rafmacalaba/datause-encoder-data huggingface dataset:
Table 9: Post-sanction Innovation Performance among Firms in the Sanctioned Technology Field

||(1)<br>**Pat**|(2)<br>**ent Applic**|(3)<br>**ation**|(4)<br>**Aver**|(5)<br>**age Colla**<br>**per Pate**|(6)<br>**borator**<br>**nt**|
|---|---|---|---|---|---|---|
||Total|Intl.|High-Tech|U.S.|Euro+|Adv. Asia|
|Treatment_×_Post|-0.069**|-0.095***|-0.035***|-0.025|0.001|0.030**|
||(0.026)|(0.011)|(0.012)|(0.015)|(0.004)|(0.010)|
|No. Obs|193,851|193,851|193,851|158,421|158,421|158,421|
|R-squared|0.410|0.425|0.481|0.409|0.269|0.390|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|Yes|Yes|

Note: We estimate the coefficients using OLS. Estimates from (4) to (6) are based on firms that started patenting before 2013. Robust standard errors clustered at the 2-digit ISIC sector level are in parentheses. ***, **, and * indicate significance at levels 1 percent, 5 percent, and 10 percent, respectively.

Table 10: Pre-Sanction U.S. Collaboration and Post-Sanction Innovation Performance

||(1)<br>**Pat**|(2)<br>**ent Applic**|(3)<br>**ation**|(4)<br>**Aver**|(5)<br>**age Collab**<br>**per Paten**|(6)<br>**orator**<br>**t**|
|---|---|---|---|---|---|---|
||Total|Intl.|High-Tech|U.S.|Euro+|Adv. Asia|
|Treatment_×_Post|0.041|0.125***|0.084**|0.357***|-0.015***|-0.066|
||(0.026)|(0.034)|(0.035)|(0.025)|(0.005)|(0.042)|
|Treatment_×_Post_×_pre-US|-0.134**|-0.268***|-0.146**|-0.464***|0.020**|0.116**|
||(0.055)|(0.033)|(0.055)|(0.026)|(0.009)|(0.051)|
|No. Obs|193,851|193,851|193,851|158,421|158,421|158,421|
|R-squared|0.410|0.428|0.481|0.410|0.269|0.390|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|Yes|Yes|

Note: We estimate the coefficients using OLS. Estimates in columns (1) to (3) are based on a set of firms that started patenting before 2013 and estimates in columns (4) to (6) are based on a set of firms that have positive patenting. Robust standard errors clustered at the 2-digit ISIC sector level are in parentheses. ***, **, and * indicate significance at levels 1 percent, 5 percent, and 10 percent, respectively.

41
are not strongly associated across categories, and that associations remain constant over time. To do this, we calculate the Kendall rank correlation coefficients of the deprivation scores across categories for the three years, as illustrated in table 5. We choose Kendall correlation coefficients since we are concerned about changes in the ordinal association between the deprivation scores and the reference variable, which turn into changes in the association between deprivation score across categories. The results are encouraging: the correlation coefficients across categories are small and remain stable over time.

The third issue—the inability to use discrete random variables as reference variables with repeated cross-sections—requires significant attention. We need to address whether the continuous variable, income, is a proper reference variable for the methodology; if rank preservation is reasonable at the sample level; and, if not, we need to stratify the sample in order to make rank preservation more plausible. The observational data is uninformative in these matters, as it is not longitudinal data. A simulation exercise is carried out to address this issue, instead.

We build a panel dataset on the basis of the 2010 observations, adding an additional simulated period of data. For this added period, we simulate changes in the deprivations of individuals, in order to replicate the actual changes that took place in the uncensored headcount ratios by dimension between 2010 and 2012. We also change household income to reflect the income growth over the period. To do this, we calculate the variation in mean income within income deciles between the two years from the observational data, and add a normal random variable centered in this mean change to each individual within the income decile. The variance of this increase is chosen to match the change in within-decile variances that occurred between both years. We change deprivations in all the categories, initially one category at a time, and then all at once. We then assess different choices in the ASN methodology. We address the performance of these choices by comparing the results of the decomposition to those that would have been obtained using the panel dataset. Some of our choices are made with the purpose of highlighting the potential pitfalls of applying the method without examining the issues. All calculations are made using the software in Azevedo et al. (2012a).

