rafmacalaba/datause-encoder-data huggingface dataset:
# App. Table 2: Sizes of industries, 2000

|||Or|ganized sec|tor|Un|organized se|ctor|
|---|---|---|---|---|---|---|---|
|NIC|IndustryDescription|Plants|Empl|Output|Plants|Empl|Output|
|15|Food products and beverages<br>|21.4|1,261.4|1,234,459|1,743.6|4,622.3|392,886|
|16|Tobacco products<br>|2.2|475.2|103,597|169.7|485.8|16,820|
|17|Textiles<br>|12.3|1,245.9|773,018|724.5|2,034.3|149,788|
|18|Wearing apparel; dressing and dyeing<br>of fur<br>|2.8|329.2|141,507|2,018.0|3,257.1|93,687|
|19|Leather; luggage, handbags, saddlery,<br>harness and footwear<br>|2.2|135.7|88,793|130.5|287.3|22,159|
|20|Wood and wood products, except<br>furniture; straw and plating<br>|2.7|45.6|18,731|1,226.7|2,646.0|76,124|
|21|Paper and paper products<br>|3.2|176.1|178,617|59.6|189.9|17,998|
|22|Publishing, printing and reproduction of<br>recorded media<br>|3.0|116.7|53,516|109.4|382.3|36,042|
|23|Coke, refined petroleum and nuclear<br>fuel<br>|0.8|66.7|771,868|5.6|19.0|2,807|
|24|Chemicals and chemical products<br>|9.9|779.8|1,439,134|38.8|198.9|40,877|
|25|Rubber and plastic products<br>|6.4|251.1|258,618|50.5|216.3|42,320|
|26|Other non-metallic mineral products<br>|10.5|428.3|288,191|623.9|2,562.2|116,946|
|27|Basic metals<br>|6.5|551.0|820,940|15.5|73.1|28,293|
|28|Fabricated metal products, except<br>machinery and equipments<br>|7.9|292.6|181,534|282.1|797.0|80,883|
|29|Machinery and equipment, n.e.c.<br>|8.9|422.9|373,526|52.5|224.1|43,967|
|30|Office, accounting and computing<br>machinery<br>|0.2|17.1|36,757|0.2|0.9|309|
|31|Electrical machinery and apparatus,<br>n.e.c.<br>|3.7|229.0|237,760|35.8|183.6|283,943|
|32|Radio, television, and communication<br>equipment and apparatus<br>|1.0|109.1|166,577|4.0|25.7|2,995|
|33|Medical, precision and optical<br>instruments, watches and clocks<br>|0.9|58.8|42,137|4.7|19.3|3,511|
|34|Motor vehicles, trailers and semi-<br>trailers<br>|2.5|257.3|389,512|9.3|45.9|9,931|
|35|Other transport equipment<br>|1.8|182.7|200,111|8.3|38.2|7,273|
|36|Furniture, manufacturing n.e.c.<br>|2.1|119.3|105,705|435.0|1,078.1|90,704|
||Traditional|70.3|4,626.0|3,167,666|7,523.1|18,342.3|1,094,037|
||Modern|42.8|2,925.5|4,736,939|225.1|1,045.0|466,225|

