rafmacalaba/datause-encoder-data huggingface dataset:
iff) with respect to Ait whereas, equation (9b) is obtained by implicitly differentiating ∆τ it+1 as expressed in equation (8) with respect to ∆Ait+1 for given initial levels of τ it and Ait.<sup>17</sup> We see that a sector with higher productivity is expected to receive more protection and a sector with a bigger increase (or a lower decline) in productivity is expected to have lower reduction in tariffs despite an exogenous shock common across sectors. Thus, we have reasons to worry about the endogeneity of tariffs with respect to total factor productivity. Accordingly, in the empirical studies where the sector level productivity is regressed on the sector level tariffs that are assumed to be exogenous, there will be a direct reverse causality problem. In the case of the firm level productivity being regressed on the sector level tariffs, this problem will be smaller. However, to the extent that the firm level productivities in a sector differ commonly from the firm level productivities in the other sectors or the more correlated the firm level productivities with the sector level productivities, the worse the endogeneity will be a problem. In the empirical section I use productivity estimates obtained at the firm level that are then aggregated to the sector level using production shares as weights to arrive at representative productivity values for each sector.

Finally, notice that the productivity-tariff linkage above is completely driven by the political economy channel. If political economy is not a concern for the determination of tariffs for a given sector, that is, if the extra political economy weight is null (ω − 1 = 0), then productivity has no effect on the tariffs. On the other hand, again because of political economy, the reduction in tariffs varies across sectors based on the productivity differences regardless of the common shock.

# 2.3 Government Perceived Learning-by-Doing

In this sub-section, I provide more structure for the liberalization process and the government perceived benefit of protection by introducing an infant industry argument. In developing countries, learning-by-doing and infant industry arguments have been a major motivation for protection which should be accounted for. Grossman and Helpman (1995) provide a comprehensive survey of the literature on technology and trade and indicate that “some countries might wish to use trade or industrial policies to alter their patterns of specialization... The short-run income loss for such a country would be small, while the policy would generate a permanent boost to its productivity growth...” (p. 1297). However, it should be noted that import substitution policies and infant industry protection have been largely abandoned especially after the 1980s and some critics have

> 17Naturally, we also have ∂τ it/∂Ait > 0 and ∂∆τ it+1/∂∆Ait+1 > 0.

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# **6.4 Life expectancy at older ages**

Hazan (2012) argues that life expectancy at birth exhibits more variation across economies and cohorts due to high infant and child mortality. He suggests that life expectancy at age five will be more appropriate to capture true effect of health on human capital investment decisions. Although the model laid out in section 4 shows that life expectancy at age 5 or 10 is endogenous because it will reflect parental decisions on health investments, it is still useful to examine the sensitivity of our estimates to alternative estimates of life expectancy. As life expectancy at exact ages is not available before 1950 for many economies, we use samples of birth cohorts beginning in 1945 and 1940. The life expectancies are only available for birth cohorts in 5-year age ranges.

Table 5 reports that the effect of life expectancy at higher ages on time spent in school is consistently positive and statistically significant, unlike Hazan’s (2012) finding of no relationship. A one-year gain in life expectancy at age 5, 10, and 15 increases time in school by 0.185 year, 0.17 year, and 0.129 year, respectively. Parents’ life expectancy turns out to be positive and statistically significant in specifications with life expectancy at birth, but not consistently for life expectancy at higher ages. Note that the value of parental life expectancy falls, at least in precision, as we measure life expectancy at higher ages, which probably indicates that parents’ endowment is not as crucial as it is in early childhood.

# **6.5 Robustness checks**

This section incorporates several economy-cohort specific measures to check the robustness of the effects of life expectancy at birth on completed years of schooling. To investigate the quality of institutions and political regimes at the time of one’s birth, we utilize a polity measure that ranks economies by their strength of democratic institutions. We use Polity IV data, which 18
# **V. A broader macroprudential surveillance**

As lessons began to emerge from the current crisis, the G20 leaders envisioned a redesign of the regulatory and supervisory frameworks for financial systems. As part of the redesign, suggested principles include a heightened micro level supervision supplemented by a macro prudential framework that requires a more inclusive approach to credit supervision. Embodied in Basel III, this ultimately would promote more prudent risk taking and a more accurate reflection of system-wide credit leverage in the banking sector.

However, the necessity for a wider oversight scope has become more evident in light of the current global financial crisis, where seeds of the crisis were sowed outside the banking sector.