Table 6 presents the results of the decompositions when deprivations in all categories are changed: this is the scenario closer to the observational data. For each method, the value on the left corresponds to the share that each category contributes to the change in the multidimensional headcount. The value on the right is the ab21
# **Figure 3 Group factors**

## **(a) Advanced countries**

<!-- Start of picture text --> CIF<br>COD<br>1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Year<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>-3 -3<br>-4 -4<br><!-- End of picture text -->

## **(b) Emerging countries**

<!-- Start of picture text --> CIF<br>COD<br>1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Year<br>4 4<br>2 2<br>0 0<br>-2 -2<br>-4 -4<br><!-- End of picture text --> 47
knowledge, this is the first implementation of the FFL methodology to explain changes in wage inequality in the Russian Federation during the last decade (2002 – 2012).<sup>6</sup> The rest of the paper is organized as follows. Section 2 describes some of the potential factors behind the reduction in wage inequality, exploring their evolution in the Russian Federation during the period of analysis. Section 3 describes the RLMS data. The decomposition methodology is explained in section 4, including the use of Recentered Influence Functions (RIF), which create a linear approximation for the moments of the distribution. Section 5 presents the main results of the RIF decompositions for the period 2002-2012. Section 6 concludes.

# **2. Factors behind the reduction of wage inequality**

The potential factors behind wage inequality patterns can be grouped into: (i) changes in institutions (such as minimum wage policies and firm characteristics), (ii) changes in the education of the workforce and in returns to skills (which may be related to changes in the supply or the demand for skills), (iii) changes in the demographic composition of the labor force and the relative remunerations between groups (for example, changes in the gender wage gap), and (iv) changes in the relative demand for skills driven by structural transformations of the economy. These factors have been explored at different instances by Katz and Murphy (1992), DiNardo, Fortin and Lemieux (1996), Autor, Katz and Kearney (2008), and Ferreira, Firpo, and Messina (2014), among many others.

## **2.1. Institutions**

The 2000s witnessed profound changes in labor market institutions in the Russian Federation. The Russian government committed to a minimum wage policy. The increase in nominal wages came alongside a steady reduction in inflation rates, which helped to protect real wages. Additionally, wage arrears—which had become a common feature of the Russian labor market in the 1990s and which largely affected low-pay workers (Earle and Sabirianova, 2002; Lehman,

> assumptions—ignorability and common support—that are unlikely to hold in this context as it has been shown there is selection bias in women’s participation in Latin American labor markets.

> 6 Recently, Gimpelson (2015) also investigates changes in wage inequality in the Russian Federation, using the FFL methodology for robustness. However, this is not the main technique used in such paper, while the author covers a different period than the present study, and includes a different set of variables in his estimations.

