rafmacalaba/datause-encoder-data huggingface dataset:
As a first step, we interact the bribe and the time tax with EU membership (Table 5). First, we observe that the sign and significance of controls remains unchanged with respect to Table 4. Second, it emerges that the time tax remains insignificant, also when interacted with the EU dummy, whereas the bribe tax remains significant with a negative sign in isolation, while displaying a large, positive and significant effect in the interaction (columns 3 and 4). This indicates that bribe payments do not have a negative impact on productivity in EU countries – the effect may even be positive.

The finding that the bribe tax is less harmful in EU countries may reflect the generally higher institutional quality in countries with higher incomes per capita.<sup>18</sup> The negative effect of the bribe tax on productivity in non-EU countries may be linked to generally poorer institutional quality and to the fact that, in high corruption environments, bribing could be regarded as the norm for most interactions with the State. Bribery will hence constitute a drag on productivity, without enabling firms to reap an efficiency advantage over competitors, as it is very likely that other firms are also paying bribes.

In order to examine the possibility of country-specific effects a step further, we explicitly consider the potential influence of broader institutional characteristics of the country. For this purpose, two variables, obtained from sources other than the BEEPS, are used. The first is the Corruption Perception Index computed by Transparency International. The second is a measure of the quality of the legal framework taken from the Global Competitiveness Report. As shown in Table A3 in the Appendix, mirroring the large differences in income per capita, these two variables also present substantial variation across the countries in the sample. We, therefore, posit that the effect of bribe and time tax on productivity may depend on the overall prevalence of corruption in the country - in the spirit of theories highlighting the role of social effects on individual behaviour – and on the effectiveness of the legal framework in preventing and sanctioning corrupt behaviour. This hypothesis is tested by estimating model (4) and the results are given in Tables 6 and 7.

> 18 One may also try to detect a specific effect of EU accession, implying that the requirements of EU accession may induce countries to improve the process of formulation and enforcement of laws and regulations, thus reducing the occurrence of bribes as experienced by firms.  However, such a conclusion would require a more detailed analysis, which takes account of the time series dimension to model pre- and post-accession environments and controls for observed and unobserved country characteristics.

29
there are a few items in the adaptive test where the fit is poor (for instance, items 62, 96, and 107 in Urdu). Re-estimating the model after eliminating these poorly fitting items does not alter the overall estimated score.

One concern with the LEAPS assessment is that it may not adequately reflect functional literacy and numeracy as documented by Banerjee et al. (2022). Therefore, we designed a separate assessment to capture respondents’ proficiency in everyday arithmetic and literacy skills. The math questions in this assessment of functional numeracy asked respondents to read an electricity bill, compute the correct amount given arrears (easier) and then recompute the right amount given electricity consumption and non-linear pricing (harder). Another math question assessed competency in a marketplace transaction where respondents purchased multiple items and collected change. To assess functional literacy skills, we asked respondents to read several messages written in Urdu and Roman Urdu (Urdu but using roman language script). We also assessed whether the individual knew how to use a phone by asking them to save a contact on a phone. Appendix C.3 details these items. We argue that someone who can complete these tasks can be considered “functionally” literate; accordingly, we use principalcomponent factor analysis to aggregate these questions into a single “functional literacy and numeracy” index.

# **II.2. SOCIOEMOTIONAL SKILLS**

Measuring socioemotional (SEM) skills in low-income countries has proven difficult, with evidence of non-classical measurement error for self-reported instruments (Laajaj and Macours, 2021). Consequently, we developed the instruments included in the Pakistan survey through an iterative process. We started with data collection in Cambodia for a related project in 2017 (we will present comparative results from that study in Section III.2.2). As in Pakistan, the sample in Cambodia also consisted of young adults from rural regions. The data collection incorporated a comprehensive assessment of socioemotional skills. However, despite our efforts to mitigate the issues later discussed by Laajaj and Macours (2021), the self-reported scales included in that survey displayed limited internal consistency (see Appendix D).

We built on that experience when designing and implementing our SEM skills assessment in Pakistan in several ways. First, in addition to self-reported measures, we developed bespoke applications on tablets. We assumed these tablet-based measures would be less subject to biases arising from social 12
constructed as the sum of the mean rate of return on assets and the mean equity-to-assets ratio divided by the standard deviation of the return on assets (Ray, 1952). A higher Z-score signals a lower probability of bank insolvency. We calculate a Z-score for a bank, if it can be based on annual accounting data for at least 4 years. Figure 8 displays relationships between the Z-score on the one hand and the income and funding shares on the other. The Z-score is shown to be lowest for banks that obtain almost exclusively fee income. Overall, the relationship between the Z-score and the fee income share appears to be an inverted U. This suggests that mixing interest-generating and feegenerating activities provides some risk diversification benefits. Specifically, bank risk appears to be lowest for banks with fee income shares between the 20<sup>th</sup> and 25<sup>th</sup> percentile of this variable’s distribution.<sup>9</sup> Figure 8 also displays the relationship between the Z-score and the non-deposit funding share. Attracting some non-deposit funding is seen to increase the Z-score and thus it appears to reduce bank risk. The Z-score peaks for the group of banks between the 80<sup>th</sup> and 85<sup>th</sup> percentile of the distribution of the non-deposit funding share, after which is goes back down. Thus, the Z-score peaks for banks high up in the distribution of the non-deposit funding share variable, although even these banks have rather low non-deposit funding shares of less than 0.1 as evident from Figure 4.

