rafmacalaba/datause-encoder-data huggingface dataset:
tition. In our data there are 306 petitions filed against 56 countries. The most frequently targeted countries in the sample are China (45 petitions), Japan (21), India (18) and the Republic of Korea (18). Altogether 139 (45%) of the petitions in 59 (56%) of the investigations resulted in an affirmative determination.<sup>18</sup> Of the 139 investigation, 32 (30%) involve steel products, while 65 (61%) occurred under Byrd Amendment rules.

In terms of the U.S. import data, the sample includes 764 HS-10 products that are included in an investigation. The sample includes 113 HS-4 categories that contain commodities named in a petition. As we describe below, we use products within the affected HS-4 categories, but not named in a petition, as reference commodities against which to evaluate changes in the named commodities. There are 2,514 products within this group of reference commodities.<sup>19</sup>

# **4 Methodology**

## **4.1 Research Design**

Since our objective is to isolate the distinctive characteristics of the products and countries named in an AD petition, we pursue a methodology based on the detail of the AD process. Consequently, our analysis is predicated not only on the very specific products and countries named in a petition but also the very specific filing date used to evaluate the merits of the claim (i.e. import competition is evaluated prior to this particular date). As a result, all three of these dimensions play a critical role in our research design.

The quasi-judicial nature of the AD process requires a very precise definition of the products in question. The boundaries of the product space are defined sharply, excluding commodities that can be quite similar in design, construction and use. Following the terminology used in AD cases, the products included in the petition shall be identified as ‘subject products.’ The set of countries named in the petition (subject countries) is also typically limited to a subset of all national sources of the imported product. The distinctions between subject and non-subject products and between subject and non-subject countries are central to our analysis as they provide two natural comparisons that we exploit to isolate the unique features of the subject countries and products.

A natural reference group for the subject imports are shipments of the same product from non-

> 18 Some investigations see affirmative determinations against only a subset of the countries that were filed against.

> 19We experimented with the definition of related products by narrowing it to non-named commodities within the same HS-6 category. The results are almost identical. We report results from the HS-4 definition since it allows us to include all cases (some cases are defined at the HS-6 level and would not have a reference group at the HS-6 level).

9
33 1987 in a recent World Bank economic and sector work illustrates that the fall in real wages continued well into the later half of the 1 980s (World Bank, 1990). By this account, real wages in urban and rural areas in 1987 were 42-47 percent of the level in 1980. For employees on a fixed payroll, during this period, these data show that they fared very badly during most of the years.

**Table 4.22: Incidence of Extreme Poverty by Occupation of Head of Household, 1985-93 (Poverty Line=Nl97.71)**

|||**1985/86**|||**1992/93**||
|---|---|---|---|---|---|---|
|**Occupation**|**P0**|**PI**|P2|Po|PI|P2|
|Profess./Technic.|0.040|0.009|0.003|0.135|0.062|0.023|
|Admin./Managers|0.044|0.020|0.009|0.075|0.084|0.030|
|Clerical Related|0.024|0.008|0.003|0.106|0.055|0.018|
|Sales Workers|0.033|0.006|0.002|0.087|0.056|0.021|
|Service Industry|0.040|0.008|0.003|0.107|0.048|0.016|
|Agric./Forestry|0.180|0.047|0.018|0.164|0.086|0.035|
|Product./Transp.|0.080|0.012|0.004|0.124|0.059|0.018|
|Manufact./Process.|0.053|0.014|0.004|0.058|0.058|0.018|
|Others|0.039|0.005|0.001|0.087|0.049|0.016|
|Student/Apprent.|0.020|0.011|0.006|0.149|0.064|0.023|
|_All Nigeria_|_0.120_|_0.042_|_0.016_|_0.136_|_0.085_|_0.034_|