Notes: Plants and employments are expressed in thousands. Output is expressed in millions of rupees. "n.e.c." stands for Not Elsewhere Classified. Taken from Ghani et al. (2013b).
|||PRE Working Paper Series|||
|---|---|---|---|---|
|||||Contact<br>|
||iLe|Author|_DA9_|for p aper|
|WPS758|Is There Excess Co-Movement of<br>Primary Commodity Prices? A<br>Co-Integration Test|Theodosios B. Palaskas<br>Panos N. Varangis|August 1991|D. Gustafson<br>33714|
|WPS759|The Profamilia Family Planning<br>Program, Columbia: An Economic<br>Perspective|Jesus Amadeo<br>Dov Chernichovsky<br>Gabriel Ojeda|August 1991|0. Nadora<br>31091|
|WPS760|How Conflicting Definitions of<br>'Manufactures'Distort Output and<br>Trade Statistics|Alexander J. Yeats|September 1991|J. Jacobson<br>33710|
|WPS761|Uncertainty and the Discrepancy<br>between Rate-of-Return Estimates<br>at Project Appraisal and Project<br>Completion|Gerhard Pohl<br>Dubravko Mihaljek|September 1991|P. Lee<br>81950|
|WPS762|Debt, Debt Relief, and Growth:<br>A Bargaining Approach|Daniel Cohen<br>Thierry Verdier|September 1991|S. King-Watson<br>31047|
|WPS763|A Valuation Formula for LDC Debt|Daniel Cohen|September 1991|S. King-Watson<br>31047|
|WPS764|African Financing Needs in the 1990s|<sup>Jorge Culagovski</sup><br>Victor Gabor<br>Maria Cristina Germany<br>Charles P. Humphreys|||
|WPS765|Withholding Taxes and International<br>Bank Credit Terms|<sup>Harry Huizinga</sup>|September 1991|S. King-Watson<br>31047|
|WPS766|Economic Crisis, Structural<br>Adjustment, and Health in Africa|Francois Diop<br>Kenneth Hill<br>Ismail Sirageldin|September 1991|0. Nadora<br>31091|
We include dummies to indicate legal origins - FRENCH, ENGLISH, GERMAN, SCANDINAVIAN, and TRANSITION. These origins proxy broadly for creditor rights, with English, common law countries being regarded<sup>asmorecreditor-</sup> friendly, whereas French, civil law countries are regarded as more<sup>debtor-friendly.</sup> However, these variables also capture other aspects, including<sup>theadaptabilityofthe</sup> legal system and elements of the efficiency of the legal system.<sup>Inadditiontoincluding</sup> legal origins, which are exogenously determined, we expect the implementation<sup>of laws</sup> to be a significant factor and therefore also include an index of the<sup>efficiencyand</sup> integrity of the legal environment, RULE of LAW, as reported for most countries<sup>byLa</sup> Porta et al. (1998) and for transition economies by Pistor (2000). As an alternative measure regarding the efficiency of the judicial system, we use a "legality"<sup>index,which</sup> is the weighted average of indexes provided by Business International Corporation<sup>of the</sup> Efficiency of the Judiciary, Rule of Law, Corruption, Risk of Expropriation,<sup>and Riskof</sup> Contract Repudiation (Berkowitz, et al., 2002).Io Finally, we use the La Porta et al. (1998) index of<sup>CREDITORRIGHTS,</sup> consisting of the summation of four dummy variables, RESTRICTIVE REORGANIZATION, NO AUTOMATIC STAY ON ASSETS,<sup>SECUREDCREDITOR</sup> PRIORITY, and MANAGEMENT DOESN'T STAY, with a highest<sup>possiblescoreof</sup> four." l We also use the individual sub-indexes.

° This index is unavailable for transition economies.

l Almost all countries have formal liquidation and reorganization laws (see<sup>Appendix2).</sup> 15
Appendix Table 16 Probability of Firms Using Public Programs: Dynamic Lockdowns, Covariate Balance Test