Table 1 reports the correlation coefficients between a bank’s fee income and non-deposit funding shares, and its risk and return outcomes. We note that the fee income and non-deposit funding shares have a positive and significant correlation coefficient of 0.30. Thus, banks that are non-traditional in having a high fee income share tend to be also non-traditional in having a relatively high non-deposit funding share. Rajan and Stein (2002) and Song and Thakor (2007) provide models of the co-existence of lending and deposit-taking within the same financial

> 9 This finding contrasts with the Stiroh (2004) who shows in his Figure 8 that the Z-score declines with the non-interest income share over its entire range.

14
# **I. Introduction**

Machinery products are important for the trade and economic development of East Asia. Studies and informal observations show that export promotion in machinery was part of the success story of Japan and the newly industrialized economies, South Korea and Taiwan, China.<sup>1</sup> In recent years, machinery has also become a key driver of trade in developing East Asia i.e., China, Indonesia, Malaysia, Philippines, and Thailand. In 2000-2004, machinery products accounted for half of the total imports of these countries. Excluding China, the share of machinery in total exports of those countries has increased to 51 percent in 20002004 from 38 percent in 1990-1994, superseding shares of other product groups.

The story for the “big dragon” China is even more fascinating. China transformed its export structure with full thrust into a machinery exporting giant with interesting dynamics. The share of machinery in China’s total exports leaped to 41 percent in 2000-2004 from 18 percent in 1990-1994. China’s share in world machinery exports has increased almost 5 times in a decade, from 2.3 percent in 1994 to 11 percent in 2004. Thanks to FDI influx, Chinese machinery products are also showing signs of moving up the technology ladder. Having started out as a mass producer of labor-intensive and low technology goods, casual observations suggest that current Chinese exports of machinery have incorporated the latest innovations and more advanced technology. The share of personal computers with the latest processor and related accessories have overtaken the share of metalworking tools in China’s total exports. China is also exporting more cellular phones, personal digital assistants and flat screen TVs rather than transistor radios as previously.

Given these developments, manufacturers in Southeast Asia i.e., “little dragons” (Indonesia, Malaysia, Philippines, and Thailand) have become anxious about China’s rising capability in producing machinery.<sup>2</sup> They now feel competitive pressure from machinery “made in

> 1 Kokko (2002) provided a survey on how Japan used government intervention to promote 44 strategic industries including steel and shipbuilding.

> 2 I exclude Singapore from the list because in my opinion it has successfully transformed its economy into a services hub for the region.

> <sup>2</sup>
forests into REDD+. The possible tradeoff, however, is that in most low-income developing countries forests provide products that are essential to the daily lives of people, including fuelwood, forest fruits and vegetables, building materials and animal fodder (Cooke et al., 2008).

The question we examine is whether, using three different measures, forest collective action in Nepal is consistent with larger carbon stocks per hectare. As carbon is not necessarily the same as forest health, we also test whether collective action results in greater tree density per hectare, additional canopy cover and more regeneration measured as seedlings per hectare.

The chain connecting better collective action and carbon stocks runs through better management and higher forest quality. Better quality forests have more biomass, because reduced fuelwood, timber and fodder collections reduce pressures on forests allowing them to regenerate. Better management is what drives these results and in community settings are potentially the result of more effective collective action.

We test our hypotheses using a nationally representative random sample of forest dependent communities and forests that are part of the Nepal Community Forestry Programme, which is the most important forest devolution program in Nepal. Over 18,000 registered forest user groups are in existence, representing over 35% of the population.

This treatment group is matched with an equal number of forests that are not part of the program. A total of 130 forests made up of 620 forest plots are analyzed and the effects of collective action, including being part of a registered community forest (CF), are evaluated using panel data regression, OLS regression and propensity score matching.

