rafmacalaba/datause-encoder-data huggingface dataset:
Figure 4-A also provides estimates of GDP per capita for higher level of R&D expenditures. In particular a 30 percent increase in R&D expenses is assumed over the period under two alternative scenarios: constant R&D elasticity of growth (8 percent) and lower R&D elasticity of growth (4 percent). A much lower rate is considered because empirical studies suggest that total factor productivity, which depends on R&D investment, is lower in developing countries [Chenery et al. (1986)].<sup>38</sup> A comparison with the base case shows a rapid increase in per capita GDP, even under lower R&D growth elasticity, suggesting significant growth and welfare returns for technological advances. For instance, if R&D expenses had been growing at 30% above their current levels, per capita GDP would be US$700 in Sub-Saharan Africa (about twice the current estimates) for a much lower R&D elasticity of growth. A higher elasticity of growth would have resulted in even larger returns, with per capita GDP increasing by over threefold.

Figure 4-B assumes that SSA’s R&D expenses are growing at OECD rates; this implies about 85 percent increase. The resulting outcome under this hypothetical technological catch-up scenario is even more impressive. A twofold increase in income is registered when R&D elasticity of growth is lower than the OECD average (4 percent). And when the elasticity is set to equal the going OECD rate, the income growth is even more impressive, increasing by over fivefold. In other words, if countries in Sub-Saharan Africa were using the same technology enjoyed by OECD countries, their income gap with advanced economies would be significantly smaller.

With the rapid increase in income under the hypothetical technological catch-up hypothesis the poverty and welfare effects of R&D improvement should be positive. In order to assess the poverty effects of increased R&D expenses, the poverty rate for Ethiopia are estimated using per capita GDP growth rates and the derived elasticities. The results are shown in Figure 5. Note that, increases in R&D spending results in a significant reduction of poverty. The gap between actual and estimated poverty rates uniformly increases over time.

**Figure 5: Implications of R&D improvement for headcount index in Ethiopia**

<!-- Start of picture text --> 110<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>Actual growth rate of GDP and -0.56 income elasticity Elasticity -0.56 and with 10% growth rate of R&D<br>Elasticity -0.56 and with 20% growth rate of R&D Elasticity -0.56 and with 30% growth rate of R&D<br>1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> 38 Chenery, Robinson and Syrquin (1986) find that increases in total factor productivity accounts for about a third of growth in valued added in developing countries and nearly one-half for the developed world.

21
starting values over the period 1992 to 2002. As mentioned previously, the food sector had the highest level of markup in the labor market while it had the lowest in the product market.

<!-- Start of picture text --> 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Year Year<br>Small Medium Small Medium<br>Large Large<br>(a) (b)<br>2 5<br>1.8 4<br>1.6 3<br>Average Markup Average Markup<br>1.4 2<br>1.2 1<br><!-- End of picture text --> Figure 3: Trend in Markups Level, By Firm Size

**_Source:_** Author’s analysis based on data from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service. **_Note:_** Markups level aggregated by firm sizes. Panel (a) shows markups computed on material input, while Panel (b) shows markups computed on labor.

Figure 3 displays the results of a similar exercise to that in fig. 2, but focusing on firm size instead of sector. Based on the cumulative distribution of the sample, the following size classification was adopted: small, 1-10 employees; medium, 11-50 employees; and, large, more than 50 employees. From panel (a) of fig. 3 it can be seen that both large and medium firms started at the same level of markup in 1992. Both categories of firm sizes registered some volatility in the markup level throughout the study period. While medium firms recorded the largest drop in markup, of 22 percent over the whole period, the markup level for large firms almost returned to the same level of 1992, with a reduction of just 4 percent. On the other hand, small firms had the lowest average level of markup on materials throughout the period. Overall, small firms recorded a decrease of 17 percent in markup levels.

