rafmacalaba/datause-encoder-data huggingface dataset:
appear to diminish latent diversification more than cross-sectional diversification, although the strength of this attenuation problem varies at different levels of aggregation.

A second way to address the variance-attenuation bias induced by aggregation is to compare the crosscountry variation of the two measures of diversification—i.e. latent diversification and cross-sectional diversification. This exercise is presented in Figure 5, Panel B.

The evidence presented in Panel B of Figure 5 supports the result found in Panel A of Figure 5—aggregated data reduces the cross-country variation of both measures of diversification. However, the varianceattenuation bias induced by aggregation appears to be stronger for latent diversification than for crosssectional diversification. These observations have important implications when it comes to empirically assessing the importance of diversification in explaining volatility, something we turn to in Section 6. In particular, the lower variance of latent diversification when using more aggregated data implies that its estimated effect on volatility is expected to decrease with the level of disaggregation. On the other hand, the significance of latent diversification is expected to increase with the level of disaggregation (see Section 6 for more on this).

In sum, the two exercises seem to point in the same direction—more disaggregation attenuates diversification with latent diversification suffering more than cross-sectional diversification. However, it appears that the problem is not necessarily “linear” in the sense that the attenuation problem appears to smaller at intermediate levels of aggregation.

# **4.1.2. Trade in Services and the Extensive Margin of Trade**

A glaring omission in most of the literature on international trade is the exclusion of analysis related to trade in services. In the past there were at least two reasons to exclude these flows. First, with some exceptions, services, which account for approximately 70 percent of world GDP and employment, were typically considered non-tradable goods. However, the rise of multinationals, outsourcing and the reduction of communication costs have made some services highly tradable, thus making this assumption unrealistic. A second reason was the lack of data on trade in services, especially at the sectorial level.

Recent efforts to record international transactions in services have produced new data on international trade in services.<sup>16</sup> One of such dataset appears in Francois and Pindyuk (2013). The authors compiled

> 16 There have also been a number of papers written in recent years studying the patterns of international trade in services. See for example Gervais and Jensen (2013) and van Merel and Shepherd (2013).

16
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2008). This may reflect constraints on national energy tax policies, which are major drivers of international energy price differences,<sup>25</sup> posed by countries’ concerns to maintain their international competitiveness in the manufacturing sector (Brack et al. 2000).

# **4 Results of Estimation of the Vintage Capital Model**

The empirical estimates of stochastic specification (3), (7), (14), and (15) applied separately to each of the five industries over the period 1990 - 2005 across 19 OECD countries are presented in Tables A.8-A.12 (technical appendix, section A.2). We present the results for both the vintage capital model, and the dynamic factor model of energy demand of Pindyck and Rotemberg (1983), in which the indices of the input efficiency of capital stock are set to 1. Tables 1, 2 and 3 present estimated own-price elasticities of input demand, cross-price elasticities of energy demand, and own-price elasticities of the input efficiency

> 25For example, in transportation sector international energy price differences are almost entirely due to gasoline tax, which accounts for nearly 60 percent of final energy price in Sweden, Germany and the United Kingdom, compared to just 13 percent in the United States (International Energy Agency 2008).

18
<!-- Start of picture text --> I ~~Policy Research<br>Trade Policy<br>WPS 808<br><!-- End of picture text --> Thispaperisaproduct oftheTrade Policy Division, Country Economics Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Dawn Ballantync, room N10-023, extension 37947 (33 pages). November 1991.

The frequent application of antidumping and countervailing measures in the United States in the 1980s has been described as a new foi-m of protection.

Harrison measures the effect not only of investigations (to evaluate claims of dumping or subsidies) but of the resulting duties, by measuring their impact on import prices.

control for differences across industries and separately measure the effects of duties and investigations.

The results suggest that for some sectors the price effect of investigations is as great as imposing a duty. Investigations that end in duties have different effects tham those resulting in no action.

The dataset combines cross-section and time series data for 1981-86, making it possible to The Policy Research Working Paper Series disseminates the findigs of work under way in the Bank. An objectivc of the scries is to get these findings out quickly, cven if presentations arc less than fully polished. The findings, irtcrprctations, and conclusions in these papers do not necessarily r.present official Bank policy.

