rafmacalaba/datause-encoder-data huggingface dataset:
Other figures available in the documents seized during the Abu Sayyaf raid, however, suggest potential problems with the Islamic State group’s own administrative data. For example, the captured documents indicate significant output from the At Tayyanah field in the Al Khayr Governorate, but for which our remote data detects no visual activity and was understood to be non-active prior to Daesh takeover. Other simple arithmetic inconsistencies also reduce confidence in the accuracy of this administrative data. It is possible that internal political motivations that may bias administrative data upwards.<sup>5</sup> In such circumstances, estimates from remote sensing serve as a complementary estimate of Daesh oil production and further allows real-time monitoring of said production.

# **3 Conclusion**

Based on our method employing satellite data, we estimate oil production in territory under the Islamic State group’s control peaked briefly above 80,000 bpd in July 2014, declined steeply before a short rise in late-2015, after which it dropped steadily until another peak from November 2015 through February 2016, after which it dropped steeply to an average of less than 16,000 bpd in the rest of 2016. These results lend support to the view that Daesh is financing itself out of other sources such as taxation and extortion rather than oil. Moreover, the results show that Daesh was ineffective in comparison to historical trends at exploiting the fields under its control. For comparison, AFPC and DZPC fields (the majority of which remained under its control during the period of study) produced around 110,000 bpd before the war [6], while Taq Taq field in the Kurdistan Region of Iraq (KRI) alone produced 103,000 bpd in 2014. With respect to revenues, there is no reliable price data on which to base estimates. Early reports recount that Daesh was selling at a discounted price, ranging from $20 to $35 dollars in 2014

> 5The most consequential example of biased administrative data is probably China’s Great Leap Forward in which falsification by lower- and mid-level officials contributed to a famine that killed millions [44]. Recently Chinese air pollution figures have also been falsified in a similar dynamic [43].

15
period. These patterns are also evident in the sub-periods. The persistence evident in India instead comes from the covariance component. Over the 1989 to 2005 period, the positive covariance component counterbalances all of the within- and between-effects; these effects are also substantial in economic magnitude at 7% of the manufacturing employment. In the most recent period, the covariance term dampens by 2.6% what would otherwise have been a 4.7% decline.<sup>10</sup> The process within services in Column 5 is somewhat different. The within component again pushes for a lower unorganized sector rate in 2006 compared to 2001. The between component, however, does not increase or decrease the unorganized sector share. To the extent that employment was reallocated across states and industries within services, it did not do so in a way that was correlated with initial unorganized sector shares in 2001. The covariance term for services is also small in economic magnitude at -0.9%.

# **Empirical Analyses of State-Industry Transitions**

Our second exercise characterizes unorganized sector transitions through a series of linear regressions at the state-industry level. We undertake separate cross-section estimations across each time period: 1989-1994, 1994-2000, and 2000-2005 for manufacturing, and 20012006 for services. This approach allows us to compare the final period for manufacturing and services directly and to see changes in behavior in manufacturing over time. We restrict the sample to state-industry observations that have both organized and unorganized employment in each survey. Our primary dependent variable is the change in the share of employment in the state-industry that is in the unorganized sector.

Our first explanatory variable is the overall employment change in the organized sector during the period relative to the initial size of the state-industry employment, combining both organized and unorganized sectors. We winsorize this metric at its 1% and 99% values to guard against outliers. It has a mean value of 0.16, with a standard deviation of 0.69. We find even stronger patterns without winsorizing, but we prefer the more cautious approach that is often taken with Indian data.

Beyond this broad growth by state-industry in the organized sector, our next two explanatory variables are indicator variables for where in the establishment size distribution this change is most occurring. We create three categories using surveyed employment levels in

> 10 The covariance effect holds for traditional and modern sectors. The within, between, and covariance components for traditional sectors from 1989 to 2005 are -0.022, -0.057, and 0.069, respectively. These components for modern sectors are 0.052, 0.006, and 0.051.