Our main finding is that within the existing institutional environment, collective action has very important, positive and large effects on carbon stocks and often on other measures of forest quality. Depending on our measure of collective action, we are able to identify effects at

**4 |** P a g e
**Table 2: Uzbekistan Enterprises Registered as "Natural Monopolies" at the Republic Level (as of October 1, 1999)**

|**Sectors with "Natural Monopolies"**|**Number of Enterprises**|
|---|---|
|Production of oil, gas condensate and natural gas|7|
|Coal mining|4|
|Pipeline transport of oil||
|Pipeline transport of natural gas|2|
|Generation, transmission and distribution of electric power and heat|48|
|Railway transport|I|
|Airport services|12|
|Postal services|16|
|Teleconimmunications|21|
|Water and sewage|30|
|**Total**|142|

_Source:_ Uzbekistan Anti-Monopoly Committee

**_When Monopolies are No Longer "Nat ural'._** International experience shows that policies that stifle the operation of inherently competitive infrastructure sectors, as well as prevent the progression of traditional "natural" monopolies into more competitive regimes, impose costs on society in the form of high consumer prices, poor production efficiency and retarded innovation. Distinguishing between competitive versus noncompetitive infrastructure sectors is thus an important policy issue in designing further transition reform initiatives. In this regard, governments worldwide typically categorize public enterprises into three groups, and this taxonomy provides a useful guide for Uzbekistan policymakers: (a) in "strategic" industries-the national military defense sector, the currency mint, and the mining of rare metals that have national defense applications-there is, with few exceptions, a compelling rationale for state involvement; (b) in "monopoly or quasi-monopoly" industries-usually local-level utilities in the energy, mass transit and communication sectors-the rationale for government involvement, either through direct ownership or regulatory oversight of nonstate-owned service providers, has historically been strong inasmuch as market forces alone can often produce suboptimal results; however, such sectors can evolve to a point where the competitive provision of such services is most efficient, and this outcome has often entailed the divestiture of utility firms to private 6ovners; and (c) in "commercial" industriesmost of the manufacturing and services sectors-there is generally little justification for state involvement, as competitive market forces often engender the greatest efficiencies.

The overwhelming majority of industrial sectors in countries around the world today possess underlying organizational structures that are _inherently_ competitive. Thus within the typical indlulstrial sector, maximum social efficiency is realized when numerous firms are producing the product (or service); the output share of each firm is not large enough to control the prevailing price in the market; any attempt by a firm to charge an above-market price will produce a loss of consumers or entry by rival 16
Égert, B. (2003) “Assessing equilibrium exchange rates in CEE acceding countries: can we have DEER with BEER without FEER? A critical survey of the literature”, Oesterreichische Nationalbank, Focus on Transition, Vol. 2/2003, 38–106.

Elbadawi, I. A. (1994) “Estimating long-run equilibrium exchange rates”, Estimating long run equilibrium exchange rates, J. Williamson, editor. Institute for International Economics, Washington, D.C.

Engel, C., Mark, N. C., and K. D. West (2007) “Exchange rate models are not as bad as you think”, NBER Macroeconomics Annual 2007, Vol. 22.

Feyzioğlu (1997) “Estimating the equilibrium real exchange rate: an application to Finland”, IMF Working Paper WP/97/109.

Frankel, J. A. and A. K. Rose (1996) “A panel project on purchasing power parity: mean reversion within and between countries”, Journal of International Economics, Vol. 40, 209–224.

Gan, C., Ward, B., Ting Ting, S. and Coehn, D. A. (2013) “An empirical analysis of China’s equilibrium exchange rate: A co-integration approach”, _Journal of Asian Economics_ , 29, p. 34–44.

Hinnosaar, M., Kaadu, H., and L. Uusküla (2005) “Estimating the equilibrium exchange rate of the Estonian kroon”, Bank of Estonia Working Papers 2005–2, Bank of Estonia.

Iimi, A. (2006) “Exchange rate misalignment: an application of the behavioral equilibrium exchange rate (BEER) to Botswana”, IMF Working Paper WP/06/140.

IMF (2007) “IMF exchange rate policy advice”, Independent Evaluation Office of the International Monetary Fund.

Iossifov, P. and E. Loukoianova (2007) “Estimation of a behavioral equilibrium exchange rate model for Ghana”, IMF Working Paper WP/07/155.

Johansen, S. (1991) “Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models”, Econometrica 59, 1551–1580.

Johansen, S. (1995) “Likelihood-based inference in cointegrated vector autoregressive models”, Oxford University Press, Oxford, UK.

Johansen, S. and K. Juselius (1992) “Testing structural hypotheses in a multivariate cointegration analysis of the PPP and UIP for UK,” _Journal of Econometrics_ , Vol. 53, 211–44.

Krugman, P. R. (1987) “Pricing-to-markets when the exchange rate changes”, in Arndt, S. W. and J. D. Richardson (eds), _Real financial linkages among open economies_ , MIT Press.

