rafmacalaba/datause-encoder-data huggingface dataset:
27 Table 3 (continued)

|Study|Results|Other Tests|
|---|---|---|
|Tyler<br>(1981)|-<br>0.57 for the whole sample.<br>Drops to_3_-<br>0.055 when 6 OPEC<br>countries are excluded.|Pearson and Spearman rank cor-<br>relations between9and<br>a) manufacturing output<br>b) domestic investment<br>c) is growth rate<br>d) manufactured X8 growth rate<br>(all were positive and<br>significant).|
|Feder<br>(1983)|1) 3- 0.4 and significant,<br>introduction<br>of exports as<br>explanatory variable increased<br>the R .<br>2)A<br>- 0.13 statistically<br>significant in both samples.<br>Evidence of substantial<br>prod-<br>uctivity differentials<br>in addi-<br>tion to the usual externalities<br>effect.|Performs regression of (1) for<br>17 developed countries and<br>found that z3 was significant<br>but 74 wasn't, i.e., product-<br>ivity benefits of export expan-<br>sion were exhausted in those<br>countries.|
|Kavoussi<br>(1984)|1)_A3_> 0 and significant for<br>the whole sample and for both<br>subsamples. But was twice as<br>large for the middle-income<br>than for the low-income<br>countries.<br>2) a4 is not significant for<br>the sample as a whole.<br>For the middle-income<br>group:<br>**A**<br>a3< 0 not significant<br>**_A_**<br>a4> 0 significant.<br>For low income groups: opposite<br>results.|Spearman rank correlations<br>between 2 and GAP for<br>a) The whole sample (positive<br>and significant).<br>b) The 2 subsamples (higher<br>for the middle income than for<br>the low income group).<br>c) The sample excluding<br>countries where Xm > 44%<br>(reduce the significance of<br>the middle income group).<br>|
|Ram<br>(1985)|-?<br>> 0 and significant in all<br>the regressions.<br>-A3 larger in the second<br>and lower for the low-income<br>countries.|-White's test is performed to<br>check for homoskedasticity<br>of<br>disturbances<br>and simultaneity<br>bias (favorable result).|
|(7)|TF45|(+9.3%)|AD06 (0.16)<br>AD08 (0.16)<br>AD11 (0.08)<br>CP15 (0.71)<br>ENV19 (0.40)<br>ENV27 (0.50)<br>ENV42 (0.16)<br>MOC26 (0.16)<br>STE37 (0.06)<br>SUB07 (0.03)<br>SUB10 (0.28)<br>TF44 (0.98)|0.96<br>stimated increase in<br> are raw correlations<br>esponding correlated|
|---|---|---|---|---|
|(4)<br>(5)<br>(6)|TBT07<br>TBT08<br>TBT33|(+3.2%)<br>(+13.1%)<br>(+12.0%)|AD06 (0.51)<br>SUB10 (0.84)<br>AD11 (-0.05)<br>AD08 (0.51)<br>TF42 (0.93)<br>ENV44 (-0.02)<br>ENV42 (0.51)<br>MOC26 (-0.10)<br>ENV44 (0.08)<br>PP08 (-0.01)<br>SPS21 (0.16)<br>SUB07 (0.08)<br>SUB07 (0.10)<br>TBT05 (0.69)<br>TBT15 (0.68)<br>TBT06 (0.98)<br>TBT34 (0.93)<br>TBT14 (0.89)<br>TBT15 (0.58)<br>TBT32 (0.69)<br>TBT34 (0.42)<br>TF42 (0.64)|0.86<br>0.86<br>0.97<br>reases in bilateral trade fows (row 1), together with the e<br>dict the provision in row 1 (rows 3-15; numbers in brackets<br>e R2 of the regression of each selected provision on the corr|
|(3)|TBT02/29|(+20.2%)|AD06 (-0.07)<br>AD08 (-0.07)<br>CP14 (0.61)<br>CP21 (0.77)<br>CP22 (0.80)<br>ENV22 (-0.01)<br>ENV42 (-0.07)<br>ENV44 (-0.01)<br>SPS11 (-0.00)<br>STE32 (0.66)<br>SUB09 (0.78)<br>SUB10 (0.90)<br>TF42 (0.98)|0.97<br> associated with inc<br> provisions that pre<br>last row displays th|
|(1)<br>(2)|AD14<br>CP23|(+41.7%)<br>(+12.5%)|AD06 (0.98)<br>AD06 (0.40)<br>AD08 (0.98)<br>AD08 (0.40)<br>ENV42 (0.98)<br>CP22 (0.80)<br>CP24 (0.89)<br>ENV42 (0.40)<br>PP08 (0.05)<br>SPS24 (-0.05)<br>STE31 (0.54)<br>TBT10 (-0.01)<br>TF42 (0.65)<br>TF43 (-0.04)<br>TF44 (0.38)|0.95<br>0.82<br>Notes: Table shows PTA provisions <br>trade fows (row 2), as well as other <br>with the provision from line 1). The <br>provisions.|