The trends exhibited in panel (b) of fig. 2 are repeated in panel (b) of fig. 3 where average markups on labor appear to be rising rather than decreasing. Medium firms were the big gainers, recording 160 percent increase in average markup on labor between 1992 and 2002. Although large firms had the highest level of markup, their overall total increase stood at 65 percent over the decade. The behavior of average markup for small firms in panel (b) of fig. 3 seems different from that of the medium and large firms. Small firms started as the category with high markup levels in the initial period. Between 1992 and 1995, the average markup decreased by 41 percent. However, over the next five years, the trend became positive with an overall increase of 62 percent. The positive trend did not 15
Table 2: Average elasticities by income and regional classification (simple averages)

|**Group Classification**|**Arming**|**ton(σ)**|**CET**|**(Ω)**|**Obs**|
|---|---|---|---|---|---|
|**1. Incomce groups**||||||
|All developing countries|0.707||0.593||128|
|Low income||0.686||0.566|26|
|Lower middle income||0.692||0.583|50|
|Upper middle income||0.731||0.616|52|
|High income|1.417||1.465||63|
|**2. Regional groups**||||||
|East Asia and the Pacsific|0.967||0.818||31|
|High income||1.515||1.353|11|
|Developing countries||0.666||0.523|20|
|Europe and Central Asia|1.142||1.206||52|
|High income||1.463||1.601|31|
|Developing countries||0.668||0.623|21|
|Latin America and the Caribbean|0.837||0.751||33|
|High income||1.080||1.271|9|
|Developing countries||0.746||0.555|24|
|Middle East and North Africa|1.008||0.966||21|
|High income||1.432||1.345|8|
|Developing countries||0.747||0.733|13|
|North America|1.668||1.442||3|
|South Asia|0.726||0.638||6|
|Sub-Saharan Africa|0.716||0.598||45|
|High income||1.071||1.240|1|
|Source: Authors' calculations.<br>Developingcountries||0.708||0.584|44|

Source: Authors' calculations. Notes: Groupings follow World Bank income and regional classifications. Income groups are defined by GNI per capita in US$ (Atlas methodology) in 2015: Low income <= 1025; lower middle income 1026-4035; upper middle income 4036-12,475; high income > 12,475.

Table 2 presents the simple averages of the Armington σ (import) and CET Ω (export) elasticities for various income and regional groups according to World Bank classification. The income classification is based on GNI (gross national income) per capita in U.S. dollars (Atlas methodology) for 2015, the base year of the constant price series in the estimation. The table averages could provide "rules of thumb" estimates for missing countries. They also show interesting tendencies by broad income categories. The average σ and Ω of high-income countries tend to be higher than 1.0, about 1.4, while those of the lowerincome groups are less than 1.0. The average elasticities for low-income and lower-middle-income countries tend to be close to one another, around 0.7 for σ and slightly less than 0.6 for Ω. The uppermiddle-income countries tend to have slightly higher values but still less than 1.0, around 0.7 for σ, and 15
Minister of Finance to promote the best inspectors (since it was based on objective criteria) and had more leeway to change/sanction inspectors since it was again based on objective and transparent criteria (which had never been really the case before). Utterly deviant practices reduced significantly since the likelihood to be sanctioned became higher and customs statements on reforms became increasingly credible for some compliant operators and act as a deterrent for the most non-compliant importers. Some interviewed informal importers explained that because of the reduction of profits due to customs changes, it had become more profitable to leave the trading business. This is therefore a virtuous circle, which has been increasingly put in place. It is however worth noting that some noncompliant practices obviously continue.

One could argue that these positive changes could have been of short-term with a gaming effect. However, over the final years, from a revenue perspective, customs reforms in Cameroon seem to be sustained<sup>16</sup> (see Figure 1). Between 2009 and 2012, revenues have increased by almost 0.3% of Cameroon GDP. If revenues would have remained at 4.45% like it was in 2009 before the experiment, Cameroon customs would have lost in 2012 30 billion FCFA or approximately 60 million US$. It is worth noting however that when revenues have increased, exemptions (which are usually signed by the Minister of Finance) have even increased more rapidly: they were equal to 0.6% of GDP in 2007 and increased to 1.5% in 2012 accounting for almost 1/3 of collected revenues.

