inherited from UCB. Interestingly, those authors do not find that the

14
26 compass, not economic destinies. . . . We are witnessing a move towards multiple poles of growth as middle classes grow in developing countries, billions of people join the world economy, and new patterns of integration combine regional intensification with global openness. (2010) As Zoellick‘s words suggest, today‘s rapidly evolving world economy is opening important opportunities for low-income countries. Following the logic of the new structural economics and its underlying flying-geese patterns in economic development, this section discusses those opportunities, most notably China‘s emergence as ―the world‘s factory‖ for labor-intensive industries and its upcoming graduation from such economic activities.

# **_Multipolarity and Its Potential Dividends_**

During the first decade of this century a burst of convergence occurred as developing countries grew substantially faster than high-income countries. As a result of this superior growth, widespread across developing regions, the world has indeed entered a new era, with emerging economies becoming new growth poles. In the 1980s and 1990s, among the top five contributors to global growth, all except China were G7 industrialized countries. But in 2000–09 all except the United States were emerging economies—with China having become the top contributor (figure 15). The trend is being reinforced in the aftermath of the 2007–09 global crisis: the recovery is characterized by a two-speed pattern, with developing countries as a group growing more than twice as fast as high-income countries.

**Figure 15. Top Five Contributors to Global Economic Growth by Decade** ( **<u>percentage</u>** )

<!-- Start of picture text --> 1980-1990 1990-2000 2000-2009<br>30 40 30<br>25 35<br>25<br>30<br>20<br>25 20<br>15<br>20<br>10 15 15<br>5 10<br>10<br>5<br>0<br>0 5<br>0<br>China US India Korea Brazil<br><!-- End of picture text --> Source: Author‘s calculations based on data from World Bank, World Development Indicators database.

That shift in economic weight is likely to produce major benefits for the world economy, with positive effects for both high-income and developing countries. For high-income countries the growth of emerging economies will expand markets for their exports of capital goods and intermediate goods. For many developing countries that are still major producers of agricultural and natural resource commodities, higher consumption and production levels in the new growth poles will continue to support adequate prices for their commodity exports. In addition, firms and governments in emerging economies will provide funds for infrastructure and natural resource investment in developing countries.
<!-- Start of picture text --> Kazakhastan's ranking chetse connectivity<br>(Ranking change from 20% increase in each type of<br>connectivity, by country or region)<br>35<br>30<br>25<br>20<br>15<br>10>0 |aD nl = oO ol. — ||mt=<br>World Germany China Russia<br>= Multidimensonal mTrade FDI Migration<br><!-- End of picture text -->
According to official statistics, Argentina has 11 million students enrolled in four education levels: preschool (ages 3–5, 15.6 percent); primary (ages 6–11, 41 percent); secondary (ages 12–17, 35.5 percent); and tertiary (ages 18–22, 7.9 percent). The majority of these students (71 percent) attend public schools (DiNIECE 2015).<sup>6</sup> Between 2003 and 2013, student numbers increased by approximately 10 percent, while the number of teachers increased by more than 20 percent over roughly the same period (DiNIECE 2004, 2015). This allowed Argentina to reach a pupil-teacher ratio of 11, the lowest in Latin America after Cuba (OECD 2016), although it is worth noting that this ratio varies considerably across provinces.

Although there have been large successes in terms of increasing coverage, the Argentinean education system fails to provide high-quality education (at least as measured by standardized test scores). While other countries in the region improved learning outcomes since 2000—measured by the OECD’s Program for International Student Assessment (PISA)—Argentina’s scores show no progress (at best) in Science, or even experienced a marginal decline between 2000 and 2012.<sup>7</sup> According to the 2012<sup>8</sup> study, Argentina ranked amongst the lowest of the participating countries (59 out of a possible 65) (OECD 2014).<sup>9</sup> CABA, being the wealthiest jurisdiction in Argentina, exhibits some specific features; namely, a lower share of students attending public schools (49 percent) and higher levels of student achievement (DiNIECE

> 6 These figures do not include special and adult education.

> 7 According to De Hoyos, <u><mark>Holland, and Troiano</mark> (2015), there is a gradual increase in Argentina’s Science scores between 2006 and</u> 2012, but it is not statistically significant at conventional levels (95 percent).

> 8 Argentina participated in PISA 2015, but its results were excluded from the main report due to problems with sample design. However, CABA participated as an adjudicated region and was included in the results.

- 9 In a similar vein, results of the UNESCO Second Regional Comparative and Explanatory Study (SERCE), applied to students of third and sixth graders, show that only 11.4 percent of sixth-grade students were able to explain everyday situations based on scientific evidence, use models to explain natural phenomena, or draw conclusions based on data (UNESCO 2009).

10
# **5 Conclusion**

“Is management good for the environment, or just hot air?”<sup>17</sup> Using firm-level data from 31 countries, our results suggest that management practices are associated with a reduction in energy expenditure intensity. However, these cost savings do not translate into reduced physical energy use, a close proxy for environmental impacts.

