rafmacalaba/datause-encoder-data huggingface dataset:
In fact, the importance of consumer preferences in shaping output demand, (and thus factor demand) is demonstrated by examples such as the move towards organic foods, or a preference for synthetic fibers away from silk. The WSJ reports recently (Oct 23<sup>rd</sup> , 2017):<sup>9</sup> _Reckitt Benckiser Group PLC, one of the world’s biggest suppliers of retail brands, is splitting the business into two divisions.... reasoning that tighter focus is more important than scale for products like Lysol, Woolite, and Mead Johnson baby food. Splitting the business into home‐and‐hygiene products and consumer‐health goods makes Reckitt the latest supplier to act amid stalling sales as consumer tastes in many key markets change fast. Shoppers are gravitating toward smaller, local products and away from mega‐brands._

**_Process (in lieu of labor) versus product innovations._** An important distinction is usually made between process innovations, where machines are replaced by humans in _existing_ production processes versus product innovations (new goods or services are produced requiring human intervention). Digital technology affects both types. Not all process innovations displace labor, however, using drones to take photos of agricultural crops may not displace any human labor being a new task, while using robots to serve at tables in restaurants does. Product innovation has tended to be employment‐friendly. Most process innovations have been in the form of embodied technical change (ETC) – technical change embodied in new capital goods. Using European Community Innovation Surveys and other data, recent micro‐econometric studies confirm that firms that spend more on R&D generally do more product innovation while ETC is linked to process innovation (Conte and Vivarelli, 2005 and Parisi et al, 2006). The literature discusses a number of “compensation” mechanisms (Vivarelli, 2014, Dobbs et al, Oxford Economic Papers,1987) whereby the initial and direct negative impact of labor‐saving technology on

> 9 Anecdotally, preferences among high income groups continue to shift in favor of organic food produced by local farmers, in smaller scale, away from the mass‐produced, pesticide‐treated agricultural products of the post‐ industrial revolution age. Maybe one day, we will eschew all goods produced by robots in favor of those solely produced by humans!

9
them to force we can use a simple analogy of driving a car. Imagine a car that is driving along at a steady speed. This is equivalent to a country with a stable momentum in their expenditures. Suddenly, the gas pedal is pushed hard (force increases rapidly), with the speed of the car growing rapidly (momentum increases). We are concerned with two possibilities of what the driver could do next. First, the driver might not feel comfortable at the new higher speed and react by applying the brakes (large negative force). This sharp deceleration lowers the speed of the car dramatically. This corresponds to our first scenario of a rapid reduction in the growth of expenditures. Another option for our driver is to maintain pressure on the gas pedal. In such a case, although the rate of speed increases stops (force is zero), by continuing to have their foot on the pedal, the driver keeps adding momentum to the speed. This corresponds to our second example, one in which the initial burst of acceleration becomes persistent, continuing to increase the speed of the car. An example of when this may happen is for rigid expenditures which will be discussed further in section 4. We can imagine a scenario where a policy, such as public sector wage increases, become not only difficult to unwind, but entrenched in annual spending increases.

To convert this analogy into something we can analyze with the data we used a statistical criterion to define expenditure episodes. Using our car analogy, we are interested in cases where the speed of the car either grew or shrunk rapidly. For our analysis, we choose those years where growth in the expenditureGPD ratio was in the top or bottom decile. Figure 1 outlines the distribution of expenditure-GDP growth rates of advanced economies, plotting the frequency of different growth rates in the sample. Figure 2 outlines the distribution for emerging economies. The vertical dashed lines in both charts indicate the cutoff from the top and bottom decile. From the charts we can see that the dispersion of growth rates for emerging economies is wider than for advanced economies.