13
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# **Appendix D:  Uncertainty analysis**

To assess the uncertainty in the projections based on the ARIMA models, 95% forecast confidence intervals were calculated. For each observation in the sample, we calculated the 95% forecast confidence intervals and used the upper and lower bounds for comparison with the observed GDP data; i.e., we calculate the differences to observed data based on these two values. Hence, there are two additional samples: one on the upper and one on the lower confidence region. The mean and median for these two samples are shown in table D1.

Table D1: Mean and median of the sample differences using either the lower bound projections or the upper bound projections of the 95 percent forecast confidence intervals.

||**t+1**|**t+**|**2**<br>**t+3**|**t+**|**4**<br>**t+**|**5**|
|---|---|---|---|---|---|---|
||low up|low|up<br>low up|low|up<br>low|up|
|**Mean**|-11.09 6.97|-22.95|14.18<br>-37.94 20.95|-56.15|27.06<br>-80.47|33.02|
|**Median**|-9.14 5.86|-19.10|13.10<br>-31.10 20.31|-44.95|27.79<br>-59.29|34.15|

A large range can be found for the differences in the post-disaster years according to these 95 percent upper and lower confidence intervals of the projections; yet there is a clear trend to negative differences. The test for the lower and upper confidence bounds of the projections are however not useful for interpretational purposes due to the high standard errors associated with mean projections, leading either to a full rejection of the Null hypothesis or not.

One remaining question regarding the ARIMA model projections and the validity of the results above is the influence of multiple disaster events. We tackle this issue by looking at a sub-sample within the full sample where 5 years before and 5 years after the disaster event no other major disaster (with losses higher than 1 percent of GDP) occurred. Table D2 again shows the mean and median as well as the sample size.

40
# 1 Introduction

“ _The UN estimates more than 200 million people in the region [Africa] will live in slums by 2020_ ‐ _(UN Habitat 2014). [...]The expansion of slums in Africa will be driven by migration and population growth, which will drive housing need, and the current lack of infrastructure for both the existing and anticipated future housing stock._ ” (Parby et al., 2015) In Ghana, a UN report estimated that 40 percent of the urban population lived in slum areas in 2010 (UN, 2013). World Bank estimates show that Ghana posted an impressive increase in consumption level over the period 1991–2012 that was accompanied by a tremendous structural transformation that brought about a significant shift of the economy out of agriculture towards services, associated with significant population flows from rural to urban areas (Molini and Paci, 2015).<sup>4</sup> The scope of this study is shedding new light on the relation between internal migration and household welfare. Do internal migrant households fare better than households left behind in terms of consumption? If so, what is this improvement attributable to? Do they gain access to different work opportunities in destination areas?

Internal migration in Ghana has been an important phenomenon since the consolidation of British colonial rule in 1896 and the twin boom of cocoa and mining sectors (Jedwab and Moradi, 2012). Beals and Menezes (1970) argue that temporary migration, motivated by regional variations in the seasonality of agricultural production, fostered economic production and growth, particularly in the agricultural sector. Using data from the 1960 census, Beals et al. (1967) find that inter-regional migration is responsive to income differentials and that education is negatively correlated with migration, remarkably among males. Caldwell (1968) studies rural-urban migration using survey data and estimates a positive effect of households’ own income, being a male, being educated and having friends or relatives at destination on the likelihood of migrating, whereas age enters negatively into the migration equation.

Molini and Paci (2015) show that internal migration has gained a continuously increasing role over time: around 1.5 million people moved to urban areas between 1991 and 1998, 1.9 million between 1998 and 2005, and 4.7 million over the period 2005-2012. Internal migration, often times from rural to urban areas, accelerated urbanization: internal migration rates increased from 38.6 percent in 2000 to 43.3 percent in 2010 according to data from the latest two Population Censuses, and the population living in urban areas grew by about 28 percent over the decade starting in 2000 and reached 24 million in 2010. In 2012, according to data from the Ghana Living Standard Survey, the population was equally split between urban and rural areas in contrast with a 7-to-3 ratio in 1991.

> 4 Table 1 shows that consumption level increased by 93 percent between 1991 and 2012.

2
Information and comments obtained from company representatives appear to confirm these conclusions. Before presenting that information, however, it is necessary to warn the reader about its low quality. Polish companies keep poor records, and company memories were seriously disrupted by the recent radical changes in management. No data were obtained from Oswiecim (producer of trichloretylene), and the information from other companies was often incomplete. There were no other sources of data to fill in the gaps.

