rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1:  Effects of discrete indicators of trade liberalization on exports**

||Dependent<br>|variable is l<br>|og(imports)<br>|Dependen<br>i<br>|t variable is<br>mport-dumm<br>|a discrete<br>y<br>|
|---|---|---|---|---|---|---|
||All<br>products|Textile, a<br>leather|pparel, and<br>products|All<br>products|Textile, a<br>leather|pparel, and<br>products|
||(1a)|(2a)|(3a)|(1b)|(2b)|(3b)|
|(a) Effects of GSP<br>|on EU imports<br>|<br>|||||
|GSP|0.0602***|0.0450*|0.0674**|0.0019*|0.0094*|0.0042|
||(0.004)|(0.020)|(0.025)|(0.001)|(0.004)|(0.005)|
|GSP+|0.0951***|0.0186|0.1407**|0.0078***|0.0017|0.0271*|
||(0.007)|(0.042)|(0.050)|(0.001)|(0.009)|(0.011)|
|EBA|0.1286***<br>(0.005)|0.0643*<br>(0.032)|0.2150***<br>(0.046)|0.0110***<br>(0.001)|0.0136*<br>(0.006)|0.0341***<br>(0.009)|
|- Textile * GSP|||-0.0418*|||0.0080*|
||||(0.017)|||(0.003)|
|Textile * GSP+|||-0.2817***<br>(0.033)|||-0.0598<br>(0.007)|
|Textile * EBA|||-0.2310***|||-0.0325***|
||||(0.036)|||(0.007)|
|- Apparel * GSP|||0.0758***<br>(0.018)|||0.0117**<br>(0.004)|
|Apparel * GSP+|||0.1371***<br>(0.035)|||0.0226**<br>(0.008)|
|Apparel * EBA|||-0.0706<br>(0.040)|||-0.0096<br>(0.008)|
|Observations|15,345,396|3,105,872|3,105,872<br>|15,345,396|3,105,872|3,105,872|
|(b) Effects of AGO|A on US impo<br>|rts<br>|||||
||(1c)|(2c)|(3c)|(1d)|(2d)|(3d)|
|GSP products|0.0313***<br>(0.006)|||0.0062***<br>(0.001)|||
|Apparel provision|<br>0.2198***|0.1526***|-0.0010|<br>0.0211***|.0150***|-0.0003|
|- AP * Apparel|(0.013)|(0.012)|(0.032)<br>0.1605***<br>(0.035)|(0.002)|(0.002)|(0.008)<br>0.0160*<br>(0.009)|
||12,926,320|3,117,620|3,117,620<br>|12,926,320|3,117,620|3,117,620|

_Notes:_ Results are based on fixed effects regressions that include all double interaction dummies: exporterproduct, product-year, and exporter-year. The results in columns (1) include all products in the sample, except for oil, art objects, products with missing MFN tariffs in some years, and products with MFN tariffs equal to zero over the entire sample period. Results in columns (2) and (3) limit the sample to textiles, apparel, and footwear & headwear (HS 50-64). Results in column (3) use footwear & headwear as the excluded category. Standard errors are clustered at the country-product level. Significance at the 10%, 5%, and 1% levels are indicated by *, **, and ***.

Effects at the extensive margin on the right show a similar pattern. They are all estimated positive and statistically significant. Here the effects of GSP, GSP+, and EBA are respectively 0.2%, 0.8%, and 1.1%. These values indicate the percentage point increase in the probability a particular product is exported from an eligible exporter to the EU once the respective GSP scheme takes effect. While these numbers are small, to put them in perspective one should compare them to the unconditional probabilities. On the sub-sample of countries and products that are ever GSP eligible, the average export market penetration is just below 15% and for GSP+ country-products it is 16.6%. It still suggests that the effects are rather small, but 23
25