**Figure 1: Customs Revenues and Exemptions as share of GDP in Cameroon (2007-2012)**

<!-- Start of picture text --> 8.00%<br>y = 0.0028x + 0.0435<br>R2 = 0.9222 revenues+exemptions/<br>7.00%<br>GDP<br>6.21%<br>5.67%<br>6.00%<br>revenues/GDP<br>4.71% 4.89% 5.28% 5.22% y = 0.0012x + 0.04<br>5.00% R2 = 0.9317<br>Linear<br>4.54% 4.58% 4.68%<br>4.00% 4.45% (revenues+exemptions/<br>4.10% 4.17% GDP)<br>3.00% Linear (revenues/GDP)<br>2.00%<br>2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> Data source: Cameroon customs and WDI.

Finally, was the success of the Cameroon customs experience due to the personality and commitment and change management skills of the head of Cameroonian customs? The head of customs has had definitely a positive impact. However, this would be too simplistic to attribute all the success to the

> 16 Whereas customs clearance time remains stable with more than 90% of declarations processed the same day declarations are lodged.

14
**_16_**

_Price Support at Any Price?_

# 3.2 Targeting EU prices: simulation experiments results

Three experiments lead to different results. First, the costs are quite different. Table 3.1 shows policy costs indicators for three experiments<sup>9</sup> (all the tables show deviations from the base run). In line with the OECD definition, the total policy cost shown in the third row does not include an increase in social security payments (covered from the general revenue of the budget).

## **Tab.3.1 Targeting EU prices Simulation experiments: POLICY COSTS**

||Fast p<br>(EU prices|ath<br>by 2000)<br>|Fast path<br>reduc<br>_(EU pr. by 20|/output<br>tion<br>00, quotas)|Last momen<br>(50%EU p<br>201<br>|t integration<br>rices by<br>0)<br>|
|---|---|---|---|---|---|---|
||2000|2010|2000|2010|2000|2010|
|_In $ billions(9) prices):_<br>(1) Cost for taxpayers<br>(2) Cost for consumers|1.67<br>1.55|5.09<br>1.77|1.00<br>1.56|0.68<br>1.81|0.00<br>0.00|0.35<br>0.89|
|Total**annual**cost<br>**(1+2)**<br>(excl.social security)|3.22|6.86|2.56|2.49|0.00|1.24|
|Increase of social<br>security expenditures|-0.06|-0.13|-0.06|-0.09|0.00|-0.03|
|Producer Subsidy<br>Equivalent (PSE) in %|33.4%|43.5%|28.8%|23.8%|0.0%|14.1%|

_Source: Model POLAGR, simulation Jan.1995_ The FAST PATH experiment leads to an extreme! expensive policy. The total policy costs reach almost $ 7bn by the year 2010 (PSE on the EU level), violating Poland's GATT commitments and putting an unbearable burden on the taxpayers and consumers.

The high policy costs are not only due to the higher (supported) domestic price levels. Other factors influencing the outcome are both increased output (supply response to higher prices) and depressed domestic consumption. This leads to increase in production surpluses, making integration of the Polish agriculture in the CAP of 2010 more costly for the EU budget (production surpluses are twice as big as in the base run) . Such a phenomenon does not appear in two other scenarios: In the FAST PATH/OUTPUT REDUCTION experiment a slowdown in domestic food consumption is accompanied by a similar slowdown in domestic output caused by quantity control, and in the LAST MOMENT INTEGRATION experiment domestic food consumption is only slightly lower than in the base run (in both experiments production surpluses are close to the base run values).