Produced by the Policy Research DisseminatLion Ccnter
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and educational attainment implications (Beard & Connor, 2003; WHO/CDC, 2004): Providing iron supplementation to children to reduce anemia has been shown to improve cognitive development (McCann & Ames, 2007) and increase school participation (Bobonis, Miguel & Puri-Sharma, 2006). It has also been demonstrated that a healthy breakfast improves same-day and long-term performance in undernourished populations; and that breakfast provided at school improves attendance and reduces tardiness.

While micronutrient deficiencies and short-term hunger are fairly easy to address through school feeding, supporting children’s long-term growth trajectory may not be. Existing evidence suggests that where there is an effect of school feeding on child growth, it is most likely modest (Powell, Walker, Chang, et al., 1998; van Stuijvenberg, Kvalsvig, Faber, et al., 1999; Grillenberger, Neumann, Murphy, et al., 2003). Anthropometric studies have confirmed that school-age children may be too old to experience catch-up growth or recover from growth faltering (Martorell, Khan & Schroeder, 1994; Martorell, 1995; Behrman, Alderman & Hoddinott, 2004; World Bank, 2006). However, where school entry is delayed and grade repetition is high, nutritionally-vulnerable older children may benefit from exposure to school feeding during pre-pubertal growth spurts.

How and whether food transfers reach the intended beneficiaries has important implications for effectiveness. School feeding formats can include on-site feeding (OSF), takehome rations (THR), or both. The impact of the alternative formats could differ, even if the value of the transfer is comparable. OSF and THR differ in the timing of receipt of the food transfer (daily vs. periodic), conditionality (daily attendance vs. average attendance), and content (OSF may include dairy productions, for example). THR do not provide short-term nutritional benefits that can boost concentration and learning effectiveness.

6
_Table 4. Impact of subsidies on nitrogen in fertilizer_

||No controls|Country FE|Country FE and<br>grid cell<br>controls|Country FE, grid cell<br>controls, and median<br>GDP indicator|
|---|---|---|---|---|
|Subsidy|0.141***|0.032***|0.031***|0.020***|
||(0.003)|(0.002)|(0.002)|(0.002)|
||(0.000)|(0.000)|(0.000)|(0.000)|
|At or above median GDP||||0.028***<br>(0.008)|
|||||(0.000)|
|Subsidy × at or above||||0.019***|
|median GDP||||(0.003)|
|||||(0.000)|
|Country fixed effects|N|Y|Y|Y|
|Grid cell controls|N|N|Y|Y|
|Observations|13,978|13,978|13,971|13,971|
|Adjusted R-squared|0.1215|0.8401|0.8406|0.8411|

_Notes:_ The dependent variable is the level of nitrogen in fertilizers. Values are in log formats. The first parentheses under the estimated coefficients show standard deviations; the second parentheses show p-values. Grid cell controls include mean elevation and CTI. Significance levels: * 10%, ** 5%, *** 1%. Y = yes; N = no.

_Table 5. Impact of nitrogen fertilizer use on nitrate storage in the vadose zone_

||No controls|Country FE|Country FE and<br>grid cell controls|Country FE, grid cell<br>controls, and high<br>subsidy indicator|
|---|---|---|---|---|
|Fertilizers|0.304***|0.217***|0.290***|0.175***|
||(0.014)|(0.031)|(0.030)|(0.032)|
||(0.000)|(0.000)|(0.000)|(0.000)|
|High subsidy||||0.481***<br>(0.052)|
|||||(0.000)|
|Fertilizers × high||||0.085***|
|subsidy||||(0.025)|
|||||(0.001)|
|Country fixed effects|N|Y|Y|Y|
|Grid cell controls|N|N|Y|Y|
|Observations|13,483|13,483|13,480|13,480|
|Adjusted R-squared|0.0346|0.1875|0.2326|0.2586|

_Notes:_ The dependent variable is the level of nitrogen in fertilizers. Values are in log formats. The first parentheses under the estimated coefficients show standard deviations; the second parentheses show p-values. Grid cell controls include mean elevation and the CTI. Significance levels: * 10%, ** 5%, *** 1%. Y = yes; N = no.

