rafmacalaba/datause-encoder-data huggingface dataset:
technological inputs of modern activities allows for productivity spillovers into the broader economy. Rodrik also emphasizes that these modern activities are largely tradable products, although the broader benefits of tradables is less dependent on whether trade actually takes place.<sup>10</sup> As resources move from traditional activities towards modern, economy wide productivity increases (see Rodrik 2009b, Stiglitz et al 2001). This can take place in agriculture, manufacturing, or services.

The distinction between traditional and modern activities will play a critical role in the remainder of our analysis. Therefore, before we analyze the composition of Malaysian production, we first offer our defining characteristics for “modern” and “traditional” production/trade separately for goods and services.

# **III.  DEFINING MODERN AND TRADITIONAL GOODS AND SERVICES**

There are two criteria that we can use to classify industrial activities (specifically, manufacturing) as either modern or traditional. The OECD classifies manufacturing industries according to several categories of technological intensity using the ratio of R&D expenditures to total value-added. The division of industries into these categories was made after ranking the multi-year average R&D ratios against an aggregate OECD average.<sup>11</sup> This distinction was used most recently in the 2009 Industrial Development Report published by the United Nations Industrial Development Organization (UNIDO). The UNIDO publishes a listing of three-digit SITC codes that correspond to four technological classifications of trade: resource-based, lowtechnology, medium-technology, and high-technology industries. Using the UN COMTRADE database, we can access the commodity-level trade of individual countries and separate it by this technological classification. Calculating the share of high-technology trade with respect to the total gives us a measure of the technological intensity of trade for that country. In addition, UNIDO also provides an equivalent classification based on ISIC codes for production-level analysis; however, the available data on the production side is more limited.

An alternative measure of the _sophistication_ of goods exports is provided by Hausmann, Hwang, and Rodrik (2007). They construct a weighted average of the per-capita GDPs of all countries exporting a particular product (which they call PRODY), and correspondingly weight each PRODY value of a country’s export basket by its share in total goods exports to arrive at a measure of the productivity level of a country’s exports, which they refer to as EXPY.<sup>12</sup> The authors argue that the PRODY measure captures a broader definition perspective of industrial

> 10 Though Rodrik emphasizes the importance of _industrial_ tradable goods, he acknowledges the increasing importance of tradable services in “modern” activities.

> 11 For a more detailed description of the OECD methodology, see Annex A of OECD (2007).

> 12 More formally, the PRODY and EXPY measures are calculated in the following way:

> _x ij X_<sup>is the value-added share of commodity j in the country’s overall export basket.</sup> _i_

9
- 11 -

leaves and products, and (v) sugar. Table 3 includes the three largest agricultural items exported from African countries whose economies are heavily dependent on agricultural production; the threshold is 30 percent of GDP. Vanilla from Comoros and natural rubber from Liberia appear exceptional in the region.

Table 2. Major agricultural products in Africa, 2004

|Product|Volume<br>(Millions of tons)|% of world<br>production|
|---|---|---|
|Cereals|121.4|5.9|
|Cassava<br>|108.1<br>|53.3<br>|
|Sugar cane<br>|84.6<br>|6.4<br>|
|Maize|43.4|6.0|
|Yams<br>|38.3|95.7|
|Plantains<br>|23.2<br>|71.3<br>|
|Cow milk, whole, fresh<br>|21.8|4.2|
|Wheat|21.7|3.5|
|Sorghum|21.0|35.7|
|Rice, paddy|18.9|3.1|
|Oil palm fruit|15.8|9.8|
|Millet|14.3|49.6|
|Potatoes|14.0|4.3|
|Tomatoes|13.7|11.4|
|Sweet potatoes|11.3|8.9|
|Groundnuts in shell|8.8|24.7|
|Taro (coco yam)<br>|8.2<br>|77.3<br>|
|Bananas|6.8|9.5|
|Seed cotton<br>|5.2<br>|7.2<br>|
|Oranges|5.0|8.0|
|Onions, dry|4.3|7.8|
|Beef and veal|4.2|7.1|
|Watermelons|4.1|4.3|
|Chicken meat|3.2|4.7|
|Mangoes|2.6|9.9|
|Cocoa beans|2.6|72.4|
|Pineapples|2.6|17.0|
|Papayas<br>|1.3|20.0|
|Coffee, green|1.0|13.1|