There may be several reasons for the divergence between the relationship of management practices to physical and expenditure measures of energy intensity. _First_ , managers may be focused on saving costs, not limiting the environmental impacts of energy use _per se_ . To the extent that management practices help managers reach their goals more expediently, it is perhaps not surprising that the correlation runs only through the energy expenditure intensity measure. _Second_ , management may enable firms to take advantage of lower prices, e.g. through investments in self-generation or by allocating production hours to take advantage of favorable time-of-day pricing. Larger one-time or repeat energy purchases may be subject to subsidies or preferential pricing, relative to smaller outlays. For some firms, it may be economically attractive to increase physical energy use in order to save costs on other productive inputs, for instance, by automating certain processes. Therefore, our results suggest that although management practices are associated with reduced energy intensity in value terms, it does not mean that physical energy use, and its related environmental impacts, will follow the same pattern.

Turning to focus on energy management practices, we find they have no additional effect on reducing the intensity of energy expense over and above generic management practices. It is possible, given the diversity of firms in our sample, that the coefficient reflects reverse causality: energy-intensive firms have stronger incentives to develop energy management. The decision to develop energy management practices may be the result of selection by subset of firms most likely to profit from them. Supporting this notion, we find that energy management scores are on average higher in energy-intensive sectors. Energy-intensive man-

> 17This is the title of the paper by (Bloom et al., 2010a).

30
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|**supply shock**||||(0.198)|(0.209)|
|---|---|---|---|---|---|
||**_upstream shockilt_**|||**3.679*****|**3.883*****|
|||||(1.058)|(1.067)|
||**_global outputmt_**|**1.408*****|**1.166*****|**1.275*****|**1.004*****|
|||(0.179)|(0.189)|(0.195)|(0.200)|
||**Constant**|**1.014*****|**0.760*****|**1.063*****|**1.374*****|
|||(0.053)|(0.020)|(0.085)|(0.108)|
||Observations|292,452|292,452|292,452|292,452|
||R-squared|0.521|0.521|0.521|0.521|
||Exporter-Time FE|Yes|Yes|Yes|Yes|
||Importer-Time FE|Yes|Yes|Yes|Yes|
||Exporter-Partner-Sector FE|Yes|Yes|Yes|Yes|
||Sector-Time FE|Yes|Yes|Yes|Yes|
||Cluster|Exporter-Partner|Exporter-Partner|Exporter-Partner|<br>Exporter-Partner|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Note: Sector-time fixed effects control for unobserved effects affecting aggregated sectors over time (see footnote 10). The variable _global outputmt_ additionally controls for global sector-time trends.

**Table 5: Supply, demand and third-country shocks and bilateral export growth, the role of sector characteristics, OLS regression results, April to June 2020 only**

|FRAMEWORK|VARIABLES|(1)<br>**_growth_**_ijkt_|(2)<br>**_growth_**_ijkt_|(3)<br>**_growth_**_ijkt_|(4)<br>**_growth_**_ijkt_|
|---|---|---|---|---|---|
|**Exporter supply shock***|<br>**_work mobilityit*remoteik_**|**1.437****|||0.766|
|**sector characteristic**||(0.559)|||(0.599)|
||**_work mobilityit*gvcil_**|**2.719*****|||**2.720*****|
|||(0.820)|||(0.832)|
|**Partner demand**<br>**shock***|**_retail mobilityjt*durablek_**||**0.809*****||**0.740*****|
|**sector characteristic**|||(0.143)||(0.147)|
||**_retail mobilityjt*gvc partnerjl_**||**-0.364*****||**-0.388*****|
||||(0.112)||(0.113)|
|**Third country**|**_competition shockijkt_**|||-1.213***|-0.594|
|**supply shock**||||(0.361)|(0.382)|
||**_upstream shockilt_**|||0.841|1.211|
|||||(1.154)|(1.164)|
||**_global outputmt_**|**0.851*****|**0.308**|**0.787*****|**0.215**|
||**Constant**|(0.195)<br>**1.092*****|(0.216)<br>**0.552*****|(0.205)<br>**0.573*****|(0.224)<br>**1.004*****|
|||(0.103)|(0.034)|(0.149)|(0.207)|
||Observations|285,783|285,783|285,783|285,783|
||R-squared|0.535|0.535|0.535|0.535|
||Exporter-Time FE|Yes|Yes|Yes|Yes|
||Importer-Time FE|Yes|Yes|Yes|Yes|
||Exporter-Partner-Sector FE|Yes|Yes|Yes|Yes|
||Sector-Time FE<br>Cluster|Yes<br>Exporter-Partner|Yes<br> <br>Exporter-Partner|Yes<br> <br>Exporter-Partner|Yes<br> <br>Exporter-Partner|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Note: Sector-time fixed effects control for unobserved effects affecting aggregated sectors over time (see footnote 10). The variable _global outputmt_ additionally controls for global sector-time trends.

23