||(1)<br>Treatment<br>Municipality|(2)<br>Control<br>Municipality|(3)<br>Dif.|(4)<br>N|
|---|---|---|---|---|
|_Panel A: All Firms in Municipality_|||||
|Elegible (Days Past Due)|0.899<br>|0.866<br>|0.033<br>|3,884|
||(0.010)|(0.006)|[2.633]||
|Elegible (Sales)|0.995<br>(0.002)|0.993<br>(0.002)|0.002<br>[0.616]|3,884|
|Elegible (Days past due and sales)|0.899<br>(0.010)|0.866<br>(0.006)|0.033<br>[2.633]|3,884|
|With Previous Loans|0.898<br>(0.009)|0.909<br>(0.005)|-0.011<br>[-1.055]|3,884|
|Firm Age (Years)|11.334<br>(0.245)|10.914<br>(0.133)|0.421<br>[1.525]|3,883|
|Debt Outstanding (Million US$)|0.199<br>(0.029)|0.278<br>(0.060)|-0.080<br>[-0.733]|3,884|
|Fogape Credit Amount (Million US$)|0.098<br>(0.008)|0.080<br>(0.005)|0.018<br>[1.894]|1,593|
|Proportion of Small frms|0.767<br>(0.014)|0.799<br>(0.007)|-0.032<br>[-2.085]|3,884|
|Proportion of Medium frms|0.164<br>(0.012)|0.138<br>(0.006)|0.026<br>[1.962]|3,884|
|Proportion of Large frms|0.063<br>(0.008)|0.056<br>(0.004)|0.008<br>[0.898]|3,884|
|_Panel B: Firms along Municipality Bor_|_der_||||
|Elegible (Days Past Due)|0.900<br>(0.017)|0.866<br>(0.022)|0.034<br>[1.235]|541|
|Elegible (Sales)|0.997<br>(0.003)|0.991<br>(0.006)|0.005<br>[0.841]|541|
|Elegible (Days Past Due and Sales)|0.900<br>(0.017)|0.866<br>(0.022)|0.034<br>[1.235]|541|
|With Previous Loans|0.899<br>(0.016)|0.897<br>(0.020)|0.002<br>[0.083]|541|
|Firm Age (Years)|11.453<br>(0.419)|9.787<br>(0.420)|1.666<br>[2.751]|541|
|Debt Outstanding (Million US$)|0.179<br>(0.049)|0.261<br>(0.105)|-0.082<br>[-0.772]|541|
|Fogape Credit Amount (Million US$)|0.100<br>(0.013)|0.053<br>(0.009)|0.046<br>[2.686]|237|
|Proportion of Small frms|0.761|0.810|-0.048|541|
||(0.024)|(0.026)|[-1.344]||
|Proportion of Medium frms|0.181<br>(0.022)|0.139<br>(0.023)|0.042<br>[1.313]|541|
|Proportion of Large frms|0.055|0.043|0.012|541|
||(0.013)|(0.013)|[0.609]||

This table reports summary statistics of the treatment and control municipalities. Treatment municipality are firms located in a municipality subject to lockdown, and control municipality otherwise. Amounts are in million US$ as of December 2019. Elegible by days past due is a dummy variable equal to one if the firm are up to date with their debt payments (no more than 30 days past due). Elegible by Sales is a dummy variable equal to one if the firm have annual sales below US $35 millions. Elegible by days past due and sales is the interaction of both variables. With Previous Loans is a dummy variable equal to one if the firm has bank credit outstanding in December 2019 or receives a bank loan over the period 2012–2019 and is zero otherwise. Small, Medium, and Large Firms are dummy variables equal to one if the firm has annual sales less than US$0.8 million, between US$0.8 and US$3.5 million, and between US$3.5 and US$35 million, respectively. Standard errors are shown in parentheses. _t_ – statistic is shown in brackets.

34
**Table 2: Explaining Capital Intensity  - OLS**

Dependent <u>variable: Log of Capital per worker</u>

||(1)|(2)|(3)|
|---|---|---|---|
|Workers(log)|0.141|0.140|-0.213|
||(0.12)|(0.12)|(0.14)|
|Program-dummie||0.006|0.122|
|||(0.27)|(0.26)|
|Contractor|||1.766***|
||||(0.35)|
|Constant|8.084***|8.083***|8.546***|
||(0.31)|(0.31)|(0.31)|
|N|222|222|222|
|R2|0.007|0.007|0.108|
|Adjusted R2|0.002|-0.002|0.096|

