rafmacalaba/datause-encoder-data huggingface dataset:
to control for the technological heterogeneity of firms (as they respond to purely crosssectional variation in business environment). The relationship between business environment and productivity that we map out in this way is a short run effect as it is conditioned on the current (or observed) spatial distribution of plants.

Profit maximizing firms would tolerate regional gaps in business environment as long as these are compensated by mobility or relocation costs, or by other sources of productivity in the current location-notably, natural geography, market access and agglomeration economies. In other words, if any two locations differed only with respect to business environment (and there were no mobility costs), all plants would eventually have (re)located away from regions of poorer business environment. This means that there would always be a “reallocation of plants” between cities and regions over any period of time in response to persistence differences in business environment.<sup>9</sup> The estimates of the effect of business environment on local or regional industrial productivity we report here are short run effects as they omit (the productivity effects) of the spatial reallocation of activities.

On the other hand, being based on longitudinal analyses of aggregate (state level) data, the productivity gaps that Besley and Burgess (2004) attribute to differences in business environment are of a long run nature as they include spatial reallocation effects (as well as the short run effects). There is, however, the downside to the use of aggregate data in this context in that we cannot separately identify short run effects from aggregate data. We think that such identification is important because firms base their location choices and investment decisions on short-run productivity premiums or shortfalls, rather than on their long-run counterparts. . We also believe that the only practical way we can achieve this identification is probably estimating the effects of business environment at the plant level. At the same time, it is the long run productivity effects that would measure the true cost of possible deficiencies in a location’s business environment. Moreover, longitudinal analysis of aggregate or industry level data seems to be a far

> 9 This is not, of course, necessarily in the sense of the physical dismantling of plants to set them up elsewhere, but in that the number of plants in areas of “poor” business environment would eventually fall through closures relative to areas of  relatively “good” environment, where more new plants would be setup than would be closed over the same period.

6
36

- Banerjee, A., D. Green, J. Green, R. Pande. 2010. “Can voters be primed to choose better legislators? Experimental evidence from rural India,” Working Paper, Kennedy School of Government, Harvard University

- Banerjee, A., S. Kumar, R. Pande and F. Su. 2010. “Do informed voters make better choices? Experimental evidence from urban India,” Working Paper, Kennedy School of Government, Harvard University

- Banerjee, Abhijit, and Lakshmi Iyer. 2005. “History, Institutions and Economic Performance: The Legacy of Colonial Land Tenure Systems in India,” American Economic Review, 95. 1190–1213.

- Becker, G. S. 1985. “Public policies, pressure groups and deadweight costs,” Journal of Public Economics, 28: 330-47.

- Bernard, T., M. Collion, A. de Janvry, P. Rondot, and E. Sadoulet. 2008. “Do village organizations make a difference in African rural development? A study for Senegal and Burkina Faso,” World Development, 36(11): 2188-2204.

- Besley, Timothy and Masayuki Kudamatsu, 2008. "Making Autocracy Work" in Elhanan Helpman (ed.) _Institutions and Economic Performance_ .  Cambridge, MA: Harvard University Press.

- Besley, Timothy, Rohini Pande, and VijayendraRao. 2004. “The Politics of Public Good Provision: Evidence from Indian Local Governments.” _Journal of the European Economic Association_ 2 (2–3): 416–26.

- Besley, T. and A. Prat, 2006, Handcuffs for the Grabbing Hand? Media Capture and Government Accountability. _American Economic Review_ 96, 720-720.

- Bjorkman, M. and J, Svensson. 2006. “Power to the People: Evidence from a Randomized Experiment of a Community Based Monitoring Project in Uganda.” Mimeo. IIES, Stockholm University

- Bratton, Michael, Robert Mattes and E. Gyimah-Boadi, 2005. _Public Opinion, Democracy and Market Reform in Africa_ . Cambridge, UK and New York: Cambridge University Press.

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- Carlitz, R. 2010. “Background paper on budget processes,” Prepared for the Review of Impact and Effectiveness of Transparency and Accountability Initiatives. Brighton, UK: Institute of Development Studies.

- Casey, K., R. Glennerster, and E, Miguel. 2011. “Re-shaping Institutions: Evidence on external aid and local collective action,” Working Paper, Department of Economics, University of California-Berkeley
- ii) the modification or withdrawal of trade concessions through renegotiation: the main procedure for tariff and non-tariff renegotiations under the GATT, which allows for interested WTO members to require compensation<sup>13</sup> , set out in Article XXVIII of the GATT. This process is launched when a WTO needs to make substantial modifications in their schedules that are likely to diminish the rights of other WTO members.