9For a definition of cost see Box 2.
28 Mexico MFA

**Table S. Fixed Effects Quality Difterence Regression Bound** Groups 5 **and 6** - **_Le_** ' Specification

|Independent<br>Variables|Coefficient|t Statistic|Intercepts a3|F Statistic (1,22)<br>Ho: a;=0|
|---|---|---|---|---|
|log a6<br>.__<br>_|0.0374<br> __ __ __ __<br> <br>(0.4339)|0.086|||
|**log pt**<br>____________<br>Pjt(0.1048)|0.591<br>**_**___<br>|**_5.6?89A_**<br>S.6?8 '|||
|log**Hjt**|0°189<br>**(0.1280)**|Ld.82**c**|||
|log**U3 t**|-0.101<br>(0.0519)|-1.1143**b**|||
|**log Vjt**|-0.186<br>**(0.0680)**|-2.734**a**|||
|log**Tt**|-0.004<br>(0.0169)|0 249|__|_|
|**DS**|0.106<br>I<br>(0.1819)|0.583<br>_<br>_|-0.068|0.056 cannot<br>reject|

Adjusted R<sup>2</sup> = 0.98, n=20.

4 significant **at one** percent.

b significant at five percent.

**c** significant at ten percent.

# Hythesis Tests

1. P' = 1, t statistic, 3.904 , reject at cne percent.

2. P' = 1, F(1,23) = 15.247, reject at one percent.

3. 1' = 1, I' = 8' = +' = 0, F(4,23)=9.854, reject at one percent.

# Test of Log Specification

Sum of squared residuals for the linear specification = 0.28936 Sum of squared residuals for the log specification = 0.34268
**Galal** & Nauriyal

13 possible appeal to the Supreme Court. Disputes over interconnection are subject to binding arbitration. Similarly, in Jamaica, conflicts pertaining to tariff adjustments are subject to binding arbitration. In addition, the operating license explicitly grants the firm the right to appeal any breach of the terms of the agreement on the part of the government to the Supreme Court, whose ruling can be subjected to review by the Commonwealth Privy Council in London.

On the other hand, although firms in Argentina have the right to bring disputes <!-- Start of picture text --> Box 1: Price setting procedus for fixed telephony in Chiled<br>1. Demand is first es1riated for aach semcotzoneArmt bundit.<br>- -2. ::For. each servlce the Incrementw cost of develoment is ten calculated based on the concept of "efficint<br>-<br>.mf. .The Incrental- c,,st: ofdevlopment :s noting but the tong-run margina cost (LRMC) a#usted for<br>-invesment: The: aw defins.the e,,nt flina tocne that statsfrorm scratch nd euss only the assets<br>necessary to provide. that- servIc. ft further stipulates that, regulated cornpanies have to have a minimum of 5-<br>year investnt am, by the copany: and preented to SUBTEL ffbEwing the detailed outin:<br>': s peifededin:tLaw 18,188 (utica 301 ).--, , -.- , . - -.- ,<br>3. Revenue t tIm d for ea e su' tat the net present value of providng the service is equal to:<br>zero. Tis revenuen. thes incrementi cost of devellopment<br>4. To .move frn the Incremontal cot of.development to the long-rmn average cost (LRAC), effirien t ariffs are<br>iricreased ,- 'n aleast dbistoting fashion so tha,t f m.ake ala rae off return.<br>. Th-Re fir rate-of reurnm is defined as the sumof rates of return on the risk-free: ass nd th premium<br>of the- acti, weighted .by te systematic risk of the. Industry. That is<br>-. . . . . . . .. ... . . .. . ....-<br>where * .s. the*rate of retum on :revalued crapitl of firm. I; 1 is the rate of return on risk-free assets, fis fisrm<br>-'9 systematlc rik an,d, *s Ethe r,ate ofretumn on adiversIfled investment portfolIo.<br>6;8-.- ' Tariffs arecalculated -evry five y rs sthe law allows firms to adjust tarff every two months, using the<br>infla,tin index of each serveandd tie ODvisia :Index..<br>- :- - - . . -. -.... - - -<br>7. DIsputes. between companis a mregulators are- settled by a commitee of three. experts, one nominated, by<br>each, paty and the third,by mutu a,reernenL<br>Source: Galal (1994)<br><!-- End of picture text --> concerning pricing, entry, or interconnection to the attention of the newly established regulatory agency (CNT), the latter's decisions can only be appealed to the minister of economy. In Malaysia, conflicts are first referred to the regulatory agency, beyond which
Table A18. Mapping between WTO model regions list ( _reg_ ) and the SUST-RUS regions ( _r_ )

