rafmacalaba/datause-encoder-data huggingface dataset:
Credit constraints can hinder green investment. Panel A of Table 2 also indicates that being credit constrained is associated with a 2.2 percentage points higher probability of making no green investment and a 3.3 percentage points lower probability of making both capital- and non-capital-intensive green investment. Average marginal effects of being credit constrained show no statistically significant impact on either capital-intensive or non-capital-intensive investments only. While credit constraints affect whether firms make a green investment, contrary to our hypothesis 3, there is no difference between the impact credit constraints have on capital-intensive versus non-capital investments in the overall sample. This could be because we do not know the actual capital intensity of each green investment the company reported. Investment in green energy generation on site, for example, could mean that the firm bought one solar panel and installed it on its roof (non-capital-intensive green investment) but it could also mean that the firm built a hydroelectric power plant (capital-intensive green investment).

**_Table 2: Multinomial logistic regression for type of green investment (average marginal effects)_**

||No green<br>investment|Capital-intensive<br>green<br>investment only|Non-capital<br>intensive green<br>investment only|Both capital- and<br>non-capital-<br>intensive green<br>investment|
|---|---|---|---|---|
|||_Panel A: O_<br>|_verall sample_<br>||
|Credit constraints|0.022*|0.001|0.009|-0.033***|
||(0.009)|(0.007)|(0.006)|(0.009)|
|Green management|-0.213***|-0.035***|0.018***|0.230***|
||(0.011)|(0.007)|(0.004)|(0.007)|
|||_Panel B: Allow all coef_|_ficients to vary by reg_|_ion_<br>|
|Credit constraints ECA|0.027**|-0.009|0.013|-0.031**|
||(0.009)|(0.007)|(0.007)|(0.011)|
|Credit constraints MENA|0.002|0.038|-0.006|-0.035*|
||(0.026)|(0.020)|(0.017)|(0.015)|
|Green management ECA|-0.194***|-0.038***|0.004|0.228***|
||(0.011)|(0.006)|(0.004)|(0.007)|
|Green management MENA|-0.281***|-0.072*|0.097***|0.256***|
||(0.036)|(0.032)|(0.011)|(0.018)|
|Observations||17|,902||
|Clusters||3|,022||

Note: This table shows the average marginal effect estimates of credit constraints and green management on the type of green investment, based on multinomial logistic regression with the type of green investment as a dependent variable. Regression controls for locality-level credit market controls (log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius) ; population size class; and region and sector fixed effects. In Panel B, estimated coefficients for all firm-level covariates as well as log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius are allowed to vary by region. Locality-clustered standard errors in parentheses. ***, **, * - statistically significant at 1%, 5%, and 10%, respectively.

Source: Enterprise Surveys and authors’ calculations.

The magnitude of the effect of credit constraints and green management quality on the type of green investment varies by region. Estimates in Panel B indicate that while credit constraints matter for some 21
# **Table 7: Specific Commitments by 'MIC' Countries** (percentage)

