rafmacalaba/datause-encoder-data huggingface dataset:
# **6. Concluding Remarks**

The analysis presented in this paper builds on the existing literature of heterogeneous firms. The primary objective of this paper is to observe how Tanzanian firms make their pricing decisions and to assess whether their behavior corresponds with price or quality competition models. The analysis undertaken draws from Manova and Zhang (2012), and the results corroborate most of these authors’ findings. Specifically, firms setting a higher export price earn higher revenue and sell lower quantities within a destination for a given product: which is preliminary evidence favoring quality competition models. Moreover, exporters that supply multiple destinations seem to have higher price dispersion across importers, indicating that firms charge different prices if they serve multiple destinations. However, when the sample is reduced to homogeneous goods, the relationship between the number of destinations served and price dispersion holds positive, while for differentiated goods it no longer holds. The exact opposite was true in Manova and Zhang (2012).

As a matter of fact, it is common to think that price dispersion is associated more with differentiated - rather than homogeneous - goods, because it relates to quality differentiation. Nevertheless, the quality argument does not have to be disregarded because it does not apply to differentiated goods. In fact, a country that exports mainly agricultural products, like Tanzania, might still be able to improve the quality and efficiency of its production. The case of TecnoServe – in Section 5 – explains that an agricultural good, such as coffee, is classified as homogeneous (according to the Rauch classification) even though its market is segmented and firms adapt its quality to different markets. Unfortunately the existing indices of vertical differentiation (which could help to establish whether price dispersion is really related to quality differentiation) are not built for “homogeneous” goods.

Even though further work is needed to establish whether price dispersion is led by quality differentiation or firms charging different mark-ups, it is interesting to find that even in an LDC like Tanzania multipledestinations exporters are doing pricing to market. This is an important issue that should be researched in future work. In fact, even though it is a standard practice to treat agricultural commodities as homogeneous goods, these commodities are perceived by consumers as differentiable in quality. Allowing for the possibility that food products can be vertically differentiable amplifies the spectrum of existing opportunities for developing countries to exploit product differentiation and market niches in countries where consumers are willing to pay more for products that satisfy specific quality dimensions.

Lastly, this study indicates that Tanzanian firms do not systematically differentiate prices for the same product based on the size of the trade partner. Nevertheless, Tanzanian exporters sell the same product at 17
||(48.65)|(9.28)|(14.86)|(0.08)|(3.96)|(0.06)|
|---|---|---|---|---|---|---|
|Pulp & paper|-39.94|47.03**|95.62|-0.26***|3.27|-0.33***|
||(69.45)|(19.59)|(72.80)|(0.04)|(3.70)|(0.07)|
|Chemicals|-18.07|27.17|86.95**|-0.15**|8.61*|-0.26***|
||(55.47)|(18.02)|(43.88)|(0.07)|(4.93)|(0.07)|
|Medical|-28.76|7.18|46.21**|-0.28***|2.59|-0.33***|
||(50.67)|(8.67)|(18.42)|(0.02)|(3.73)|(0.06)|
|Fiber, rubber &|-33.66|19.83|9.34|-0.26***|20|-0.33***|
|plastic|(65.93)|(26.68)|(15.45)|(0.04)|(12.85)|(0.07)|
|Smelting|(52.41)|4.56|11.52|-0.29***|0.81|-0.33***|
||(66.49)|(12.51)|(14.89)|(0.03)|(5.51)|(0.07)|
|Machinery|(100.65)|11.87|12.53**|-0.28***|0.50|-0.33***|
|manufacture|(89.26)|(11.87)|(6.02)|(0.02)|(1.63)|(0.06)|
|Utilities|145.69*|5.53|13.66|-0.12***|33.50|-0.27***|
||(74.49)|(16.52)|(39.68)|(0.03)|(35.80)|(0.08)|
|Transportation|(58.63)|20.67*|(2.78)|-0.30***|(1.72)|-0.32***|
||(67.32)|(11.46)|(8.49)|(0.06)|(3.95)|(0.08)|
|Others|-24.54|9.74|12.69|-0.24***|1.12|-0.31***|
||(25.03)|(16.18)|(8.75)|(0.04)|(3.19)|(0.06)|
|Time trend|13.04|1.67|2.46|0.00|3.28|0.00|
||(8.84)|(2.50)|(6.27)|(0.02)|(4.18)|(0.01)|
|Constant|377.03***|(0.18)|2.11|0.43***|18.97***|0.36***|
||-107.85|-20.45|-34.02|-0.08|-6.75|-0.07|
|No. ofobs|1320|1128|1296|1158|1229|1104|
|within_R_<sup>_2_</sup>|0.15|0.61|0.73|0.99|0.49|0.82|
|between_R_<sup>_2_</sup>|0.16|0.52|0.84|0.97|0.33|0.61|
|overall_R_<sup>_2_</sup>|0.14|0.39|0.77|0.99|0.33|0.62|
|Breusch and Pagan|Lagrangian M|ultiplier test|for random|effects|||
|Test statistics: χ<sup>2</sup>(1)|0.04|0.19|0.05|75.13***|5.27**|21.68***|

