rafmacalaba/datause-encoder-data huggingface dataset:
private rental stock is typically in worse physical condition and has fewer amenities and lower standards than the postwar municipal rental stock, the grading scales result in the lower average private rents.<sup>61</sup> This will be changing, however, with new regulations allowing substantial rent increases in this rental stock.

In the uncontrolled markets of Romania and Russia, in contrast, private rents are both significantly higher than public rents and less affordable. Although distorting factors in the LSMS and HBS is a possibility - for example, strategic behavior of interviewees to conceal true rent levels because they might violate regulations or lead to higher taxation - we believe that these results by and large reflect the low quantities of rental housing supply in those countries determined earlier in the chapter.

- The data for Romania confirm our evidence on the scarcity of rental housing supply - especially in Bucharest. According to our interviews, for example, flats in public rental units in Bucharest in 2004 were let for just one-tenth of the rent level in the private market.

- The data for Russia indicate somewhat lesser relative scarcity of private rental units than in Romania, but certainly still yield a high differential. This is consistent with both the findings of a low base effect concerning sector size reported in this study and the dynamics of new rental supply noted by Kalinina and Klepikova (2002).

The countries of Armenia and Serbia are both in special situations:

- The low mark-up for private rents in Armenia seems to reflect the combined effects of a lack of effective demand and a lack of net emigration, which leaves many units vacant. Low general affordability makes public rents appear very high - which they are not, if compared to costs.

- Serbia has by definition the highest mark-up of private rents, because our survey does not record rent payments made for public rental apartments.

It would be worthwhile to refine the results obtained by the LSMS and HBS by location or available information reflecting housing quality and tenant income. Such a hedonic pricing approach would lead to a greater accuracy about whether the matching of pricing and incomes works. Also, tracing rent levels over time would be important to test the interaction with the improving mortgage market conditions. However, the data presented here seem to provide an accurate snapshot of overall demand and supply conditions.

# **_Household Effort Ratios for Housing Expenditures_**

Quite expectedly, our results indicate that households’ rent-to-income ratios are significantly higher in the private than in the public rental sector, with the exception of Poland. The gap for Romania is reduced somewhat by the fact that private tenants have higher average incomes than public tenants. For Russia and Armenia, the affordability relations reflect the price relations.

> 61  Although newly agreed contracts in these buildings, on vacated dwellings, are released from this rent setting regime.

39
<!-- Start of picture text --> Figure 4.2a. Aggregate Pattern of Growth (1999-2002)<br>1.6<br>1.4<br>1.2<br>1.0<br>0.8<br>0.6<br>0 10 20 30 40 50 60 70 80 90 100<br>Cumulative Percentage of the Population<br><!-- End of picture text --> Table 4.6. A Decomposition of Aggregate Measure of Pro-poorness (1999-2002)

||Watts|Poverty Gap|Squared Poverty Gap|
|---|---|---|---|
|Rice|-0.173|-0.125|-0.062|
|Other Food|0.044|0.037|0.015|
|Education|0.005|0.003|0.002|
|Health|0.009|0.006|0.003|
|Other Non-Food|0.077|0.057|0.027|
|Aggregate|-0.039|-0.022|-0.014|

Finally, the available household survey data for 1999 and 2002 allow us to look beyond aggregate results for this period and consider the contribution of expenditure components to the observed outcome. We also use the same data to look at pro-poorness at percentiles. Figure 4.2a shows the aggregate pattern of growth for the period under consideration while figure 4.2b represents a disaggregation of this overall pattern and is 24
# GRAPHS

GRAPH Is STANDARDIZED SCHOOL LEVEL RESIDUALS (Y) PLOTTED AGAXNST PREDICTED AVERAGE SCHOOL ZJC RESULTS IN MATHEMATICS (X) . . . . . . . . . . 41 GRAPH 2: STANDARDIZED SCHOOL LEVEL RESIDUALS (Y) PLOTTED AGAINST PREDICTED AV&RAGE SCHOOL ZJC RESULTS IN ENGLISH (X) . . . . . . . . . . . . 42 APPENDIX 1: List of Variables . .. . . .54 APPENDIX 2: School Coding . . . . . . . . . . . . . . . . . . . . . . 55 This Study was commissioned by the Southern Africa PopUlation and Human Resources Division of the World Bank as a background paper for a review of primary and secondary education in Zimbabwe.
Table 16: IV split regression innovation score over IT use

