rafmacalaba/datause-encoder-data huggingface dataset:
28 if there are spillovers then shouldn't the effect of education be evident both in changes (to represent the standard private productivity augmenting effect) and levels (to represent the externality)? Second, if the entire return to education at the aggregate level is primarily spillover type effects, why the wage premia observed at the individual level? Third, the threshold type findings at the aggregate level seem inconsistent with the assertion from micro estimation that wage increments for individuals are falling (or at least not rising) as a function of the level of schooling (a point acknowledged by Azariadis and Drazen, 1990). Fourth, the time series properties of regressing growth rates on the level of education are wrong because the stock of education has an obvious upward trend while GDP growth rates do not. Growth rates are stationary (or at least driftless) while the stock of education is non-stationary<sup>42</sup> . This is a criticism that applies to all endogenous growth models that make growth rates a function of any variable (such as the magnitude of R&D or the stock of knowledge) that are non-stationary (Jones, 1995).

# IT) Why **dneR schooling** nnt mitte-r<sup>9</sup>

So far I have I have shown that a simple, but standard and widely accepted, growth accounting framework provides no support for the idea that the investments made in schooling accelerated the rate of economic growth. I have shown that this result is not the result of simple measurement error of years of schooling and is robust to a variety of choices of specification and sample. I have also shown that widely accepted evidence about the importance of education in macroeconomic growth is far more problematic and ambiguous than is often acknowledged as, it typically relies on a clearly erroneous use of enrollment rates. The data are definitely telling us something about education and growth, but what?

42 In the current econometrics jargon education is an 1(0) series while the stock of education is clearly 1(1), where l(n) is the notation for a series that needs to be differenced in times to achieve stationarity. It generally does not make sense to regress an 1(0) series on an 1(1) series as it cannt be the case that there is a linear relationship between a trending and mn-trending series (unless one incbudes several 1(1) series on the rhs that are cointegrated , so that some combination of the 1(1) series might be 1(0)). Thus it may be the case that a higher level implies a transitorily high rate of growth but no effect on long-run levels. Pappell and Ben David (1995) find, using Madison's historical data that growth rates are stationary after allowing for one structural break.
In developing countries, including those in South Asia, access to electricity is especially low among farmers. Nightlight intensity has been used to estimate electrification rates at local levels (Min 2011); based on this approach, it has been suggested that close to half of the rural population of South Asia lacks access to electricity (Doll and Pachauri 2010). And even when they do have access, farmers tend to use the electricity for activities such as pumping water, which do not generate nightlight. Therefore, nightlight intensity is more strongly correlated with economic activity in manufacturing and services than in agriculture. When considering a large cross-section of countries covering the whole world, the inverse Henderson elasticities are 0.13 for agriculture, 0.37 for manufacturing, and 0.43 for services and the relationship is statistically significant for all three sectors. When focusing on South Asia, however, the relationship becomes statistically insignificant for the agricultural sector. For manufacturing the elasticity is 0.25 and for services it is 0.35.<sup>5</sup> _Table 2: The short-term relationship of GDP and nightlight intensity._

||**W**<br>|**orld**<br>|**SA**<br>|**R**<br>|
|---|---|---|---|---|
||Δln(GDP)<br>(1)|Δln(GDP)<br>(2)|Δln(GDP)<br>(3)|Δln(GDP)<br>(4)|
|Δln(lights/area)|**0.0640*****|**0.0547*****|**0.0890*****|**0.0741*****|
||(0.0168)|(0.0161)|(0.0154)|(0.0154)|
|Country fixed effects|no|yes|no|yes|
|Observations|3,778|3,778|158|158|
|Countries|187|187|7|7|
|(within country)R2|0.093|0.094|0.335|0.338|

_Note:_ The following regression is estimated: Δln(GDPi,t) = a + bi + ct + `δ` Δln(lighti,t) + `ε` i,t, where ln(GDPi,t) is the natural logarithm of GDP of country _i_ in year t measured in constant local currency, ln(lighti,t) is the natural logarithm of lights per km<sup>2</sup> , bi is a country-fixed effect and ct is a year fixedeffect. The regressions in (1) and (2) are estimated using data until 2013. The regressions in (3) and (4) are estimated using data until 2016 and excludes Maldives. Robust standard errors, clustered by country, are in parentheses _._ *** p<0.01.

When considering longer periods of time, economic growth is accompanied by improvements in energy infrastructure. In this context, the strength of the

> 5 See the regression tables in Appendix A.