23
Table 4: Relationship between management sub-scores and post-COVID outcomes; services.

||(1)<br>∆Sales|(2)<br>Temp.|(3)<br>Perm.|(4)<br>Furloughed|(5)<br>Laid Of|(6)<br>Pivot<br>Product|(7)<br>Remote<br>Work|(8)<br>Arrears|
|---|---|---|---|---|---|---|---|---|
|MS: Operations|-0.001|0.082|-0.204<sup>_∗∗_</sup>|0.022|0.024|-0.039|0.033|-0.017|
||(0.010)|(0.085)|(0.096)|(0.044)|(0.055)|(0.041)|(0.042)|(0.046)|
|MS: Monitoring|-0.008|0.172<sup>_∗_</sup>|0.084|0.045|0.060|0.028|0.091<sup>_∗_</sup>|0.020|
||(0.010)|(0.088)|(0.084)|(0.054)|(0.067)|(0.049)|(0.049)|(0.051)|
|MS: Targeting|-0.014|-0.126|-0.014|-0.078|-0.183<sup>_∗∗_</sup>|0.050|0.085|0.016|
||(0.011)|(0.099)|(0.100)|(0.057)|(0.073)|(0.053)|(0.053)|(0.055)|
|MS: Incentives|-0.007|0.107|-0.175|0.029|0.059|0.161<sup>_∗∗∗_</sup>|0.172<sup>_∗∗∗_</sup>|-0.075|
||(0.013)|(0.101)|(0.124)|(0.065)|(0.082)|(0.061)|(0.060)|(0.063)|
|Size (log)|0.003|-0.176<sup>_∗_</sup>|-0.127|0.095|0.186<sup>_∗∗_</sup>|0.017|0.187<sup>_∗∗∗_</sup>|-0.085|
||(0.014)|(0.101)|(0.116)|(0.061)|(0.076)|(0.056)|(0.056)|(0.060)|
|Sales (log)|0.019<sup>_∗∗_</sup>|0.002|0.026|-0.034|-0.067|0.043|0.071<sup>_∗_</sup>|-0.034|
||(0.009)|(0.059)|(0.062)|(0.040)|(0.048)|(0.036)|(0.037)|(0.038)|
|Observations|1173|867|843|1020|1034|1215|1213|1130|
|Adjusted _R_<sup>2</sup>|0.266||||||||
|Pseudo _R_<sup>2</sup>||0.305|0.133|0.120|0.070|0.153|0.172|0.116|
|df<br>m|31|26|26|29|28|31|31|31|

Notes: Standard errors in parentheses All specifications include controls for country, sector, number of days from lockdown, firm age, and export dummy. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01

**18**
- 3 -

linear demand curve and has constant costs. The process innovation results in a downward shift of the cost function from C0 to C1 . A patent will allow the innovating firm to choose between two strategies. On the one hand, the firm can license its innovation or sell it to other firms. Given the competitive nature of the industry, the maximum royalty pet. unit of output that the innovating firm can charge to its competitors is CoC1.

The firm can also choose to displace its competitors and supply the market demand making a profit equivalent to the area CONCI. It will be noted. that the innovating firm will alter its pre-invention profit-maximizing price-output decision only in the case of inventions which imply major cost reductions.

The social benefit of the patent is represented by the area of the rectangle COENC1 during the life of the patent an3 this area plus the consumer surplus triangle EAN (or COEACI), from the moment the patent expires to infinity or until a new innovation becomes more cost effective. On the other hand, the social cost is given by the consumer surplus loss during the life of the patent, or the area EAN plus the resources used in RD triggered by the patent system. Given these considerations, what is the optimal level of RD? What is the optimal life of a patent? What is the efficiency of the patent **system?**