Source: _FAOSTAT_ .
> - **'4** -

Figure 6. The impact of weather on African agriculture (deviations from trend in cereal yield) <!-- Start of picture text --> 0.08 -<br>0.06<br>0.04 -<br>0.02-<br>0 -<br>-0.02-<br>-0.04-<br>-0.06<br>-0.08<br>-0.1AI I I I I<br>1970 1972 1974 1976 1978 1980 1982 1984 1986<br><!-- End of picture text --> i~~~~~~~~~~~~~
season but since then the problem appears to be under control, although as we will see subsequently, cotton cultivation in this area seems to be balanced on a knife edge and much needs to be done before sustainability in cotton production can be achieved.

# Losses due to Pest attacks

Table 3 has the data on cotton production (in bales of lint, each bale weighing 170 Kgs) and the acreage for the years 1970/71 to 1991/92. (Output for 1991/92 is an estimate, as the cotton season is not yet over). Of the 22 observations available to us, five are identified as outliers and eliminated from the estimation of a trend line. These are: 1977/78, in which season a severe cyclone damaged a large proportion of the standing crop, 1983/84, when the output was abnormally high in Guntur and also all over India because of very favourable weather conditions, 1985/86, -rhitefly damage year, and 1987/88 and 1988/89, both years when Heliothis damage was very significant. The remaining ("normal") years were used for curve fitting. Two approaches were tried, the first by using output as the dependent variable and a second in which yield of cotton lint per hectare was used as the dependent variable. Several different variables,. including the use of time and (time)<sup>2</sup> as the independent variables, and several different functional forms (semilog, sigmoid, quadratic, etc.) were tried. Appendix 1, has a brief discussion on the reliability and validity of the approach, but the best fitting form was obtained by using total output (Q) as the dependent variable and the acreage (A), 57
influencing both the construction of water plants and youth education. A useful falsification test to shed light on omitted variable bias is to examine whether a plant water program is significantly related to the education of older individuals whose education is unlikely to be affected by this program. If other factors lead to the plant water effects, we would likely see the water program to be significantly related to the education of older individuals as well.

To implement this placebo test, we construct a sample of males who had passed 30 years of age when the program was implemented in their villages. We exclude females from the sample because their current residence locations likely differ from where they lived when they were in school. In contrast, males are relatively stable due to the social norms of male-biased inheritance in China. Similar to the construction of the youth sample, we only keep the observation of an individual at his/her oldest age in the CHNS data. In total the placebo sample has 2,708 individuals. The outcome variables, the same as before, reflect their final educational achievement.<sup>28</sup> For the adults from these older cohorts, their current household information is different from that when they made decisions on schooling. We thus do not have their household characteristics during schooling ages, and have to resort to more limited control variables here. Table 3 shows the regression results. As expected, none of the “treatment effects” of plant water are statistically significant. The placebo test thus renders support for our identifying strategy.<sup>29</sup>

# **Endogeneity of Water Plant**

> 28 The average grade of education completed in this sample is 7.33 years. About 50 percent of them graduated from a middle school and 15.5 percent from a high school. This placebo sample is comparable to the baseline one in their environment. Their means of the distances to schools are quite similar in magnitudes. For example, the distance to a middle school in the placebo sample is 2.35 km, as compared to 2.03 km in the baseline sample.