In this context, two initial scenarios appear a priori possible: 1) the EU/UK seek only to rectify the current EU list, by making two lists out of it with identical substantial content; or 2) the EU/UK wish to make substantial amendments to their respective lists, thus launching a renegotiation process. The choice for these two procedures, however, is not necessarily mutually exclusive, as a rectification process could lead to a renegotiation if a third WTO Member objects to the proposed amendments. Box 1 offers further details on the rectification and modification procedures.

**<mark>Box 1.</mark>** <mark>Modification vs. Rectification of Concessions</mark>

The GATT Contracting Parties adopted the 1980 Procedures for Modification and Rectification of Schedules of Tariff Concessions 27 (Decision on Rectification), which provides that changes resulting from formal modification or rectification could be certified by the Director-General if no objection is raised by any other Member within a 3 month period. This procedure is subject to the following conditions:

- The proposed rectification shall be limited to amendments or rearrangements which are of a purely formal character and which do not alter the scope of the concessions.

- The proposed modifications resulting from action under Article II, Article XVIII:7, Article XXIV:6, Article XXVII or Article XXVIII, shall be a correct reflection of the modification.

- If objections are raised (e.g., because another member considers the change alters the scope of the concession) on any of these grounds, the member wishing to modify/rectify its schedule could end up in Article XXVIII of the GATT 1994 renegotiations.

- <mark>Source: WTO Secretariat, n/d.</mark>

# _Rectification of schedules_

While the rectification process would appear more straightforward in terms of procedure, it also raises some substantial legal questions regarding its feasibility. In principle, it is conceivable that the UK merely mirrors the current EU schedules, with only formal amendments to reflect that it is now an individual UK list. It could be expected that UK and the EU may have a preference for this simpler and straightforward procedure. The limit of this procedure is that the proposed

> 13 In this context, Article XXVIII of the GATT requires the modifying party to seek an agreement with any WTO who has an “initial negotiating right” or a “principal supplying interest”, as well as to consult with any other WTO member who has a “substantial interest” on the matter, as interpreted in the Understanding on the Interpretation of Article XXVIII of the GATT 1994.

10
<u>Table 4: Dimensional deprivation rates by gender and by place of residence</u>

|||||By gender||By|place of resi|dence|
|---|---|---|---|---|---|---|---|---|
||Dimensions|All|_Male_|_Female_|_Diff. _|_Rural_|_Urban_|_Diff. _|
|**o**|_Access to credit_|52.6|50.2|54.6|-4.4*|53.7|49.9|3.8**|
|**Fas**|_Employment_|49.5|31.9|64.2|-32.3*|55.0|35.7|19.3*|
|**a**|_Education_|71.6|63.4|78.4|-15*|83.4|41.7|41.7*|
|**kin**|_Housing_<sup>_~~a~~_</sup>|53.8|53.1|54.3|-1.2*|69.3|14.5|54.8*|
|**ur**|_Assets_<sup>_~~a~~_</sup>|69.5|68.5|70.3|-1.8*|78.1|47.9|30.2*|
|**B**|_Basic utilities_<sup>_~~a~~_</sup>|54.1|52.5|55.4|-2.9*|69.1|16.1|53*|
||_Access to credit_|56.5|49.9|61.7|-11.8*|62.5|48.1|14.4*|
||_Employment_|11.5|5.0|17.0|-12*|15.6|5.3|10.3*|
|**go**|_Education_|18.6|16.6|20.3|-3.7*|20.7|15.7|5.0*|
|**To**|_Housing_<sup>_~~a~~_</sup>|41.3|38.6|43.4|-4.8*|65.0|8.2|56.8*|
||_Assets_<sup>_~~a~~_</sup>|63.6|59.9|66.5|-6.6*|86.0|32.2|53.8*|
||_Basic utilities_<sup>_~~a~~_</sup>|45.0|42.4|47.1|-4.7*|69.9|10.2|59.7*|

(<sup>~~a~~</sup> ) indicates that deprivation rates are computed using the mean value of each index as the threshold.

(*) and (**) mean that the differences are significant at 5% and 10% level respectively.

Multidimensional poverty rates are estimated using equal weights for all dimensions. The results confirm the existence of gender inequalities in both countries. Tables 5 and 6 illustrate the situation for Burkina Faso and Togo, respectively. In the case of Burkina Faso, inequalities seem to be generally higher when the cutoff value is quite high (3 or more). They are observable both in rural and in urban areas. For a cutoff _d_ = 3, one can notice gender gaps in deprivation headcount, in terms of percentage points, of about -12, -9 and -15 respectively at the national, rural and urban levels. The differences are higher for _d_ = 4. The MPI measure also confirms such gender inequalities (cf. Table 5).

