rafmacalaba/datause-encoder-data huggingface dataset:
12 4. Workers themselves paid for much of the training they received. Of the non SENA-trained individuals working as bookkeepers, secretaries, seamstresses, and electricians, from one-sixth to one-third paid for training in a training institute. Except for SENA-trained metal workers and plumbers, about 20 percent of both short-course and longcourse SENA trained workers paid for additional (non-SENA) institution-based training.

# V. OCCUPATIONAL TRAINING MAPS

# MaDping Training Patterns:

One limitation in presenting results in tabular form, as in Tables 4 - 6, is that much relevant information is lost for those workers that have pursued multiple forms of training. It omits details on the particular combinations of training programs, and their sequences. The results of reverse tracer studies may be presented more comprehensively in the form of "occupational training maps." These are charts that identify, and illustrate diagrammatically, the main sequences (i.e., alternative training paths) workers in particular occupations pursued to reach a given job.

In the following section we present occupational training maps relating to, and extending, our findings for two of the occupation groups considered in Table 4 and 5. In preparation, we outline briefly the salient features of the particular form of training map that is employed in this study. One potential problem associated with training maps is that they can be made very rich in detail; on the other hand, too much information will inhibit clarity. A training map can be constructed in many different ways; in the training maps that follow, we have attempted to strike a balance between detail and clarity. But this balance has been achieved at a cost: much available information that was available (such as whether the worker paid for institutional training) is absent from the maps presented here.
projections (Lutz and Butz, 2014). Government consumption is assumed to fall by 5.6% per period, as during 2000-14, investment is assumed to increase by 10% per period such that it reaches a share of 26.7% by 2040-44. The openness index is expected to gradually improve back to the level of 1990-2000, and inflation is assumed to remain at a comparable high level of 26.6. The projections of oil rents in GDP follow the oil price forecasts of the World Bank’s Commodity Price Forecasts as in Scenario 1, but in this scenario also non-oil resource rents develop in line with the respective price forecasts. This implies a further decline in 2015-19, and a gradual increase to present values until 2040-44, and altogether this is less favorable for the country than a continuation of the current trend, but at the same time a more realistic assumption. The rule of law and corruption indicators remain at the 2010-14 level.

**Table 5** – Values of covariates in Scenario 2

|**Year**|**Educ.**|**Gov.**|**Invest-**|**Openness **|**Infation**|**Oil, 4th**|**Non-oil**|**Rule of**|**Corruption**|
|---|---|---|---|---|---|---|---|---|---|
|||**cons.**|**ment**|||**quart.**|**rents**|**law**||
|2015|40|7.53|16.61|-12.10|26.63|8.18|1.56|17.41|-0.29|
|2020|46|7.10|18.27|-10.91|26.63|10.31|1.67|17.41|-0.29|
|2025|52|6.70|20.10|-8.88|26.63|11.79|1.80|17.41|-0.29|
|2030|58|6.32|22.11|-5.43|26.63|13.48|1.95|17.41|-0.29|
|2035|63|5.96|24.32|0.44|26.63|15.41|2.11|17.41|-0.29|
|2040|69|5.62|26.75|10.43|26.63|17.61|2.29|17.41|-0.29|

Growth forecasts under these assumptions are significantly revised upwards as compared to Scenarios 0 and 1, and are projected to cross the 5% threshold in 2030. Particular to this scenario is that as opposed to the first two scenarios, the contributing factors can—at least to a certain degree—be influenced by policies. The top right graph in Figure 6 displays how the contribution of various factors to GDP per capita growth differs between Scenarios 1 and 2. In particular the influence of human and physical capital accumulation is striking. Also the assumed slight reduction in government consumption and the gradual improvement of the openness measure contribute positively to growth. Merely the tying of non-oil natural resource rents to commodity price forecasts causes a lower contribution of that factor to growth. This latter assumption, however, is important, and more realistic than the presumption of constant non-oil natural resource rents in the upcoming decades.