Note: *, **, *** indicate significance at the 10%, 5% and 1% level respectively

**Table 3: Explaining Input-Intensity - OLS**

Dependent <u>variable: Log of Input per worker</u>

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Workers(log)|-0.135|-0.359**|-0.346**|-0.283*|
||(0.13)|(0.14)|(0.14)|(0.15)|
|Program-Non-Contractor|||-0.145||
||||(0.26)||
|Program-Contractor|||0.536||
||||(0.63)||
|Contractor||1.901***|1.502***|1.586***|
|||(0.36)|(0.53)|(0.38)|
|Capital-Labor-Ratio||||0.201***<br>(0.06)|
|Program-dummie||-0.049||-0.097|
|||(0.25)||(0.25)|
|Constant|9.459***|9.750***|9.767***|7.955***|
||(0.32)|(0.30)|(0.30)|(0.62)|
|N|185|185|185|175|
|R2|0.006|0.143|0.148|0.186|
|Adjusted R2|0.0005|0.129|0.129|0.166|

Note: *, **, *** indicate significance at the 10%, 5% and 1% level respectively

-13-
Figure 1: Financial openness The figure displays the evolution of the average Chinn-Ito index of financial openness for all countries and for the group of advanced and emerging countries.

<!-- Start of picture text --> 1<br>0.9<br>Advanced countries<br>0.8<br>All countries<br>0.7<br>0.6<br>0.5<br>0.4<br>Developing countries<br>0.3<br>0.2<br>0.1<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text -->

**A** (L) = **0** but still imposes **β** (L) = **0** , so that each country’s GDP growth is described by an AR process; experiments with both first- and second-order processes proved the former to be sufficient.<sup>21</sup> The third specification is a factor-augmented AR model with **A** (L) = **0** and **β** (L) = **0** . All three specifications allow for unrestricted cross-country heteroskedasticity and parameter heterogeneity.

Table 1 reports the estimation results and forecasting performance of the three specifications. The top half of the table summarizes the parameter estimates (except for the iid specification). With the AR(1) model, shown in the second column, the average of the country-specific autoregressive parameters equals 0.24, and is significantly different from zero. Twenty-five of the fifty individual estimates are significantly different from 21Standard tests could not reject the joint null that all the coefficients on twice-lagged growth equal zero, so the results are not reported here.

17
# **III. Estimation**

We study the extent to which the time to move goods from the factory to the ship influences the volume of exports. Longer time delays act as a tax on exports, especially on high-value goods, since they are effectively depreciating during the delay. In addition, the exporter must expend capital on the exporting process and storage/transport of the goods during the delay. Finally, long time delays are likely to be associated with more uncertainty about delivery times, which would further depress exports.<sup>12</sup> For comparison purposes, we begin by estimating an augmented gravity equation that takes the remoteness of a country into account. The intuition is that more remote countries are likely to trade more with their actual trading partners because they have fewer alternatives. For example, Australia is likely to trade more with New Zealand than Austria is with Portugal—even though their other bilateral characteristics are similar—as the latter have more source countries to choose among.

Since this impact is likely to be correlated with our explanatory variables of interest, there is a potential omitted variable bias. Indeed, Anderson and Wincoop (2003) show that the failure to control for what they call the “multilateral resistance” is a major reason that McCallum (2000) found a large border effect in bilateral trade between Canadian provinces and U.S. states. In our case, countries that have more opportunities for export may have higher returns to enhanced local trade facilitation, and invest more in time efficient means. In addition, countries in market centers are more likely to

> 12 The Doing Business data contain information on the maximum time for exporting.  To control for uncertainty, we added maximum-time and also maximum-time-less-average-time variables to the regression equation.  The coefficients on these variables were not significant when either was included along with the average time variable (which remained robust) and coefficients were very similar to those reported here when they were included without the average time variable. The correlation between maximum time and average time is 0.92. This high correlation means it is difficult to pick up the individual effect of uncertainty.