First, company statistics do not indicate that intentional dumping occurred. As a rule, export prices were higher than domestic prices. There were three exceptions. One was sodium carbonate; export prices received by Jarikowo in 1984 were some 19 percent lower than those received for domestic sales, while for Inowroclaw in the period 1982-85, the difference averaged about 33 percent (note, however, that the antidumping case was initiated in 1982). According to both producers, the price differential resulted because of the low, regulated COMECON prices. The losses were made good through subsidies. For Janikowo, subsidies amounted to almost 56 percent of export revenue; for Inowroclaw, they amounted to 5.3 percent in 1982, 14.7 percent in 1983, 13.3 percent in 1984, and 8.9 percent in 1985. The second case is that of copper sulphate, for which export prices were below domestic prices in 1984. In the third case, silicon carbide, export prices were lower than domestic prices in 1985 and 1986. In neither case did producers receive subsidies to cover any of their losses.

Second, all the producing companies claimed that the EC actions had no significant impact on their performance either at the time the actions were taken or later and. therefore, that they made no adjustments in production and price policies or structure. This claim is supported by data on production output in the year of initiation of the action and in the three preceding and three following years for four companies that provided full information (table 4). In all four enterprises, output was larger in each of the years following the year of initiation than in the year of initiation or in the preceding period. Clearly, the antidumping actions did not disrupt total production,

- 14 _
**_Table 3._ HIV Status and Occupation**

|VARIABLES|Textile workers|w/controls|Domestic<br>workers|w/controls|
|---|---|---|---|---|
|Textile worker|0.28***|0.10**|||
||(0.06)|(0.05)|||
|Domestic worker|||0.12***|0.05*|
||||(0.03)|(0.03)|
|Controls|No|Yes|No|Yes|
|Observations|3,318|3,318|3,318|3,318|

_Notes_ : The table shows marginal effects calculated at the mean from probit coefficients. Robust standard errors in parentheses are corrected for the clustering of the residuals at the cluster level. Controls include age, age sq, dummies for age, age sq, dummies for education (primary, secondary and tertiary), urban status, durable goods, religion, and dummy for whether she is widowed or divorced.

Although many factors may contribute to domestic and textile workers’ higher infection rates, one potential vulnerability that arose during qualitative work was condom accessibility. While almost all (96%) of the interviewed textile workers believed condoms were readily accessible, only 84% of interviewed domestic workers believed so. Further, more textile workers had been tested for HIV (97% compared to 83% of domestic workers), as have their partners (51% of textile workers’ and 37% of domestic workers’). These differences are also likely linked to accessibility: 76% of textile workers have a clinic _at their workplace_ , while 27% of domestic workers have a health clinic _near their workplace_ . That said, women employed in garment factories are less likely to participate in the decision about whether or not to use contraception, with only 88% of surveyed women reported to do so, compared to 93% of all women and 97% of domestic workers (DHS 2014).

Women working in the textile sector used to benefit from the HIV prevention and treatment services offered by the Apparel Lesotho Alliance to Fight Aids (ALAFA). Established in 2006 in response to the high prevalence of HIV among textile workers, ALAFA provided free condoms, HIV testing, training on safe sexual practices, and other activities to raise awareness across garment factories. The program’s crucial role in addressing the issue of HIV/AIDS was recognized by surveyed textile workers, who credited ALAFA with increasing their knowledge and awareness of HIV and for helping to provide condoms at work (“Yes we negotiate using condoms with partners, women are now smart. We have also received a lot of capacity sessions on HIV issues in our textiles. We know our rights.”). In fact, the workplace was mentioned as the main source for condoms by the majority (53%) of textile respondents, ahead of stores (11%) and clinics (24%) during the interviews conducted in 2015. The initiative also led to some small adjustments in 43
Table 1: Summary Statistics