8
38 example, **Zi** may be negatively<sup>correlated with PitUs if the greater</sup><sup>**Zi**</sup> is, the poorer is the quality of<sup>US goods relative to Hong Kong goods;</sup><sup>**and**Zi</sup> is positively correlated with Pi.HK<sup>because the greater Zi is, the better</sup> is the quality of Hong Kong goods<sup>relative to domestically-produced</sup> goods, and the high3r will be the Hong Kong<sup>price. If thlis</sup> is the case, both effects work to bias the estimate of p downward<sup>in Equation (1),18 thus creating an</sup> impression of rent sharing. Furthermore, we can expect a negative correlation between Zi and Vit -- it has been shown<sup>19</sup> that more restrictive quotas tend to induce imports of a higher<sup>quality, since quotas are typically volume,</sup> rather than value restrictions. Therefore, the omission of **Zi**<sup>**would cause**</sup> the estimate of **0** in regression (1) to be biased downward.<sup>There is no clear</sup> relation between quality differences<sup>and licence concentration</sup> or quota utilization, so that we have no prior<sup>expectation as to the effect on the</sup> coefficients on Hit and Uit<sup>caused by the omission of Zi.</sup> There are two ways to control for<sup>the unmeasurable "fixed effects", Z;.</sup> One way is to make use of our panel<sup>data to con.truct "within" estimators of</sup> the regression parameters ir. equation (9). A simpler way, however,<sup>is to</sup> introduce nine apparel group-specific<sup>intercepts, D</sup> 1<sup>,..., Dg, in the matrix of</sup> explanatory variables: (10) Pit H **al0** + **alDl** + + aD9 + a'Pitus + r'Hit + 6'Uit + **0Vit** + (it Note that the coefficient O' now<sup>stands for the marginal component of</sup> rent sharing net of the effect of<sup>product differentiation.</sup> The constant term **aCl** is exactly (a'+rZ10<sup>) and the coefficients on the apparel group dummies,</sup> **ai,** represent f(Zi-Zlo) for i-1,...,9.

Table 5 gives the results of regression<sup>(10). The estimate of ' is</sup>
<!-- Start of picture text --> = oO<br>fa)<br>io}<br>x)<br>&Ca<br>c<br>zEa<br>=a,<br>vy<br>a,3<br>7<br>t-1 t t+ t+2 +3. t4 t+5 +6 +7 tB to<br>Horizon, h (years)<br><!-- End of picture text --> <!-- Start of picture text --> z*<br>fa)<br>xro)<br>zo<br>g<br>&<br>aa<br>Be<br>=aa<br>&3°<br>v<br>a,3<br>Eq<br>t-1 t t+1 t+2 +3. t+4 t+5 +6 t7 tB to<br>Horizon, h (years)<br><!-- End of picture text --> <!-- Start of picture text --> oO<br>=io}fa)<br>x)<br>&on<br>c<br>zEa<br>a,=<br>vy<br>a,3<br>7<br>t-1 t t+ t+2 +3. t4 t+5 +6 +7 tB to<br>Horizon, h (years)<br><!-- End of picture text --> <!-- Start of picture text --> We)<br>fa)<br>xro)<br>&<br>gc<br>&<br>a<br>Be<br>=a<br>&go<br>v<br>a,3<br>&<br>=<br>t-1 t t+1 t+2 +3. t+4 t+5 +6 t7 tB to<br>Horizon, h (years)<br><!-- End of picture text -->
are available in both survey rounds, we can apply survey-to-survey imputation methods. In particular, Dang, Lanjouw, and Serajuddin (2017) propose a simple imputation framework that builds on earlier studies (Elbers, Lanjouw, and Lanjouw, 2003; Tarozzi, 2007).<sup>23</sup> Compared to previous studies, the Dang _et al._ (2017) method provides a more explicit theoretical modeling framework, with new features such as model selection and standardization of surveys of different designs (e.g., for imputing from a household survey into a labor force survey).

Assume that the explanatory variables �� are comparable for both surveys (Assumption IV.1), and that the changes in �� between the two periods can capture the change in poverty rate in the next period (Assumption IV.2). Suppressing the subscript for households in the following equations, Dang _et al._ (2017) define the imputed consumption y�� as and estimate it as where the parameters ��� are estimated using Equation (3), and ����,� and �̂��,� represent the sth random draw from their estimated distributions, for s= 1,..., S. The poverty rate<sup>_P_</sup> 2<sup>in period 2 and its</sup> variance can then be estimated as

> 23 Elbers _et al._ (2003) provide a method that imputes household consumption from a survey into a population census. Adapting this approach for survey-to-survey imputation, Christiaensen _et al._ (2012) impute poverty estimates using data from several countries, including China, Kenya, the Russian Federation, and Vietnam; other studies analyze data from Uganda (Mathiassen, 2013).