11
# **Box 7: Summary of Findings of the Service Delivery Dimension**

|**STRENGHTS**|**CHALLENGES**|
|---|---|
|There is a well-structured network of preventive,<br>diagnostic, treatment, palliative and support<br>services for people living with HIV/AIDS.<br>The coverage level and the quality of services have<br>improved over the years.|The complexity of the insurance system<br>determines the national response in terms of:<br>Geographical inequities and by type of insurance<br>regime.<br>Problems of availability, access and quality of<br>medicine for human use (poorly developed<br>regulatory framework).|
||Continuity of care within the subsidized regime (it<br>is especially problematic).<br>Programs and interventions for targeting most at<br>risk groups are still limited, mainly due to the lack<br>of sound evidence of vulnerable populations at the<br>territorial level.|

**Source:** Técnicas de Salud. 2011.

52
1

# Table of Contents

|I.|Introduction|1|
|---|---|---|
|IR.|Methodology|3|
||General Benefits<br>|6|
||Specific Benefits and Transfer Payments<br>|7<br>|
||Capzital Expenditures<br>|8|
||Interest Payments<br>|8|
||Rent-Seeking and Side Payments<br>|8|
||Measurement Issues<br>|9|
||Computable General Equilibrium Models<br>|12|
||Two Measures of Benefits for Local Public Goods|13|
|III.|Methodological Issues Specific to Developing Countries|13|
||Public Enterprises<br>|14|
||The Geographic Distribution of Benefits<br>|14<br>|
||In-Kind Transfers and Informal Markets<br>|15|
||Govemment Benefits and the Intrafamily Distribution<br>||
||of Resources<br>|15|
||Food and Agricultural Programs<br>|16|
||Migration|18|
|IV.|Developed Country Benefit Incidence: Empirical Results|18|
||Speific Govermnent Services<br>|19|
||General Public Goods|22|
|V.|Developing Country Benefit Incidence: Empirical Results|23|
||Studies of Total Public Expenditure Benefits|23|
||Education<br>|26|
||Health<br>|31|
||Electricity, Water, and Sewage|34|
||Food and Agricultural Programs<br>|37|
||Consumer Price Subsidies|39|
|VI.|Conclusions and Suggestions for Further Research|39|
|Endno|tes|43|
|Refere|nces|47|

|References|
|---|
**Table 16 How Constraining Labor Regulations Are?**

||Basic|Interaction|
|---|---|---|
|Smallt‐3|‐0.131|‐0.167|
||(0.040)***|(0.070)**|
|Mediumt‐3|‐0.148|‐0.122|
||(0.034)***|(0.052)**|
|Larget‐3|‐0.107|‐0.098|
||(0.060)*|(0.089)|
|Aget‐3|‐0.003|‐0.003|
||(0.001)**|(0.001)**|
|Foreign|‐0.033|‐0.024|
||(0.072)|(0.070)|
|Govt|0.038|0.025|
||(0.042)|(0.044)|
|Exporter|0.018|0.014|
||(0.033)|(0.032)|
|LaborReg|0.414|0.230|
||(0.195)**|(0.378)|
|LaborReg*Small||0.457|
|||(0.466)|
|LaborReg*Medium||‐0.238|
|||(0.431)|
|LaborReg*Large||‐0.076|
|||(0.555)|
|Constant|0.170|0.181|
||(0.085)*|(0.093)*|
|Obs|668|668|
|R<sup>2</sup>|0.214|0.220|

Robust standard errors clustered by region and 2‐digit industry are in parentheses. Additionally control for 2‐digit industry and region fixed effects. *** p<0.01, ** p<0.05, * p<0.1.