<u>Table 5: Multidimensional poverty rates in Burkina Faso</u>

|<br>||Headco|unt H(%)|||MP|I(%)||
|---|---|---|---|---|---|---|---|---|
|Level<br>Cutoff|All|Men|Women|Gender<br>diff.|All|Men|Women|Gender<br>diff.|
|<br>_d = 1_|98.3|97.8|98.7|-0.9*|65.1|59.9|69.3|-9.4*|
|**nal**<br>_d = 2_|91.6|89.0|93.7|-4.6*|63.4|57.7|68.0|-10.4*|
|**tio**<br>_d = 3_|78.3|71.8|83.6|-11.7*|57.7|50.4|63.7|-13.4*|
|**Na**<br>_d = 4_|52.6|40.7|62.5|-21.8*|42.6|32.1|51.2|-19.1*|
|<br>_d = 5_|18.4|9.3|25.8|-16.5*|16.8|8.5|23.6|-15.2*|
|_d = 1_|99.9|99.9|100.0|-0.1|71.7|66.6|75.7|-9.1*|
|**al**<br>_d = 2_|98.1|97.3|98.8|-1.6*|71.2|65.9|75.4|-9.6*|
|**ur**<br>_d = 3_|90.3|85.4|94.1|-8.7*|67.8|60.8|73.4|-12.6*|
|**R**<br>_d = 4_|64.9|51.6|75.3|-23.8*|52.8|40.9|62.1|-21.3*|
|_d = 5_|23.6|12.0|32.8|-20.7*|21.7|11.0|30.1|-19.0*|
|**r**<br>**a**<br>_d = 1_|93.9|92.7|95.0|-2.3*|47.3|43.4|51.0|-7.6*|
|**U**<br>**b**<br>_d = 2_|74.0|68.8|78.9|-10.1*|42.3|37.4|46.8|-9.4*|

21
**Table 4. Value-Added by Sector as a percent of the Value-Added of the Regional Market**

|Sector|Central|Far East|North|Siberia|South|Urals|Moscow|St.<br>Petersb.|Tumen|North-<br>west|
|---|---|---|---|---|---|---|---|---|---|---|
|MAR|0.6|0.6|0.7|0.7|0.5|0.8|0.3|0.6|0.7|0.6|
|AGR|14.8|7.6|3.7|8.7|15.8|8.7|1.2|3.9|0.9|10.0|
|AIR|0.5|0.6|0.5|0.5|0.6|0.6|1.2|0.5|0.3|0.4|
|CHM|1.9|0.1|0.8|1.1|1.2|2.0|0.6|0.7|0.0|2.8|
|CNM|1.4|0.5|0.4|0.5|0.8|0.7|0.7|0.8|0.1|0.4|
|COL|0.0|2.3|1.6|3.3|0.2|0.1|||||
|CON|10.2|3.1|3.0|5.6|13.3|8.5|11.2|9.7|2.5|7.6|
|CRU||1.9|8.3|1.0|4.8|5.5|||29.4|0.7|
|ELE|3.6|3.4|3.5|3.6|2.9|3.5|3.5|3.5|2.0|3.9|
|FIN|5.8|7.2|7.0|6.2|5.6|6.0|9.3|6.8|4.9|5.9|
|FME|2.5|0.2|0.8|0.9|0.5|1.9|0.2|0.4||7.5|
|FOO|3.3|4.0|1.8|1.8|3.0|1.6|2.7|5.8|0.1|3.1|
|GAS||0.1|0.2|0.0|0.2|0.2|||5.4|0.0|
|HEA|5.7|7.6|7.2|6.0|5.7|5.7|10.2|7.1|4.2|5.9|
|HOU|3.0|3.8|3.3|3.0|3.1|2.9|5.5|3.7|1.4|3.0|
|MWO|5.2|4.9|1.3|2.0|2.3|5.7|3.7|6.2|0.3|2.4|
|NFM|0.3|3.5|4.6|11.6|0.6|2.6|0.6|1.4||0.1|
|OIL|1.6|2.1|0.8|1.8|1.3|2.2|0.4|2.4|0.4||
|OTH|1.0|0.9|1.1|0.9|0.6|1.0|0.5|1.1|0.8|1.2|
|OTI|1.8|0.2|0.3|0.3|0.5|0.5|0.8|0.8|0.0|0.6|
|PIP|0.2|0.2|0.2|0.2|0.2|0.3|0.1|0.2|0.5|0.1|
|PST|0.4|0.4|0.4|0.4|0.4|0.4|0.6|0.4|0.3|0.4|
|RLW|3.1|3.0|3.1|3.1|2.6|3.0|2.8|3.1|1.7|4.1|
|SCI|1.2|1.4|1.2|1.1|1.0|1.3|1.3|1.4|1.0|1.0|
|TMS|1.0|1.2|1.1|1.0|1.0|1.1|1.7|1.2|0.8|1.0|
|TPP|0.9|1.9|7.5|1.3|0.3|0.7|0.5|2.4|0.1|2.8|
|TRD|27.5|34.2|33.0|30.8|28.3|30.1|36.8|33.3|40.2|31.9|
|TRK|1.4|1.5|1.5|1.4|1.4|1.4|1.9|1.5|1.3|1.5|
|TRO|0.6|0.7|0.7|0.6|0.6|0.6|0.9|0.7|0.6|0.6|
|CLI|0.5|0.7|0.6|0.5|0.6|0.5|1.0|0.7|0.2|0.5|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|