**Scenario 3:** This scenario differs from Scenario 2 with respect to the adopted institutional characteristics. While in Scenario 2, the rule of law and corruption were assumed to remain constant at the current levels, Scenario 3 shows predicted growth rates that could be materialized given moderate improvements of the institutional quality and the corruption indicators (for a graphical representation of the expected development, please see Figure A.6 in the Appendix).

22
**Table 4: Robustness checks for Probit regression of firm death on enterprise characteristics before crisis**

||Probit|IV Probit|Probit|Probit|
|---|---|---|---|---|
|**Observations**|1779|1618|1726|666|
|**Country Dummies**|Yes|Yes|Yes|Yes|
|**Sector Dummies**|Yes|Yes|Yes|Yes|
|**Month of Interview Dummies**|Yes|Yes|Yes|Yes|
|**Bank Credit**<br>|||||
|Firm has loan, line of credit or overdraft|-0.358***|-2.211***|-0.231**|-0.355**|
|(dummy)<br>|(-2.65)<br>|(-4.25)|(-2.23)|(-2.09)|
|Firm has loan, line of credit or overdraft*Medium-sized firm<br>|0.316<br>||||
|(interaction)<br>|(1.49)<br>||||
|Firm has line of credit or loan *large firm|-0.051||||
|(interaction)|(-0.22)||||
|**Firm Characteristics**|||||
|Firm is medium-sized|-0.327*|0.187|-0.088|-0.061|
|(dummy)|(-1.88)|(1.28)|(-0.84)|(-0.38)|
|Firm is large|-0.102|0.170|-0.115|-0.205|
|(dummy)|(-0.50)|(1.01)|(-0.86)|(-0.90)|
|Age of firm|-0.145*|-0.055|-0.129|-0.105|
|(natural log)|(-1.72)|(-0.72)|(-1.49)|(-0.83)|
|Percent foreign-owned|-0.002|-0.005**|-0.002|0.002|
|(percent)<br>|(-1.18)<br>|(-2.51)<br>|(-0.99)<br>|(0.32)<br>|
|Firm was privatized|0.051|-0.029|0.004|-0.545|
|(dummy)|(0.32)|(-0.23)|(0.03)|(-1.38)|
|Exports|-0.001|0.000|-0.001|-0.005|
|(% of sales)|(-0.66)|(0.02)|(-0.80)|(-1.18)|
|**Firm Performance**|||||
|Firm has no need for external financing|-0.119|-0.662***|-0.091|-0.259*|
|(dummy)|(-1.29)|(-4.52)|(-0.98)|(-1.71)|
|Firm has ISO certification|-0.206**|-0.093|-0.177*|-0.176|
|(dummy)|(-2.08)|(-0.90)|(-1.76)|(-1.06)|
|Firm Growth 2005-2007|0.002|0.001|0.003|0.002|
|(employment growth, %)|(0.75)|(0.27)|(0.93)|(0.51)|
|Firm invested in 2007|-0.029|0.261**|-0.050|-0.043|
|(dummy)|(-0.31)|(2.06)|(-0.53)|(-0.29)|
|**Additional Control Variables**|||||
|Firm has female owner|||0.031|0.018|
|(dummy)|||(0.35)|(0.13)|
|Firm has audited accounts|||-0.058|0.059|
|(dummy)|||(-0.62)|(0.40)|
|<br>Obstacles to doing business|||0.095**|0.039|
|(index)|||(2.09)|(0.58)|
|Level of Competition||||0.020|
|<br> (index)||||(0.19)|
|**Pseudo R-Squared**|0.07||0.07|0.11|
|***,**, * Significant at 1,5, and 10 percent level. T-statistics in parenthese|s.||||
|Source: Authors‘ calculations based upon World Bank Enterprise Surveys<br>Note: See Table 3 for additional notes|||||

42
_5_ experienced more unstable growth with significantly higher inflation rate than the low debt countries.

**Table 1: Growth Performance of High Debt vs. Low Debt Countries (1971-1990)**

||High Debt Countries'<br>(38 countries)|Low Debt Countries<br>(63 countries)|
|---|---|---|
|Average annual real GDP<br>growth rate(%)|2.7|_4.5_|
|Standard deviation of<br>growth rate<sup>2</sup>|16.8|1.7|
|Average annual inflation<br>(CPI: %)|56.4|16.8|

'High debt countries are those whose average foreign debt was higher than 50 percent of their GDP and low debt countries are those with the ratio below 50 percent during 1971-1990. 2 Mean-adjusted standard deviation.

