rafmacalaba/datause-encoder-data huggingface dataset:
$8-$9 per month from the investment. For a small firm earning $100 a month, this would be a 8-9 percent increase in profits. If we instead use a market interest rate of approximately 2 percent per month, then firms would earn $3.50 more per month from training, which would be a 3.5 percent increase in profits for a firm earning $100 a month. If firms are more profitable to start with (small rather than micro), the percent increase in profits needed for training to pay for itself becomes even smaller.

From either this return to education or this return to investment approach, we see that an opportunistic estimate of the increase in profits we should see from training is in the 4 to 5 percent range, and the return could be smaller and still compare favorably with regular education and pass a cost-benefit calculation for relatively profitable firms. This will present a challenge for detecting and measuring the impacts of training, since most studies do not have enough statistical power to detect impacts of such a magnitude.

_How can we learn whether or not business training works?_ The approach that many training providers use to measure impact is to simply compare before and after outcomes for participants. For example, as of June 2, 2020, CEFE International’s website claims an impact of 18 million jobs created based on interviewing 459 participants who had participated in training 2 years earlier, and asking them how many additional workers they had hired since training, and then multiplying this average of 1.4 by their total number of participants.<sup>7</sup> Organizations often complement this by case studies of their most inspiring success stories – for example, the ILO’s SIYB website features 10 inspiring stories.<sup>8</sup> Such approaches almost certainly overstate the benefits from training.

The key challenge to learning the impact of business training is being able to compare outcomes after training to a reliable counterfactual of what would have happened without training. The problem with before-after comparisons is that individuals may decide to take part in business training when they are planning on starting a business or expanding it anyway. Even if they do not receive training, they might still grow their businesses. De Mel et al. (2014) provides an example of this from the SIYB training in Sri Lanka. They randomly allocated women without businesses

> 7 - <u>https://cefe.net/cefe impact/</u> [accessed June 2, 2020].

> 8 <u>https://www.ilo.org/empent/areas/start-and-improve-your-business/WCMS_729986/lang--en/index.htm</u> [accessed June 2, 2020].

8
||**(i)**|**(ii)**|**(iii)**|
|---|---|---|---|
|GDP|0.03**|0.04**|0.03|
|GNI|-0.02|-0.02|0.01|
|Employment|-0.01|-0.01|0.12|
|**Aggregate demand:**||||
|Private cons|-0.02|-0.02|-0.00|
|Gov cons|0.00|-0.00|-0.01|
|Aggregate inv|-0.02|-0.02|-0.01|
|Priv investment|0.06***|0.06***|-0.03|
|Gov investment|-0.06**|-0.09*|0.33|
|Exports|0.01|0.01|0.03|
|Imports|-0.03|-0.03|-0.03|
|**External sector:**||||
|Trade balance/GDP|0.05**|0.05**|0.05|
|Current account/GDP|-0.01|-0.01|0.00|
|Terms of trade|-0.04|-0.04|-0.07**|
|**Monetary/Fiscal policy:**||||
|Inflation|0.04**|0.03**|0.02|
|Fiscal balance/GDP|-0.01|0.01|0.02|
|Public expenditures|-0.01|-0.02|-0.05|
|Public revenues|-0.01|-0.01|-0.03|
|Source:|Table A3|Table A7|Table A11|

Note: * p<0.10, ** p<0.05, *** p<0.01 Column (i) of Table **7** makes it clear that

1. Larger countries tend to have higher correlation between GDP and real interest rates. The same result applies to the correlation between real interest rate and private investment, trade balance, and inflation.

2. The correlation between government investment and the real interest rate, on the other hand, tends to decrease with size.

3. All other correlations seem to be unaffected by country size.

# **Persistence**

Next, we turn to the persistence of the key macroeconomic aggregates. Table **8** summarizes the average first-order autoregressive coefficient for various variables, separately for large and small countries, where as before, country size is measured based on population. The results for labor force and land area are presented in appendix B.