**Figure 1: Distribution of Growth Rates in the ExpenditureGDP Ratio of Advanced Economies** <u>(percent)</u>

**Figure 2: Distribution of Growth Rates in the ExpenditureGDP Ratio of Emerging Economies** <u>(percent)</u>

<!-- Start of picture text --> 25 10<br>20 8<br>15 6<br>10 4<br>5 2<br>0 0<br>-30 -24 -17 -11 -5 1 8 14 20 26 33 -36 -30 -23 -16 -10 -3 4 10 17 24 31 37<br><!-- End of picture text --> Note: For presentational purposes, observations 3 standard deviations above or below the average are excluded from the calculation. The percentiles in the charts are calculated across all observations. In the later analysis we calculate percentiles for each country individually. Source: IMF GFS data and authors’ calculations
# **Table 9. Effect on students’ perception**

||Sequence<br>vs.<br>Control<br>(1)|Coaches<br>vs.<br>Control<br>(2)|Wald Test<br>(3)|
|---|---|---|---|
|Standardized captivate index (A+B+C+D)|0.197**<br>(0.094)<br>974|0.051<br>(0.117)<br>964|1.91<br>[0.166]|
|A. This class (of Science) keeps my attention|0.100**<br>(0.044)<br>1016|0.118**<br>(0.046)<br>1015|0.13<br>[0.722]|
|B. My teacher (of Science) makes learning enjoyable|0.064<br>(0.063)<br>1016|-0.049<br>(0.067)<br>1038|2.82<br>[0.093]|
|C. My teacher (of Science) makes lessons interesting|0.118<br>(0.073)<br>1021|-0.012<br>(0.078)<br>1043|3.10<br>[0.078]|
|D. I like the way we learn in this class (of Science)|0.127***<br>(0.043)<br>1002|0.062<br>(0.064)<br>991|1.27<br>[0.260]|
|E. I like the class of Science|0.153***<br>(0.055)<br>1007|0.151**<br>(0.070)<br>999|0.00<br>[0.970]|

_Source:_ Author's analysis based on experimental data generated for this study.

_Note:_ ** significant at 5 percent; *** significant at 1 percent. Robust standard errors in parentheses clustered at school level. The _p_ - values are in brackets in the third column. Controls: (a) students’ characteristics (gender, age, nationality, parent’s education, if the student missed, at most, one class per month, if the student has internet in his home, and if he/she has a car), (b) teacher characteristics (gender, age, years of experience, if she/he has postgraduate certificates), and (c) school characteristics (school size, seventh grade size, seventh repetition rate, FEPBA score in Language, school district, or location). The dependent variable in A-E represents a four-point scale, where 1 means strongly disagree, 2 disagree, 3 agree, and 4 strongly agree. The captivate index is a combined scale standardized in terms of the Control Group, whose construction is described in Appendix 4. All the regressions exclude the classrooms where the Human Body unit was not taught.

6
In conclusion, between 60 and more than 80 percent of variability in the global income position can be attributed to the circumstances beyond individual control. Sixty percent represents the lower bound where only the mean income of the country and country’s inequality are allowed to play a role. Eighty percent or more is obtained when we include person’s parental income as part of circumstance, and use either base-case or pessimistic assumptions regarding income mobility in various parts of the world. In any case, the part which remains for effort and “episodic luck” (to use John Roemer’s felicitous phrase) remains relatively small.

Finally, we compare the actual role of location to a hypothetical case where all countries’ mean incomes are equal.<sup>24</sup> We still “allocate” people to different countries and income classes in our Rawlsian lottery, but now location implies only a difference in the income distributions between the countries (different Gini coefficients), not the difference in the average income. The results are shown in column (9) Table 3. The coefficient on income class more than doubles compared to regression (3), and when we decompose the two effects, income class is found to explain more than 90 percent of variability in the global income position, while location (through its specific inequality) accounts for less than 5 percent.<sup>25</sup> This counterfactual also allows us to conclude that location really matters through its mean income effect, not through its specific (national) inequality.

# _Median global position and its variability when income class is given_

A different way to look at effort is to consider by how much one’s position in the world improves, on average, if he is able to move up the income ladder within his country. For example, for a person in the bottom income class, the median position in the world is the 7<sup>th</sup> percentile. Suppose now that he manages to climb up to the 5<sup>th</sup> income

> 24 This is  the situation referred by Roemer (2007) as Equality of opportunity of degree  1. Incidentally, if all mean incomes were equalized the global Gini would be only 37.4 vs. the actual Gini of 64.2 (based on the World Income Distribution 2002 dataset).