- **The scope of prudential oversight** . Besides banks, the stability of financial systems is also affected by the operations of other creditors that are seldom regulated and supervised. The genesis of the subprime crisis has raised awareness on the systemic importance of “shadow banking systems.” However, other sources of non-bank credit that “shadow” the banking system can be as important in other latitudes. For example, in some countries of Latin America this is evident by considering large retail stores disbursing lines of credit, which at times comprise a large segment of the credit market and that is neither regulated nor subject to oversight. Although they do not intermediate deposits, they are interconnected with the financial system through a variety of channels:

- i. They obtain loans from the banking sector.

- ii. They fund indirectly from the banking sector, when banks hold significant portions of their commercial papers or securitization tranches; these securities are backed by pools of credits granted with unknown credit standards.

- iii. When banks act as underwriters or enhancers of their securitizations (exposing banks to more subtle, although not least important, types of risks).

- iv. Since they provide credit to banks borrowers, particularly families, typically by issuing their own non-bank credit cards.

On the ground of potential spillover effects and to shed more transparency in this area, PCRs could include credit data from large non-bank creditors. Although they do not fall under the sphere of bank regulators and supervisors, preliminary lessons of the current crisis suggest that the scope of financial regulation should be widened to account for the operations of non-bank systemic players. These non-bank creditors may overstretch the repayment capacity of bank borrowers, making the banking sector vulnerable to their credit policies. This is of paramount importance if they are heavily intertwined with their activity as creditors, sponsors, investors or enhancers of their ABSs.

- **Macroprudential indicators** . In addition to serving the supervisors’ need to monitor an array of indicators that summarize bank’s financial and economic condition, indicators must also be constructed to monitor the stability of the overall financial system.

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used as a new metric for the monitoring and evaluation of the efficiency of investment and to better understand the benefits of improving accessibility and resilience of roads.

- **Identifies socioeconomic benefits of rural infrastructure investments.** Economic analysis of rural transport investments has traditionally focused on reduction of road user operating cost by improving road surface conditions. The framework presented here allows to bring social benefits to rural transport investment as gain in accessibility to human capital development centers such as for health and education. At the same time, introducing the gain in accessibility to markets by productive agricultural land, the method identifies a new metric for economic growth that goes one step beyond the more traditional methods focusing on measuring road operating cost.

- **Supports policy dialogue on rural development and resilience.** The data, maps, and results from this model engage decision makers, allowing them to identify areas with lower accessibility and high climate vulnerability. This information can inform strategic goals and action plans for the national and provincial governments.

The analysis has also identified the next avenues for further improvement of this model. The first is to include supply chain resilience, based on enterprise and business census. This will bring an additional dimension to the analysis as it will evaluate economic losses of business interruption. This method has already been developed in the context of Tanzania (Colon, Hallegatte, and Rozenberg 2019), and it is another step toward operationalizing some of the analysis included in the report of Hallegatte, Rentschler, and Rozenberg (2019). There is also potential to assess poverty impacts of road disruptions using the methodology by adding the results of the model to poverty maps. Another avenue is to scale up the analysis for all 27 provinces across Cambodia to produce whole-network analysis which can inform government strategies for closing the infrastructure and accessibility gaps.

# **5. Acknowledgments**

The authors would like to thank the counterparts at the Ministry of Rural Development of the Royal Government of the Kingdom of Cambodia, especially the management and technical staff of the Ministry of Rural Development, Department of Rural Roads, and General Technical Department for their collaboration and support. We would like to thank World Bank colleagues who provided very useful advice and insights during data collection and analysis, especially Bowen Wang, Transport Analyst; Nareth Ly, Senior Health Specialist; Voleak Van, Health Analyst; Fata No, Education Specialist; Hideaki Hamada, Senior Financial Sector Specialist; Antoine Bavandi, Senior Financial Sector Specialist; and Catherine Ansell, Financial Sector Specialist. The authors also appreciate valuable comments provided by peer reviewers Dung Anh Hoang, Senior 19
In a first stage, we assume that the occupation polarization is due only to changes of the parameters (“utility” returns) linking individual characteristics to specific occupations. To do that individual characteristics are kept at the levels they had in the initial year, but the parameters are changed from those of the initial year to those of the final year. This simulation assumes, for example, that college graduates at the end of the period have a different probability than college graduates at the beginning of the period to be in high‐skill occupations, but that the share of college graduates in the pool of workers is constant in the two periods. This simulated occupation structure is then used to calculate a simulated (“intermediate‐parameters”) earnings distribution. This is done by recalculating the earnings for each worker who has switched occupation with the returns to the occupations kept equal to those of the initial year.