**Table 4.15: Decomposition of Extreme Poverty Incidence by State, 1985-93 (Poverty Line=N197.71)**

|||1985/86|||1992/93||
|---|---|---|---|---|---|---|
|**STATE**|**P0**|**P1**|P2|**P0**|P,|P2|
|Lagos (Urban)|0.4|0.2|0.2|1.1|0.4|0.3|
|FCT|0.0|0.0|0.0|1.7|2.9|3.4|
|Anambra|2.9|0.9|0.8|2.9|0.6|0.5|
|Bauchi|7.9|12.6|11.9|6.4|7.6|7.6|
|Bendel|3.5|1.4|1.1|1.6|_0.5_|_0.5_|
|Benue|6.4|6.7|5.3|4.7|5.8|7.5|
|Bomo|6.9|7.1|7.3|6.0|6.3|6.3|
|Cross Rivers|2.4|2.5|6.8|5.3|5.2|5.4|
|Gongola|5.4|5.7|6.8|3.3|1.7|1.7|
|Imo|3.3|1.2|1.0|3.9|1.6|1.3|
|Kaduna|10.1|9.0|7.9|4.4|2.1|2.1|
|Kano|10.8|7.2|5.9|12.6|10.6|9.3|
|Kwara|1.9|0.7|0.5|2.7|2.4|2.7|
|Lagos (Rural)|1.2|0.6|0.4|4.1|2.7|2.1|
|Niger|5.1|16.3|23.8|3.3|4.4|5.2|
|Ogun|1.6|0.7|0.6|1.4|0.5|0.3|
|Ondo|5.3|5.1|4.3|3.6|2.4|2.4|
|Oyo|0.8|0.0|0.0|4.8|1.9|1.8|
|Plateau|4.1|4.3|4.8|6.5|7.9|8.5|
|Rivers|0.7|0.1|0.1|3.3|2.7|2.3|
|Sokoto|17.1|17.8|14.8|17.4|29.4|28.5|
|All**Nigeria**|**100.0**|**100.0**|**100.0**|**100.0**|**100.0**|**100.0**|

Source: NCS, 1985/86 and 1992/93.

# **4.3 Evolution of Poverty and Household Composition**

Table 4.16 shows the incidence of poverty by age groups. It should be noted that in the rural areas, the largest decline in headcount of 15.4 percentage points has been in the 66 and over age cohort, followed by the 36 to 45 age group with 14.7 percentage points. The 16 to 25 age cohort showed a 1.1 percentage points decline in headcount. The large decline in the headcount of the 56 and older heads of households in the rural areas can be explained by the fact that most of these are households whose heads are retired and returned to the rural areas thus helping to reduce poverty in these age groups.
R&D or technological upgrading makes no sense if Russian firms have no access to sophisticated customers who are demanding these higher value added services and financial resources -- either from commercial banks or retained earnings -- to finance the investments that would be required to serve these customers. The manner in which these linkage/value chain issue are addressed will have a major impact on whether Russia makes a successful transition to a knowledge economy.

- **Encourage Educated Russians to Live, Work and Invest in Russia.** Applications to elite technical universities in Russia are at an all-time high. Normally, this would be a positive development, a sign that students believe that there is a productive rewarding future in Russian science. But anecdotal survey evidence suggests that students are enrolling in elite Russian technical universities because they believe it is the best way to get a visa for entry to the US and a job with leading US high tech firms. Until Russia manages to convince these students to use their skills and training domestically -- in other words until it creates a business climate and business opportunities that discourages wholesale brain drain -- Russia will have difficulty making a successful transition to a Knowledge Economy.

- **Improve the Business Climate.** Improving the business and investment environment so that both Russian and foreign businessmen are willing to invest in Russia is an absolute prerequisite for supporting the transition to a Knowledge Economy. Without improvements in this area, including measures designed to ease the entry of new high tech firms, it is unlikely that Russia will be able to tackle such items as reversing the brain drain, establishing more productive linkages between SMEs and larger national and international firms, or commercializing Russian innovations.

Why is it important for Russia to address these challenges and answer these questions?