Source: World Bank National Account Database, World Debot Tables, various issues, World Bank Intemational Financial Statistics, IMF One interesting comparison may be the different growth patterns achieved be-tween two resource rich countries: Malaysia and Ghana. At the point of independence in 1957, both countries inherited strong administrative machinery, high education levels and high external reserves. Their major products and expurts were however highly concentrated on commodities: Malaysia on rubber and tin and Ghana on cocoa and gold. Beginning in the late 1960s, Malaysia adopted an export diversification strategy, introducing one major export prmduct roughly every five years: timber, palm oil, oil and gas, and finally manufacturing, financed significantly by foreign direct investments. On the other hand, 99 percent of Ghana's exports were concentrated on primary commodities in 1965, improving only slightly to 92 percent by 1989 (Table 4). While both countries undertook different historical paths, and other factors also have affected the actual growth outcome, the diversified economy achieved more stable and higher annual average growth rate (6.9%) over the period 1965-89, even though both countries started with almost comparable levels of per capita income in 1965 (Ghana US$260 and Malaysia US$330).

Without underemphasizing other factors in the political economy of growth, the point here is that different national risk management strategies: asset diversification and openness to foreign direct investment (liability management) does matter in terms of long-run growth.
- 8 -

of metering. High system losses, whether technical or nontechnical, would incur large implicit fiscal deficits along with underpricing and nonpayment. This is often a significant problem in the electricity sector of developing countries. The quasi-fiscal deficits can amount to as much as 1 to 2 percent of GDP.<sup>5</sup>

**Figure 2. Electricity Transmission and distribution losses (percent of total)**

<!-- Start of picture text --> 0 10 20 30 40 50 60<br>Moldova 50.4<br>Albania 2003 41.4<br>2005 40.9<br>2007 36.5<br>2009 35.4<br>Kyrgyz Republic 28.2<br>Macedonia, FYR 22.4<br>Bosnia & Herzegovina 18.8<br>Tajikistan 16.9<br>Latvia 16.7<br>Serbia 15.8<br>Azerbaijan 14.2<br>Turkmenistan 14.0<br>Turkey 13.9<br>Armenia 13.1<br>Georgia 12.8<br>Ukraine 11.7<br>Belarus 11.7<br>Romania 10.9<br>Bulgaria 10.9<br>Russian Federation 10.3<br>Kazakhstan 9.6<br>Poland 9.1<br>Uzbekistan 8.8<br>Lithuania 8.2<br><!-- End of picture text --> Sources: KESH data for Albania; World Development Indicators for other countries.

In Albania, the electricity system losses have been a long controversial issue, because they are extremely high by both global and regional standards (Figure 2). The losses, though gradually declining, were estimated at 35 percent in 2009. Despite some recent improvements, the Albania’s system is still losing about one-third of total energy produced (and imported).

On paper, the meter penetration rate is already high in Albania. But the remaining deficiency has long been persistent, negatively affecting the whole governance in the electricity sector. According to the ERE data, about 100,000 electricity customers or about 11 percent of total

> 5 In Africa, the quasi-fiscal deficits in the electricity sector were estimated at almost zero to 4.7 percent of GDP with an average of 0.84 percent (Briceno-Garmendia _et al._ , 2008). In the Europe and Central Asia (ECA) region, the deficits were estimated at nearly zero to 1.3 percent of GDP in 2003 (World Bank, 2006).
2010, and 2015. The 2005 and 2015 data have been used because they match more closely the end-point years of the trade data. (See Enamorado et al. [2016] for a detailed description of the data.) For labor market and migration indicators, tabulations from the 2000 and 2010 Census of Population and Housing and the 2015 Population Count are used to obtain labor market indicators, as well as information on the demographic characteristics of the population at the municipal level.<sup>5</sup> Given that publicly available data from the Mexican Institute of Statistics do not allow for more than two cross-tabulations, the census microdata samples of the Integrated Public Use Microdata Series are used to estimate labor market indicators on more detailed groups (Ruggles et al. 2007). Because the 2005 data do not include detailed information on labor market outcomes, 2000 data are used instead as the initial year for these indicators. The analysis also relies on the absolute index of migratory intensity between Mexico and the United States in 2000–10 to capture emigration and immigration with respect to the United States, as well as the remittances received in Mexico from that country (CONAPO 2014). The analysis uses the exports from developing to developed countries at the 2-digit level from the BACI international trade database to construct the instrumental variable (Gaulier and Zignago 2010).<sup>6</sup> Low- and middle-income countries are defined as developing economies, and highincome countries are defined as developed, following the World Bank’s income classification.