|Totals or<br>Average||Australia|Austia|Canada|EU|Finland|Hong<br>Kong|Japan|Korea|Mexico|New<br>Zealand|Norway|Singapor|e Sweden|Switzer-<br>land|Turkey|USA|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|330.4|Count (No. of conunitments)|360|412|352|392|328|200|408|311|252|276|360|232|320|400|276|384|
||Market Access:|||||||||||||||||
|40.6|Average sectoral coverage|46.6|55.4|43.3|44.4|42.4|18.4|50.4|31.5|22.3<br>|36.5<br>|45.3<br>|27.0<br>|42.8<br>|50.7<br>|34.4<br>|50.6<br>|
|53.3|CountVGNS list total|58.1|66.5|56.8|63.2|52.9|32.3|65.8|50.2|40.6<br>|44.5<br>|58.1<br>|37.4<br>|51.6<br>|64.5<br>|44.5<br>|61.9<br>|
|76.1|Av. Sec. Cov./[CountVGNS list]|80.3|83.4|76.3|70.3|80.2|57.0|76.6|62.7|55.0|81.9|78.1|72.2|83.0|78.6|77.4|81.8|
|188.9|Count of l's (no restrictions)|222|283|186|169|202|90|230|134|79|189|210|118|215|247|168|244|
|56.4|No restrictions/count|61.7|68.7|52.8|43.1|61.6|45.0|56.4|43.1|31.3|68.2|58.3|50.9|67.2|61.8|60.9|63.5|
||National treatment:|||||||||||||||||
|42.4|Average sectoral coverage|49.2|55.6|46.0|49.6|51.6|7.3|47.9|37.3|25.9|36.9|47.5|25.0|43.0|51.4|38.1|55.8|
|53.3|CountVGNS list total|58.1|66.5|56.8|63.2|52.9|32.3|65.8|50.2|40.6|44.5|58.1|37.4|51.6|64.5|44.5|61.9|
|79.5|[CountVGNS list ]/Av. Sec. Cov.|84.7|83.6|81.1|78.4|97.6|22.8|72.8|74.3|63.7|83.0|81.8|66.8|83.3|79.6|85.7|90.1|
|218.7|Count of l's (no restrictions)|257|287|220|233|317|41|199|187|124|182<br>|237<br>|152<br>|217<br>|251<br>|213<br>|324<br>|
|65.1|No restrictions/count|71.4|69.7|62.5|59.4|96.6|20.5|48.8|60.1|49.2|65.9|65.8|65.5|67.8|62.8|77.2|84.4|
||MA and NT comparisons:|||||||||||||||||
|1.2|No. of NT I's/No. of MA I's|1.2|1.0|1.2|1.4|1.6|0.5|0.9|1.4|1.6|1.0|1.1|1.3|1.0|1.0|1.3|1.3|
|173.4|No. of matched l's: MA=NT=1|219|268|180|163|199|31|155|133|78|166|204|115|207|241|154|218|
|51.4|Matched Is/count|60.8|65.0|51.1|41.6|60.7|15.5|38.0|42.8|31.0|60.1|56.7|49.6|64.7|60.3|55.8|56.8|
|28.0|Matched Is/GNS total|35.3|43.2|29.0|26.3|32.1|5.0|25.0|21.5|12.6|26.8|32.9|18.5|33.4|38.9|24.8|35.2|
|18826|GDP (S billion)|295|185|494|6662|94|78|3671|296|329|41|113<br>|46<br>|221<br>|241<br>|100<br>|5920<br>|
|81.6|World GDP share|1.3|0.8|2.1|28.9|0.4|0.3|15.9|1.3|1.4|0.2|0.5<br>|0.2<br>|1.0<br>|1.0<br>|0.4<br>|25.7<br>|
|38.3|MA cov. weighted by GDP|0.6|0.4|0.9|12.8|0.2|0.1|8.0|0.4|0.3|0.1|0.2|0.1|0.4|0.5|0.1|13.0|

NOTE: 'HIC' group averages include Iceland and Liechtenstein 21
been completely phased-in, and therefore incorporates the long-run supply and demand adjustments that will result from the liberalization process. The predicted changes in world prices are relative to the numeraire, which is the price of OECD manufacturing exports. Given the long-run nature of the policy reforms, the studies focused on the impact on the long-ran average level of prices, rather than on short-run year-to-year price changes. The changes are specified as percent deviations from the benchmark levels at the close of implementation period.

Brandao and Martin (1993) and Goldin, Knudsen, and van der Mensbrugge (1993) both utilized the Rural-Urban North-South (RUNS) model developed by the OECD and the World Bank, with different stylization of partial liberalization in agriculture. The results in column 1 by Brandao and Martin (1993) assumed trade liberalization along the lines of the Dunkel (GAYT, 1991) proposal, where only positive protection was reduced by 36 percent for import protection and by 20 percent for domestic support. The results in column 2 by Goldin, Knudsen and van der Mensbrugghe (1993) refer to an experiment in which all agricultural protection (positive or negative, domestic or border) was reduced by 30 percent for all commodities in all regions (including regions which are not currently members of the GATT/WTO. i° The price changes estimated by Brandao and Martin (1993) are broadly similar to the estimates by Goldin, _et al_ (1993), except for several commodities (rice, coffee, and dairy products) where the absence of discipline in negative protection in the Dunkel package scenario resulted in larger price increases. In both studies, dairy, sugar, beef and wheat are the commodities for which the largest price increases are observed, reflecting the high levels of protection in the OECD countries.