**Decomposition of Extreme Poverty Incidence by Occupation**

|||**1985/86**|||**1992/93**||
|---|---|---|---|---|---|---|
|**Occupation**|**P0**|**P1**|**P2**|**Po**|**P1**|**P2**|
|Profess./Technic.|2.6|0.6|0.5|6.3|4.6|4.3|
|Admin./Managers|0.2|0.1|0.1|0.2|0.3|0.3|
|Clerical Related|0.9|0.2|0.2|4.2|3.5|2.9|
|Sales Workers|4.0|0.6|0.4|10.2|9.3|8.8|
|Service Workers|1.2|0.2|0.2|2.2|1.4|1.2|
|Agric./Forestry|86.6|97.4|97.9|67.4|72.4|75.3|
|Product./Transp.|1.5|0.4|0.4|2.3|1.9|1.5|
|Manufact./Process.|0.8|0.3|0.2|1.0|1.3|1.1|
|Others|2.2|0.2|0.1|3.9|3.3|2.7|
|Student/Apprent.|0.1|0.0|0.0|2.4|1.9|1.8|
|_AllNigeria_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|
|Source: NCS, 1985/86|and 1992/93||||||

Tables 4.23 and 4.24 provide the employment status breakdown of poverty. Here self employed appear to be the hard hit. Here again the pattern remains same between the two periods. Poverty has declined in the wage earner category from an incidence of 51.5 percent to 27.7 percent in the rural areas, and 33.6 percent to 27.8 percent in urban areas. They constitute a substantially large proportion of the poor than their corresponding proportion in the national population.
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Table N8: SNAP participation and effect of SNAP-benefit changes on real sales by product department, grocery stores

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Product department|Health & Beauty Care|Food|Non-food Grocery|Alcohol|General Merchandise|
|VARIABLES|||Log real sales|||
|_Baseline_||||||
|Log benefts p.p.|0.00913|0.0613|-0.265*|-0.199|0.0385|
||(0.145)|(0.108)|(0.138)|(0.280)|(0.160)|
|_Interaction_||||||
|Log benefts p.p.|0.00337|0.0387|-0.247*|-0.199|0.0320|
||(0.143)|(0.103)|(0.132)|(0.264)|(0.156)|
|x Participation rate|0.0918|0.358***|-0.276|0.00306|0.104|
||(0.200)|(0.118)|(0.188)|(0.399)|(0.184)|
|Observations|381612|382140|381900|364044|380988|
|Number of units|7951|7962|7957|7585|7938|
|Number of clusters|48|48|48|48|48|
|Revenue share|0.0512|0.768|0.0949|0.0653|0.0206|

Notes: Robust standard errors are in parentheses, clustered at the state level. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Controls refer to the full set of controls including log housing prices, log unemployment rate, log employment to population ratio, log population, log average wage, poverty rates at both the state and county levels, as well as log SNAP participants at the state level. Store and period fixed effects are also included. Log benefits p.p. refers to log benefits per population. Participation refers to the ratio of SNAP participants to population in each county fixed to the pre-period of 2006.

Table N9: Proportion of revenue earned by each product department by store type

|Store Type|||Product department|||
|---|---|---|---|---|---|
||Health & Beauty Care|Food|Non-Food Grocery|Alcohol|General Merchandise|
|Drug|0.479|0.216|0.171|0.052|0.083|
|Food|0.053|0.765|0.094|0.065|0.023|
|Merchandise|0.219|0.312|0.225|0.008|0.237|

Notes: This table lists the proportion of revenue earned by each product department by store type across all stores from 2006 to 2015.