22
Similar evidence on endogeneity of caseloads is seen in several other studies. Spaić and Đorđević (2022) investigate the relationship between the relative number of judges within a court and the efficiency of the judiciary. To determine how the number of judges influences effectiveness, they compare data on the judiciary from six countries: Serbia, Croatia, Slovenia, France, Austria and Norway. The authors conclude that judicial effectiveness is static with the number of judges in a jurisdiction. Dimitrova-Grajzl et al (2012), Alencar and Ponticelli (2016), Grajzl and Shikha (2020) and Dimitrova-Grajzl et al (2016) study courts in Europe and Asia and find that the number of serving judges, a key court resource, matters only in a subsample of courts, a result suggesting that judges adjust their productivity based on the number of judges serving at a court.

Also, there is some evidence that restructuring the judiciary with a view towards increasing the quality of judgments pays off. Garoupa and Ginsburg (2009) provide a theory of the formation of judicial councils and their effect on judicial quality and from there to economic growth. They test the extent to which different designs of judicial council affect judicial quality and find a modest relationship between councils and quality. This result echoes Voigt and El Bialy (2013) in their findings on reforms in European courts. Further research is sorely needed to provide a detailed mapping of the path from quality of judgments to judicial effectiveness.

There is robust evidence on who benefits from judicial effectiveness. Improvements to judicial effectiveness disproportionately benefit the least privileged groups of society. Chemin (2021) studies data on 36 African jurisdictions and documents that the perception among the poorest decile of the population that the head of state will never ignore the courts and laws is lower at baseline by 11 percentage points compared to other more well-off groups in society, and that this perception differentially increases by 10 percentage points after a judicial reform, nearly closing the gap between groups.

# **4. Conclusions**

Courts around the world are perceived to be often ineffective in the delivery of justice. The resolution of civil and criminal cases takes too long, costs too much and judgments are often 25
**Table 6: Model Parameters**

||**parameter**|**value**|
|---|---|---|
|ψ<br><br>|weight of leisure in utility|3.36|
|β<br><br>|subjective discount factor|0.99|
|δ<br><br>|depreciation rate|0.08|
|γn<br><br>|population growth rate|2.1%|
|γz<br><br>|productivity growth rate|-1.0%|
|α<br><br>|capital share in non-agriculture|0.35|
|C<br>|capital share in agriculture|0.25|
|μ|labor share in agriculture|0.45|
|_Source_:|authors.||

Most of the parameter values are standard and have been extensively used in the literature. These are the weight of leisure in utility, equal to 3.36; subjective discount rate equal to 0.99 and annual depreciation rate equal to 8 percent.<sup>18</sup> We estimate average annual population growth to be equal to 2.1 percent during 1991-2010 in Paraguay, while the average productivity growth during the same period is negative equal to -1 percent. We also find that this average aggregate productivity growth conceals some important sectoral differences in productivity. Specifically, we find that during the same period, agricultural productivity was growing at about 3 percent per annum, while productivity in non-agriculture was falling at about 1.6 percent per annum. Given the larger size of non-agricultural sector in Paraguayan GDP, the aggregate productivity growth was negative.

Parameters governing production technology are calibrated as follows. We assume that the labor share in non-agriculture is equal to 0.65, while the capital share in non-agriculture is equal to 0.35. These numbers are standard in developed countries and are close to the estimates for Ecuador (labor share = 0.57) and Bolivia (labor share = 0.63) computed by Gollin (2002) after accounting for self-employed and assuming that labor and capital shares for self-employed are approximately the same as in private unincorporated enterprises. For the agricultural sector, we use labor and capital share estimates from Abler, Tolley, and Kripalani (1994) computed for India. They estimate that labor share is equal to 0.45, while capital share is equal to 0.25 in agriculture.<sup>19</sup> The rest is returns to a fixed factor such as land.