|Dependent variable: Innovation score|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Low com**|**petition **|**with Chin**|**a**|||
|Products and processes||||||
|∆Computers-per-worker|-6.954*|-6.372*|-6.715|-5.958|-6.228|
||(4.012)|(3.854)|(4.534)|(4.518)|(4.631)|
|F-frst stage|9.7|10.2|7.1|6.5|6.6|
|Sargan|0.92|0.72|0.75|0.96|0.95|
|Marketing and Organization||||||
|∆Computers-per-worker|-4.461|-3.678|-2.779|-2.353|-2.328|
||(3.069)|(2.925)|(3.375)|(3.404)|(3.474)|
|F-frst stage|9.7|10.3|7.0|6.5|6.6|
|Sargan|0.56|0.73|0.85|0.72|0.71|
|Observations|336|336|336|336|336|
|**High com**<br>|**petition **|**with Chin**|**a**|||
|Products and processes||||||
|∆Computers-per-worker|9.295**|9.020**|7.823**|7.846**|8.599**|
||(3.870)|(3.841)|(3.445)|(3.524)|(3.779)|
|F-frst stage|13.1|13.1|13.5|13.0|12.0|
|Sargan|0.64|0.64|0.48|0.48|0.54|
|Marketing and Organization||||||
|∆Computers-per-worker|5.971**|5.882**|5.254**|5.404**|5.824**|
||(2.773)|(2.772)|(2.559)|(2.629)|(2.817)|
|F-frst stage|13.1|13.1|13.5|12.9|9.2|
|Sargan|0.31|0.31|0.23|0.24|0.27|
|Observations|365|365|365|365|365|
|Controls||||||
|Age|No|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|
|FDI share|No|No|No|No|Yes|
|Time efects|No|No|No|No|No|

Standard errors in parentheses Source: Authors’ calculations using data from ENTIC, INEGI

* _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01

40
Table B3: Results Pooled: Job Benefits - Heterogeneity by gender

||(1)<br>Area<br>Studies|(2)<br>Contract<br>Written|(3)<br>Contract<br>Permanent|(4)<br>Job<br>Benefts|(5)<br>Registered<br>Firm|
|---|---|---|---|---|---|
|Offered Training|-0.00|0.04|0.03|0.01|0.09<sup>_∗∗∗_</sup>|
||(0.04)|(0.03)|(0.02)|(0.03)|(0.03)|
|Offered * Female|0.03|-0.00|0.01|-0.02|-0.06|
||(0.05)|(0.05)|(0.03)|(0.04)|(0.05)|
|Control Mean: Men|0.53|0.32|0.10|0.18|0.42|
|Control Mean: Women|0.35|0.27|0.07|0.12|0.39|
|Observations|2,597|2,597|2,597|2,597|2,597|
|p-value female|0.40|0.23|0.03|0.94|0.41|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of follow-up data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. We control for the baseline value of the dependent variable, stratification dummies and survey wave dummies. Standard errors are clustered at the respondent level.

Table B4: Results Pooled: Channels - Heterogeneity by gender

||(1)<br>Searched For Job<br>in Last Month|(2)<br>Confdence Approaching<br>Employers (0-3)|(3)<br>CV in<br>Digital Format|(4)<br>People Discusses<br>Jobs with|
|---|---|---|---|---|
|Offered Training|0.03|0.06|0.03|0.98<sup>_∗∗∗_</sup>|
||(0.04)|(0.05)|(0.02)|(0.25)|
|Offered * Female|-0.04|-0.01|-0.00|-0.17|
||(0.05)|(0.07)|(0.03)|(0.35)|
|Control Mean: Men|0.64|2.51|0.88|5.47|
|Control Mean: Women|0.64|2.53|0.92|5.37|
|Observations|1,728|2,597|2,597|2,597|
|p-value female|0.91|0.23|0.12|0.00|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of followup data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. We control for the baseline value of the dependent variable, stratification dummies and survey wave dummies. Standard errors are clustered at the respondent level. Earnings are top winsorized at the ninety-ninth percentile; we replace earnings with 0 for participants who do not work.

41
- [46] Pallage, Stéphane, and Michel A. Robe, “On the welfare cost of economic fluctuations in developing countries,” International Economic Review , Vol. 44 (2003), pp. 677-698.

- [47] Pieschacon, Anamaria, “The value of fiscal discipline for oil-exporting countries,” Journal of Monetary Economics, Vol. 59 (2012), pp. 250-268.