> 25 Historically, something similar might have obtained in the early 19th century when, according to the Bourguignon and Morrisson (2002) study of  the long-run global inequality,  class (within-national inequality) explained about 90 percent of world inequality between individuals.

20
21 using weekly data for 1989 and 1990, changes in New York's fuel oil prices explain only 4% of MARAVEN's changes in fuel oil prices. However, if we include the changes in N.Y. prices lagged two weeks, New York fuel oil prices explain 61 % of the volatility in MARAVEN's weekly fuel oil prices. For diesel, we compared MARAVEN's diesel prices with Gulf Coast diesel prices. Gulf Coast diesel price volatility explains 18% of MARAVEN's diesel price volatility and 45 % if we include price changes lagged two weeks. For jet fuel, Gulf Coast price movements explain 15% of MARAVEN's prices and 45% if we include price changes lagged two weeks.

Comparing the weekly with the monthly results, the following observations can be made: first, MARAVEN's product (gasoline, diesel, fuel oil, and jet fuel) prices do not respond to market changes (as reflected by New York and Gulf Coast prices) oi a week to week basis but when prices are aggregated over a month they are closely related to New York and Gulf Coast price movements. Second, the fact that the lagged New York and Gulf Coast product price movements influence MARAVEN's product price movements more than current prices reflects some inefficiencies in the way MARAVEN prices its products. Observations of the price data reveal that MARAVEN keeps its product prices fixed for a long time, sometimes for up to eight to twelve weeks.<sup>16</sup> 16For example, between the 4th and 12th week of 1989 MARAVEN's gasoline price was fixed at $20.06/bbl. Meanwhile, the New York gasoline price fluctuated between $21.71/bbl and $25.07/bbl. Similarly, in 1990, between the 20th and 32nd week MARAVEN's price was unchanged at $26.15/bbl, with the New York gasoline price fluctuating between $24.96/bbl and $37.07/bbl.
efficiency are comparable across countries, since it is likely that there were only minor or similar changes in the bank environment of the five countries during the period 1992-96. We can thus pool the data on efficiency across countries and time. This increases the number of observations and therefore the power of our tests.

To explain the variation in changes in output efficiencies through time we specify a second-step regression model. For each country, the second-step regression model is specified as a two-factor fixed effects model with ownership dummies where Ey(xf', yf') is the vector of efficiencies calculated in the first step using DEA, a, / and 8 are vectors to be estimated, _B f'_ is a matrix of J explanatory variables for bank f in period t, A denotes that a variable is in first differences, for example, ABf = _Bft - Bft_<sup>_1_</sup> _),_ g denotes one of the five countries, _D f[g_ are ownership dummies corresponding to K different ownership forms and -ftg is normally distributed with mean zero and variance _cr2._ Equation (8) has an overall constant (ao ) as well as a country effect for each of the five countries (ag ) and a time effect for each period (at). As explanatory variables we include the capital adequacy ratio', the ratio of loan loss provisioning to loans, the amount of loans, and the amount of short term borrowings. The capital adequacy ratio and the ratio of loan loss provisioning to loans are to a large extent imposed upon the banks by the regulators. They are included as explanatory variables, because it is likely that, while imposing their rules upon the banks, regulators discriminated across banks and that regulators became more lax regarding enforcement of their rules during the period 199296, as it became clear to them that some banks were facing financial difficulties. An increase in the amount of loans, and especially an increase in the amount of short termn borrowings is a good indicator for the degree of bank risk-taking. They are included as explanatory variables because risk-taking is likely to be related to changes in bank efficiency. All explanatory variables are in first differences. To avoid multicollinearity in estimating (8), we will suppress one of the ownership dummies.

We will use the DEA calculated efficiency measures to calculate our measure of risktaking. The measure of risk-taking is comparable across countries, since it is likely that there were only minor or similar differences in risk-taking behaviour across the five countries during S We have calculated capital adequacy ratios as the ratio of equity capital to the sum of loans and securities.