11
- 1.57 -

# Flgure 1.C.11 COCOA SUPPLY RESPONSE UNDER POPULATION AND INCOME GROWTH

(Constant Wag) <!-- Start of picture text -->
1.5<br>14  -<br>12<br>0.  o  14<br>0.5<br>0.7~~~7/<br>0.3<br>0.2<br>0.1-19207-<br>198769  07  2017  2027  2037<br>PERIOD MIDPOINTS<br>12~0  *  140  o  IS0  IGO  AK  20<br><!-- End of picture text -->
platform were only known by 10 percent or fewer of the firms. Only one provider had a brand recognized by at least half of the firms in the program. This shows that most small firms lack information about most business service providers in the market.

# **2.2 How easy is it to find information about business service providers and how do they signal quality?**

To better understand what the marketplace for business service providers looks like, we conducted a survey of providers in Lagos and Abuja between November 2017 and June 2018. A key challenge for conducting such a survey is that there was no master list, website, or database of service providers. This points to the challenge facing small firms who wish to use one of these services: it is difficult to know exactly which companies provide these services. We constructed a sample frame by using multiple methods: we began with the set of providers that had been selected for the GEM program, then contacted the professional industry associations (Association of National Accountants of Nigeria; Institute of Chartered Accountants of Nigeria; the National Institute of Marketing of Nigeria) to get a list of their members, and supplemented this with web searches, snowballing, and word of mouth. This resulted in a survey of 271 service providers in total, of which 39 were providers for the GEM program.<sup>2</sup> By specialization, we have 68 human resource specialists, 102 accounting firms, and 101 marketing firms.

Table 1 summarizes key data from these provider surveys. The first few rows provide information on the characteristics of business service providers in these markets. Most of these providers have been in operation for some time, with a median of 9 years and mean of 11.5 years. They have an average of 10.9 professionals working for them (directors, managers, accounting experts, marketing experts, and human resource experts), with a 10-90 range of 3 to 18 professionals. 38 percent have more than one branch. The mean provider works with about 30 customers a month and has US$142,000 in annual sales, but there is a lot of variation: the 10-90 range for annual sales is US$5,500 to US$274,000.<sup>3</sup> Their typical charge for a full day of work has a median of US$96 and mean of US$285. These providers work with firms of a range of different sizes: on average 21

> 2 The survey also included an additional 62 general purpose business consulting firms. That type of provider is not part of the marketplace we set up for the information experiment, and so we do not include their data here. 3 We use an exchange rate of 365 Naira = 1 USD for these calculations.

6
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33
framework.<sup>4</sup> This global macro-micro model combines a set of price and volume changes from the CGE model with expected changes in demographic structure to create a simulated distribution of income in 2030.

# **2.1 LINKAGE: A global dynamic multi-sectoral model**

At its core, LINKAGE is essentially a neo-classical growth model, with aggregate growth predicated on assumptions regarding the growth of the labor force, savings/investment decisions (and therefore capital accumulation) and productivity. Unlike more simple growth models, however, LINKAGE has considerably more structure (see van der Mensbrugghe, 2011) for a detailed description). First, it is multi-sectoral. This allows for more complex productivity dynamics including differentiating productivity growth between agriculture, manufacturing and services and picking up the changing structure of demand (and therefore output) as growth in incomes leads to a relative shift towards manufactures and services. Second, it is a multi-regional model allowing for the influence of openness—via trade and finance—on domestic variables such as output, wages, and productivity, and thus growth. Third, the LINKAGE model has a diverse set of production factors including land and natural resources, and labor is split between unskilled and skilled categories.

In each period t, capital stock is equal to the depreciated capital stock from period t-1 augmented with the investment of period t. Investment is driven by savings which, in turn, depend on per capita income growth and the population youth and elderly dependency ratios (the number of persons aged under-15 or over-65 respectively per 100 in the working age population). According to the UN demographic projections the global average youth dependency ratio is expected to decrease from 44 to 35 over 2005-2030. All regions will experience a declining trend with the highest drop recorded in South Asia and Sub-Saharan Africa, with developments in LAC being close to the world average.