In a second stage, we assume that the occupational structure changes only because the characteristics of the labor force are shifting. This exercise produces another simulated (“intermediate‐characteristics”) earnings distribution and accounts for the share of occupational changes and earnings inequality driven by factors such as changing age and gender composition of the labor force or educational upgrading, under the assumption that the probability of having a certain occupation conditional on individuals’ characteristics is the same as in the initial year. In a final stage, the model allows to simulate changes in earnings driven directly by changes in returns to specific occupations and workers (“intermediate‐ returns”).

Comparing the position of these three simulated intermediate earnings distributions – the “parameters”, the “characteristics”, and the “returns” one – with respect to the initial and the final distribution allows to decompose the total distributional change into parts that are associated to each of these specific factors.

Using repeated cross‐sections of household surveys for Germany, Poland and Spain from the early 1990s to 2013, this paper shows that this period was characterized by a significant increase in earnings inequality and polarization of occupations. We find that holding everything else constant, the occupational _parameters_ account for at least 44 percent of the increase in the P90/10 earnings ratio over the considered period and group of countries. In terms of the P50/10 earnings ratio, the rise of low‐skill occupations and the decline of mid‐skill ones account for more than its full observed rise; so the model over‐estimates the role of occupational change for the increase of inequality at the bottom part of the distribution. In contrast, the decomposition model shows that simulated occupational shifts explain none or very little of the inequality increase at the top of the income distribution. These findings confirm those 5
Appendix box 2. Sivadasan’s modified Levinsohn and Petrin methodology This section is taken from Appendix B of Sivadasan (2009) where he reports his methodology. This has been appropriately adjusted for our panel analysis over the 1994-2009 period. We assume that our value-added production function v = f(1,n,k,w) is part of a more general production function separable in all intermediate inputs Y = g(f(inkw),h(lw)), where I’ is a vector of intermediate inputs. Let : be one intermediate input, which LP assumes has a demand function of the form 4; = t:(@z,kiz). Other possible state variables not explicitly included in the above input demand function include prices of inputs and output(s). We assume input and output prices are fixed across firms within the same industry, but allow for the common prices to change over time by indexing the input demand function by t.t Assuming monotonicity, 1.e., input choice is strictly increasing in productivity for all relevant capital levels, the input demand function can be inverted to yield a representation for the unobserved productivity: wi = w;(ti,kit). Then, assuming the monotonicity condition holds, we can estimate the coefficients on the labor inputs by estimating the following regression: vit = Bile + Bune + Ot (unkiz) + it (Step 1) where i(tiki) = Pickin + wr(tink). We use quantity of electricity consumed ¢; as the input proxy 1. We specify wy (ti,ki,) as a polynomial function in its arguments (including the absorbed intercept term and dropping the firm index i for expositional convenience) as follows: wr(Cek:) = ace + M126? + argc? + arake + aiskece + ar6kecp +ay7k? op wigk? Ce +E ayok; +aoito¢r + Mootec? + wastec? + woataky + aastakece + aactokic? + Wort? + aogtakt Gr + Waotok; +w3it3Gr + Wsatsce + Wastsct + Maatsky + agstakics + 126} + aaetakec? +agzt3kP + aggtsk¢Gy + agotgk; where tz = 1 for years 1990, 1991 and 1992, and 3 = 1 for years 1993, 1994 and 1995.
principals. Nevertheless, most of the information available relies on small samples and cannot be assumed to be representative for statistical inference.

Results from qualitative interviews conducted by Portaluppi Castro (2012) and FabaraGarzon (2013) show, for instance, that teachers identify several problems with the training courses offered by the SIPROFE program. Focus group participants indicated that the training programs offered under SIPROFE lacked relevance and that courses were theoretical and provided little space for dialogue, analysis, and contextualization. Participants also indicated that there was little follow-up (for example, mentoring, coaching, feedback mechanisms) after training courses were completed. Some participants complained about the quality and pedagogical approaches of the trainers.

On the positive side, participants agreed that the efforts made through the SIPROFE program are a clear signal of the interest of the state in teachers’ professional development. Participants also indicated that some teachers participated in the program to be promoted. Finally, participants suggested that the program could be improved by (a) having a better diagnostic of teachers’ training needs and offering progress to address them; (b) complementing training courses with more follow-up support, such as coaching and mentoring; (c) making it easier to obtain permission to attend training during working hours; and (d) making courses more interactive, flexible, and relevant to local contexts.