Numbers in parentheses are standard errors of the estimated coefficients. ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.
# **Table 5: Effects on Retail-World Margin**

||**Average Effe**<br>**reform S**<br>|**cts in Post-**<br>**ample**<br>|**Sustainability**<br>|**of Reform**<br>|
|---|---|---|---|---|
||Same Period<br>Prices|2 Weeks<br>Lagged|Same Period<br>Prices|2 Weeks<br>Lagged|
|**Announcement Effects**|||||
|**INTERCEPT**|**27.22**|**11.46**|**24.09**|**11.86**|
||(1.462)|(0.475)|(1.248)|(0.473)|
|**SLOPE**|**-0.245**|**-0.0489**|**-0.208**|**-0.0536**|
||(-1.066)|(-0.161)|(-0.873)|(-0.170)|
|**Effects of Policy Implementation**|||||
|**Average Effects in Post Reform**<br>**Period**|||||
|**INTERCEPT**|**37.29*****|**27.55*****|||
||(10.36)|(6.106)|||
|**SLOPE**|**-0.381*****|**-0.261*****|||
||(-8.171)|(-4.444)|||
|**Effects during first 9 months**|||||
|**INTERCEPT**|||**57.33*****|**49.81*****|
||||(12.33)|(8.649)|
|**SLOPE**|||**-0.655*****|**-0.556*****|
||||(-10.80)|(-7.394)|
|**Effects after first 9 months**|||||
|**INTERCEPT**|||**25.83*****|**12.49*****|
||||(6.998)|(2.734)|
|**SLOPE**|||**-0.215*****|**-0.0654**|
||||(-4.733)|(-1.142)|

NOTES: (1) ‘t’ statistics in parenthesis. *** denotes significant at the 1 percent, ** at the 5 percent, and * at the 10 percent level. (2) Slope denotes the slope of margin with respect to world market crude oil price. (3) The first column report estimates where the same period world market price of crude palm oil is used to calculate trading margins, and the second column for the case when two weeks lagged crude price is used. (4) The regressions include year, quarter, product fixed effects, relative prices, a dummy for upswing in the world market, Ramadan dummy and fuel price (diesel).
higher followed by the “Professionals” and “Skilled agricultural, forestry and fishery workers”. On the contrary, “Managers”, “Technicians”, “Craft and related trades workers” and “Plant and machine operators and assemblers” are the types of occupations with a lower incidence of NSE in the region.

Figure 16: Prevalence of NSE among salaried employees. (Early 2000s/Mid-2010s) <!-- Start of picture text --> 30%<br>starting point<br>25% ending point<br>20%<br>15%<br>10%<br>5%<br>0%<br>Russia Georgia Kyrgyz Republic Turkey Armenia Albania Moldova<br><!-- End of picture text -->

# Source: Own calculations based on Household surveys

Figure 17: Prevalence of NSE by categories of occupations ISCO. (Mid-2010s) <!-- Start of picture text --> 50%<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Managers Professionals Technicians Clerical Services and Skilled Craft and Plant and Elementary<br>Support Sales Agricultural Related Machine Occupations<br>Workers Workers Workers Trades Operators<br>Workers<br>Albania Armenia Georgia Kyrgyzstan Moldova Russia Turkey<br><!-- End of picture text -->

# Source: Own calculations based on Household surveys.

As in the case of Latin American countries, the observed prevalence of NSE across types of occupations suggests a strong heterogeneity across non-standard employees. Actually, as we will analyze in detail in next sections, the productivity and task profile of workers in the categories of “Professionals” and “Elementary workers” is very different, even though both types of occupations are characterized by a higher prevalence of non-standard employment arrangements.

Page 25 of 51
evidence that board composition matters for firm performance, board members may also simply passively collect payments. Also, it cannot be easily differentiated from a legitimate hiring and any lack of technical competence is not directly damaging to the firm, as it would be the case if the politician were appointed to an executive position.