While youth dependency ratios decline, elderly dependency ratios increase in all regions. The world average elderly dependency ratio increases from 11 to 18. The highest elderly dependency ratio is typical for the high income countries, where the share of the elderly in working age population increases to 23% in 2050 from 15% in 2005. However the elderly dependency ratio increases rapidly for all regions. Despite being relatively low at 6% in 2005, the elderly dependency ratio in LAC doubles by 2030. In China, the old-age dependency ratio increases from 11 in 2005 to 25 in 2030. The aging of population is likely to affect not only the saving rates and

> 4 For more information see www.worldbank.org/prospects/GIDD

5
44. Within the Middle East, most of the conflicts have occurred in a handful of countries. One simple way to make comparisons is by adding up the number of years that each country had an ongoing conflict; if a country had more than one conflict in a given year, the additional conflict is added to the total. Figure 14 shows the distribution of conflict-years among MENA countries from 1960 to 2009. About two-thirds (65 percent) occurred in Israel (including the Palestinian territories), Iran, or Iraq. Algeria, Yemen, and Morocco accounted for another 21 percent of conflict-years.<sup>23</sup> Most of the region‘s twenty-odd countries had little or no conflicts at all.

**Figure 14. MENA Conflicts by Country, 1960-2009**

<!-- Start of picture text --> Tunisia<br>Saudi Arabia<br>Oman<br>Syria<br>Djibouti<br>Egypt<br>Lebanon<br>Morocco<br>N Yemen/Yemen<br>Algeria<br>Iran<br>Iraq<br>Israel<br>0 10 20 30 40 50 60<br><!-- End of picture text --> _Source:_ Calculated from data in Gleditsch et al. (2002) and Harbom and Wallensteen (2010) 45. This underscores the surprising and distinctive quality of recent conflicts in Libya, Syria, Tunisia, Egypt, and Bahrain – and to a lesser extent, protests in Jordan, Saudi Arabia, Oman, and Kuwait. Over the last fifty years, these societies have been both undemocratic and had low levels of violent conflict. In all cases except Libya, the recent upheaval might not be classified by political scientists as ̳civil wars,‘ but government violence against peaceful protest movements. Only in Libya, where the military has fractured, and anti-regime forces have acquired a rudimentary political organization and military capacity, are we witnessing an unambiguous civil war. We return to a discussion of recent events, and their economic implications, in Section 7 below.

# **5. Oil, Political Stability, and Conflict: MENA Case Study Evidence**

46. Can the prevalence of violent conflict in MENA countries be explained by the fact that many of them are significantly dependent on natural resource revenues? At first blush, when we consider the individual histories of natural resource producers in the MENA region we do

> 23 For the purposes of this analysis, we include the conflicts in North Yemen in the total for the Republic of Yemen.

12
Table 8. Informal and formal savings

||Any savings (formal|Total informal|Livestockvalue (in||
|---|---|---|---|---|
||or informal)?|savings (inFCFA)|FCFA)|LSU|
|PanelA: Household randomization|(1)|(2)|(3)|(4)|
|Access to warrantage|-0.040|3.208.059|186,945.551*|1.218**|
||(0.062)|(7,239,277)|(107,942.106)|(0.499)|
|Observations|516|516|518|518|
|Controlmean|0.518|37482|872290|4.880|

Notes: In Panel A, the sample is restricted to lottery participants. In Panel B, the sample is restricted to all control households and non-lottery participants. Standard errors are clustered by village and presented in parentheses. *, **, *** indicate significance at the 1, 5, and 10 percent respectively.
These results are in contrast to the results obtained by looking at countries grouped by World Bank income categories.<sup>16</sup> In this case, there is uniformity in the level of satisfaction with the language skills of local MBAs. The interesting exception is that 32% of respondents from Income Category V (the category with the highest income levels) found the language skills of local MBAs to be lacking or inadequate. This is due mainly to the dissatisfaction with these skills expressed by the East Asian and Latin American respondents.

The analysis of international language skills of MBA graduates has been completely ignored in previous analyses, which may imply that U.S. businesses are less concerned with this issue than international businesses, or that U.S. MBA programs do not consider this a necessary priority. But the evidence above shows that international businesses are very concerned with the issue, and programs that focus on business education in the developing and transition countries should include training in international business languages.

> 16 World Bank income categories are based on the per capita incomes of countries.  The range of the income categories is I-V, with I being the lowest and V being the highest:  Countries in Category I have a per capita income of $746 or less;   those in Category II,  between $746 and $1435;  Category III,  between $1436 and $2975;  Category IV,  between $2976 and $5185;  Category V, over $5185.

17
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