# **III. Teacher assessments in Ecuador**

The _Ser Maestro_ assessment, the government’s main instrument to quantify the level of knowledge of Ecuadoran teachers, is implemented by Ecuador’s National Institute of Evaluation and is an important pillar of the country’s teacher evaluation model. The assessment measures the level of knowledge of teachers in the field they teach, including specific topics they must master to promote an adequate learning process in their subjects. The assessment is applied to all teachers in active service (that is, it is census-based). The last assessment was conducted in 2016.

The assessment includes 19 knowledge areas ( _Saberes_ ) that include subjects spanning Ecuador’s three main educational cycles: Early Education (pre-K), Basic General Education (BGE) (grades 1 to 10), and Unified General Baccalaureate (UGB), equivalent to high-school (grades 1 to 3). Each subject includes several “curricular areas,” which assess the level of knowledge of the teacher in specific aspects of the national curriculum within the subject matter.

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learning, there is no significant difference in their effects. Interestingly, the average treatment effect of the Coaches Group increases as the content evaluated (or items) becomes more complex (see column 2). This implies that, in these cases, the Coach treatment was more effective in promoting higher-order skill development in students than either the four-hour training session or the provision of the structured curriculum unit.

The mechanism through which both the Sequence and Coaches treatments appear to increase Science test scores involves an increase in the percentage of correct answers. Students in the Sequence and Coaches Groups exhibit 10 percent more correct answers than students in the Control Group (panel C). The treatments also reduce the number of omitted and incorrect answers. This suggests that the interventions did not only increase Science learning (which is shown in both the increase of correct answers and the reduction of incorrect ones), but also motivated students to answer more questions.<sup>22</sup> These findings are especially important given the large difference in treatments’ costs. While the cost per student for the Control Group was 1.4 dollars, for the Sequence Group the cost per student was 4.6 dollars and for the Coaches Group it was 14.7 dollars.<sup>23</sup> This includes the costs of hiring and training the tutors, teacher seminar materials, as well as curriculum unit design and printing. The estimates of <u>table 6</u> allow us to calculate the cost-effectiveness of the program. Providing teachers with short-term training complemented with a structured curriculum costs (per student) 0.84 dollars per 0.1 standard deviations, whereas providing teachers with the same short-term training complemented with ongoing coaching through the use of the same structured curriculum costs (per student) 2.28 dollars per 0.1 standard deviations. In other words, it costs 0.84 dollars to move a child from the 50th to the 53th percentile, approximately, in the first intervention, and 2.28

> 22The results of estimating <u>equation (1)</u> without controls are consistent with those reported in <u>table 6</u> and are available upon request.

> 23 Our calculations correspond to 2016 US dollars.

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**Table 5**

||1<br>α|2<br>α|3<br>α|
|---|---|---|---|
|∆_Agr_|-0.038|-0.006|**0.260**|
||(-0.78)|(-0.19)|(3.83)|
|∆_Manu_|-0.042|0.006|**-0.159**|
||(-0.94)|(0.20)|(-2.51)|
|∆_Transp._|**-0.152**|**-0.068**|-0.098|
||(-3.24)|(-2.04)|(-1.49)|
|∆_Commerce_|**-0.151**|-0.012|**0.323**|
||(-2.51)|(-0.27)|(3.80)|
|∆_Fin._|**0.303**|**0.727**|-0.196|
||(-1.77)|(5.93)|(-0.81)|
|∆_Gov._|**-0.446**|**0.248**|0.070|
||(-5.88)|(4.57)|(0.65)|
|∆_Petrol_|0.695|**2.019**|-0.558|
||(1.33)|(5.41)|(-0.76)|

_Note:_ ∆ indicates a variable in first differences. _Source:_ Authors’ own calculations.

The columns of β s’ are the cointegrating parameter vectors or, in other words the β s’ spans the cointegration space. The coefficients of the α s’ can be interpreted as adjustment coefficients measuring the relative importance of a deviation from equilibrium on a given endogeneous variable.

# **4.2  Identifying the Cointegration Space**

The significance of the adjustment parameters can be used as a first guideline for identifying cointegration relationships. The first cointegration relationship suggests a relationship between the public sector, the non-oil industrial sectors and private service sector components. Agriculture and oil do not seem to be part of this first cointegration relationship, since the agricultural sector and the oil sector enter the cointegration relationship with a coefficient close to zero and further have insignificant adjustment coefficients.

The second cointegration relationship seems to be centred around oil sector and the public sector. This relationship could be interpreted as a “petrolization” equation describing the capitalization of the public sector through rent from nationalised oil 8