MacDonald (1997), R. “What determines real exchange rates? The long and short of it”, IMF Working Papers 1997/21, International Monetary Fund.

MacDonald, R. (1996) “Panel unit root tests and real exchange rates”, Economic Letters, Vol. 50, 7–11.

MacDonald, R. and J. Nagayasu (1998) “On the Japanese yen-U.S. dollar exchange rate: a structural econometric model based on real interest differentials”, _Journal of the Japanese and International Economies_ , Vol. 21, 75–102.

30
Figure 3.10 Average years of schooling of employed labor force by gender 2014 and 2015 <!-- Start of picture text --> 10.3<br>10.2<br>10.2<br>10.1<br>10.1<br>10.0<br>10.0<br>9.9<br>9.9<br>9.8<br>9.7<br>9.7<br>9.6<br>9.6<br>9.5<br>9.4<br>9.3<br>9.2<br>total male female total male female<br>2014 2015<br>Years<br><!-- End of picture text --> _Source:_ National Bureau of Statistics of China 2015b and 2016a.

# Vocational versus academic tracks

A key feature of upper secondary education in China is the dichotomy of academic and vocational upper secondary education. Depending on the track, high school graduates either advance to tertiary education or enter the labor market directly. Through a provincial-level standard examination upon finishing Grade 9, the final year of compulsory education, the placement of students into two tracks directly follows their academic performance in the exam—betterperforming students are able to secure places at academic upper secondary schools. The selection process is highly competitive: nationwide, less than 60 percent of the total upper secondary school enrollment was in academic schools in 2016.

The structure of the post-compulsory education enrollment has gone through fundamental changes during the past decade. In 2007, less than 19 million students enrolled in tertiary education, and this figure rose to nearly 27 million in 2016, an 8 million increase. The scale of this increase is even more astonishing considering that during the same period the youth cohort at ages 15–17 was decreasing (Figure 3.11). In 2016, there were 7.5 million entrants into tertiary education, compared with 5.7 million in 2007, while during the same period, the number of graduates from academic senior secondary education largely remained constant—7.88 million in 2007 versus 7.92 million in 2016.<sup>9</sup> 31
– such as the East Asian financial crisis or the 2008‐2012 Great Recession – and time‐invariant country characteristics may also influence policy choice, we add country ( _θk_ ) and year dummies ( _τt_ ) to equation (7). Indexing countries by _k_ , we get:<sup>8</sup>

# **3. Data**

In this study we analyze the association of World Bank operations with the quality of economic policy. Following Smets and Knack (2016), one of our dependent variables measures the quality of economic management, as derived from the World Bank’s CPIA ratings.<sup>9</sup> The CPIA assessments are subjective ratings of 16 policy indicators, grouped into four “clusters”, updated annually by World Bank staff.<sup>10</sup> Possible scores on each indicator range from one to six, including half‐point increments (e.g. 3.5). For this analysis, our main dependent variable is the simple average of CPIA clusters A and B, which reflect policies aimed at achieving economic growth and poverty reduction by means of encouraging the market mechanism, and directing government activity towards rule‐making, the regulation of markets, and compensation for market failures such as natural monopoly. Cluster A covers macroeconomic and debt policy, and cluster B structural policies, including those about trade, the financial sector, and the regulation of private enterprise.<sup>11</sup> The mean score of this CPIA‐based policy quality indicator in our sample is 3.6, with a standard deviation of 0.48.<sup>12</sup> On the one hand, the CPIA is arguably an appropriate policy measure because its content reflects the views of World Bank management and staff regarding what policies are most conducive to poverty reduction and the effective use of aid resources. On the other hand, the CPIA indicators reflect the subjective judgments of World Bank staff. However, they are correlated with conceptually‐related objective indicators, as well as with subjective indicators produced by other organizations. The CPIA cluster A and B average is correlated in the expected direction with macroeconomic indicators such as inflation (r = ‐0.12) or government debt (r = ‐ 0.43). It is also strongly correlated with the ‘economic risk’ composite of the International

> 8 Since equation (8) indicates that residuals are serially correlated, we cluster standard errors at the country‐level in our estimations.

> 9 See the definitions of variables, and their sources, in Table 13, p. 29.

> 10 See OPCS (2009) for a detailed description of the 16 indicators and the assessment procedure used to generate them.  A brief description of the CPIA is given in Annex B.

> 11 In addition to the macroeconomic and structural policies of clusters A and B, the CPIA includes cluster C on human development and social and environmental policies, and cluster D on public sector governance and institutions.  See Bogetic and Smets (2017) and Smets and Knack (2018) for an analysis of the impact of World Bank lending on these clusters.

> 12 For further detail on summary statistics see Table 14, p. 29.

6