78
Table 7b: District-industry-year regressions for absolute deviation in actual loans and expected loans

|||Ui dii|Panel es|Ui<br>timation with dis|Addi<br>trict-industry,st|ate-year and indu|Milli<br>stry-year FE|Milli||
|---|---|---|---|---|---|---|---|---|---|
|||sng strct||sng state-|ng||saocaton|saocaton||
||Baseline<br>estimation|<br>covariates<br>instead of FE|Dropping<br>weights|<br>industry-year<br>FE|<br>employment<br>control|Using balanced<br>panel|without industry<br>aggregation step|using OLS<br>TFP metric|Using average<br>deviation|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||||A|. Using land and|labor misallocat|ion in organized|sector|||
|Land misallocation index|0.038++|0.036++|0.036+|0.037+|0.045+++|0.041+|0.055++|0.049++|0.037++|
||(0.019)|(0.015)|(0.019)|(0.020)|(0.016)|(0.024)|(0.022)|(0.022)|(0.018)|
|Labor misallocation index|-0.013|-0.041++|-0.012|-0.011|-0.029+|-0.024|-0.001|0.010|-0.019|
||(0.021)|(0.017)|(0.020)|(0.021)|(0.017)|(0.027)|(0.024)|(0.024)|(0.020)|
|Log employment in|||||0.224+++|||||
|district-industry-year|||||(0.011)|||||
|Observations|14839|14839|14839|14839|14839|6546|14839|14839|14839|
|Adjusted R-squared|0.259|0.097|0.270|0.259|0.318|0.224|0.259|0.259|0.147|
||||B. Usi|ng land and labor|misallocation i|n total manufactu|ring sector|||
|Land misallocation index|0.049++|0.044++|0.047+|0.045+|0.034|0.057++|0.062++|0.044+|0.032|
||(0.025)|(0.019)|(0.024)|(0.026)|(0.022)|(0.027)|(0.024)|(0.023)|(0.026)|
|Labor misallocation index|-0.034|-0.057+++|-0.039+|-0.032|-0.021|-0.024|0.025|0.051++|-0.020|
||(0.023)|(0.018)|(0.023)|(0.024)|(0.019)|(0.025)|(0.025)|(0.023)|(0.021)|
|Log employment in|||||0.223+++|||||
|district-industry-year|||||(0.011)|||||
|Observations|14839|14839|14839|14839|14839|6546|14839|14839|14839|
|Adjusted R-squared|0.259|0.098|0.270|0.259|0.317|0.225|0.261|0.262|0.146|

Notes: See Table 6a. Misallocation variables and loan deviations are expressed in unit standard deviations.
natural resource income effect is small and insignificant. In column 3, the natural log of total PPP GDP, which is almost universally available, replaces natural resource income. Its interaction with distance is now substantially negative, but noisy, and the main coefficient of interest, while slightly smaller, remains negative and significant. The results in this table suggest that oil prices, not overall commodity income fluctuations, are driving the effect shown in the baseline specification.

The evidence shown so far does not rule out the possibility that oil prices are having an effect for reasons not directly related to intercity transport costs. However, any confounding effect must also be correlated with distance to the primate city. Table 5 provides evidence against three potential alternative mechanisms. Because some sample countries are oil producers, oil prices could also affect the within-country pattern of economic activity on the oil supply side. When oil prices rise, cities near production or exploration areas are more likely to benefit from increased employment and wages. Because oil wells in this set of countries are largely near the coast, oil well proximity is correlated with primate city proximity. However, while transport costs to the primate increase continuously away from the coast, it is unlikely that local oil industry effects persist throughout the country.<sup>40</sup> In Table 5 columns 1 and 2, I report results for the baseline specification when all cities at least partially within 50 and 100 kilometers of an oil or gas field are excluded.<sup>41</sup> They are very similar to the baseline specification results. This suggests that local oil industry effects are not driving my results.

It is also possible that the price of oil (and gas and coal, whose prices tend to co-vary with oil’s) is directly reducing the size of distant city lights, because light is produced by electricity, and some electricity is produced by fossil fuels. It is unlikely that this is driving any results, for two reasons. First, nearly all countries in this region have national grids, and many are connected to international ones. Power companies are almost all either state monopolies or former state monopolies wholly or partially privatized as a single entity. Their posted rate structures are characterized by quantity discounts, or more often, premia, and differentiated by sector (residential, commercial, industrial), but with no explicit within-country geographic variation.<sup>42</sup> Second, to the extent that transmission costs proportional to distance matter in practice, more than a third of electricity in the region is produced from hydropower, with the remainder pro-

> 40The oil industry could also have national effects that are correlated with the oil price, but these are removed by the country*year fixed effects.

> 41Oil and gas field centroid locations were manually georeferenced from Persits et al. (2002). There are 24 cities within 50 kilometers, and 50 cities within 100 kilometers.