We find that GDP exhibits the highest persistence, but the estimated coefficient does not differ much by country size. GNI, government consumption and private consumption are also quite persistent, but again, are also quite similar for small and large economies. The real interest rate, inflation and current account are the least persistent of all the variables considered, and more so in smaller economies.

24
The role of the external environment has been extensively studied in cross-country comparisons of transition economies' growth performance (Sachs, 1996). Fast liberalization, for example, has been shown to lead to productivity growth (World Development Report 1996 and Gelb et al., 1996). Financial discipline imposed by external parties is an essential part of this external environment: when no one financing losses, firms have no choice but to eliminate losses by increasing productivity.<sup>4</sup> There is less agreement on the privatization method that leads to the most effective corporate governance and can be implemented over a significant share of a country's industrial enterprises.

Previous studies (Caves, 1990; Carlin et al., 1995) have argued that the primary rationale behind privatization is to create owners who have the power and incentives to monitor managers and ensure that they act in the firm's best interest. Each approach to privatization, however, may lead to different results. Table 1 illustrates the existing hypotheses on the trade-offs among the three prevalent privatization methods. While management-employee buy-outs (MEBOs) and mass privatization lead to speedy transformation of ownership, they are inferior (or questionable at best) to direct sales to outside owners in ensuring effective corporate governance and better access to skills and capital. This is particularly the case if firms are sold to foreign owners who (as the argument often goes) are able to implement deep restructuring.

It should be noted that important relationships exist between micro factors (initial conditions and internal factors) and the external environment. The influence of external discipline, for example, depends on managerial expectations regarding how binding (credible) these are. Thus a belief that governments will bail out loss-making finns affects enterprise restructuring. A number of studies have examined these relationships, e.g., Pinto et al., (1993) and Claessens and Peters, (1997).

6
- McKenzie, D. and Yang, D. (2015). Evidence on policies to increase the development impacts of international migration. The World Bank Research Observer, pages 155192.

- Mendola, M. and Carletto, C. (2012). Migration and gender dierences in the home labour market: Evidence from Albania. Labour Economics, 19(6):870880.

- Ministry of Finance (2014). Economic survey: Fiscal year 2013/14. Technical report, Government of Nepal Ministry of Finance.

- Mitchell, J. C. (1969). The concept and use of social networks. Bobbs-Merrill.

- Mu, R. and van de Walle, D. (2011). Left behind to farm? women's labor re-allocation in rural china. Labour Economics, 18:S83S97.

- Murard, E. (2016). Consumption and leisure: The welfare impact of migration on family left behind.

- Nandini, A. (1999). Engendered mobilization-the key to livelihood security: Ifad's experience in south asia. Rome: IFAD.

- Seddon, D., Adhikari, J., and Gurunga, G. (2001). The New Lahures: Foreign Employment and Remittance Economy of Nepal. Nepal Institute of Development Studies.

- Stark, O. and Lucas, R. E. (1988). Migration, remittances, and the family. Economic development and cultural change, 36(3):465481.

- Stock, J. H. and Yogo, M. (2005). Testing for weak instruments in linear iv regression. In Andrews, D. and Stock, J. H., editors, Identication and inference for econometric models: Essays in honor of Thomas Rothenberg, pages 80108. New York: Cambridge University Press.

- Taylor, J. E., Rozelle, S., and De Brauw, A. (2003). Migration and incomes in source communities: A new economics of migration perspective from china. Economic Development and Cultural Change, 52(1):75101.

- Thieme, S. (2006). Social networks and migration: Far West Nepalese labour migrants in Delhi, volume 7. LIT Verlag Münster.

- Tilly, C. and Brown, C. H. (1967). On uprooting, kinship, and the auspices of migration. International Journal of Comparative Sociology, 8(2):139164.

21
probability density functions (pdfs) of the parameters. We assume uniform probability density functions, with upper and lower values of the pdfs given by the upper and lower values in the piecemeal sensitivity analysis table. We include initial rent capture in the systematic sensitivity analysis, with the rent capture parameter allowed to take values between zero and one with a uniform pdf.