The most recent World Bank analyses of the Uruguay Round (Goldin and van der Mensbrugghe, 1995; Harrison, Rutherford and Tarr, 1995; Hertel, Martin, Yanagishima and Dimaranan, 1995) utilized the detailed evaluation of final commitments in agriculture carried out by Ingco (1995). Goldin and van der Mensbrugghe (1995) also consider non-agricultural liberalization in the new simulations; thus, including the considerable liberalization of manufacturing protection which raises world import demand and the average price of OECD price of manufacture exports, the deflator of agricultural prices used in the RUNS model. These l°If a country protections its agriculture, this involves a 30 percent reduction in import tariffs or a 30 percent reduction in export subsidies. If a country is taxing agriculture, Goldin, et al. assumed a 30 percent reduction in its import subsidy or a 30 percent reduction in its export tax. The reduction in the tariff equivalent is assumed to occur entirely in the year 1993, and the new rate is held constant for the rest of the simulation period at the lower level. All agricultural input subsidies are reduced by 30 percent.

9
(English origin). Examination of the long-term debt ratio reveals a weak relation between legality and long-term debt. French and German legal origin firms seem to have roughly the same amount of long-term debt which is substantially more than English legal origin firms. Chinese firms have an overall reliance on debt and equity similar to firms from German legal origin but exhibit a higher (lower) reliance on short-term (long-term) debt.

To test our hypotheses more precisely we estimate regression models using firm-level data with equity or debt ratios as the dependent variables, legality as an explanatory variable and a set of control variables to account for other probable determinants of external finance levels. The first two columns of Table 2 show results from tests with the market capitalization and total debt ratios as the dependent variables using all firms in our sample. As suggested by the findings in Table 1 legality is strongly positively related to the amount of firms’ external equity capital and strongly negatively related to the amount of total debt. The positive relation for external equity is consistent with both Hypothesis 1 and its alternative. However, the negative relation for total debt is inconsistent with Hypothesis 1 (that total debt and legal quality are positively related). Instead, it is consistent with the alternative (that total debt and legal quality are negatively related). Thus, these results support the monitoring models of Diamond (1991, 1993).

It is interesting to note the relations between external capital and the other variables included in the regression. Larger firms with less tangible assets tend to have both less (or less valued) external equity and less debt. In contrast, more profitable firms have more (or more highly valued) equity but less debt which is consistent with the pecking-order theory and the results of many prior empirical studies (see, for example, Rajan and Zingales, 1995).

The remainder of Table 2 presents results from similar tests that examine firms based on their use of foreign currency debt. The third and fourth set of results repeat the analysis in the first two columns but only for firms we do not identify as having foreign currency debt. These results are nearly identical to those for the full sample in so far as there exists a strong positive relation between legality and external equity and a strong negative relation between legality and 12
Figure 1: Essential SPS and TBT Provisions in PTAs <!-- Start of picture text --> Essential SPS provisions Essential TBT provisions<br>Total number of new PTAs Total number of new PTAs<br>New PTAs with less than 25% of essential SPS prov. New PTAs with less than 25% of essential TBT prov.<br>New PTAs with more than 25% of essential SPS prov. New PTAs with more than 25% of essential TBT prov.<br>1960 1970 1980 1990 2000 2010 2020 1960 1970 1980 1990 2000 2010 2020<br>20 300 20 300<br>10 150 10 150<br>Cumulative Cumulative<br>Number of Agreements Number of Agreements<br>0 0 0 0<br><!-- End of picture text --> Note: the graph plots the number of new PTAs entering in force over the period 1958-2017. It disantangles PTAs including zero, less than 25% and more than 25% of the total number of essential SPS provisions (left) and PTAs including zero, less than 25% and more than 25% of the total number of essential TBT provisions (right). Deepest agreements include 54% of essential SPS prov. and 47% of essential TBT prov.The red line shows the cumulated number of PTAs. Source: Mattoo, Rocha, Ruta (2020).