|**Regional code in the RUS-**<br>**ENV (WTO model)**|**Regions of the SUST-RUS model**|**Name of the macro-region**|
|---|---|---|
|msc|FD1-Central|"Moscow"|
|stp|FD2-Northwest|"Saint-Petersburg"|
|tmn|FD5-Urals|"Tumenskaya region"|
|vgd|FD4-Volga|"Northwest"|
|nor|FD2-Northwest|"North"|
|cen|FD1-Central|"Central"|
|sou|FD3-South|"South"|
|url|FD5-Urals|"Urals and near east"|
|sib|FD6-Siberian|"Siberia"|
|far|FD7-FarEast|"Far East"|

_Source: Authors’ estimations_ Table A19. Detailed list of subjects of the Russian Federation corresponding to WTO 10-regions macro-regions and Federal Districts of the Russian Federation (coincides with regional division in the SUST-RUS model)

|**Federal District**<br>**Subject of the Russian**<br>**WTO 10-**|**WTO subj**|**Russian Name**<br>**SR FD**|
|---|---|---|
|**Federation**<br>**reg code**|**of RF code**|**code**|

121
Table 1c: Average misallocation metrics

||1989|1994|2000|2005|2010|
|---|---|---|---|---|---|
|||A. Basel|ine misallocation|metrics||
|Organized sector||||||
|Financial loans|-0.260|-0.164|-0.099|-0.140|-0.013|
|Land and building|-0.215|-0.169|-0.113|-0.209|-0.092|
|Labor|-0.155|-0.091|-0.090|-0.163|-0.043|
|Combined organized and<br>|unorganized sectors|||||
|Land and building|-0.800|-0.437|-0.703|-0.476|-0.371|
|Labor|-0.684|-0.258|-0.555|-0.446|-0.270|
||B. Misall|ocation metri|cs without the in|dustry aggregatio|n step|
|Organized sector||||||
|Financial loans|-0.244|-0.061|-0.087|-0.193|0.132|
|Land and building|-0.461|0.001|0.027|-0.038|0.075|
|Labor|-0.248|0.020|-0.017|-0.029|0.086|
|Combined organized and|unorganized sectors|||||
|Land and building|-0.621|-0.433|-0.628|-0.406|-0.307|
|Labor|-0.468|-0.391|-0.409|-0.343|-0.245|
|||C. Misalloc|ation metrics usi|ng OLS TFP||
|Organized sector||||||
|Financial loans|-0.114|-0.117|-0.024|-0.009|0.055|
|Land and building|-0.069|-0.111|-0.037|-0.070|-0.029|
|Labor|0.014|-0.016|0.004|-0.016|0.063|
|Combined organized and|unorganized sectors|||||
|Land and building|-0.391|0.023|-0.087|-0.023|-0.080|
|Labor|-0.305|0.230|-0.002|0.085|-0.105|

Notes: See Table 1a. Authors' calculations using the OP decomposition index and other variants described in the text.
**Table 7**