# **3.2. Descriptive Statistics**

Mexico has experienced a substantial increase in exports since the 1990s (figure 2). Exports of goods and services increased from US$96.7 billion to US$480.0 billion in 1990–2018. As a share of GDP, the rise was from 18.7 to 39.2 percent. This analysis focuses on 2004–14 because the municipal-level export data set only covers those years. While this excludes the initial period of NAFTA, it still captures a significant increase in exports, from about 28.4 percent to about 31.9 percent of GDP.

According to various measures, poverty declined almost every year, with the main exception of the 1994–95 tequila crisis (figure 3). This is particularly true of the US$3.20-a-day and

> 5 Even though the 2015 population count is from a survey, the sample size is sufficiently large to provide statistics representative at the municipality level. See Enamorado et al. (2016).

> 6 These include the following sectors: agriculture, mining, food products, textiles, wood, paper, printing, chemicals, rubber and plastics, metal, electronics, machinery, automotive, other manufacturing, utilities, construction, education, and other nonmanufacturing.

7
# **Impact of Rising Rice Prices and Policy Responses in Mali: Simulations with a Dynamic CGE Model**<sup>1</sup>

Kofi Nouve and Quentin Wodon JEL codes: I32, D1, Q12 Key words: Food prices, productivity, import tax, CGE, poverty, Mali

> 1 This paper and the broader research project it is part of have benefitted from discussions with and/or comments from among others Douglas Addison, Harold Alderman, Antonella Bassani, Shanta Devarajan, Hinh Dinh, Wilfried Engelke, Louise Fox, Delfin Go, Ana Revenga, Sudhir Shetty, Kenneth Simler, Linda Van Gelder, Jan Walliser, Vera Wilhelm, and Hassan Zaman.  All potential mistakes or omissions remain obviously ours.

1
Stock, James H., and Motohiro Yogo (2005). Testing for weak instruments in linear IV regression. In D.W.K. Andrews and J.H. Stock (eds.) Identification and Inference for Econometric Models: Essays in honor of Thomas Rothenberg. Cambridge: Cambridge University Press.

- Tiruneh, Addis, Teklu Tesfaye, Wilfred Mwangi, and Hugo Verkuijl (2001) Gender differentials in agricultural production and decision-making among smallholders in Ada, Lume, and Gimbichu Woredas of the Central Highlands of Ethiopia. Mexico D.F.:International Maize and Wheat Improvement Center (CIMMYT).

- Udry, Christopher (1996). Gender, agricultural production, and the theory of the household. Journal of Political Economy, 104 (5): 1010–1046.

USGS FEWS NET GeoWRSI (2014). Available at: chg.geog.ucsb.edu/tools/geowrsi/index.html.

Wooldridge, Jeffrey (2002). Econometric Analysis of Cross Section and Panel Data. Cambridge: The MIT Press.

Yamano, Takashi, Otsuka, Keijiro and Frank Place (2011). Emerging Development of Agriculture in East Africa: Markets, Soil, and Innovations. New York and Heidelberg: Springer.

- Yorobe, Jose M. and Melinda Smale (2012). Impacts of Bt maize on smallholder income in the Philippines. AgBioForum 15(2): 152-162.

- Zerfu, Daniel and Donald F. Larson (2010). Incomplete markets and fertilizer use: evidence from Ethiopia. Policy Research Working Paper Series 5235. Washington: World Bank.