28
# Figure 5.3: SOUTH AFRICA, evaluation of TFP measures under different imputation methods

## I. Kernel<sup>1</sup> estimates of TFP densities

<!-- Start of picture text --> 0 2 4 6<br>log-TFP<br>Complete case ICA method<br>Random ICA ICA on inputs<br>EM alg. [1] EM alg. [2]<br>EM alg [3]<br>1.5<br>1<br>Density of TFP .5<br>0<br><!-- End of picture text -->

## II. Table of descriptive statistics of TFP measures

||**# Obs**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|Complete case|1265|3.50|0.70|-3.74|10.34|
|ICA meth.|1585|2.99|0.84|-4.34|10.28|
|Random ICA|1585|3.38|0.90|-4.97|10.31|
|ICAon inputs|1576|2.94|0.84|-4.39|10.21|
|Em alg [1]|1784|2.78|0.80|-4.47|10.26|
|Emalg [2]|1581|3.21|0.72|-4.01|11.21|
|Em alg [3]|1578|3.22|0.72|-4.00|11.18|

## III. Correlation matrix between TFP measures

||Complete<br>case|ICA meth.|Random ICA|ICA on<br>inputs|Em alg [1]|<br>Em alg [2]<br>Em alg [3]|
|---|---|---|---|---|---|---|
|Complete case|1.000||||||
|ICA meth.|0.996|1.000|||||
|Random ICA|0.998|0.993|1.000||||
|ICA on inputs|0.996|1.000|0.993|1.000|||
|Em alg [1]|0.992|0.999|0.988|0.999|1.000||
|Em alg [2]|0.982|0.991|0.975|0.990|0.992|1.000|
|Em alg [3]|0.982|0.991|0.975|0.990|0.993|1.000<br>1.000|

Notes: 1 Epanechnikov kernel. Each point estimated within a range of 300 values.

Complete case: TFP measure from the sample without replacement of missing values; likewise, input-output elasticities are obtained from estimating equation (1) in the complete case (see I-O elasticities in Table 9.3). ICA method: TFP measure with inputs and output replaced by ICA method and input-output elasticities from Table 8.3. Random ICA: TFP measure with inputs and output replaced by random ICA method and input output elasticities from Table 9.3.

ICA on inputs: TFP measure when only inputs are imputed by the ICA method (not sales),.The I-O elasticities and semielasticities used are in Table 9.3.

Em alg. [1]: TFP measure obtained under imputation of inputs and output by the EM algorithm described in section 5.1.1. Likewise, the I-O elasticities are in Table 9.3.

Em alg. [2]: In this case the EM algorithm used is that described in section 5.1.2. The I-O elasticities are in Table 9.3. Em alg. [3]: The description of the EM algorithm is in section 5.1.3. The I-O elasticities are in Table 9.3. Source: Authors’ estimations with ICSs.

85
nologies, changes in the production process, or changes in the organization of work are more complementary to skilled workers, so that shifts in the level of technological capabilities of the economy increases the demand for skilled labor relative to unskilled labor and the wages of skilled labor. While the skill bias technological change impact on wages has received some empirical backup, there is no full consensus on the importance of this effect (Card and DiNardo, 2002), and some studies looking at the use of particular technologies, for example, computers, also find that the premium can be exaggerated if not controlled for the sorting of higher-paid workers. For example, Entorf et al. (1999) study the effects of computer use on wages and find that workers who use computers are better paid than non-users; however, computer users were already better paid before, with only a small wage increase afterwards.