# 1. Optimal level of RD I/

The answer to the first question is facilitated by the introduction of Figure 2. Here, the vertical axis shows percentage cost reductions. The 1/ The graphs that follows are taken from Scherer (1972).
**Table A4: Contribution of Each Explanatory Factor to Wage Differentials, Using Potential Years of Schooling by Region 20032007**

||**#1**|**#2**<br>**NCR**|**#3**|**#4**|**#5**<br>**Luzon**|**#6**|**#7**|**#8**<br>**Visayas**|**#9**|**#10**|**#11**<br>**Mindana**|**#12**<br>**o**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|
|**Residual**|**58.1**|**66.0**|**52.6**|**52.7**|**54.2**|**48.8**|**49.9**|**54.1**|**44.1**|**48.8**|**54.1**|**40.5**|
|**Personal Attributes**|**27.7**|**20.0**|**32.0**|**23.5**|**19.0**|**28.9**|**27.0**|**23.9**|**30.1**|**31.3**|**27.7**|**36.0**|
|_Gender_|_3.2_|_--_|_--_|_1.5_|_--_|_--_|_1.4_|_--_|_--_|_0.9_|_--_|_--_|
|_Experience_|_1.0_|_1.5_|_0.1_|_1.3_|_3.2_|_-0.5_|_1.9_|_3.4_|_0.3_|_3.9_|_4.2_|_2.8_|
|_Years of completed schooling_|_23.5_|_18.4_|_31.9_|_20.7_|_15.8_|_29.3_|_23.7_|_20.5_|_29.9_|_26.6_|_23.5_|_33.1_|
|**Sector**|**0.5**|**-0.3**|**1.1**|**6.5**|**13.9**|**1.3**|**4.1**|**9.8**|**0.7**|**1.6**|**6.3**|**-0.9**|
|**Occuption**|**13.6**|**14.2**|**14.2**|**17.1**|**12.7**|**20.9**|**18.6**|**12.0**|**24.3**|**17.8**|**11.7**|**23.3**|
|**Year**|**0.1**|**0.2**|**0.1**|**0.1**|**0.2**|**0.1**|**0.4**|**0.1**|**0.8**|**0.4**|**0.1**|**1.1**|

**Table A5: Contribution of Each Explanatory Factor to the Difference in Inequality Index (Gini Index)**

||**NCR **|**Luzon **<br>**Bo**|**Visayas **<br>**th Sexes**|**Mindanao**|**NCR **|**Luzon **<br>|**Visayas **<br>**All Men**|**Mindanao**|**NCR **|**Luzon **<br>**Al**|**Visayas **<br>**l Wome**|**Mindanao**<br>**n**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Gini Index|0.31|0.35|0.40|0.39|0.27|0.32|0.36|0.36|0.36|0.39|0.46|0.44|
|**Residual**||**0.12**|**0.23**|**0.14**||**-0.07**|**-0.08**|**-0.08**||**0.08**|**-0.46**|**-0.87**|
|**Personal Attributes**||**-0.08**|**0.25**|**0.45**||**0.14**|**0.45**|**0.68**||**-0.04**|**0.11**|**0.78**|
|_Gender_||-0.11|-0.04|-0.08||--|--|--||--|--|--|
|_Experience_||0.04|0.05|0.14||0.12|0.13|0.18||-0.07|0.02|0.31|
|_Years of completed schooling_||0.00|0.24|0.38||0.02|0.32|0.50||0.03|0.09|0.47|
|**Sector**||**0.52**|**0.16**|**0.06**||**0.88**|**0.62**|**0.40**||**0.03**|**-0.04**|**-0.23**|
|**Occupation**||**0.43**|**0.35**|**0.33**||**0.05**|**0.01**|**-0.01**||**0.92**|**1.31**|**1.20**|
|**Year**||**0.01**|**0.01**|**0.02**||**0.00**|**0.00**|**0.00**||**0.00**|**0.09**|**0.12**|

19
|Kerosene|0.371|0.371|0.004|0.949|
|---|---|---|---|---|
|Candles/Batterty|0.134|0.133|0.009|0.924|
|Other|0.142|0.147|0.474|0.491|
|Main heat source for cooking|.|.|.|.|
|Firewood|0.659|0.645|1.346|0.246|
|Charcoal|0.009|0.010|0.185|0.667|
|Kerosene/Oil|0.288|0.293|0.182|0.670|
|Electricity/Gas|0.016|0.017|0.105|0.745|
|Other|0.029|0.036|3.023|0.082|
|Type of Garbage disposal|.|.|.|.|
|Bins|0.146|0.150|0.164|0.685|
|Compound|0.329|0.317|1.385|0.239|
|Empty Lots/streets/rivers|0.525|0.534|0.505|0.478|
|**Assets**|||||
|TV|0.428|0.427|0.012|0.915|
|Fridge|0.195|0.194|0.012|0.912|
|Stove|0.494|0.499|0.182|0.669|
|Bicycle|0.191|0.187|0.163|0.686|
|Car|0.098|0.097|0.031|0.861|
|Iron|0.389|0.390|0.001|0.971|