- [48] Rasmussen, Tobias, and Agustin Roitman , “Oil shocks in a global perspective: Are they really that bad?,”IMF Working Paper No. 11/194 (2011).

- [49] Richaud, Christine, Arthur Galego Mendes, Firmin Ayivodji, Samer Matta, and Sebastian Essl, “Fiscal vulnerabilities in commodity exporting countries and the role of fiscal policy,”MTI Discussion Paper No. 15 (2019), World Bank.

- [50] Roch, Francisco, “The adjustment to commodity price shocks in Chile, Colombia, and Peru,”IMF Working Paper No. 17/208 (2017).

- [51] Schmidt-Hebbel, Klaus, “Fiscal institutions in resource-rich economies: Lessons from Chile and Norway,” Working Paper No. 416 (2012), Pontificia Universidad Catolica de Chile.

- [52] Talvi, Ernesto, and Carlos A. Vegh, “Tax base variability and procyclical fiscal policy in developing countries,” Journal of Development Economics, Vol. 78 (2005), pp. 156-190.

- [53] Tornell, Aaron, and Philip R. Lane, “Are windfalls a curse? A non-representative agent model of the current account,”Journal of International Economics, Vol. 44 (1998), pp. 83-112.

- [54] Tornell, Aaron, and Philip R. Lane, “The voracity effect,” American Economic Review, Vol. 89 (1999), pp. 22—46.

- [55] Tornell, Aaron, and Andres Velasco, “Fixed versus flexible exchange rates: Which provides more fiscal discipline?,”Journal of Monetary Economics, Vol. 45 (2000), pp. 399-436.

- [56] Vegh, Carlos A., Open Economy Macroeconomics in Developing Countries (MIT Press, 2013)

34
consumption of electric energy. Therefore, we use a series of price indexes to convert all the nominal variables into 1986 constant pesos. We obtain implicit price indexes for different types of capital goods and producer price indexes (PPI) at 3-digit ISIC (revision 2) from DANE, and construct our own indexes for domestic and imported raw materials and for exports. Details on the construction of price indexes and other data issues are provided in Appendix B.

The main variables for our analysis are output, labor, intermediate inputs, skill intensity, wage premium, capital, vintage, production experience, and export experience. Output _Y_ it is obtained as the sum of the value of domestic sales plus net inventory accumulation deflated by PPI and the value of exports deflated by the exports price index. Labor _L_ it is the total number of workers. Intermediate inputs _M_ it is the sum of raw materials consumption and energy consumption in constant pesos. Raw materials consumption in constant pesos is the sum of the values of domestic and imported raw materials consumed during the year deflated by the price indexes of, respectively, domestic and imported raw materials. Energy in constant pesos is the sum of electric energy consumed during the year valued at 1986 prices plus consumption of fuels and lubricants deflated by the PPI of the petroleum refineries sector.

Our measures of labor quality are skill intensity _S_ it, defined as the ratio of the number of white collar workers, managers, and technicians to the total number of workers, and the wage premium _W_ it, defined as the ratio of the plant’s average wage in a given year to the average wage paid that year in the region where the plant is located.<sup>8</sup> Bahk and Gort (1993) use the plant’s average wage as a measure of labor quality on the grounds that variations in wages "mainly

> 8 We consider thirteen regions: eight major metropolitan areas (Bogotá, Medellín, Cali, etc.), four regions in the interior, and the rest of the country.

15
# Relationship between corruption risks and prices

This section analyzes the price impact of corruption risks in public procurement in North Macedonia. We attempt to quantify the potential savings that can be achieved if corruption risks were eliminated. To measure how pricey a contract is, we use the relative price which is a ratio between the final contracted price and the estimated contract value (i.e. auction reference price). The relative price captures the discount achieved by procurement authorities. We expect that tenders with high corruption risks suffer from higher relative prices. In other words, the ratio between the contracted price and the estimated contract value would be higher or, in more extreme cases, the final contracted prices would be higher than the estimated prices.