> 29 The model specification for this falsification test differs slightly from the baseline specification--some individual and household characteristics (e.g. the relationship to household head, household size and income) are not controlled for due to the lack of data when those older cohorts were in school age. However, the estimates of “the treatment effects” for this placebo sample should not change much since the program implementation is relatively exogenous and, thus, uncorrelated to those characteristics at the individual and household level.  To ensure that the insignificance of the “treatment effects” in this falsification test is not caused by  the change in the set of control variables, we apply the same specification here to the male youth aged between 18 to 25.  The regression results show that the estimated coefficients remain similar to our baseline ones. For instance, the coefficient of Water Plant on grades completed is 0.87 with the extra controls, and 0.9 without them. The results on the other two discrete outcomes are also very similar.

17
Table 8 provides the values of _OEW_ * at mean points for _X_ and for two reference utility levels defined by the middle two cut-off points from the OP from Table 4, specification (1). The proportion of the population reporting that they are below the lower of these two cut-off points (i.e., that they are either ‘very unsatisfied’ or ‘unsatisfied’) corresponds closely to prevailing estimates of the poverty rate for Malawi.

* Focusing first on panel (a) in Table 8, we find that for _u_ at the estimated cut-off point below which one is either ‘very unsatisfied’ or ‘unsatisfied,’ the value of _OEW_ * tends to fall as _FEW_ rises (at given _NEW_ ) and similarly _OEW_ * falls as _NEW_ rises (at given _FEW_ ). This pattern reflects the positive utility gains from having better off friends and neighbors.  However, the pattern reverses when we shift to the higher reference utility level (panel b), when the RD effect kicks in.  Then the poverty lines in the space of perceived economic welfare start to look like * relative poverty lines.  However, note that in all cases in Table 8, the gradients in _OEW_ are * small; to the nearest ladder rung, the poverty line is at _OEW_ = 3 for 80% of cases in panel (a) * and it is _OEW_ = 4 for 80% of cases in panel (b).

# **6. Conclusions**

Relative deprivation is seen to stem from social comparisons that generate negative externalities from economic gains to friends and neighbors. Other theories point instead to positive external effects arising from institutions for risk-sharing, the provision of local-public goods, local employment opportunities or productivity-enhancing spillovers. These arguments predict that it is an asset to have well-off friends and neighbors.

30
# **1 Introduction**

In June 2005, the Office of the U.S. Global AIDS Coordinator (OGAC) claimed that there were over 32,000 citizens of Botswana receiving antiretroviral treatment (ARV) because of PEPFAR (U.S. President’s Emergency Plan for Aid Relief). In response, Segolame Ramotlhawa, director of Botswana’s treatment program, called the U.S. figures “a gross misrepresentation of the facts” and reported that no patient was dependent on PEPFAR for treatment. Politically, the credit claiming was important to OGAC because it needed to demonstrate progress to the U.S. Congress. For Botswana’s President, Festus Mogae, receiving credit was important because he promised universal ARV treatment and has made HIV/AIDS one of his top issues. President Mogae did not want Botswana’s citizens to think that the U.S. was the only one responsible for the country’s treatment success (Patterson, 2006; Timberg, 2005, 159-161).

This exchange between Botswana’s government and OGAC officials illustrates one of the dilemmas that foreign aid poses for donors, non-governmental organizations (NGOs), governments, and citizens. If the priorities of a government mirror those of PEPFAR and PEPFAR provides much of the money needed to meet them, who receives the credit for success or the blame for failure? Different actors have funded the various components of the ARV treatment program: the Gates Foundation has built AIDS clinics; Merck Pharmaceutical Company has donated ARVs; the Government of Botswana pays doctors and nurses; PEPFAR money has purchased laboratory equipment; and Harvard has trained lab technicians (Patterson, 2006, 159-161). In this context, who do recipients believe is ultimately responsible for the HIV/AIDS program?

These questions raise a broader concern about the conditions under which the provision of services by donors and non-state actors is likely to undermine or strengthen the fiscal contract between citizens and a government and citizens’ legitimating beliefs. Before addressing this concern, I will first examine whether the logic of the fiscal contract, which is largely based on the statebuilding experiences of Europe, is relevant to developing countries with very low and idiosyncratic taxation levels. I will then assess whether there is empirical support for the fiscal contract argument using cross-sectional survey data, the Afrobarometer, which was collected across 19 Sub-Saharan African countries in 2008.