The results for preferential reduction of barriers with African partners on welfare, output and labor are shown in figures 3-5 and similar figures for the preferential trade agreement with the EU in figures 6-8.. The sample distribution of the welfare results for preferential reduction of barriers in services with the Africa region is depicted in figure 3. We find that in 1.9 percent of the 30,000 simulations yield a negative welfare result, which we interpret as a 1.9 percent probability that preferential liberalization with the Africa region will yield a negative result. A 95 percent confidence interval for equivalent variation as a percent of consumption is: 0.008 to 0.417 around a sample mean of .203.<sup>27</sup> The sample distribution of the welfare results for a free trade agreement with the EU that includes services are depicted in figure 6. In the 30,000 simulations, there are no negative values. A 95 percent confidence interval for equivalent variation as a percent of consumption is: 0.37 to 0.94 around a sample mean of 0.63.<sup>28</sup> In figures 4 and 7, we show ―box and whisper‖ diagrams for the sample distribution of the percentage change in output by sector. Sectors are on the horizontal axis and the percentage change in output is shown on the vertical axis. The bars in the box are the means of the distributions. Fifty percent confidence intervals are depicted by the boxes, while the vertical lines show 95 percent confidence intervals.

The means of the systematic sensitivity results show a similar pattern to the point estimates regarding the expansion of the services sectors. While the confidence intervals are rather tight for most sectors, they reveal a large range of uncertainty for several sectors. With respect to the EU agreement, while the sign of the direction of change does not change within the 95 percent confidence interval, the confidence intervals of expected output change are large for other manufactured food, maritime transportation, coffee and mining (among the expanding sectors) and sugarcane, non-metallic products and other manufactures (on the negative side). For

> 27 90 percent and 99 percent confidence intervals are 0.033 to 0.384 and -0.029 to 0.479, respectively. 28 90 and 99 percent confidence intervals are 0.41 to .89 and 0.30 to 1.07, respectively.

33
<!-- Start of picture text --> Born rural lives rural Born rural lives urban Born urban lives urban<br>.8<br>.7<br>.6<br>.5<br>.4<br>Predicted probability<br>.3<br>.2<br>.1<br><!-- End of picture text --> _Source_ : World Bank Staff estimates based on data from Perceptions of Poverty, Prosperity and Economic Mobility in Nepal, 2014

# **3. Mobility within Generations**

While the idea of lack of correlation between parental income, education or occupation and the outcomes of their children is the fundamental benchmark of a society with high intergenerational mobility, it is unclear whether the same principle would carry over in the analysis of mobility within a generation. A society where a person’s income or living standards 10 years ago, for example, had no bearing on their income today would be a society with a significantly large amount of inter-temporal variation in these outcomes. The lack of serial correlation would imply a lot of random churning in living standards and would not necessarily be desirable.

So how can mobility within generations be measured and analyzed? One possibility is to consider directional income/living standards movement for the same individual or household over a lifetime. A mobile society would be one in which poorer and socially marginalized households can climb up the economic ladder and attain higher living standards. This would require the income of this group to grow faster than the income growth realized by the average households. So in a way the key statistic to measure intra-generational mobility then is simply the growth rate of income of the household between two points in time. But a pre-condition to being able to measure this is the existence of nationally representative data that allow us to observe the same household at two points in time. Unfortunately for Nepal, the panel data that have been collected with the three rounds of the living standards survey have suffered from a high degree of attrition between rounds and are thus not ideal for this analysis. Instead, we use what is referred to as the synthetic panel methodology (Dang and Lanjouw, 2013).

7
girls complete middle school. Overall, results summarized in Table 6 indicate that 12-17 year-old girls who were exposed to the program (for different periods) between 2003-04 and 2007-08 are on average around 3.3 percentage points more likely to have completed middle school (grade 8) relative to the control cohorts. This is equivalent to an increase of 4.5 percent relative to the baseline value of the treatment group.<sup>26</sup> The impacts on middle school completion are larger for girls aged 15-16 (6 percentage points with 5 percent significance) but none for girls older than 16.