15
44

## **_4.1.1  Mortality Improvement Rates_**

As already mentioned, improvement rates can be computed from both sources (both of which are available at 10-year intervals). As can be seen from Figure 18, the improvements are similar, although the FIF data exhibits greater variability with age, most probably due to the relatively small number of annuitants on which the mortality rate estimates are based. For the computation of MWRs the difference does not seem to be quantitatively important. The reported numbers in this chapter are derived with population improvement rates.

It is important to mention that most pension funds do not update the mortality rates on a yearly base. They compute the liabilities based on the published FIF mortality rates and increase these by 0.4 percent to 0.5 percent on a yearly basis until the new FIF mortality rates are available, i.e., every 10 years. The 0.4 percent per year delivers an increase in longevity over 10 years that coincides almost exactly with the increase in longevity using the SFSO or FIF improvement rates for all groups of annuitants. It thus constitutes a good approximation for the anticipated increase in longevity. The 0.5 percent is a more prudent estimate.

**Figure 18  Mortality Improvement Rates**

# **Improvement Rates per Age**

<!-- Start of picture text --> 0.045<br>0.035<br>men (BfS)<br>0.025<br>women (BfS)<br>0.015 men (EVK)<br>women (EVK)<br>0.005<br>-0.005<br>50 60 70 80 90 10 0<br>age<br><!-- End of picture text --> Improvement rates per year computed from pooled mortality rates. SFSO = between 1980 and 1990, FIF = between 1990 and 2000. Source: SFSO, FIF

## **4.2 Yield Curves and Discount Rates**

The choice of the discount rate in Switzerland is somewhat tricky due to the specificities mentioned below. We will use three different strategies to discount the annuity streams:
Table A3: Correlation between reading proficiency, schooling and age

||(1)<br>Pooled<br>(countryFE)|(2)<br>Pooled|(3)<br>Bolivia|(4)<br>Colombia|(5)<br>Ghana|(6)<br>Kenya|(7)<br>Vietnam|
|---|---|---|---|---|---|---|---|
|Years of schooling|0.172***|0.230***|0.188***|0.132***|0.263***|0.099***|0.153***|
||(0.011)|(0.014)|(0.033)|(0.037)|(0.024)|(0.034)|(0.035)|
|Age|‐0.001|0.016***|‐0.007|‐0.007|0.013**|‐0.019*|‐0.016**|
||(0.003)|(0.004)|(0.009)|(0.007)|(0.006)|(0.010)|(0.006)|
|Age*years of schooling|‐0.001***|‐0.002***|‐0.002|‐0.001|‐0.003***|0.001|‐0.000|
||(0.000)|(0.000)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Female|‐0.129***|‐0.114***|‐0.073|‐0.019|‐0.340***|‐0.139***|‐0.053|
||(0.022)|(0.025)|(0.054)|(0.054)|(0.056)|(0.048)|(0.035)|
|Colombia FE|0.568***<br>(0.043)|||||||
|Ghana FE|‐0.523***<br>(0.047)|||||||
|Kenya FE|‐0.263***<br>(0.046)|||||||
|Vietnam FE|0.634***<br>(0.041)|||||||
|Constant|‐1.227***|‐1.902***|‐1.228***|‐0.574|‐1.925***|‐0.889**|‐0.604|
||(0.118)|(0.139)|(0.326)|(0.433)|(0.445)|(0.371)|(0.501)|
|Observations|10,627|10,627|1,550|1,878|2,221|2,487|2,491|
|R‐squared|0.411|0.200|0.315|0.274|0.264|0.125|0.362|

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.