The method can be briefly summed up as follows: We run a linear regression model using CRI as the main predictor of the logarithm of relative prices (we use logarithm of prices to account for the uneven price distribution). The model controls for differences in contract value deciles, procuring authority type and location, supply type, product market (2-digit CPV divisions), year and month. We also test the corruption risk-price relationship with the individual corruption risk indicators and calculate the expected savings achieved if policy makers were to focus their efforts on reducing the risk scores of a specific corruption risk indicator. Based on the best fit estimation model (i.e. highest explanatory power), we predict the relative prices for each contract in the case of zero CRI (lowest possible corruption risk score). These no-risk contract value estimations enable us to estimate the savings to be made in each sector, time period, or geographical location. This method is further discussed in Abdou, <mark>Basdevant, David-Barrett & Fazekas (2022).</mark> We need accurate contract/lot level estimated contract values to calculate relative prices achieved by procuring authorities in the contracting process. However, the North Macedonia’s data portal only publishes estimated contract values at the tender level with no way to accurately break it down to the contract or lot level. Therefore, the analyzed sample had to be restricted to single-lot tenders where tender and lot values are necessarily the same. Furthermore, we also restrict relative prices to fall between 0.5 and 1.3 – as ratios outside this range might be due to data reporting errors. In Figure 15, panels A and B, we display the distribution of tenders in the dataset split over single-lot and multi-lot tenders.<sup>5</sup> Single-lot tenders represent 78.3% of all tenders (132,902 tenders) and 65.6% (335.5 billion MKD) of the total contract value available in the dataset.

> 5 We count the single-lot tenders based on the CFT notice url appearing only once in our lot level dataset.

24
The nature of many of the armed conflicts following the end of the Cold War refocused attention on the consequences of ethnicity, religion, and other forms of cultural heterogeneity<sup>1</sup> on social outcomes (Alesina et al. 2003; Collier 2001a; Ellingsen 2000; Fearon and Laitin 2003; Fox 2004; Gurr 1993; Harrison and Huntington 2000; Horowitz 1998; Ramet 2004; Reynal-Querol 2002; Sambanis 2001; Varshney 2001; Wimmer et al. 2004).<sup>2</sup> The focus on ethnicity and religion surely intensified following the 9/11 terrorist attacks in the US, although the tradition of explaining Third World violence in the years after World War II as ethno-nationalist rebellion under conditions of weak states has deep roots (Drake 1957; Gurr 1970; Huntington 1968). The popular wisdom is that ethnic and, if less so, religious conflict are ‘endemic’ and ‘everywhere on the rise’.<sup>3</sup> Theories built around the concepts of ethnicity, religion and security dilemmas are particularly prominent in the literature (Kaufmann 1996; Petersen 2001; Posen 1993; Snyder and Jervis 1999; Walter and Snyder 1999). Ethnic and religious conflict occurs because groups are unable to coordinate mutual security fears (Woodward 1995), particularly over questions of national integration and exclusionary politics (Cederman and Girardin 2005; Wimmer 1997), leading to relative deprivation and spirals of dissent, repression, and outright violence.

Some theories of ethnic fractionalization and polarization argue that violence may occur because of the ways in which state power is distributed that allow some to access state resources to greater extents than others (Caselli and Coleman 2006). If a majority hogs state power, then it has an incentive to militarize in order to prevent a challenge from other groups. This would lead to cycles of repression, causing security dilemmas (Kaufmann 1996; Posen 1993; Snyder and Jervis 1999). In fact, the idea of an ethnic security dilemma borrows heavily from the international relations literature where states operating in an anarchical international system are driven to seek security, but create insecurity among others in so doing. The concept of an ethnic security dilemma is applied in the situation where states have collapsed and ethnic groups 1 We use the terms heterogeneity, diversity and fractionalization interchangeably. 2 It is reported that one English-language scholarly journal database records 249 articles published since 1990 containing ‘ethnic conflict’ in the title as opposed to just 23 with ‘class conflict’ (Gilley 2004: 1155).

3 See Kaplan (1994) for a recent explication of the primordialist argument that suggests ethnic and, more broadly, cultural conflict to be endemic. Huntington’s (1993, 1996) hypothesis of a _Clash of Civilizations_ provides a related argument. Others report that the incidence of ethnic conflict and inter-communal violence is declining (Gurr 2000).

2
India).

A longstanding concern in the literature has been whether the observed correlations are primarily mechanical, driven largely by genetic transmissions from parents to children (see, for example, the discussion by Black and Devereux (2011)). We address this issue in two ways. First, we develop a simple but plausible approach to check whether the estimated intergenerational persistence could be due solely to genetic correlations. The approach combines the recent evidence on intergenerational correlation in cognitive ability from the behavioral genetics and economics literature with the Altonji, Elder, and Taber (2005) biprobit sensitivity analysis (henceforth called augmented AET (AAET) analysis). Second, the theoretical foundation for the empirical specification allows us to use economic mechanisms as a test for the importance of parental economic choices.