We also find some evidence that the program helps girls transition from middle to high school and complete grade 9, especially those younger than 17.<sup>27</sup> The first outcome analyzed is the probability that a girl enrolled in grade 8 during the program period progressed to grade 9 or beyond.<sup>28</sup> The average impact for the entire cohort of 12-19 years old girls is not statistically significant (Table 6). However, for girls aged 15-16, the impacts are positive and significant (5.5 percentage points relative to a baseline value of 58.5 percent). This cohort is also 5 percentage points more likely to complete grade 9 than the control group (Table 7). One reason for this is that these younger girls benefit more from the FSSP as they are not only exposed longer but they joined the program when there was more awareness of its existence. Second, and perhaps more important, only the younger cohorts have the additional incentives induced by the expansion of the FSSP, which began in 2006, to also provide conditional transfers to enrollees in high school.

# **_Labor Market Outcomes_**

Theoretically, the direction of the effects of the FSSP on working behaviors of the beneficiaries in the longer-term is unclear. While the program introduces incentives for girls to spend more time in school and work less, if girls are an important source of labor for the household, they may give up leisure time rather than working less. Moreover, girls are expected to obtain better jobs and higher wages as they progress through school and accumulate more education. At the

> 26 Evidence of similar impacts has been also found for Bolsa Escola and PRAF II, two CCT programs in Brazil and Honduras (de Janvry and others 2006; Glewee and Olinto 2004).

> 27 2007-08, the year of the follow-up survey, may be too early to capture the impacts on this younger cohort (that is, age 15-16) of the program on high school (that is, grade 10) completion, as the majority of girls enrolled in grade 10 at the time of the follow-up survey are aged 15-16

> 28 The transition from middle to high school is a critical juncture in schooling attainment in Punjab as well as the rest of Pakistan, particularly in poor rural communities. In fact, rates of transition from middle to high school were low at baseline. The distribution of school attainment (measured as the number of years of schooling) for adolescents and adults exhibits a significant drop between grades 8 and 9 in both stipend and non-stipend districts.

19
# Table 8

# Loans and Discounts of Government Financial Institutions (billion yen)

||Total|JDB|SBFC|PFC|EXIM|AFFFC|HLC|
|---|---|---|---|---|---|---|---|
|1955|685|374|45|48|39|82|85|
|1960|1519|538|148|125|125|195|202|
|1970|7836|1705|895|709|1522|955|1070|
|1980|41958|5018|4351|4024|5077|3706|12733|
|1990|83722*|9473|7309|7096|6252|5324|40303|

- Notes: JDB: Japan Development Bank.

- SBFC: Small Business Finance Corporation. PFC: People's Finance Corporation.

- SXIN: Export Import Bank of Japan.

- APFFC: Agriculture Fisheries and Forestry Finance Corporation. HLC: Housing Loan Corporation.

* refers to 1989.

Source: Bank of Japan, Economic _Statiatics_ Annual, various issues.