There are at least two ways in which bankers can interact with politicians in a privateinterest setting: the tollbooth and the capture mode (a distinction borrowed from Djankov et al (2003)). The tollbooth view would have politicians benefiting from the bankers and using the bank to extract rents or for other political purposes.<sup>2</sup> The more traditional capture view sees the bankers capturing the politician in order to get more favorable treatment.<sup>3</sup> Both stand in sharp contrast to the public interest theory and fit better the evidence above because they imply a higher prevalence where the politician has more power, where the authority is less accountable, and where there lower costs of engaging on it. Both are independent of the degree of financial expertise of the politician, and both should have a cost in terms of worse-functioning banking systems. But the two flavors differ critically in the way the benefits of the relationship are split between the two groups: while the board seat is an imposition on the bank and an enforcement device for the politician, in the tollbooth story it is a reward for accommodating regulators. We are able to test the alternative views based on three differences in their implications. First, the tollbooth view would predict a higher prevalence when the politician’s sector is in government because the politician would be acting as an agent in charge of extracting rents or favors from the regulated firm. Second, banks would be more profitable under the capture than the tollbooth view; leverage should be higher in the latter because it allows to shield shareholders from expropriation. Third, while rents created can be measured and should accrue primarily to bankers in the capture story, under the tollbooth view rents are dissipated via higher costs.

Overall the evidence favors the capture view: former politicians are not more likely to be directors when their side is in power, banks are more profitable without being more leveraged, rents can be measured and a higher fraction of these accrue to bankers, and regulation becomes more pro-banker where the occurrence is higher.

Our paper is most closely related to the literature emphasizing political factors as a necessary complement to initial factors in understanding the determinants of financial development (see Haber and Perotti (2008) for a thorough recent review). As such, it follows insights from recent work by Rajan and Zingales (2003), Pagano and Volpin (2005), and Perotti and von Thadden (2006), and systematic evidence for a large number of countries from Barth,

> 2 De Soto (1990).

> 3 Stigler (1971).

3
- 36 -

Tyler, WilLiam. 1981. "Growth and Export Expansion in Developing Countries," _Journal of Development Economics_ 9, 1981, pp. 121-130.

United Nations Conference on Trade and Development. _Handbook of International Trade and Development Statisftcs._ New York: United Natinns Publicaton. Various years. United Nations. _Internaonal Trade Stadstical Yearbook._ 1992. United Nations Statistical Office. Uzawa, H. 1965. "Optimum Technological Change in an Aggregative Model of Economic Growth.' _Internatonal Economic Review_ 6 (January). pp. 18-31. The World Bank. _World Development Report._ Various years.

The World Bank. 1990. _World Tables of Economic and Social Indicators._ International Economics Department, Computer file. Am Arbor, MI: Inter-university Consortium for Political and Social Research (distributor).

Zhang, Xiaoming. 1994. "Different Sectors of Exports and Economic Growth.' Mimeo, Department of Economics, MIT.

Zou, Heng-fu. 1993. "Product Inovation, Capital Accumulation and Endogenous Growth." Mimeo, Policy Research Dqetment, The World Bank.
<!-- Start of picture text --> Ethiopia 2015- 2016<br>2018- 2019<br>Malawi 30123945«2013<br>- 2016<br>2018- 2019<br>Mali sons<br>. 2011<br>Niger 2014<br>Nigeria 2019<br>2008<br>; 2012<br>Tanzania 2010<br>2014<br>2019<br>a ,i |<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90%<br>Frequency of plots recording a shock<br><!-- End of picture text -->

<!-- Start of picture text --> Ethiopia :<br>2013 - 2014 ——<br>2015 - 2016 4<br>2018 - 2019 el<br>Malawi<br>2018 - 2019 re<br>Mali<br>2014 —_e<br>2017 jo<br>Niger<br>2011 a<br>2014 facia<br>Nigeria<br>20 30 40 50 60 70 80<br>Mean percent of harvest value lost on plot (%)<br><!-- End of picture text --> <!-- Start of picture text --> a|a<br>0 20 40 60 80 100<br>Fraction of aggregate potential harvest lost (%)<br><!-- End of picture text -->
# **3.2 comparing price distortion between countries with different incomes**

While it is not likely that the average level of distortion eJ greatly affects income per capita, if countries with low income levels are more likely to have high average distortion levels, then using income per worker as an instrument for nontraded prices may bias estimates of ai - a. Erzan et al find that in 1985 average tariffs were between 50 and 66 percent for countries with GDP per **capita** less then $500 and between 3 and 5 percent for countries with GDP per capita greater than $5000. NTB's coverage ratios were found to be highest among middle income countries<sup>5</sup> .