Second, extending the logic of the fiscal contract arguement, there is 2
To explore the dynamics of HEs and the relationship to welfare, we have classified households into four groups:

- 1) _Never HE_ - households that did not report HE activity in either 2003 or 2008

- 2) _HE Start-up_ - households that reported an HE in 2008 but not in 2003

- 3) _HE Survivor_ - households that reported an HE in both 2003 and 2008, and

- 4) _HE Closer_ - households that reported an HE in 2003, but not in 2008.

Descriptive analysis of these four groups shows that a large number of households started up an HE between 2003 and 2005. Start-up of HEs took place in all consumption terciles, and in urban and rural areas in more or less equal proportions (table 3). This type of entrepreneurship is not confined only to better off or urban households. In total 40 percent of households were engaged in HEs in either 2003 or 2008. Among HE active households roughly one-quarter of HEs were present in both time periods, while one-quarter closed and half were start-ups. This shows that about half of all HEs reported in 2003 had closed by 2008. The actual mortality rate of for HEs may be even higher, as some of the survivors may have closed one enterprise and started another, and some identified as never having an HE may have started and closed an HE over the period. But others may have been missed in 2003. Start-ups took place in all regions, but a higher share of households started up an HE in Maputo City and Province.

**Table 3 Sample distribution households engaged in HEs**

|Never<br>HE|HE<br>Survivor|HE<br>Start-up|HE<br>Closer||
|---|---|---|---|---|
|National<br>60|10|22|9|100|
|Location|||||
|Rural<br>66|6|21|7|100|
|Urban<br>45|18|25|12|100|
|Consumption terciles in 2003|||||
|Poorer<br>64|9|19|8|100|
|Middle<br>64|7|25|4|100|
|Richer<br>51|13|21|15|100|
|Observations in<br>sample<br>697|186|290|151|1324|

_Source:_ NPS Panel

# _HEs and upward mobility_

Table 4 shows the results of interest for a first difference regression of consumption ranking in each year on changes in income sources (HE, wages and agriculture) and demographic composition of the employment in the NPS panel but not in the national data (table A7). This can be explained the different timing of the surveys IOF 2008/09 show two percentage point higher NFE activity reported as primary employment during the period of the NPS survey, which was post harvest.

15 | P a g e
48

# **Figure 17: Credit to Government and SOEs in Egypt over time**

<!-- Start of picture text --> Credit to Government and SOEs / GDP (%)<br>45.0<br>40.0<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Value Observed Expected median<br><!-- End of picture text --> Source: Global Financial Development Indicators, own calculations The example of Egypt shows the importance of considering an array of different data sources to gauge the efficiency and depth of financial systems. Benchmarking based purely on aggregate data can be mis-leading if not accompanied by more detailed analysis with micro-level data.

# **8. Conclusions**

This paper discusses the concept of the possibility frontier as assessment tool for financial sector development across countries. The financial possibility frontier indicates the constrained optimum of depth, outreach or breadth of a country’s financial system given certain state variables that cannot be changed in the short term. We show how the benchmarking exercise can be used to operationalize the frontier concept on the aggregate level. We also discuss three examples of how to apply the frontier concept in analytical work. These three examples are intended to give a flavor of the possibilities that the frontier concept 48
increase in cognitive/SEM skill is associated with $8.3/$16.5 higher monthly earnings for men. For women, point estimates are smaller, less stable, and lack statistical precision.

This association between labor earnings and skills for men varies by respondents’ migration status.<sup>4</sup> Among the 65% of men still resident in their native village (non-migrants), we find a precisely estimated _zero_ association between labor earnings and years of schooling and cognitive skills, but a strong correlation between SEM skills and labor earnings. This result is robust to multiple specifications and sample definitions. Among men who have left the village (migrants), we find a significant correlation between labor earnings and years of schooling. The differences in the association between labor earnings and years of schooling for migrants and non-migrants reflects not just the fact that LFP is higher among migrants, but also that conditional on LFP the association between earnings and years of schooling is also significantly higher for migrants. Finally, we also find a strong correlation between labor earnings and cognitive skills for men in median regressions, but this result is less stable across multiple specifications.