Notes: Least squares regressions weighted by sampling weights. Dependent variable: standardized reading proficiency score. Sample: population with 1‐12 years of schooling. Pooled specification includes country fixed effects and gives same weight to each country. Data source: World Bank (2016a) 34
<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR APD EC NAM<br>1000<br>Base year<br>f= -- 2010<br>=<br>5 Scenario<br>2 Oo Zerolnvest<br>e) 500 : E oO Invest<br>oa | | Maxinvest<br>20302050 20302050 20302050 20302050 20302050 20302050 20302050 20302050 20302050<br><!-- End of picture text -->
# _Table of Contents_

|Ir,tro|duction and summary<br>|1<br>|
|---|---|---|
|I.<br>|Overall imports, primary commodities, and energy<br>|5<br>|
|II.|Imports of manufactures by the European Communities<br>|13<br>|
|m.|The EC, the United States, and Japan in comparison<br>|15<br>|
|IV.<br>|Manufactures imports from developing countries<br>|17<br>|
|V.<br>|A case study: textiles, clothing, and the MFA<br>|20<br>|
|VI.<br>|The determinants of imports: growth and exchange rages<br>|22<br>|
|VII.<br>|Imports, efficiency, and de-industrialization<br>|26<br>|
|Conc|lusion|31|

### TABLES

Table 1 Import elasticities of industrial countries 6 Table 2 GDP, manufacturing production, imports, and exports 8 Table 3 Nominal and rea, exchange rates 22 Table 4 Inports and PPP-corrected GDP 24

### CHARTS

Chart 1 GDP and imports Chart 2 Investment ratios <!-- Start of picture text --> 7<br>27<br><!-- End of picture text -->

## ANNEX TABLES

Merchandise trade by regions, major trading partners, and product Annex Table I - European Community Annex Table II - the United States Annex Table m - Japan <!-- Start of picture text --> 31<br>35<br>38<br><!-- End of picture text -->
**Appendix table A1: Size of training samples and F1 scores for winter and summer crops**

|**Year**|**Total **|**Sample size**<br>**Winter crops**|**Summer crops**|**F1 s**<br>**Winter crops**|**core**<br>**Summer crops**|
|---|---|---|---|---|---|
|2019|2,481|779|1,702|99.3|97.9|
|2020|3,155|806|2,349|97.0|94.1|
|2021|3,644|991|2,653|99.9|98.5|
|2022|2,132|470|1,662|95.1|98.1|
|2023|3,285|404|2,881|97.5|96.5|

_Source:_ Original table for this paper.

_Note_ The F1 score is TP/(TP+0.5*(FP+FN)) where TP is true positives, FP is false positives and FN is false negatives.

32
<!-- Start of picture text --> $100,000,000<br>y= 224,54)0085<br>x RF= 0.8079 ee<br>= $10,000,000<br>iu e a]<br>oe et<br>i os +<br>7 $1,000,000 a<br>Es .<br>2wu $100,000nn oon Ps -<br>=fic} “a *<br>E5 . -<br>ad §10,000 .<br>51,000<br>10 100 1,000 10,000 100,000<br>Contained Copper in Reserves<br><!-- End of picture text -->
Table 2 displays pairwise correlations of the components of infrastructure by sector (used in Figure 1) and the correlations between the representative components from each sector. As shown in the top panel of Table 2, all the components within sector are significantly mutually correlated at either the 5 or 10 percent level of significance. The bottom panel also indicates that the representative components are correlated across sectors at the 5 percent significance level.

Among all the indicators listed above, our main focus is on the following five indicators: total road length in km per square root of the country’s 1,000 workers multiplied by its mean arable land, paved roads as ratio of total road length, the number of main phone lines per 1,000 workers, electricity generating capacity in megawatts per 1,000 workers, and power loss (% to total output). These five indicators are used to construct sectoral infrastructure indices for transport, telecommunications, and electricity, which are explained in detail in Part C in this section. The definitions and sources of the entire set of infrastructure indicators used in Figure 1 as well as Figures 1-A and 1-B in Appendix 2 are shown in Table 1.

# **B. Cross-Regional Comparison Using Trend Data**

In this section, we assess the time trends in the main infrastructure indicators over the last few decades, comparing Egypt with developing countries as well as the group of both developing and developed countries (called “World”).

We select five main indicators of infrastructure quantity and quality from the three core infrastructure sectors, transport, telecommunications, and electricity. A quality indicator for telecommunications, telephone faults per 100 main lines, is excluded as the time dimension of the indicator is very limited and is only available for the last few years<sup>17</sup> .

> 17 The cross-country comparison of telephone faults by using the most recent data is shown in Panel (b) of Figure 1.

16