See Table 1 for sector codes.

Source: Regions of Russia, Roskomstat.

40
9 explanations for this are that (i) banks did not want to overtly oppose the central bank, and (ii) banks in fact used other evasive measures to allow them to stay within the growth limit while extending the amount of credit that they would have desired.

The latter explanation is plausible given the numerous evasive measures resorted to by banks. For example, banks cut back on holdings of securities and on unused lines of credit, both items included in the credit aggregate, and used this to offset growth above 4 percent in other categories. In addition, banks with affiliated leasing companies encouraged clients to take leases rather than loans. Finally, banks with parent banks abroad referred clients to the parent bank, with all of the screening and paperwork carried out in Croatia, but the final contracts and funding, at least formally, coming from the headquarters outside of Croatia (Galac and Dukić 2008).

The 2007 version of the credit growth reserve closed the line of credit loophole by limiting on- and off-balance sheet items separately and dealt with the leasing problems by capturing funding of the leasing company within the credit limit. However the issue of direct parent bank lending to clients was not resolved.

The CGR was removed at the beginning of 2004 since it was felt that bank credit growth had been successfully slowed and the current account deficit had improved.<sup>9</sup> However, as Figure 1 shows, credit growth began to accelerate again in mid-2004, and the current account deficit moved upward (Figure 2). The central bank, aware that the CGR did produce evasion, tried a less direct approach with the marginal reserve requirement (MRR). It imposed the CGR again only when credit growth had become quite strong, the current account deficit was accelerating, and the central bank was convinced that the other measures in force, including the MRR, were not adequate.

**Figure 2. Current account deficit and GDP growth**

<!-- Start of picture text --> 10<br>9.2<br>7.4 7.5 8<br>6.9<br>6.6<br>5.8<br>5.35.1 5.5 4.9 5.1 5.5 6<br>4.3 4.34.1 4.5<br>3.8 3.7<br>3.2 4<br>2.4 2.4<br>2<br>2<br>0<br>-1<br>-2<br>-4<br>Current account deficit GDP growth -5.8<br>-6<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> 9 The current account deficit fell from 7.4 percent in 2002 to 5.3 percent in 2003 and 4.3 percent in 2004. It then climbed to 5.5 percent in 2005 and 6.9 percent in 2006, leading the central bank to tighten its policy stance, including by imposing the CGR.
accounts methodology for domestic investments, domestic savings, and foreign savings. In order to obtain the EOCK for the economy, the result of the overall tax externality per unit of capital invested must be combined with the minimum supply price of capital in the market ( _i_ **m** in percentage terms per year), which comes from debt and equity sources. This part of the discussion assumes that we are dealing with the opportunity cost of capital supplied to an investment project that represents the financing of the foregone investment and the same capital funds supplied in the top-down case. This discount rate thus serves as the economic reference price for public sector projects in the absence of variations in the cost of capital for different projects. We will show that, in principle, following the top-down or bottom-up methodology for the average investment should yield similar or comparable results.

**First, the tax collection per unit of capital was estimated based on data available for general government tax revenues by major category.** The financial sum of taxes collected from the operations of the private sector (income taxes, property taxes, indirect taxes paid out of capital income, plus the share of indirect taxes in the product markets attributable to the contribution of capital to value added) was divided by the sum of capital stocks of the private sector, including the value of land and inventories, to obtain an average rate of taxation to capital in Mexico for the 2003–2011 period. The result was 2.3 percent of capital stocks, including land and inventories (Table 9).