**Regression Results of Low Congestion depending on size of cities (country fe)**

|**(Megacities)**<br>**PANDEMIC & LOCKDOWN EVOLU**<br>***|**(Small cities)**<br>**TION**<br>***|**(Intermediate)**<br>***|
|---|---|---|
|**Covid death (per Million)**<br>0.060<br>(0.018)|0.150<br>(0.009)|0.072<br>(0.011)|
|**Public transport closure**<br>1.056***<br>(0.061)|1.041***<br>(0.056)|1.167***<br>(0.042)|
|**Public transport facial covering**<br>0.453***<br>(0.041)|-0.112***<br>(0.034)|0.205***<br>(0.025)|
|**CONTROLS**|||
|**Working days**<br>-2.671***<br>(0.123)|-3.683***<br>(0.111)|-2.983***<br>(0.082)|
|**Min temp**<br>0.019*<br>(0.012)|-0.016**<br>(0.007)|0.003<br>(0.007)|
|**Avg temp**<br>-0.051***<br>(0.011)|-0.010<br>(0.007)|-0.017***<br>(0.004)|
|**Precip**<br>-0.020<br>(0.031)|-0.224***<br>(0.039)|-0.041***<br>(0.006)|
|**City density**<br>-0.002**<br>(0001)|-0.010<br>(0023)|-0.050<br>(0055)|
|.<br>**TRENDS IN MOTORIZATION**<br>***|.<br> <br>***|.<br>***|
|**Car registration**<br>-1.155<br>(0.139)|-0.168<br>(0.037)|-0.518<br>(0.005)|
|**COUNTRY FE**<br>|||
|**Country FE**<br>Yes|Yes|Yes|
|**Time dummy**<br>Yes|Yes|Yes|
|**Constant**<br>2.557***<br>(0.731)|2.064***<br>(0.427)|1.327***<br>(0.491)|
|**N**<br>9882|28182|30012|
|**N cities**<br>27|77|82|
|**Wald χ2**<br>2022.59***|5586.96***|5158.68***|
|**Log likelihood**<br>-3046.4335|-7540.9512|-7626.5672|
|**LR (chi2)**<br>228.28***|861.46***|674.77***|

Note: *, **, *** indicate respectively level of significance of 10, 5 and 1 percent.

20
# **Political Reservations and Women’s Entrepreneurship in India**

Ejaz Ghani, William R. Kerr and Stephen D. O’Connell January 2014

**Abstract:** We quantify the link between the timing of state-level implementations of political reservations for women in India with the role of women in India’s manufacturing sector. While overall employment of women in manufacturing does not increase after the reforms, we find significant evidence that more women-owned establishments were created in the unorganized/informal sector. These new establishments were concentrated in industries where women entrepreneurs have been traditionally active and the entry was mainly found among household-based establishments. We measure and discuss the extent to which this heightened entrepreneurship is due to channels like greater finance access or heightened inspiration for women entrepreneurs.

**Keywords:** Women, female, gender, entrepreneurship, political reservations, development, informal sector, India, South Asia.

**JEL Classification:** D22, E26, H11, J16, L10, L26, L60, M13, O10, R00, R10, R12.

_Author institutions and contact details:_ <u>Ghani: World Bank, Eghani@worldbank.org; Kerr:</u> Harvard University, Bank of Finland, and NBER, <u>wkerr@hbs.edu; O’Connell: CUNY Graduate Center, soconnell@gc.cuny.edu.</u> _Acknowledgments_ : We thank Nina Pavcnik, David McKenzie, Petia Topalova, and two referees for helpful comments on this work. We also thank Mani Shankar Iyer, Pratap Banhu Mehta, Meera Chatterjee, Prabir De, Ria Mukherji, Reema Nanavaty, Bhishma Rout, and Nupur Tiwary for extremely useful discussions on Panchayati Raj Institutions. We thank the World Bank's South Asia Labor Flagship team for providing the primary datasets used in this paper. We are particularly indebted to Shanthi Nataraj for sharing her wisdom regarding the industrial survey data during the nascent stages of this work. Funding for this project was provided by World Bank and the Multi-Donor Trust Fund for Trade and Development. Our online appendix that contains additional materials noted throughout this study and code for replicating the analysis is available at Stephen O'Connell's website. The views expressed here are those of the authors and not of any institution they may be associated with.

1