Looking more directly at the impact of innovation on the wage premium, Reenen (1996) uses a panel of British manufacturing firms to analyze the wage differential between innovative and non-innovative firms. Innovating firms are found to have higher wages due to the rent sharing associated with the R&D projects. Martinez-Ros (2001) also finds for a panel of manufacturing firms in Spain a positive wage premium associated with product and process innovation and that, in fact, innovation and wages are jointly determined. Cirillo (2014) estimates the wage premium for Chile using both factor and cluster analysis, as well as panel data techniques, and finds that the innovation premium is not significant when including firms fixed effects. However, as these papers lacked information on workers’ characteristics, it is not clear whether the observed premium is due to the sorting of high (low) paid workers into high (low) paying firms or due to firms paying higher (lower) wages to seemingly similar workers. Aghion et al. (2019) are the first to use matched employer-employee data for larger firms in the United Kingdom to investigate the innovation wage premium. The authors find a positive innovation premium that is larger for low-skilled workers, which in further theoretical and empirical analysis, the authors associate with increasing complementarity between workers in high-skilled occupations and those in some low-skilled occupations in innovative Overall, the literature documents several wage premiums associated with firm characteristics. The few existing studies looking at innovative firms suggest a positive wage premium associated with innovation. However, what is less clear from the evidence is what part of this premium is associated to sorting of workers into more innovative firms, what part is explained by innovative firms paying higher wages before turning innovative and what is the change in the wage premium once firms become innovative. These questions are explored empirically in the next sections.

6
# **I. Introduction**

One of the more commented upon facts about the Indian economy is its small and dwindling importance in world trade. At independence, India accounted for over two percent of world exports and imports, and by the early 1 990s, this share had declined to about half a percent (Chart 1 below). A plausible case can be made that India's economic fortunes have been related to this de facto international disengagement which is attributable, at least in part, to India's inward looking economic and trade policies. Protectionist polices, in turn, have shaped India's attitude to participation in the multilateral trading system, both in the old GATT and its successor, the World Trading Organization (WTO). India's stance in the GATT/WTO has always tended to be defensive, seeking freedom to use restrictive policies, which has been one of the two prongs of the so-called special and differential (S&D) treatment embraced by developing countries as a whole. Since the early 1990s, India has embarked on serious trade and economic reform, without, however, a corresponding change in the nature of its multilateral engagement. This paper examines whether India should adopt a proactive role in the multilateral arena founded on its recent adoption of more open policies.

**Chart 1: India's Share in World Imports and Exports**

<!-- Start of picture text --> 3.5 3.5<br>3 . -3<br>2.5 -A /Export Sha- 2.5<br>22<br>1.5 1.5<br>0.5 0.5<br>1947 1951 1955 1959 1963 1967 1971 1975 1979 1983 1987 1991 1995<br>Year<br><!-- End of picture text --> _Source:_ IMF's International Financial Statistics The collapse at Seattle of the negotiations to launch a new round provides time and perspective for India to reassess more carefully its attitude toward the multilateral trade system. The failed talks are both sobering and heartening in their lessons for India. Sobering because of what failure signals about the external political and negotiating environment. The hardening of partner country attitudes toward opening their own

**1** The other prong was to seek preferential access for one's own exports in industrial country markets.

3
interviews with a total of 519 lawyers working for banks directly and as outside counsel (ITAM/GMA, p. 32).

While the relevant commercial laws are national in scope, state laws and state legal enforcement have an important effect on the ability of claimants to enforce verdicts. State laws vary, for example, on the ease with which collateral can be claimed by a victor in a court decision. We construct a measure of the efficiency of legal enforcement in each state by taking an average of the responses to seven different questions. Each of these questions reflects the judgment of lawyers in the survey, and each is scaled of one to five. The questions relate to: 1) the quality of judges (mean value 3.76); 2) the impartiality of judges (1.94)<sup>13</sup> ; 3) the adequacy of judicial resources (1.88); 4) the efficiency of enforcement of rulings (2.71); 5) the efficiency of the judicial administration more generally (2.69); 6) the cost, ease of use, and completeness of property registries (3.33); and 7) the adequacy of local legislation related to contract enforcement (3.14). The index is shown in the first column of Table 4. The Appendix shows the correlation among the 7 components of the index and also three more quantitative measures of the legal system which are included in the ITAM/GMA study: the percentage of the state budget spent on the judicial branch, the salary of judges relative to that of private sector workers in the same state with a university education, and the average number months a judicial proceeding takes.<sup>14</sup> The seven-component index of judicial effectiveness is correlated

> 13 Since the survey was administered to layers who generally work for banks, it could be that a high rating on “impartiality” actually reflects a bias in favor of the banks. Given Levine’s (1998) finding that rules favoring creditors are associated with higher levels of financial development, we do not see this as a great concern.