29
For countries that crossed the threshold more than once, in our baseline specification we consider only the first crossing in defining the instrumental variable. We then show, however, that our results are robust to changes in this criterion. Following the convention of the literature, we smooth out fluctuations in the annual data by using period averages. Due to the length of our panel dataset, and (more importantly) because IDA has a three-year replenishment cycle, we group years into 8 three-year periods that roughly coincide with the IDA replenishment periods.<sup>10</sup> The first period, with data from calendar years 1987-1989, corresponds roughly to IDA8, covering fiscal years 1988-1990 (July 1, 1987 to June 30, 1990). The final period, with data from 2008-2010, roughly corresponds to IDA15 (July 1, 2008 to June 30, 2011). Donors pledge contributions for each replenishment period, rather than annually. Moreover, policies for allocating IDA funds (e.g. the relative weights assigned to poverty, quality of economic policies, and quality of governance) among eligible recipients are often modified between IDA periods but never within an IDA period. For this reason country allocations should be more correlated from one year to the next within an IDA period than across two replenishment periods. The 3-year IDA periods are therefore a natural way of grouping the data. The timing of actual graduations from IDA also tends to coincide with the end of replenishment periods. The baseline sample contains 247 country-period observations.<sup>11</sup> Table 2 shows the summary statistics for the baseline sample. Real per capita GDP of the sample countries grew at an average annual rate of 2.9%. Investment on average accounted for 25% of GDP. ODA equaled about 8% of GNI for a typical country in a typical year in the sample. Of total ODA, about 9% is from IDA, 67% is from DAC

> 10 Recent studies that use panel data often group years in 4- or 5-year periods. As Temple (1999, 132) observes, “The question of when to test for growth impacts plagues the entire growth literature, not just aid-growth research. Empirical research on the determinants of growth cannot escape the selection of a fixed observation period, but “selecting the time intervals over which to study growth ... is a question that remains largely unsettled.” (excerpted from Clemens et. al., 2012). We nevertheless follow the convention of the literature by using non-overlapping periods.

> 11 Appendix Table E lists the definitions and sources of data for variables used in this paper.

8
**_Table 6: Walking speed by landcover_**

|**Landcover**|**Walking speed on a**<br>**flat surface**|
|---|---|
|Build‐uparea|5.0 km/h|
|Barren Area|3.0 km/h|
|Grassland|4.86 km/h|
|Scrubland|3.6 km/h|
|Agriculture area,<br>Forest|3.24 km/h|
|Water body|0.06 km/h|
|Snow|1.62 km/h|

We merge slope information with the land cover data to calculate a final walking speed. Results from Tobler’s hiking function and a model by van Wagtendonk and Benedict (1980)<sup>1</sup> were compared to reported walking times from the household survey. We found Tobler’s function yielded results most consistent with survey responses and consequently employed it within the model. Formally, Tobler’s hiking function is:

### **_Equation 4_**

Where: W= 6 e<sup>−3.5 abs (tanθ+0.05)</sup> W: Walking speed (km/h) Θ: Angle of slope The speed of a level surface is calculated to be around 5.04 by Tobler’s hiking function. The following formula is used to combine the walking speed by land cover and slope.

### **_Equation 5_**

Where: V: Modified Walking speed (km/h) V = v<sup>W</sup> 5.04 v: Walking speed on flat surface by land cover

## Monsoon modification

A uniform 25% reduction in walking speeds has been applied to all off‐road surfaces in the monsoon model.

# Combining on- and off-road results

We calculated each raster cell’s “cost” in units of time by overlapping the separate off‐road and on‐road speed surfaces and selecting the highest value in each cell. This ensured that vehicle‐based speeds are used wherever roads exist, and walking speeds are used where roads do not exist or the monsoon so badly degrades roads that walking is faster. A generic time (in hours) to travel across a cell (surface friction) is then calculated by dividing the cell travel cost (distance) by the cell travel speed (in kilometers / hour). A final travel time raster surface for each facility was computed by applying an algorithm to choose the least travel time route on the resulting Cost Time layer. By extracting these raster values to administrative areas and/or the location of households, the travel time is summarized.

> 1 V = V0 _e_ - _ks_

> Where: v = off road foot based speed over the sloping terrain, v0 = the base speed of travel over flat terrain, 5km/hr in this case, s = slope in gradient (meters per meter) and, k = a factor which defines the effect of slope on travel speed

For this case we assume a base walking speed of 5km/hr and k = 3.0 and constant for uphill and downhill travel.
44 Figure 4

# INFLATION ARGENTINA

<!-- Start of picture text --> 200-<br>180 -- -<br>BB PLAN<br>160-<br>140- --<br>_-<br>120-<br>100---<br>ERMAN 11<br>80-_<br>60- .-..<br>_p 1._<br>40- USTRAL PRIMAVERA t l ------ l ICONVERT<br>201 1 1<br>t1985 1986 1987 1988 1989 1990 1991<br><!-- End of picture text -->
6. Trade Linkages between China and other B&R Economies