To understand if the lower returns to years of schooling and cognitive skills among non-migrants is a facet of this particular sample, we turn to the second dataset from Cambodia. As in Pakistan, this sample comes from a rural area (poorer than in Pakistan) and includes children who have been tracked and re-surveyed in adulthood. Here, we again find that the correlation with years of schooling is close to zero, and although the results are more imprecise, the association with SEM skills is positive. Thus, for those who have remained in the village, the labor market appears to reward skills that are only weakly correlated with years of schooling; but for those who have left their village, the labor market rewards both the cognitive skills that are produced by schools as well as years of schooling itself. This result is reminiscent of Schultz’s (1975) argument that schooling allows individuals to adapt better to changing circumstances (exemplified here by migration).

> 4 The high rates of migration mirror Beegle et al.’s (2011) previous study of migration from an initially rural sample in Kagera; one difference in our sample is that 10.5% of the men are working outside the country, with more than 90% in the Arab countries of Saudi Arabia, UAE, Bahrain, Qatar, and Oman. In our sample, female migration is almost entirely due to marriage (of those who have migrated, 90% are married, and only 4% are working), while male migration primarily reflects work opportunities.  Migration and occupations for men are linked—those living in the village are either engaged in daily labor, salaried occupation, self-employment (or in their family’s business) or in agriculture. Men who have migrated are more likely to be in salaried employment at the expense of other occupations.

5
A large body of the literature has documented the increase in wage inequality that took place in the Russian Federation during the transition to a market economy and the financial crisis of 1998. Using data from the Russian Longitudinal Monitoring Survey (RLMS), Flemming and Micklewright (1999) find an increase in the 90-10<sup>th</sup> percentile ratio of monthly earnings from 6.2 in 1992 to 9.2 in 1995. Lukiyanova (2006) finds that inequality in earnings, measured by the Gini index, peaked in 2000 as a consequence of the financial crisis of 1998, and then started to decrease during the economic recovery. Yet, less evidence exists on the reduction in wage inequality experienced over the last decade (2002-2012) and its underlying causes.

This paper aims to fill in this gap, by investigating the determinants of the change in wage inequality during the 2002-2012 period. To do so, we make use of the decomposition technique recently proposed by Firpo, Fortin and Lemieux (2009, 2011) (FFL henceforth). Following Firpo, Fortin and Lemieux (2011), changes in the wage distribution may be decomposed into changes in the observable characteristics of the workers, the _composition effect_ ; and changes in the return to these characteristics, the _wage structure effect_ . Moreover, the FFL methodology presents two main advantages with respect to those previously used: (i) it allows measuring the composition and wage structure effect at different percentiles of the wage distribution, and (ii) it allows further decomposing each of them into the contribution of individual covariates (such as education, firm characteristics, etc.), leading to a greater understanding of the factors behind changes in inequality. In this way, it can be speculated the importance of supply versus demand effects.<sup>4</sup> The FFL methodology has recently been used to decompose changes in inequality only in a few settings. Firpo, Fortin and Lemieux (2011) first illustrate the application of the methodology looking at wage inequality in the United States between the 1980s and the early 2000s. More recently, Ferreira, Firpo, and Messina (2014) use the technique to explain changes in wage inequality in Brazil between 1995 and 2012. Atencio and Posadas (2013) use this methodology to assess the gender wage gap in pay in the Russian Federation.<sup>5</sup> Yet, to the best of our

> 4 Other papers have used a different approach by measuring the elasticity of substitution between different types/groups workers: high skilled vs. low skilled, following Katz and Murphy (1992).

> 5 Carrillo, Gandelman and Robano (2014) use FFL to measure sticky floors and glass ceilings for women in 12 Latin American countries. However, their results should be taken with caution as the FFL is based on two

7