Our estimated wedges are plotted in Figure 1.8. Panel (a) presents labor, capital and government wedges, as well as GDP; while panel (b) shows GDP and efficiency wedges in agriculture and non-agriculture. All wedges and GDP are normalized by their initial value in 1991. Since all variables in our calculations are de-trended using the average productivity growth, agriculture and non-agriculture efficiency wedges show the changes in sectoral productivity relative to the aggregate trend. The sectoral labor and capital allocation wedges are presented in Figure 1.8.

> 18 The depreciation rate for the capital stock is taken from Acosta-Ormaechea and Dabán Sánchez (2011). 19 We also analyze how sensitive our results are with respect to the factor share parameters. We find that these parameters affect primarily the average size of the wedges but not their trends or dynamics.
<!-- Start of picture text --> Expected<br>SEEChosenChosen LoB's pee threshold threshold | Pareto alpha? alpha?<br>[Motor vehicleliability vehicleliability | =. =. | 3,000,000 [100,000,000] No | 25<br><!-- End of picture text -->

Expected SEEChosenChosen LoB's pee threshold threshold | Pareto alpha? alpha? [Motor vehicleliability vehicleliability | =. =. | 3,000,000 [100,000,000] No | 25

|Line of business|Lower loss<br>threshold<br>= <br>=CHF im =||Lower loss<br> |threshold<br>|=CHF 5m|
|---|---|---|
|MVL (Motor vehicle liability)|2.5|2.8|
|MvVcC-hail (Motorvehicle comprehensive hail)|1.85|1.85|
|Property|1.4|1.5|
|Liability|1.8|2|
|UVG incl. UVGZ|2|2|
|Health Collective and Individual|3|3|
|Transport|1.5|1.5|
|Finance and surety|0.75|0.75|
|Others|1.5|1.5|
Table 43: Correlation Matrix of Variables Used in Panel Regression Analysesa (Sample of Developed countries avoce the diagonal; sample of LDCs below the diagonal)

|1. RGDPpercapita<br>1985|(1)|(2)<br>.93|(3)<br>.78|(4)<br>.84|(5)<br>.14|(6)<br>.37|(7)<br>-.01|(8)<br>.14|(9)<br>.20|(10)<br>-.02|(11)<br>.24|(12)<br>.04|(13)<br> -. 24|(14)<br>-. 81|(15)<br>-.08|<br>(16)<br>-. 66|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|2. RGDP per capita<br>1960|.85||.69|.82|.13|.39|-.06|.14|.12|-.02|.23|.05|-.17|-. 80|-.07|-.58|
|3.PrimayEducation<br>1960|.73|.61||.66|.03|.39|.04|.01|.16|-.05|.13|.13|-.22|-.63|-. 06|-. 39|
|4. Secondary Educaton<br>1960|.77|.74|.60||.08|.20|-.03|.04|.20|.08|.22|.02|-.09|- 1 4|-.19|-. 64|
|5. Toal Instructional<br>Hours|-.04|-.09|-.05|.00||.52|.36|.51|.20|.36|_.50_|.08|.26|-.19|.05|-.09|
|6. MathInstructional<br>Hours|.22|.21|.37|.11|.42||.22|.32|.00|-.05|.37|-.10|-.07|-.39|.10|-.19|
|7. Science Instructional<br>Hours|.01|-.06|.06|-.10|.42|.35||-. 34|.14|.31|.25|-.10|.33|.01|-.00|.16|
|8. Lang. Instructional<br>Hours|-.11|-.15|-.16|-.22|.46|.10|-.33||-.13|-.18|.04|.03|-.36|-.18|.07|-. 22|
|9. ArtsInstructional<br>Hous|.06|-.09|.07|.04|.21|.02|.11|-.16||.23|.39|-.22|-.06|-.26|_.15_|-.17|
|10. Social Sci. Instr.<br>Hours|.08|.13|.02|.20|_.S3_|.27|.46|-.16|.20||.12|-.14|.24|.02|.00|.09|
|11.Physical Ed. Instr.<br>Hours|.18|.14|.11|.31|.38|.22|.22|-.07|.44|.16||.03|.06|-. 28|.02|-. 25|
|12. MoralRel. Instr.<br>Hours|.05|.04|-.15|.10|.05|-.06|-.13|.07|-.40|-.21|.12||.01|.08|-.07|-.02|
|13. VocationalInstr.<br>Hours|-.17|-.05|-.14|.14|.21|-.18|.32|-. 44|.03|.33|-.01|.00||.14|-.06|.19|
|14. TotalFertility<br>Rate 1965|-.54|_-. 53_|-.52|-. 72|-.07|-.21|.02|.12|-.09|-.10|-. 29|.08|-.09||.23|.70|
|15. Dunmny for Mining/<br>Oil Counuies|.16|.21|.07|.00|.12|.30|-.01|.16|.24|-.02|.09|-.10|-.12|.00||.38|
|16. Export Commodity<br>Concentration|-.35|-.14|-.12|-.37|.04|.16|.22|-.11|.00|.06|-.13|-.14|.06|.36|.32||