If indeed low and middle income countries have higher average distortion levels than upper income countriOs<sup>6</sup> . then the measured d.stortions in low and middle income countries will understate the true price distortions, and estimates of _a,_ - a will be biased.

The importance of this bias in comparing price distortions for two countries will increase with the difference in the countries' income levels. Let **ei,** be the estimated error eij - **_ej,_** let **_A3,_** be the estimated a, - _a_ and _,30_ be the true a, - a. Comparisons of the relative distortion for good i between countries j and _k_ can be expressed as the difference where y, is the nontraded price predicted by per capita income in country j and _Yk_ is the same in country k. According to equation 11, the estimated difference between two countries of price distortion equals the true difference plus a bias which increases with the difference in nontraded

> **5 kierage** tariff **lefelc mid,ir'tatv Ilic Impiiortalice** of tra(de harrier' **il de floleld ronninrie,:** tarifR are **amonig tfie** least **llill ort:otit harriers to** t rathI- wit Imii I **it** ( . Ilext to ailmni,iit rafile hbarritr'. tecIntiical rfeitIatioiis. atiid fronlier lelav,s,e LEitersont et al. I **_14X)_**

> 61 nles thie lhigher aerage distortion ,I **catiseuI** h\ tiniforiiil! hiilger di'tortion' e,, for all goo(d. leavilig ., -, constant **s(ee** abio%e} Ezran **t a** t **1187.** for example. fotnid( thlat for **(levelopling** coniittrie, average tariff **levek** were rottghll **tie** samiie for food aiiti for iiia,.-ifacture,.

13
|Table II<br>Summary Stat<br>|I<br>isticsa<br>|||
|---|---|---|---|
|Variable ||Obs|Mean|Std. Dev.|
|Scheduled Election|513|0.16|0.36|
|Midterm Election|513|0.08|0.27|
|Political Affiliationb|513|0.39|0.49|
|Growth in National Highwaysc|318|19.5|98.4|
|Growth in State Roadsc|330|1686.3|3583.8|
|Commodity Taxes|456|58.1|40.4|
|Sales Taxes|445|28.3|21.2|
|Excise Taxes|456|9.3|10.8|
|Trade Taxes|443|6.1|6.3|
|Development Spending [current]|456|95.9|57.4|
|Non-development Spending|456|46.3|20.8|
|Total Current Spending|456|144.5|76.1|
|Development Spending [capital]|469|23.4|11.7|
|State Debt|470|48.9|26.7|
|State Domestic Product|464|1101.6|<br>576.7|
|Share of Agriculture in SDP|464|0.43|0.11|
|Total Populationd|513|41626|25195|
|Proportion of Rural Population|483|0.79|0.08|

- a. Taxes, spending and SDP variables are in percapita 1973 rupees

- b. Indicator variable equals 1 if incumbent is affiliated with central govt.

- c. In kilometers

- d. In thousands

39
Policy ReseaRch WoRking PaPeR 4559

# **Abstract**

An effective tax system is fundamental for successful country development. The first step to understand public revenue systems is to establish some commonly agreed performance measurements and benchmarks. This paper employs a cross-country study to estimate tax capacity from a sample of 104 countries during 1994-2003. The estimation results are then used as benchmarks to compare taxable capacity and tax effort in different countries. Taxable capacity refers to the predicted taxgross domestic product ratio that can be estimated with the regression, taking into account a country’s specific economic, demographic, and institutional features. Tax effort is defined as an index of the ratio between the share of the actual tax collection in gross domestic product and the predicted taxable capacity.

The authors classify countries into four distinct groups by their level of actual tax collection and attained tax effort. This classification is based on the benchmark of the global average of tax collection and a tax effort index of 1 (when tax collection is exactly the same as the estimated taxable capacity). The analysis provides guidance for countries with various levels of tax collection and tax effort.

This paper—a product of the Poverty Reduction and Economic Management Network—is part of a larger effort in the department to support developing countries in revenue policy reforms. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at bmorenododson@worldbank.org@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
- Dechezleprêtre, A., M. Glachant, I. Haščič, N. Johnstone, and Y. Ménière (2011). Invention and transfer of climate change–mitigation technologies: a global analysis. _Review of environmental economics and policy_ .