**Source:** Author’s calculations using the Tiva database.

LOOKING AT THE DIFFERENCE BETWEEN INTERMEDIATE AND FINAL GOODS, WE NOTICE THAT CHINA IMPORTS MORE INTERMEDIATE GOODS THAN IT EXPORTS. HOWEVER, EXPORTS TO OTHER B&R ECONOMIES HAVE BECOME MORE AND MORE INTENSIVE IN INTERMEDIATES (FIGURE 38), REACHING 59 % IN 2010. FROM THE IMPORTING SIDE, WE SEE THAT CHINA’S IMPORTS ARE MOSTLY INTERMEDIATE GOODS ( Figure 39). The results suggest that China has stronger GVC linkages as a buyer of value-added than as a seller.

FIGURE 38: SHARE OF INTERMEDIATE GOODS IN CHINESE EXPORTS TO B&R AND NON-B&R ECONOMIES, 1995-2010 <!-- Start of picture text --> 61<br>59<br>52<br>50 50 51 49 50<br>48<br>46<br>43 43<br>1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010<br>non-B&R B&R ROW<br>60<br>40<br>20<br>Share of Intermediate Goods in Gross Exports<br>0<br><!-- End of picture text -->

**Source:** Author’s calculations based on GVC indicators extracted using the Wang et Al. (2013) methodology on the Tiva database.

FIGURE 39: SHARE OF INTERMEDIATE GOODS IN CHINESE IMPORTS FROM B&R AND NON-B&R ECONOMIES, 19952010 <!-- Start of picture text --> 87 89 91<br>80 80<br>77 79<br>74 73<br>70<br>67 67<br>1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010<br>non-B&R B&R ROW<br>100<br>80<br>60<br>40<br>20<br>Share of Intermediate Goods in Gross Exports<br>0<br><!-- End of picture text --> 32
- 33 -

municipal absorptive capacity. As such, however, the Law raises a number of issues about:

- (a) the form of the sharing formula for small municipalities;

- (b) the restriction of tax effort just to those municipalities; and

- (c) the speed with which municipal absorptive capacity can be increased.

35. First, the elimination of the 30% portion of IVA shared equally by departments and the introduction of a "fiscal effort" factor for small municipalities are both improvements. However, the formulas that have been used raise questions. The formulas suggested by the Government and by the World Bank account for fiscal effort adding a factor to the population share which reflects a community's efforts relative to average efforts. The Government's formula is:<sup>2 4</sup> and the World Bank's recommended reformulation is:<sup>2 5</sup> where CAT is total transfer to be divided among . municipalities; CAHi is the transfer received by the ith municipality; P1 is its population and Pop is the total population of small municipalities; AC1 is the total cadastral value (less tax exempt properties) in the ith municipality and 24/ See DNP Proveccion de la ParticiDacion de los Hunicipios **en** la Cesion del ImDuesto al Valor Aaretado (Bogota; Septiembre 1988), p. 6 25/ See IBRD (1987), Op. cit., p. 42.
||by the ES. In the baseline sample using in<br>Table 1, there are 28 3-digit industries.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|---|---|
|Year fixed effects|Dummy variables indicating the year the ES<br>was conducted in the country.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Firm Size (logs)|Log of total number of permanent full-time<br>workers at the firm in the initial year<br>(beginning of the growth period which is 2<br>fiscal years ago for most countries and 3 fiscal<br>years ago for other countries).<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|GDP per capita (lagged, logs)|Log of GDP per capita lagged by 2 years from<br>the fiscal year covered by the Enterprise. GDP<br>per capita values are in constant 2011<br>International Dollars and PPP adjusted. In<br>cases where the Enterprise Survey spanned<br>more than one calendar year, the GDP per<br>capita figure is the weighted average over the<br>calendar years covered (lagged by 2 years),<br>where the weights used are the proportion of<br>months covered in the calendar year.<br>Source: World Development Indicators,<br>World Bank.|
|Age of Firm (logs)|Log of age of the firm. The age of the firm is<br>defined as the current year minus the year the<br>firm first started operations.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Manager Experience (logs)|Log of the number of years of experience the<br>top manager of the firm has working in the<br>industry.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Exports (proportion of sales)|The proportion of the firms’ annual sales made<br>abroad in the last fiscal year. Only direct<br>exports are included.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Female Top Manger Y:1 N:0|Dummy variable equal to 1 if the top manager<br>of the firm is a female and 0 otherwise.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|

55
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22