- The existing S&T base is a wasting resource that has already eroded significantly and is in danger of continuing to erode rapidly. Russia cannot maintain a world class scientific establishment if it is built on a declining industrial base. As one observer from INTAS noted recently, "To date, rather than the S&T sector _pulling up_ the rest of the economy, what we have witnessed in p ost-Soviet Russia is the rest of the economy _pulling down_ the S&T sector." [emphasis in original.]

- Unlike the fixed capital stock sunk into obsolete, poorly located industrial facilities, the existing scientific human capital stock is potentially much more flexible and mobile. This can be both a virtue and a defect. Like financial resources, it can be a source of capital flight or brain drain. But it can also be a potential engine of economic growth and private sector development if properly

Daniel Berger, "The Russian Science & Technology Sector: _Quo Vadis?,"_ unpublished manuscript, Novemrber 2000.

_-4 -_
# **<u>Appendix Table 4: Results, Decomposing Property Value into Components, OLS</u>**

||<br>(1)|
|---|---|
|Area in sq. m., Gasabo district|25.17***<br>(0.802)|
|Area in sq. m., Kicukiro district|5.288***<br>(0.553)|
|Area in sq. m., Nayarugenge district|45.49***<br>(1.985)|
|Volume of buildings in cubic m., Gasabo district|38.64***<br>(0.764)|
|Volume of buildings in cubic m., Kicukiro district|22.24***<br>(0.793)|
|Volume of buildings in cubic m., Nayarugenge district|35.83***<br>(2.139)|
|Gasabo district|-1,027*|
||(611.4)|
|Kicukiro district|8,030***<br>(671.5)|
|Nayarungenge district|-10,860***<br>(1,088)|
|Observations|12,592|
|R-squared|0.565|

Standard errors in parentheses: *** significant at 1%; ** significant at 5%; * significant at 10%.

30
as well as various empirical studies analyzing the impact of trade reforms on productivity. These studies normally found that the “average” effect of increased competition is positive (Tybout and Westbrook 1995, Fernandes 2007, De Hoyos and Iacovone 2006, Nickell 1996). In other words, our model extends Aghion, Burgess, Redding, and Zilibotti (2004) and Aghion and Griffith (2005) by making their predictions more in line with previous empirical studies but mantains intact its central feature, which is that liberalization has an unequal effect and advanced firms benefit more from it.

# 4 Econometric Analysis

The econometric analysis is split into two sub-sections. In the first we briefly describe the data while in the second section we analyzed the relationship between liberalisation and productivity. In particular we will build or empirical model in order to explore the potential firm heterogeneity and allow the impact of liberalization to be dissimilar for different types of firms.<sup>20</sup>

## 4.1 Data

The data used in this analysis are provided by INEGI and cover the entire period of NAFTA reforms, 1993-2002. The data are collected at plant level, and individual establishments are identified through a unique key that allow us to build a panel.<sup>21</sup> After having cleaned the dataset the number of establishments varies between 6,500 and about 5,000 because of attrition.<sup>22</sup> The sampling structure of the survey is such that the data tend to be more representative of larger firms, overall the survey covers 85% of industrial output excluding the“maquiladoras”. The survey frequency is yearly and the variables collected cover various aspect of the firm’s operations: workers, wages, electricity usage, intermediate inputs usage<sup>23</sup> , production, inventory, sales<sup>24</sup> , investment in different types of physical assets, investment in R&D and in technology transfers.<sup>25</sup>

> 20We will focus on one specific dimension of firm heterogeneity, namely productivity.

> 21This work was carried out while I was in Aguascalientes and in compliance with Mexican Laws protecting the confidentiality of the data.

> 22It is important to note that entry is not systematically captured by INEGI as the sample is refreshed only once every ten years. However a specific department of INEGI tends to follow up on newspapers and other sources of information the entry of new plants, in this way the most important new firms tend to be included even if this is not systematic.

> 23The intermediate inputs used are split into imported and domestic inputs.

> 24As for intermediate inputs also the sales are split into domestic and foreign sales.

> 25For more details on the data see appendix A or refer to Iacovone (2008).