> 14 Both the impressionistic data on which we focus and the quantitative data have drawbacks. On the one hand, the lawyers’ responses to questions related to the impartiality and efficiency of judges and their courts reflect precisely the measures we would expect to impact the decisions of firms. The drawback of these data is obviously that they reflect the subjective opinion of lawyers. On the other hand, data on the percentage of the state’s budget spent on courts are objective, but more spending may reflect less rather

23
instruments that are exogenous to the error term but are correlated with their own contemporaneous values. To choose appropriate lags for the instruments and check their exogeneity and validity, the study applies the Hansen test of overidentifying restrictions and tests for the second- and third-order autocorrelations in the error terms. In addition to applying instruments, both the two-step difference and system GMM methods address the endogeneity stemming from the omitted country fixed effects. The two-step difference GMM estimation approach uses the first-difference for the variables in the regressions and the level indicators for the instruments, while the two-step system GMM approach augments the two-step difference GMM approach with regressions that have level variables and instruments in first-differences. If the Hansen test of overidentifying restrictions and autocorrelation tests verifies the exogeneity of chosen instruments, system GMM would be preferred to difference GMM, as adding level equations increases the amount of information that can be exploited to estimate the sector elasticities in a panel with small T and large N.

# **3. Data and Descriptive Statistics**

This study compiles panel data of annual value added and electricity consumption for 126 countries with sector level observations in agriculture, manufacturing, and services during the period of 1996 to 2014. The empirical estimations also include control variables obtained from various sources that are country or sector specific. The WDI database and the Penn World Table 9 provide data for the sector-level value added and other country- and sector-level control variables, and the IEA has data on sectoral electricity consumption. About a one-third of the data set covers high-income countries, another third includes upper-middle-income countries, and 20 percent and 10 percent of the sample represent, respectively, lower-middle-income and low-income countries.<sup>6</sup> Naturally, the coverage of data from IEA and WDI is better for high-income countries, which have few missing values, including for value added, electricity consumption, and control variables across all sectors. By contrast, low-income countries are missing an average of 12.8 out of 54 observations across all sectors and years. Of the three sectors, the agriculture sector suffers more from missing observations than the other two sectors, for which low-income countries lose only about 2 observations (out of 19 sector-years) on average.

The WDI provides sectoral value added across countries, measured in constant prices in U.S. dollars, and defines these sectors based on the United Nations’ International Standard Industrial Classification 4 (ISIC

> 6 See Appendix A1 for more details.

6
**Table 2 Survey** **<u>Summary- Panel Firms</u>**

|Panel Years|Percent|
|---|---|
|ECA 2002-2005|36|
|ECA 2005-2008|24|
|LAC 2003-2006|40|
|Total|2,911|

# **3.1 Industry Summaries**

The manufacturing industries that are included in the analysis are listed in Table 3. The classification of the industries is made according to ISIC revision 3.1. Firms are divided into four groups according to their trade orientation: two-way traders, only importers, only exporters and non-traders. Table 3 shows the fraction of firms in each trade group. In almost all industries, nontraders make the largest group. Among the firms that trade, only importers have the largest share except textile industry. The high ratio of importers can be due to the imperfect substitutability between foreign and domestic inputs. It might also show that sunk cost of importing is less constraining than the sunk cost of exporting.

To see how engaged firms are with foreign markets, in Table 4, I show the percentage of imported intermediate goods by importing firms and percentage of output that is exported by exporting firms<sup>12</sup> . The table shows that the trading firms in the sample trade quite intensively. Amount of imported intermediate goods make 54% of total intermediate goods used for production and amount of exported goods make 41% of total revenues for two-way traders. The median values are close to the mean values especially for import intensity which supports the significance of participation in foreign markets of the firms.

**Table 3 Manufacturing Industries – Trade Orientation of Firms**

|||Tra|de Shares in|Industries|(in %)|Totals|
|---|---|---|---|---|---|---|
|ISIC|Industry|Export/<br>Import|Import<br>Only|Export<br>Only|None|(%)|
|15|Food|21.0|34.7|7.3|36.9|10.54|
|18|Garments|17.6|38.0|4.9|39.5|10.18|
|17|Textiles|34.2|25.1|9.7|31.0|22.84|
|29|Machinery & Equipment|28.5|23.9|13.4|34.1|7.24|
|24|Chemicals|18.9|42.7|3.9|34.5|20.23|
|31|Electronics|14.0|18.4|9.3|58.3|5.96|
|26|Non‐metallic Minerals|10.8|21.5|8.6|59.1|5.83|
||Other Manufacturing|22.3|33.0|11.4|33.4|17.18|
||Total|4947|6829|1768|7963|27,754|

> 12 I have done this table separately for each survey year and the percentages do not change much across regions or over time.