# **Table** **<u>9</u> Taxes** **<u>on capital over GDP, 2003–2011 averages</u>**

|Type of tax|Taxes as share<br>of capital|
|---|---|
|Taxes on capital income and assets of corporate and capital share of non-corporate businesses<br>as a share of private reproducible capital + land|0.7%|
|as a share of private reproducible capital + land + inventories|0.5%|
|Taxes on property values||
|as a share of private reproducible capital + land|0.1%|
|as a share of private reproducible capital + land + inventories|0.1%|
|Indirect taxes paid out of capital income (taxes on mining and oil)||
|as a share of private reproducible capital + land|1.7%|
|as a share of private reproducible capital + land + inventories|1.2%|
|Share of indirect taxes in product markets (VAT, customs and excise) attributable to capital||
|as a share of private reproducible capital + land|1.0%|
|as a share of private reproducible capital + land + inventories|0.6%|
|Total tax distortion on capital assets||
|as a share of private reproducible capital + land|3.4%|
|as a share ofprivate reproducible capital + land + inventories|2.3%|

_Source:_ SHCP; OECD tax data.

**Second, the weighted average externality for invested capital was estimated.** We combine the tax externality on capital of 2.3 percent (from Table 9) with the tax gain from domestic

23
# **Measuring Green Jobs:**

# **A New Database for Latin America and Other Regions**

Hernan Winkler, Vincenzo Di Maro, Kelly Montoya, Sergio Olivieri, Emmanuel Vazquez<sup>*</sup> _Keywords_ : Green Jobs, Green Sectors, Climate Change, Labor Markets, Structural Transformation. _JEL Codes_ : Q5, Q52, Q56, J01, J21,

> * Di Maro, Montoya, Olivieri, and Winkler are with the World Bank Poverty and Equity Global Practice, Vazquez is with CEDLAS (IIE, FCE) - Universidad Nacional de La Plata, Argentina. We are grateful to Guillermo Beylis, Miki Doan, Alvaro Gonzalez, Ruth Hill, Nancy Lozano, Ghazala Mansuri, Carlos Rodriguez-Castelan, Trang Van Nguyen and Hugo Nopo for useful comments and discussions.
cratic firm growth strategies<sup>4</sup> is a public policy priority that will shape policies that promote or regulate high-value firms. The dominant concern is that these firms are gaining their distinction through market power by restricting competition which enables them to charge high prices without investing much.

Using conventional ROIC measures, in Figure 1, we find that there has indeed been a run-up in the ROIC of the top decile of large, non-financial sector, publicly-listed U.S. firms.<sup>5</sup> Over the period 1965-2015, the ratio of the 90<sup>_th_</sup> percentile ROIC firm to the median ROIC firm has increased by over 69%. Importantly, we find that the star firms whose returns are diverging from the rest of the firms are in industries that require high cognitive skills or that rely on high levels of intangible capital and that in these industries average returns are higher. In industries where the tasks involve routine manual skills and which score low on non-routine cognitive and complex problem solving skills, we see lower returns and do not see the star firms pulling away from the rest of the economy.

However, conventional return metrics do not capitalize research and development, brand capital, or other forms of organizational capital. The consequences of not measuring intangible capital are far-reaching because they affect measures of firms’ earnings, identification of variable costs, capital investment and estimates of pricing power, outcomes which are subject to controversy. For instance, De Loecker and Eeckhout [2017] show that there is a dramatic rise in firm pricing power in the U.S. using Cost of Goods Sold (COGS) as a measure of variable cost, Traina [2018] argues that once we include Selling, General, and Administrative Expenses (SGA) which are an increasingly vital share of variable costs for firms and accounts for intangible organization/management capital, there is no rise in markups. To address these issues, we adjust the conventional ROIC measures, measures of capital stock, variable costs and measures of pricing power to take into account investment in intangible capital.

Once we re-compute the ROIC calculations to factor in estimates of intangible capital from the

> 4Successful idiosyncratic growth strategies may also be due to successful innovation or superior management practices (e.g. Bloom and Van Reenen [2007]).

> 5We restrict our sample to large firms (defined as firms with assets more than $200 million in 2009 dollars, adjusted for inflation) to replicate the equivalent figure in the previous studies. However, the evidence in Council of Economic Advisors [2016], Furman and Orszag [2015], and Koller et al. [2017] is based on a proprietary dataset of US firms from McKinsey & Co. whereas Figure 1 is based on publicly available Compustat data. If we were to use the full sample of Compustat firms without restricting to large firms, we get much higher increases in ROIC for the top decile of

2