39
Table 2: Descriptive Statistics Enterprise Survey

|**Characteristic**|**Overall**, N = 1,802<sup>_1_</sup>|**Small**, N = 814<sup>_1_</sup>|**Medium**, N = 738<sup>_1_</sup>|**Large**, N = 250<sup>_1_</sup>|
|---|---|---|---|---|
|Experienced Power Outages|||||
|Don’t know|0.3%|0.1%|0.1%|7.0%|
|Yes|45.6%|45.2%|48.8%|40.7%|
|No|54.1%|54.7%|51.2%|52.3%|
|Obstacle: Electricity|||||
|Don’t know|0.0%|0.0%|0.0%|0.0%|
|Does not apply|0.3%|0.0%|1.5%|0.2%|
|No Obstacle|30.0%|30.9%|26.0%|25.2%|
|Minor Obstacle|10.5%|10.2%|13.5%|4.6%|
|Moderate obstacle|13.3%|12.7%|17.3%|10.1%|
|Major obstacle|21.5%|23.0%|13.0%|23.4%|
|Very Severe Obstacle|24.4%|23.2%|28.6%|36.5%|
|Obstacle: Transport|||||
|Don’t know|0.0%|0.0%|0.0%|0.0%|
|Does not apply|1.4%|1.6%|0.5%|0.8%|
|No Obstacle|31.8%|31.5%|37.2%|9.8%|
|Minor Obstacle|7.4%|6.8%|10.7%|7.5%|
|Moderate obstacle|31.8%|32.9%|26.3%|32.1%|
|Major obstacle|17.1%|17.3%|14.0%|29.2%|
|Very Severe Obstacle|10.4%|9.9%|11.4%|20.7%|
|Importance in Relocating: Roads|||||
|Don’t know|0.0%|0.0%|0.0%|0.0%|
|Does not apply|0.3%|0.1%|1.1%|0.9%|
|Not important|15.7%|17.2%|10.1%|3.8%|
|Slightly important|4.1%|4.2%|4.4%|2.1%|
|Important|21.3%|21.9%|20.1%|9.2%|
|Very important|26.7%|25.7%|27.9%|50.3%|
|Extremely important|31.8%|30.8%|36.4%|33.7%|
|Importance in Relocating: Energy Reliability|||||
|Don’t know|0.0%|0.0%|0.0%|0.2%|
|Does not apply|0.0%|0.0%|0.1%|0.9%|
|Not important|16.5%|18.9%|6.1%|3.3%|
|Slightly important|7.4%|7.6%|6.3%|9.7%|
|Important|25.9%|28.0%|15.9%|20.3%|
|Very important|25.2%|21.4%|43.8%|35.5%|
|Extremely important|24.9%|24.2%|27.9%|30.1%|
|Number of Power Outages|1.75 (4.76)|1.81 (4.75)|1.46 (4.84)|1.46 (4.58)|
|Duration of Power Outages|1.74 (5.15)|1.92 (5.61)|0.93 (1.79)|0.96 (2.09)|

> _1_ %; Mean (SD). Observations weighted on firm eligibility.

10
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- 32 -

# 4.2. INDIGENOUS ENVIRONMENTAL RESEARCH AND MANAGEMENT

# 4.2.1. Alaska Eskimo Whaling Commission

In 1976, the International Whaling Commission learnt from the United States delegation that the current estimate for the north Pacific bowhead whale population was 1,200 and that the numbers of whales struck with explosive harpoons and lost had suddenly increased. Two factors were suggested: higher incomes from oil development financing more crews; a decline in a major food alternative, the Northwest Arctic Caribou Herd. (Langdon 1984) While the US delegation abstained, the IWC revoked the standard exemption for indigenous bowhead whaling and imposed a complete ban to take effect in 1977. The Alaskan Eskimo whaling community responded with court actions asserting their aboriginal rights to subsistence whalirg and to self-regulation of the hunt. Over the next few months, the whaling community took a series of initiatives which led eventually to a resolution of the crisis. The Alaska Eskimo Whaling Commission (AEWC) was formed and charged with three missions (ibid): To ensure that whaling was conducted in a traditional, nonwasteful manner.

To communicate to the outside world the cultural and nutritional importance of bowhead whaling.

To promote scientific research designed to ensure the continued existence of the bowhead whale without unnecessary disturbance to Eskimo society.

The AEWC formed a Scientific Advisory Board which, upon discovering that the US government had no regime in place for bowhead whales, proceeded to formulate one and submit it to the IWC. The AEWC regime proposed a quota, measures for registering whaling captains and regulations regarding weaponry and the conduct of the hunt. Debate continued over the size of the quota but it was in principle accepted and there was no absolute ban.

Since 1980, the Science Advisory Board has organised a series of four international conferences on the bowhead whale and administered an annual research program costing about $1 million, with over half the funding from native sources. The board has also worked with outside biologists on improved census methods. The results have validated the claims by Inuit whalers that there are more bowhead than the 1,200 estimate that in p'rt precipitated the controversy. Successive surveys have arrived at increased figures: 2,250 in 1979, 3,900 in 1983, 4,417 in 1985.

Earlier census methodology had relied upon counting bowhead from the edge of the land fast ice as they moved along open leads. Inuit whalers, from observing breathing cracks and hearing the breathing itself, maintained that bowhead continued to move after new ice had formed. They