a) Zeo-order correiadons in bold are significant at least at the .05 level.
ZAMBIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE comprehensive information on existing spending; however, based on the experience of other African countries, it is safe to assume that a funding gap for this sector is small or nonexistent.

**Table 13. Funding** **<u>gaps by sector</u>**

||ICT|Power|Transport|WSS|Total|
|---|---|---|---|---|---|
|Spending needs|(218)|(631)|(289)|(471)|(1,609)|
|Existing spending|90+|180|245|158|673|
|Efficiency gains|n.a.|160|59|96|315|
|Funding gap|n.a.|(291)|15|(217)|(493)|
|Potential for reallocation|0|30|65|0|95|

_Source:_ Briceño-Garmendia, Smits, and Foster 2008. n.a. = not applicable.

Every year about $95 million more is spent than needed to meet Zambia’s estimated infrastructure requirements (recall table 9). Most of this overspending is on the transport sector ($65 million), which in recent years is being funded at rates apparently over and above long-term requirements. There is also evidence of overspending on O&M in the power sector, due to utility inefficiencies and an overextended distribution network. Since there is a large funding gap in this sector, these resources would be better diverted to finance power investments.

# What else can be done?

There are a number of ways of addressing the infrastructure funding gap; filling it with money is not the only relevant approach. A number of policy choices relating to technology selection and regional approaches to infrastructure development could reduce the gap by lowering the costs of meeting infrastructure targets. Alternatively, there is the possibility of taking a longer period of time to meet defined goals.

Adopting lower-cost technologies for meeting the MDG targets for WSS could reduce Zambia’s infrastructure funding gap by $0.2 billion. The estimated cost of reaching the MDG targets is based on Zambia maintaining its current mix of WSS technologies, which as noted tends to be skewed toward higher-end solutions such as private taps. If, instead, the service expansion needed to meet the MDG targets was undertaken entirely through lower-end solutions, such as standposts and boreholes, the associated cost could fall substantially, by $218 million or almost one-half.

Eventually, importing power from the DRC through an enhanced SAPP could reduce the funding gap by $0.2 billion. Although not feasible at present, in the medium to long term (as the DRC develops the hydropower at Inga), a larger volume of low-cost power would become available through the SAPP. Since the DRC’s hydropower resources are more cost-effective than those of Zambia, the latter could reduce its power-sector development costs substantially in the longer term by moving toward increased reliance on power trade, with potential cost savings of $160 million per year.

Adopting more appropriate standards for paved roads could shave a further $0.1 billion from the funding gap. The spending needs for the transport sector assume that regional and national connectivity standards will be met by standard asphalt paved roads. But, in practice, it may be possible to reduce costs 32