16
# **1.** **<u>Introduction</u>**

In recent years a strong belief that ‘entrepreneurship’ is a crucial driver of economic growth for both developed and developing nations has emerged among both scholars and policy makers (see, for instance, Audretsch, Keilbach and Lehmann, 2006; Koellinger and Thurik, 2012; and, for a comprehensive survey, Van Praag and Versloot, 2007). However, moving from macroeconomic scenarios to the micro foundations of entrepreneurship, since the seminal contribution by Baumol (1990) we have known that ‘Shumpeterian innovative entrepreneurs’ coexist with ‘defensive and necessity entrepreneurs’, the latter being those who enter a new business not because of market opportunities and innovative ideas, but merely because they need an income to survive. For obvious reasons, this kind of ‘survival-driven’ self-employment is particularly diffused in developing countries (DCs) (see Naudé, 2009 and 2010), where poverty and lack of formal opportunities in the wage sector often push a large number of people into ‘entrepreneurial’ activities ranging from street vending to traditional and personal services (in most cases within the informal sector of the economy, see Ihrig and Moe, 2004; Maloney, 2004; Sonobe, Akoten and Otsuka, 2011).

Empirically a world-wide research project, the ‘Global Entrepreneurship Monitor’ (GEM), has been collecting survey data using standardized definitions and collection procedures on potential and actual entrepreneurship since 1999, and now covers 60 developed and developing countries (see Zacharakis, Bygrave and Shepherd, 2000; Reynolds _et al._ , 2005; Acs, Desai and Klapper, 2008). This project reports the rates of business start-up and of self-employment across different countries of the world, but makes it clear that these statistics comprise both ‘opportunity-motivated’ entrepreneurs and those driven by necessity, the latter being defined as those who have started their own firms as a consequence of the following personal situation: “because they cannot find a suitable role in the world of work, creating a new business is their best available option” (Reynolds _et al._ , 2005, p.217).

Within this context, the purpose of this paper is to provide a contribution to the identification of the role of entrepreneurship in economic growth by mapping out: 1) the different microeconomic determinants of new firm formation; 2) the relationship between 2
camps comprising a total of 1,953 majhee blocks.<sup>18</sup> The camps were spread across 10 mauzas and five unions in the two sub-districts of Ukhia and Teknaf.<sup>19</sup> The average number of households within the 1,953 blocks was 103.

The survey was designed to visit 200<sup>20</sup> clusters of 13 households each in the refugee camps, and a two-stage stratification was implemented to identify the refugee sample. In the first stage, blocks (enumeration areas for camps) were selected for a full household listing, using a probability of selection proportional to the size of the block. After selecting the block, a full listing was carried out in each block as will be explained in the next subsection. In the second stage, 13 households were randomly selected from the full household listing.<sup>21</sup> For the adult module, a third stage was implemented within each household, in which two adults<sup>22</sup> were randomly selected from all the adults living in the household.

# _Selection probability for a Rohingya displaced household_

Where, is the target number of enumeration areas to be selected (target of 200 blocks) zz is the number of refugee households in enumeration area according to IOM NPM R12 RR kk nn is the number of listed refugee households in enumeration area kk (selection target of 13 households RR per block) kk ll kk On the right-hand side of the equation, the first term represents the probability of selecting the block in the first stage, and the second term the probability of selecting the household in the second stage. kk

> 18 In the Rohingya community, local leaders are referred to as “majhee(s).” A majhee block thus refers to a collection or subset of households within the same area in a camp that are overseen by the same local or community leader. In the absence of a standard division of camps into sub-blocks across all 34 camps, IOM NPM executed population counts using majhees as their focal points, and the areas governed by individual majhees as the lowest geographical units.

> 19 Bangladesh is divided into eight divisions (bibhag) and 64 districts (jela, zila, zela). For the purposes of local government, the country is divided into upazilas (sub-districts), union councils (or rural councils), and then mauzas. Urban areas are divided into municipalities or city corporations and then into wards. The administrative structure of Bangladesh thus includes: Division (admin 1)

> >> district/zila (admin 2) >> sub-district/upazila (admin 3) >> union/municipalities (admin 4) >> mauza/wards (admin 5).