Source: Authors’ estimations based on GHS Data 30
16 The results of steel trade liberalization at bilateral (United States-Mvexico) and trilateral level (NAFTA) mirror the results obtained in their analysis of the textiles and apparel industry." The main impact for the ROW is smaller quota rents, reflecting terms of trade effects. However, their analysis suggests that another important secondary effect of the preferential liberalization may occur. As a consequence of lower steel prices, US steelconsuming industries experience an increase in their competitiveness _vis-a-vis_ the ROW.

# (iv) The Automobile Indlstry

Trade in automobiles and auto parts constitutes the most important component of North American intra-trade. Most of this trade is conducted either duty-free or under preferential (low) tariffs. Accordingly, one should not _a priori_ expect a major impact from the NAFTA on the ROW as far as these trade flows are concemed.

The it. st detailed auto industry model available is provided by Hlunter, Markusen, and Rutherford (HMR) (1992). This study analyzes the production of finished autos using a four region CGE model (Canada, Mexico, United States, and the ROW) having two goods (autos and a composite of the remaining goods and services), and two factors of production, and assumes increasing returns to scale in the auto industry (with frins following Coumot behavior) and

**"** As in **the** case of textiles and apparel, even the most sophisticated models cannot incorporate all aspects of North American steel trade. The Trela-Whalley model, for instance, assumes that the direction of trade before and aiter the liberalization is from Mcxico to the United States. Accordingly, trade liberalization would bring about a contracion of the US steel sector. This forecast seems to be at odds with the "qualified" support offered by the American Iron and Steel Istitute to the NAFTA talks. The explanation, however, is quite simplc- The United States is a net steel exporter to Mexico. Hence, the preferential liberalizaion of Mexican steel tariffs, as well as of governmcat procurement policies based on a buy-Mexican provision, is probably attractive enough for the US steel industry for it to accept the preferential quot and tariff removal on the US side This, in turn, suggests that steel exporters from the ROW will also experience trade diversion in the Mexican market as a rcsult of the NAFTA.
a toilet or latrine.<sup>3</sup> _Height_ is the average height of children under 5 or children under 3, used in separate regressions. As robustness checks, results are replicated with country fixed effects _αc_ , year fixed effects _γy_ , and time-varying controls _Xcy_ , including the log of GDP per capita, all of which are added separately in stages. Standard errors are clustered by 65 countries.

# **2.2 Regression results**

Figure 1 depicts the main result of this section: a negative association between open defecation and child height is visible across country years both for children under 3 and children under 5. Regression lines are plotted with and without weighting by country population. The three largest circles are India’s National Family and Health Surveys (only one large circle appears in panel (b) because only the 2005 survey measured height of children up to age 5). Average height in India is, indeed, low. However, the fact that the Indian observations are on the regression line – and not special outliers – is an initial suggestion that sanitation might help resolve the “Asian enigma” of Indian height.

Table 1 reports estimates of regression 1 and will be referenced throughout this section of the paper. The main estimate of a linear decrease in height of 1.24 standard deviations associated with changing the fraction openly defecating from 0 to 1 is qualitatively similar to Spears’s (2012a) estimates of 1.15 to 1.59, where effects of sanitation are identified using heterogeneity in the implementation of an Indian government program. In column 1, sanitation alone linearly explains 54 percent of the country-year variation of children’s height in DHS surveys. Because sanitation and height are both improving over time, column 2 adds year fixed effects; the point estimates slightly increase and standard errors decrease, suggesting that the result is not an artifact of time trends.

Does the significance of open defecation merely reflect general economic development? Column 3 adds a control for GDP per capita; the coefficient on sanitation remains similar,

> 3For example, in India’s NFHS-3, the survey asks “What kind of toilet facility do members of your household usually use,” with the relevant answer “No facility/uses open space or field.” This importantly distinguishes latrine use from latrine ownership.

8