24
—o— BBR (Default model) -——SSR (Default model) —>— BBR (EB model) ——SSR (EB model) — — HR (Default model) - — BBR-HR (EB model) +++**SSR-HR (EB model) <!-- Start of picture text --> 90<br>80 «Baseline<br>—Scenario<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0 _ OO ee |<br>2020 2025 2030 2035 2040 2045 2050<br><!-- End of picture text -->

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Average tariff rates by commodity are also included in the table. The rates implied by the social accounting matrix originally developed for this analysis are presented under the heading “average tariffs” and the rates used in the GTAP model of global trade are presented under the heading “GTAP tariffs”. Generally, the tariffs implied by the SAM correlate well with those employed in the GTAP model (the correlation is about 0.58), even though the methodologies for developing these tariff aggregates have been rather different.

Table 3 is meant to provide a better sense of the degree of competition between imports and domestic production. The results in the table are derived from an analysis of local production and imports comprising all economic activity divided into 144 sectors. Each of the 144 sectors was put into one of three groups. The first group contains sectors where production accounts for at least 90 percent of total availability (production plus imports). The second group contains sectors where imports account for at least 90% of total availability. The third group contains all remaining products. This third group contains sectors where neither domestic supply nor imports dominate the total supply of the commodity. The first two groups are considered to be “specialized” while the remaining third group is considered “non-specialized”.

Table 3 indicates that, in general, sectors tend rather strongly to be either dominated by imports or by domestic production. Overall, about 89% of the value of domestic production is specialized with the large majority of these facing minor to no import competition in their particular product category.<sup>6</sup> The sectors that compete most

> 6 Substitution across commodities would amplify competition. So, for example, maize production faces little direct import competition in the form of imported maize. However, significant volumes of wheat and rice are imported. Since maize meal and bread are substitutes, domestic maize competes indirectly with imports through the potential for consumers to alter dietary choices.

12
- 32 -

|Libannes|Hungary|_Poland_<br>|Romania<br>|CSFR<br>|China|Vietnam|Bulgau|Ruoo|
|---|---|---|---|---|---|---|---|---|
|||Partial<br>peogmbliy|No(book<br>purchase<br>thimh the<br>budgs of the<br>Miniiry of<br>_OdhnC_<br>_)_|No|No||Lal<br>iaui|Speci ibaqy<br>aevicca**at**tde<br>**eblM**levde an<br>mml<br>ibay<br>ices at he<br>isyo lve-ld.|
|Poke<br>ccs<br>Sitti||||_No_|-<br>--||Sofia has a<br>Withdie<br>Wiliti; ed_er|Road (rafic)|
|anaon<br>garageclection)<br>__________________<br>S|Garbaecollection<br>|Ownerhipand<br>Provisionof<br>coldwaterat<br>~~~~~~all<br>levels<br> <br>|||Rcflu colection|||Patofgarbage<br>colgcticurat_both_<br>oebband rayou<br>levels.|
|ewge<br>|Garbage coLection<br>and<br>eleme<br>clekanig<br>_ _ _ _ _<br>_ _<br>_<br>_<br>|Owneri<br>ad<br>pwviaoaof<br>ewage evceo<br>_ _<br>_<br>_<br>_<br>_<br>|Sewage<br>colhecti<br>_<br>_<br>_||_<br>_<br>_<br>_||Gar<br>clectioGand<br>i<br>wm<br>b<br>ln die<br>se_mea<br>_<br>_<br>_<br>_|Moat of the<br>epetcioa<br>e<br>_pendluSt doe<br>rayon leveland<br>some op_Son<br>_ezpendiuresat_<br>the<br>~~~~~~~~~~<br>levd|
|Public utiies (ps.<br>electricity, and war)<br>-________________|Water<br>snanaeetand<br>rmuenance of<br>public<br>cemteries|Supply me<br>ligh<br> _|Ditict<br>heating_and_<br>wtder||||Wdcarspply<br>iDn_uctn.<br>.|Subsdi_to_<br>hmboJs (nt<br>cep_e)<br>**at**the<br>yonlev|