> 42The one exception to this is a small additional tax on some rates for Abidjan, the primate city of Cˆote d’Ivoire.

18
10

# **3.3.3 Re-setting price controls and the treatment of capital expenditure**

The price control will periodically need to be reset. It is preferable to specify this period _ex ante_ to preserve incentives in the absence of profit sharing mechanisms built into the formula. Prices may be reset by moving the regulated price level to the prevailing level of costs (through a so-called Po cut), and thereafter a new X factor would apply. Thus, companies keep the profits from extraordinary or unanticipated efficiency improvements for up to five years, when the gains are transferred to customers through lower prices. Alternatively, the regulator could choose not to impose a PO cut and instead set the X factor so that expected economic profits are zero at the end of the period rather than throughout the period, thus allowing the company to enjoy the profits of its efficiency gains for rather longer. In either case, a kind of inter-temporal profit sharing system operates.

During the price control period, however, the treatment of capital expenditure poses some rather difficult questions. Investment can be postponed or even cancelled-often because demand is overestimated by the bidders on a new concession based on the information made available by the governments because typically public enterprises have very little relevant information on demand. It is also often lumpy rather than occurring uniformly over a number of years, and the assets invested in are long-lived. This makes forecasting investment extremely difficult for regulators and it is almost inevitable they will make errors in the early years of concessions. Indeed, having made that forecast, the incentive is on the firm to pass the cash that would have been used for investment to shareholders, thus boosting the value of the business. For example, in the UK, the Northern Ireland Electricity spent £50m less on investment than was anticipated at the time of privatisation.

However, the regulatory treatment of this problem has implications for the incentive to invest. When the regulatory asset base is updated from one price review to the next there is an issue of whether the _ex ante_ or _ex post_ data is used. The choice of value has implications for the level of incentives for the company to undertake the investment at a lower price than that forecast. These incentives are summarised in Table 1. It shows that the decision between whether to use _ex ante_ or _ex post_ data, or a possible compromise between the two which might allow efficiency savings to be kept for a maximum of 10 years rather than five<sup>5</sup> , depends on a number of factors. These include:

- the overall importance of the investment program;

- the expected level of efficiency savings; and

- the ability of the regulatory body to establish the efficient level of investment for the company.

5 This is the system that is being put in place in the water industry in England & Wales.
mean); these are very close to the errors in predicting ln _PPP_ 05 _i_ by simply adding ln _PPP_ 93 _i_ to (4). It can be seen that the density function of the errors implied by the inflation-adjustment method has thicker tails (large errors in both directions, but more so in the upper tail) and is not centered on zero, with underestimation at the mean; the mean error is 0.071, or roughly a 7% underestimation of the PPP (or 7% overestimation of GDP at PPP).

It can be argued that this comparison is biased against the inflation-adjustment method, since the proposed alternative is calibrated using data that include the 2005 PPPs from the ICP, which would not (of course) be available when updating the 1993 PPPs prior to release of the 2005 ICP. However, virtually identical results are obtained if one simply assumes that the PPP has an elasticity of unity to the market exchange rate and that the DPE coefficient is 0.293— based on the cross-sectional value estimated above using the 1993 PPPs. I also give the density function for this simple and feasible estimator in Figure 8. This does not yield zero-mean error, but the mean error is much smaller than the inflation-adjustment method (-0.017 versus 0.071), and it also trims the large errors in the tails generated by the latter method.

# **7. Conclusions**

The substantial downward revisions to the estimates of the real GDP of developing countries implied by the 2005 ICP have surprised many observers. Some have questioned the data. Concerns about the comparability of ICP data across survey rounds have loomed large, and have reinforced past practices of not mixing PPPs across ICP rounds.

This paper has tried to make sense of the PPP changes between ICP rounds. The paper reports new evidence consistent with the existence of a Balassa-Samuelson effect over time such that the PPP rises relative to the exchange rate in a growing economy. There are signs that this only starts to happen with a sufficiently high growth rate. The paper finds that this “dynamic Penn effect” is even stronger in initially poorer countries. Thus the widely-observed static Penn effect (whereby the price level index is lower in poorer countries) has been attenuated over time.