14
# Introduction

1.1 This paper presents a transparent, user-friendly framework for analyzing macroeconomic policy issues in Kenya. It adheres to flow-of-funds consistency, covers the fiscal, monetry, national and private sector accounts, and balance of payments, and is an analytical tool for grappling with the economic relationships and institutional realities which ultimately dictate the design, implementation and effectiveness of macroeconomic policies. The model is programmed in JAVELIN.

1.2 A recent report concluded that Kenya's "... recovery ... is somewhat fagile as it has relied on foreign savings, leaving the economy vulnerable to external shocks, and the remaining domestic macroeconomic imbalances, unless corrected, threaten to undermine recent economic and stabilization gains." (World Bank, 1990). The model presented in this paper is usefil to the policy analyst who may wish to project the broad magnitude of these imbalances under different fiure growth assumptions. With these results in hand, the policy analyst may choose to revise growth targets in an ad hoc, piecemeal manner until acceptable" imbalances are obtained and then focus on mobilizing inflows to fill the resource gaps. Alternatively, the policy analyst may wish to determine what levels of imbalances are sustainable, financeable and consistent with other development goals. Here judgment about institutional/political feasibility is indispensable. But so too is a map of inviolable accounting macro-relationships, their evolution under different behavioral assumptions, and the technical limitations they impose on policy selection and effectiveness. The model developed in ftis paper is such a map which could also demonstrate
9 measures of total factor productivity (TFPlow and TFPhigh).'<sup>0</sup> Initial diagnostic tests pointed toward a first-order integrated process, so that all variables are expressed as differences in the regressions.

The estimation results in Table 4 indicate that the ratio of public to private investment is positively correlated with total factor productivity growth, and generally significant, which suggests that public capital has exerted positive externalities for the private capital that was not crowded out. As before, the public-private investment ratio was included in squared form among the explanatory variables to test for a possible nonlinearities in the relationship, but this yielded insignificant coefficients in all cases.

**Table 4: Public Investment and Total Factor Productivity in Mexico**

**(1.a)** A(TFPhigh) = -0.010 + 0.167 A(IG / **IP)** _(0.005)_ (0.059)* R = 0.25, D.W. = 1.62, F-stat = 7.93 **(l.b)** A(TFPhigh) - -0.008 - 0.004 DUM + 0.167A(IG **/ IP)** - 0.102DUM* A(IG **/IP)** _(0.005)_ (0.010) (0.056)* (0.041)* R<sup>2</sup> '=0.43, D.W. =1.51, F-stat=5.19 (2.a) A(TFPlow) = -0.009 + 0.134 A(IG/I) (0.004) (0.052)* R<sup>2</sup> =0.21, D.W. =1.71, F-stat = 6.54 (2.b) A(TFP1ow) = -0.007 - 0.003 DUM + 0.137A(IG / **IP)** - 0.092DUM* **A(IG** / lP) _(0.005)_ (0.009) _(0.050)*_ (0.037)* R<sup>2</sup> _=_ 0.40, D.W. = 1.51, F-stat = 4.66

Source: Same as Table 3 and Bosworth (1997). Each regression is based on 26 observations referring to annual data from 1970 to 1995. Terms in parentheses are standard errors. Asterisks indicate significance at 5%.

Finally, Chow tests pointed to a significant break in the mid- 1 980s. In contrast to the earlier results, this time the coefficients associated with the Dummy variables indicate a significant change in the relation between productivity and the publicprivate investment ratio. In particular, the results indicate that the positive relationship between both variables appears to have weakened after 1985. This comes as a surprise again: as discussed earlier, the existence of a positive relationship between both variables can be interpreted to imply a certain complementarity between public and private

> 0 Both measures of total factor productivity are aggregative concepts derived in the standard manner as a residual after netting out the contribution of factor accumulation. To derive these residuals, output is measured by GDP, labor input is measured by the economically active labor force, and the physical capital stock is constructed using a perpetual inventory method with two alternative depreciation rates; 6% (low) and 12% (high).
# **I. Introduction**