The substantive conclusions of this paper can be summarized as follows. Intergenerational educational mobility was substantially lower for sons in rural India compared to sons in rural China for the cohorts that went to school in the 1970s-1990s. The point estimates of intergenerational persistence are larger in nonfarm households in both countries. The difference between farm and nonfarm households is statistically significant in rural India, but not in rural China, and this conclusion applies to both the slope and the intercept of the intergenerational educational persistence regression. The evidence suggests that while parent’s education and nonfarm occupation were complementary in determining a son’s education in rural India, they were separable in rural China.<sup>14</sup> The long-term variance in schooling was significantly higher for the sons born into nonfarm households in rural India, and structural change from agriculture to the nonfarm sector contributed to educational inequality. The evidence from the augmented AET sensitivity analysis shows that the observed persistence in rural India cannot be accounted for by genetic correlation alone, while the persistence in China can be explained away by an ability correlation of plausible magnitude.<sup>15</sup> An important advantage of this approach is that the conclusions refer to the whole population of interest, rather than a subset as usually is the case with other standard approaches such as instrumental variables strategies. 14It is important to recognize that separability in rural China does not imply that nonfarm income did not play any role in rural income inequality; in fact, we cite a substantial literature to the contrary. The evidence in this paper suggests that intergenerational educational linkage is unlikely to be an important mechanism through which the nonfarm sector influenced the observed inequality in rural China in the 1980s and 1990s.

> 15This evidence on rural China is similar to the recent analysis on urban China based on a sample of monozygotic twins by Behrman et al. (2020). They find that the estimated impact of father’s education can be explained fully by genetic correlations and unobserved family endowments.

4
# **1 Introduction**<sup>1</sup>

Global trade volume growth reached a post-crisis low of 2.4 percent in 2016—significantly below the pre-crisis average of 7.6 percent. Cyclical factors, such as weak global demand, low commodity prices, and slower growth in China have all contributed to the trade deceleration. In addition, structural factors have lowered trade’s responsiveness to global output expansion (World Bank 2015a; Constantinescu, Mattoo, and Ruta 2015).

The maturing of global value chains is a key structural factor contributing to the recent trade slowdown.<sup>2</sup> Global value chains often involve numerous cross-border operations, conducted either “intra-firm,” that is, between firms related through ownership or control, or between unaffiliated firms at “arm’s-length.” A firm’s decision between arm’s-length and intra-firm transactions has its roots in the underlying motivation for vertical integration (or lack thereof) and foreign direct investment. Firms choose to internalize transactions if the cost of performing these through the market is higher than internal costs (Coase 1937). In particular, contract enforcement imposes costs when contracts are incomplete (Williamson 1985; Grossman and Hart 1986). When contracts are incomplete and their enforcement is costly, firms may prefer vertical integration and internal ownership of assets (Hart and Moore 1990; Antras 2015).<sup>3</sup> In cross-border trade transactions, additional considerations come into play. Firms may favor arm’s-length transactions when they seek access to export markets similar to their home markets and when technology, knowledge, or resource transfers are not required (Dunning and Lundan 2008; Lanz and Miroudot 2011). As a result, arm’s-length transactions are more prevalent in low-skilled sectors and among less productive firms (Corcos et al. 2013).

In practice, multinationals employ intra-firm and arm’s-length transactions to varying degrees. In 2015, intra-firm transactions are estimated to have accounted for about one-third of global exports (UNCTAD 2016). Vertically integrated multinational companies, such as

> 1This paper draws from a background study featured in World Bank 2017. Figures and background data presented here are available at www.worldbank.org/gep.

> 2The expansion of global value chains contributed significantly to the rapid rise in trade growth during 1985-2000. However, during 2000-16, growth in value chains has stabilized (Haugh et al. 2016; Constantinescu, Mattoo, and Ruta 2015).

3Incomplete contracts can result in underinvestment when firms undertake significant relationship-specific investments. Parties to a contract may underinvest in expectation of their counterpart not complying with the terms of a contract. As suppliers often customize their products to fit the needs of a specific buyer and buyers undertake significant investment specific to a particular supplier, such cost could be significant.

1