The higher price levels (lower real exchange rates) for many developing countries implied by the 2005 ICP are accountable in part to their economic growth. On its own, the dynamic Penn effect accounts for about one fifth of the variance in the proportionate changes in the price levels over 1993-2005, rising to one half over 1985-2005. An augmented version of the 18
Index the data series available are too short, these economies are not dealt with in the following analysis and only IFC's Index economies are included.<sup>5</sup> The 19 economies are grouped according to geographical areas and Tables 6 to 9 present the results of correlation analysis, while Figures 7 to 10 show the distribution of MCY, M1Y, QMY and DCPY over time in Korea, Malaysia, Chile and Greece, selected each from one group. For reference, Figures 11 and 12 show the distribution of the four variables in the United States and Japan from the early 1950s.

On the whole, the correlations in the Asian economies (except Pakistan and Philippines) are much stronger than in the other economies. In fact, the contrast between the Asian economies and the other economies is very dramatic. While the correlations are significant for most of the variables in Korea, Taiwan, India, Indonesia, Malaysia and Thailand, the correlations are not significant for most of the variables in other economies.

MIY remained relatively stable over time in most of the economies, while the patterns of movement of the other three variables are more heterogeneous. In most of the Asian economies (except in Pakistan and Philippines), there was an upward trend in MCY, QMY and DCPY, and they were highly correlated. (In Indonesia, the stock market remained very small until 1989, although a slight upward trend in MCY can be observed). On the other hand, the upturn in MCY around 1986 was generally much more dramatic than the increase in the indicators of financial deptn. In other economies, there was no consistent pattern of movement in these variables. In Brazil, Chile, Colombia, Greece, Jordan, and Philippines, while financial C:pth as measured by QMY has been increasing, there was no apparent trend in MCY, and there was no consistent pattern in the movement of DCPY either. For example, in Chile, the stock market declined continually in the early 1980s and the direction was reversed in 1985, and the direction of the movement of domestic credit to the private sector was exactly opposite.

> 5 Zimbabwe is aot included since the time series available is too short for statistical analysis.

7
shorten the period of study, starting in 1960, because the data before that year were scattered and not strictly comparable.<sup>7</sup> The initial selection of variables is taken from the literature on the determinants of the female labor participation rate. We mainly focus on analyzing the relationship between the female participation and fertility rate, education (primary, secondary, and tertiary, for both men and women), infant mortality (total, male and female), maternal mortality, marriage rate, male labor force participation rate, homicide rates (for both men and women), female political participation in both the senate and the chamber of representatives, and some macroeconomic variables such as the real GDP per capita growth and real minimum wage for a total of twenty-two variables. Tables A1.1 and A1.2 in Appendix 1 present the variables used in this paper, their definitions and sources, and their main descriptive statistics, respectively.

# **III. Results**

In this section, we examine the findings of the principal component analysis and identify the variables that have the most significant impact on the variability of the female labor force participation rate ( _f_lfp_r_ ). We also evaluate the effect of individual variables on the _f_lfp_r_ in Colombia using the TVEM method.

# **A. Principal Component Analysis**

In the PCA we set three main component scores to reduce the original process of dimension twentytwo. The results are shown in Figures 1, 2, and 3. In particular, the left panel of Figure 1 shows the size of the eigenvalues on the y-axis against their index on the x-axis. We note that the first eigenvalue is large compared with the second and third ones. Thus, the eigenvalue of the first component is approximately 15.5, while the second and third components have eigenvalues under 2.0. Moreover, the lower curve in the right panel, the contribution in percentage to the total variance, and the upper curve shows the cumulative contribution to the total variance. The largest eigenvalue contributes over 70 per cent of the total variance, and the three principal components account for almost 90% of the total variance. Despite that an elbow at the second eigenvalue is visible, the similar value of the third eigenvalue led to decide that three principal components are required to represent the data.

> 7 Data on the female labor participation rate from 1960 to 1983 was constructed using census data, and the missing observations were fit by a _spline_ curve to the observed values.

7