10
# TABLE OF COWTBNTS

|PAGE NO.|
|---|

|PREFACE|*..................<br>..<br>........<br>ii|
|---|---|
|I. Introduction<br>.1... . .<br>. . . . . . .<br>.|. . . . . .<br>. . .<br>..|
|II. The Planning Process<br>... . . . . . . .|. . . . . . . . . .<br>.<br>7|
|III. Setting Objectives<br>.... . . . . . . .|. . . . . . . . . .<br>.<br>10|
|IV. The Marketing Plan .....<br>. . . . . . .|. . . . . . . . . .<br>.<br>14|
|V. The Transportation Plan . . . . . . . .|. . . . . . . . . . . .<br>19|
|VI. Project Identification and Definition ..|. . . . . . . . . .<br>.<br>24|
|VIZ. Project Evaluation<br>.... . . . . . . .|. . . . . . . . . .<br>.<br>33|
|VIII. Financial Impact of the Plan<br>. . . . .|. . . . . . . . . . . .<br>36|
|AtNXS||
|1. The planning process: An example .... .|. . . . . .<br>. . . .<br>.<br>37|
|2. The marketing plan: Table of contents for|a|
|marketing<br>plan<br>for<br>passengers<br>and for<br>a co|mmodity<br>. . . . . . . .<br>40|
|3. FinancialImpact<br>of<br>Investment<br>Plans:<br>Case|Studies<br>. . . . . . .<br>47|
|_, X_)_−E_(_Y |T_ = 0_, X_)<br>(4)<br>(5)<br>OLS<br>IV<br>0.256***<br>0.279**<br>(0.085)<br>(0.109)<br>0.213**<br>0.242**<br>(0.082)<br>(0.112)<br>0.248***<br>0.294**<br>(0.083)<br>(0.115)<br>-0.000<br>-0.017<br>(0.054)<br>(0.077)<br>0.063**<br>0.068*<br>(0.029)<br>(0.038)<br>Yes<br>Yes<br>0.0106<br>-<br> per individual for variable 5.<br>bed nets, irrespective of their<br>le means restricted to control<br>ment and control groups using<br>eived the spraying campaign<br>ndicator (model3). Standard<br>iage status of the respondent,<br>ies and village characteristics<br>the treatment indicator on the<br>the selected variables.|
|---|
|_E_(_Y |T_ = 1<br>(3)<br>OLS<br>0.200*<br>(0.110)<br>0.166*<br>(0.096)<br>0.222**<br>(0.100)<br>-0.024<br>(0.065)<br>0.049<br>(0.035)<br>No<br>0.1831<br>e observation <br> refers to any <br>) report samp<br>between treat<br>ort to have rec<br>tment group i<br>ation and marr<br>b-zone dumm<br>regression of<br>coeffcients on|
|(2)<br>Treatment<br>1.774<br>[1.279]<br>1.444<br>[1.206]<br>1.142<br>[1.222]<br>0.558<br>[0.938]<br>0.429<br>[0.495]<br>p-values =<br>variables 1-4, on<br>rviewer. “Nets” <br>lumns (1) and (2<br>rt the difference<br>seholds who rep<br>tion with the trea<br>ender, age, educ<br>characteristics, su<br>, we run a probit<br>nifcance of the|
|(1)<br>Control<br>_Household level_<br>1. Number of nets owned by household<br>1.575<br>[1.207]<br>2. Number of ITNs owned by household<br>1.278<br>[1.126]<br>3. Number of observed nets used the night before<br>0.920<br>[1.050]<br>4. Number of observed nets left unused the night before<br>0.581<br>[0.938]<br>_Individual level_<br>5. Reported net use<br>0.380<br>[0.486]<br>Controls<br>Joint tests on variables (with comparable sample size):<br>1-4<br>Note: *** p_<_0.01, ** p_<_0.05, * p_<_0.1. One observation per household for <br>Variable 1 and 5 is self-reported, while variables 2-4 are observed by the inte<br>treatment status, “ITNs” includes only LLINs and properly treated ITNs. Co<br>and treatment group, standard deviations in brackets. Columns (3) and (4) repo<br>OLS regression (model 2). Column (5) estimates the difference between hou<br>in the last 5 months and those who didn’t by instrumenting program participa<br>errors clustered at village level are reported in parentheses. Controls include g<br>household size, tribe and religion, information about access to water, dwelling<br>(size and share of women living in the village). To control for joint signifcance<br>selected groups of variables and we report p-values of an F-test for the joint sig|

29