In-service teacher training is an essential component of Ecuador’s teachers’ professional development strategy. However, available training interventions offered by the state do not necessarily respond to the teachers’ skills gaps. This occurs because available training programs do not systematically rely on diagnostics of the teachers’ needs. This paper proposes a roadmap for improving the design of in-service teacher training in Ecuador using available data from student and teacher assessments. Current teacher assessments in Ecuador have an active cognitive component capable of assessing the level of knowledge of teachers in the field they teach. They provide valuable information for determining the specific areas of knowledge that teachers have problems with. Coupling such information with available data on student academic achievement, the state could develop not only more pertinent in-service training programs, but also target interventions to teachers that serve the student populations with the greatest needs.

Many countries in Latin America and the Caribbean (LAC) face shortages of good teachers, which undermines the capacity of their systems to improve student achievement in the short, medium, and long term (Rivkin, Hanushek, and Kain 2005; Hanushek and Woessmann 2006; Figlio and Kenny 2007; Chetty, Friedman, and Rockoff 2014). Individuals who become teachers in LAC, most of whom are female, tend to be socioeconomically vulnerable and to underperform in university entrance exams compared to the average university student (Bruns and Luque 2014). Many structural factors explain this phenomenon, such as the lack of incentives to choose teaching as a profession and high turnover of teachers (Ingersoll 2004; Woessmann 2011; Muralidharan and Sundararaman 2011). Moreover, salaries for teachers in the LAC region tend to be substantially lower than in the average profession (Moura-Castro and Ioschpe 2007), which limits the capacity of the sector to attract the best professionals.

Despite remarkable progress in teachers’ educational attainment (at present, most teachers in the LAC region have completed a tertiary degree), graduates from the teaching profession enter classrooms with significant cognitive and pedagogical gaps (Bruns and Luque 2014).<sup>2</sup> These deficiencies weaken education systems in the region, as teachers’ cognitive skills influence the achievement of their students (Hanushek, Piopiunik, and Wiederhold forthcoming).

> 2 According to data from Ecuador’s Ministry of Education (MINEDUC), the public system of primary education in Ecuador employs 163,999 teachers. Of this total, 81 percent have a bachelor’s degree in education, 14 percent have a postgraduate degree (mainly in education), and the remaining 5 percent are not college graduates.

2
**_Table 2: Types of transitions between Market Structures_**

|**Transitions between**<br>**Market Structures**|**East**<br>**Asia &**<br>**Pacific**|**Europe &**<br>**Central**<br>**Asia**|<br>**Latin**<br>**America &**<br>**the**<br>**Caribbean**|**Middle**<br>**East &**<br>**North**<br>**Africa**|**North**<br>**America**|**South**<br>**Asia**|**Sub-**<br>**Saharan**<br>**Africa**|<br>**TOTAL**|
|---|---|---|---|---|---|---|---|---|
|**VIU to SBM**|22|27|18|17|0|7|30|121|
|**SBM to WRC**|5|26|3|1|1|1|1|38|
|**VIU to WRC**|2|16|10|1|1|0|0|30|
|**WRC to SBM**|0|0|1|0|0|0|0|1|
|**SBM to VIU**|0|8|1|0|0|0|0|9|

Source: (Akcura 2024)

# **_3.2 Transition Drivers_**

While countries followed a wide range of paths to transition between different market structures, most market movements observed over the past three decades were from state-owned VIU into the SBM. This transition route reflects the standard reform model, which placed the SBM as the first step before moving into more competitive structures. Historically, SBM has been favored by countries as it allows governments to retain some control, thought to be essential to meet certain political and social objectives. In addition, some markets do not yet meet the preconditions to transition to more competitive market structures.

The historical global mapping of market structures highlights several overarching factors that have prompted countries to transition from a VIU to an alternative market structure. These factors include the following: _Sector performance issues_ : In the early 1990s, many public utilities in developing regions experienced inadequate financial and operational performance. Revenue flows were insufficient to cover a reasonable share of investment costs, as well as O&M due to multiple factors including low tariffs, revenue collection and high losses. Technical, operational and maintenance issues, as well as management and staff performance, were also causing frequent plant breakdowns and unreliable service. In this adverse scenario, many countries started a reform process in the power sector, as a first step, followed by introducing IPPs.