||**Formosa **|**provinces**|**Contro**|**l provinces**|
|---|---|---|---|---|
||Mean|S.E.|Mean|S.E.|
|**_Panel I. Fishery Characteristics: Indiv_**|**_idual Level _**|**_(Labor Force _**|**_Survey _**|**_2015)_**|
|Total Monthy Income (’000 VND)|7,475.4|8,294.5|6,232.0|12,073.8|
|Monthly Income from Fishery (’000 VND)|7,396.3|8,321.3|6,191.7|11,976.7|
|Work Hours (per week)|62.11|15.03|54.47|13.49|
|Age|39.52|12.73|37.46|11.01|
|Gender:|||||
|_Male (%)_|98.53|12.04|92.28|26.69|
|_Female (%)_|1.47|12.04|7.72|26.69|
|Educational Attainment:|||||
|_No Training (%)_|26.65|44.27|42.90|49.50|
|_Primary School (%)_|38.88|48.81|41.27|49.24|
|_Secondary School (%)_|31.54|46.52|12.79|33.40|
|_High School (%)_|2.93|16.90|2.12|14.40|
|_College (%)_|0.00|0.00|0.93|9.58|
|Observations|4|09||2,268|

**_Panel II. Fishing Intensity: Monthly Boat Detection (VIIRS 2013-2015)_**

a. Near-shore ( _≤_ 20 _nm_ ):

|_Boat Detection Likelihood (%)_|59.13|49.16|39.78|48.94|
|---|---|---|---|---|
|_Number of Boats Detected_|4.62|8.20|3.33|7.84|
|Observations (grid-month)||25,560|8|4,960|
|b. Of-shore:|||||
|_Boat Detection Likelihood (%)_|72.84|44.48|54.62|49.79|
|_Number of Boats Detected_|5.09|7.10|3.00|5.38|
|Observations (grid-month)||51,624|20|7,324|

Note: this table presents the descriptive baseline statistics for the primary fishery characteristics – using data from the Labor Force Survey 2015 (Panel I), and fishing intensity – using monthly-interval satellite data from VIIRS Boat Detection module 2013-2015 (Panel II). “Formosa provinces” refers to observations belong to the four Formosa-affected provinces (Ha Tinh, Quang Binh, Quang Tri, and Hue). “Control provinces” refers to observations belong to all provinces south of Phu Yen, i.e. distant from the Formosa-affected region. 1 USD=22,550 VND (Vietnam Dong) as of December 31st, 2015. In Panel II, “Boat Detection Likelihood” refers to the probability that a marine pixel was “lit”, i.e. was detected with at least one boat in a given month. “Number of Boats Detected” refers to the total number of boats detected in a pixel in a given month. A pixel’s resolution is 10-mile-square. “Off-shore” region covers all marine pixels located between 20 and 80 nautical miles from the shoreline.

31
We begin by estimating the prevalence of firm death. Existing estimates of the rate at which small firms die in developing countries range from as low as 3 percent per year (Frazer, 2005; Davies and Kerr, 2015) to over 30 percent annually (Fajnzylber et al, 2006; Nagler and Naudé, 2017). We find the rate of firm death is approximately linear over time horizons up to five years, averaging 8.3 percent per year, but then declines over longer time horizons, so that firm death rates average less than 5 percent per year when looking over intervals of 10 years or longer. The result is that half the stock of firms operating at a given point in time will die within the next six years.

We then examine which firms are more likely to die, through examining the extent to which firm and owner characteristics predict firm death. Younger firms are more likely to die, with the first year particularly failure-prone: a firm in its first year is twice as likely to die in the next year as a five-year old firm. Retail firms and less profitable firms are more likely to die. Death rates are higher for younger and older entrepreneurs than for middle-aged owners, and higher for female-owners than males. Firm death rates are higher in richer developing countries than poorer countries.

Finally, we turn to why firms die. We consider three separate theories of firm death. The first views firm death as arising from firm competition and firm-level shocks causing firms to make losses and exit. Here firm death is involuntary, tends to cull less productive firms, and lowers the income of the owner. A second theory predicts firm death as arising from occupational choice decisions of the owner. Firm death can be voluntary here, with owners choosing to shut down when better outside opportunities arise, with less impact on poverty and less selectivity on productivity. A final theory is that firm death results from non-separability of business and household decisions due to imperfect markets. The result is that illness and shocks in the household can cause the business to have to close, resulting in income loss for the owner and not necessarily the least productive firms closing.