> 20 During the listing, eight blocks were not listed due to collective refusal by households.

> 21 The survey covers at least one block in all the camps except Camp 20 Ext (CXB-234).

> 22 An adult is defined as any household member aged 15 years or older.

11
- 19 -

Table 2.1c: CENTRAL GOVERNMENT INTEREST PAYMENT AS PERCENTAGE OFTOTAL EXPENDITURES 9/

||197n|197b|1977|1978|1975|1980|1981|1982|1983|1984|
|---|---|---|---|---|---|---|---|---|---|---|
|LAC SAMPLE<br>9i|5.5|?.1|5.1|8.2|8.3|'.4|9.0|12.2|16.6|22.0|

.......................................................................................................... V **Totot** expendltures incLude expenditures **and** lening minus repe,unst.

Toble 2.ld: CENTRAL GOVERNMENT INTEREST PAYMENTS AS PERCENTAGE OF GDP S/

||1975|1976|1977|1978|1979|1980|1981|1982|1983|1984|
|---|---|---|---|---|---|---|---|---|---|---|
|LAC SAMPLE<br>ty|1.1|1.3|I.7|1.8|1.7|1.6|2.2|3.5|4.6|5.3|

.........................................................................................................

a/ Interest payments by subnatlonaL governTents are generalty negligible except for Brazil, where subnational goverrmwnt Interest payments reached about 1X of GDP In 1983. _/ Weighted average.

Source: IMF, Goverrwnent Finance Statistics Yearbook, various years.

Table 2.2: DOMESTIC INFLATION IN LAC COUNTRIES -Annual Percent Change of Consumer Price Index-

||19Tn|<br>1976|<br>1977|<br>1978|1979|<br>1980|<br>1981|<br>1982|<br>1983|<br>1984|<br>1985|
|---|---|---|---|---|---|---|---|---|---|---|---|
|ARGENTINA|182.3|**443.2**|176.1|<br>175.5-|159.5|<br>100.8|<br>104.5|<br>184.8|<br>343.8|626.7|<br>672.1|
|BOLIVIA|8.0|4.5|8.1|10.4|19.7|47.2|28.6|133.3|<br>269.v|-181.|4 11749.6|
|BRA21L|29.0|42.0|43.7|38.7|52.7|82.8|105.6|97.8|142.1|ifr.|0226.9|
|CHILE|374.7|211.8|91.9|40.1|33.4|35.1|19.7|9.9|27.3|19.9|30.7|
|COLONMIA|22.9|20.2|33.1|17.8|24.7|26.5|27.5|24.5|19.8|16.1|24.0|
|COSTA RICA|17.4|3.5|4.2|6.0|9.2|18.1|37.1|90.1|32.6|12.0|15.1|
|DOMINICAN<br>REP|14.5|7.8|12.8|3.5|9.2|16.8|7.5|7.6|4.8|<br>27.0|37.5|
|GUATEMALA|13.2|10.7|12.3|8.3|11.3|10.8|11.4|0.3|4.5|3.4|18.7|
|MEXICO|15.2|15.8|29.0|17.5|18.2|26.4|27.9|58.9|101.8|65.5|57.7|
|PANAMA|5.5|4.0|4.5|4.2|7.9|13.8|7.3|4.3|2.1|1.6|1.0|
|PARAGUAY|6.8|4.6|9.3|10.6|28.3|22.4|14.0|6.8|13.4|20.3|25.2|
|URUGUAY|81.4|50.6|58.2|44.5|66.6|63.5|34.0|19.0|49.2|55.3|72.2|
|VENEZUELA<br>.......|10.3<br>....................|7.6<br>..................<br>i........a|7.8<br>...........<br>1...............|7.1<br>.................|12.4<br>................|21.5<br>....|16.2|9.6|6.3|12.2|11.4|

**Sorxuce: lINF Intern tfaont Finorc Statistics,** 1987.