- Delbono, F. and V. Denicolo (1993). Regulating innovative activity: the role of a public firm. _International Journal of Industrial Organization 11_ (1), 35–48.

- Downing, P. B. and L. J. White (1986). Innovation in pollution control. _Journal of environmental economics and management 13_ (1), 18–29.

- Dutz, M. A. and S. Sharma (2012). Green growth, technology and innovation. _World Bank Policy Research Working Paper_ (5932).

- Fischer, C. and R. G. Newell (2008). Environmental and technology policies for climate mitigation. _Journal of environmental economics and management 55_ (2), 142–162.

- Foster, C. and R. Heeks (2013). Conceptualising inclusive innovation: Modifying systems of innovation frameworks to understand diffusion of new technology to low-income consumers. _The European Journal of Development Research 25_ , 333–355.

- Gambhir, A., R. Gross, and R. Green (2014). The impact of policy on technology innovation and cost reduction: a case study on crystalline silicon solar pv modules. _Imperial College WP_ .

- Haruna, S. and R. K. Goel (2019). Optimal pollution control in a mixed oligopoly with research spillovers. _Australian Economic Papers 58_ (1), 21–40.

- IRENA (2022). _Renewable Power Generation Costs in 2021_ . International Renewable Energy Agency (IRENA).

- Ishibashi, I. and T. Matsumura (2006). R&d competition between public and private sectors. _European Economic Review 50_ (6), 1347–1366.

- Jung, C., K. Krutilla, and R. Boyd (1996). Incentives for advanced pollution abatement technology at the industry level: An evaluation of policy alternatives. _Journal of environmental economics and management 30_ (1), 95–111.

29
**Figure A6 – Respondents’ answers to the question “Do you trust the COVID-19 vaccine?”, disaggregated by willingness to get the COVID-19 vaccine**

<!-- Start of picture text --> Do you trust the COVID-19 vaccine?<br>Planning on being vaccinated Not planning on being vaccinated Unsure<br>Yes 62.4 Yes 2.1 Yes 8.2<br>No 5.1 No 73.7 No 23.9<br>Not Sure 32.5 Not Sure 24.2 Not Sure 67.9<br>0 20 40 60 80 0 20 40 60 80 0 20 40 60 80<br>Share of respondents that selected this answer<br><!-- End of picture text --> Note: This chart is based on answers to question 3 and 8 in Appendix B.

**Figure A7 – Whether people thought the COVID-19 vaccine was important for their health disaggregated by their willingness to get vaccinated**

<!-- Start of picture text --> Do you think the COVID-19 vaccine is important for your health?<br>Planning on being vaccinated Not planning on being vaccinated Unsure<br>Yes 68.4 Yes 3.0 Yes 8.3<br>No 5.1 No 67.3 No 22.1<br>Not Sure 26.5 Not Sure 29.7 Not Sure 69.6<br>0 20 40 60 80 0 20 40 60 80 0 20 40 60 80<br>Share of respondents that selected this answer<br><!-- End of picture text --> Note: This chart is based on answers to question 3 and 9 in Appendix B.

33
# **7. Conclusion**

While the hypothesis that firms improve their productivity when exposed to competitive export markets—learning-by-exporting—is intuitively appealing, the corresponding empirical evidence has been inconclusive. Researchers have often favored the alternative hypothesis that firms that improve their productivity self-select into export markets. In this paper we consider the parallels between learning-by-exporting and learning-by-doing. From Arrow’s (1962) classical study of learning-by-doing, we know that learning occurs when workers and managers gain experience in solving new technical and organizational problems, and that learning associated with repetitive tasks is subject to sharply diminishing returns. Arrow’s characterization of learning-bydoing applies to learning-by-exporting because firms breaking into export markets need to solve new problems, such as adopting new technical standards, introducing more efficient equipment, and ensuring product quality to satisfy sophisticated consumers. Drawing on this characterization, we focus our empirical investigation of learning-by-exporting on young plants, which are much more likely than old, established plants to face new technical and organizational challenges. We also favor using measures of export experience to study whether productivity improvements are associated with the extent of exposure to export markets.

We find strong evidence of learning-by-exporting for our sample of young Colombian manufacturing plants. First, we find that young plants that enter export markets experience annual average rates of TFP growth between 3% and 4% higher than those of young plants that never export. This gap is robust to the use of matching methods and to the use of the plant percentile in the industry-year distribution of TFP as an alternative measure of performance. Second, we find 33