_Large power demand growth_ : During the same period, electricity demand in developing countries was on the rise, with a projected growth rate of 7 percent per annum. This affected countries with otherwise wellperforming utilities, for example, the Republic of Korea, Costa Rica, Thailand, and Morocco among others (Escay 1990).<sup>13</sup> Additional growth in GDP required much larger additions of electricity per unit of output because of the ongoing process of industrialization, which in turn meant large investment needs, estimated at about $100 billion per year in developing countries at the time. For context, concessional financing from multilateral banks to the power sector in emerging markets amounted to $12 billion at the time (Schramm 1993). Private sector investment was seen by many countries as a bridge to finance growing needs of the sector.

> 13 Based on data retrieved from: (Escay 1990).

19
42

**Table 4.28 (Cont...): Targeting Indices by Various Indicators 1992-93 (Poverty Line=N395.41)**

|**Groups and**<br>**Indicators**|**Targeting I**<br>**Additive**|**ndicator for**<br>**Transfers**|**Targeting I**<br>**Multiplicat**|**ndicator for**<br>**ive transfers**|
|---|---|---|---|---|
|**Main Occupation**|**Urban**|**Rural**|**Urban**|**Rural**|
|Profess/Technic.|82.2|70.9|75.76|78.01|
|Admin. Managers|60.5|93.5|80.65|103.91|
|Clerical Relat.|67.5|62.9|76.00|64.49|
|Sales Workers|61.1|64.9|58.52|59.01|
|Service Industr.|64.4|53.5|68.67|55.86|
|Agric/Forest.|89.5|104.1|101.57|104.71|
|Product/Transp.|71.4|78.7|96.78|77.96|
|<br>Manufact/Process.|65.7|42.7|55.11|58.87|
|Others|59.1|55.3|66.70|54.91|
|Student/Apprent.|93.1|70.9|100.37|59.74|
|**State of Residence**|**Urban**|**Rural**|**Urban**|**Rural**|
|Anambra|42.95|27.49|28.86|24.12|
|Bauchi|125.83|144.01|193.71|254.11|
|Bendel|26.77|42.21|31.52|34.30|
|Benue|95.86|107.97|89.83|103.10|
|Borno|95.13|114.38|119.85|126.31|
|Cross River|72.57|95.94|76.25|80.68|
|Gongola|70.60|78.51|82.57|85.30|
|Imo|40.66|37.97|28.51|30.21|
|Kaduna|48.94|62.18|50.70|50.41|
|Kano|106.79|122.46|224.29|186.58|
|Kwara|49.46|90.21|64.45|71.16|
|Lagos|77.29|90.79|100.75|113.61|
|<br>Niger|92.42|130.55|166.09|136.78|
|Ogun|58.90|55.98|73.84|54.79|
|Ondo|53.57|77.08|50.01|63.47|
|Oyo|54.25|50.14|44.04|46.71|
|<br>Plateau|103.99|118.17|123.61|90.13|
|Rivers|85.88|100.11|122.65|105.89|
|Sokoto|160.08|148.62|230.01|202.90|
|FCT|166.56|130.49|140.70|179.71|

# **4.6 Sensitivity Analysis of Poverty**

The poverty line of N 395.41 has been the basis for poverty incidence, depth and severity indices for 1985/86 and 1992/93 periods. However, these poverty measures are dependent on the chosen poverty line. Hence, it would be meaningful to do some sensitivity analysis of the poverty indices over time and cross sectionally by changing the poverty line. This is done in the tables that follow. Table 4.29 shows that if the poverty line ofN 237.246 was used as the poverty line, then only 18.67 percent of the population would have been in poverty. Similarly, by using N 553.574 as the poverty line almost 48.9 percent of the population would have been in poverty.