We bring these three theories to our data by examining the extent to which firm death occurs for more, or less, productive firms, measuring the change in total labor earnings of the owner after death, and using cause of death data. On average, firms that die are less productive to begin with, and their owners suffer a fall in labor earnings. This is most consistent with the firm competition theory, and least consistent with firm death being a voluntary occupational choice. However, we find considerable heterogeneity in the cause of death, with subsets of firms 3
# Table 3.6 REGIESSION COEFFICIES ON SOURCES OF INCWME, WITH CONSUMPTION OF NUTRIENTS AS DEPENDENT VARXABLES

||Calories|Protein|Fat|Carbohydates|Calcim|Iron|VitaminA|Thiamine|Riboflavin|Niacin|VitaminC|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||(gr)|(qr)|(qr)|(in)|(mg)|Il|(ug)|(mg)|_(uq)_|(u)|
|Java||||||||||||
|Source<br>of||||||||||||
|Household<br>Income:||||||||||||
|Agriculture|0.1296|0.1103|*|0.1445|*|0.0739||*|0.1099|0.1669||
|Irdustry|0.:037<br>|- 0.0783|*|0.1178|0.1051|0.0901|*|0.0918|0.0740|0.0792|0.2297|
|Grvernment|*|*|*|*|*|^|*|*|*|*|**a**|
|Other|-0.1086|-0.0764|*|*|*|-0.0976||*|*|*|*|
|Outer<br>Islands||||||||||||
|Source<br>of||||||||||||
|Household<br>Income:<br>Aqriculture|0.1747|0.0932|0.0560|0.2076|0.0837|0.1279||0.1626|0.1085|0.1'L4|0.2612|
|Industry|0.0592|^|*|0.0789|*|*|||0.0509|0.0567|0.1817|
|Government|*|*|*|*|*|*|*|*|*|,|*|
|Other|*|*|*|*|*||-0.1551||*|'|*|
The latter might have social benefits that greatly exceed the private returns, because private agents can benefit from the knowledge embodied in the product innovations of their competitors, as suggested by the Information Spillovers literature. Trade-policy distortions seem to reduce national product innovation rates, although it is international rather than inter-sector differences in tariffs that affect the probability of product innovation. Hence an open trade environment with a dynamic and dense export sector seems to be an important ingredient for maintaining an innovative private sector.

# **References**

Aghion, P., Bloom, N., Blundell, R., Griffith, R. & Howitt, P. 2005. Competition and innovation: an inverted-U relationship. _The Quarterly Journal of Economics,_ 120(2): 701-728.

Aghion, P., Blundell, R., Griffith, R., Howitt, P. & Prantl, S. 2006. The effects of entry on incumbent innovation and productivity. Harvard University, Cambridge, Massachusetts, http://post.economics.harvard.edu/faculty/aghion/papers.html.

Aghion, P. & Howitt. P. 2005. Appropriate growth policy: a unifying framework. Harvard University, Cambridge, Massachusetts, <u>http://post.economics.harvard.edu/faculty/aghion/papers.html.</u> Bamberger, P. 2008. Beyond contextualization: using context theories to narrow the micro-macro gap in management research. _Academy of Management Journal,_ 51(5): 839-46.

Benito, G.R.G., Grogaard, B., & Narula, R. 2003. Environmental influences on MNE subsidiary roles: economic integration and Nordic countries. _Journal of International Business Studies,_ 34: 443456.

Cohen, W. M. & Levin, R.C. 1989. Empirical studies of innovation and market structure, in R.

Schmalensee & R.D. Willig (eds.) _Handbook of Industrial Organization, Volume II_ , New York: Elsevier Science Publishers.

Criscuolo, C., Haskel, J.D. & Slaughter, M.J. 2005. Global engagement and the innovation activities of firms. National Bureau of Economic Research Working Paper 11479, Cambridge, Massachusetts. Djankov, S., Ganser, T., McLiesh, C., Ramalho, R. & Shleifer, A. 2007. The effect of corporate taxes on investment and entrepreneurship, World Bank and Harvard University, <u>http://www.doingbusiness.org/MethodologySurveys/</u> .

15