rafmacalaba/data-use-mentions-extended huggingface dataset:
14

# Box 3: Methodology Used to Construct the Modified IEF Index.

We modify twenty percent of the index constructed by Johnson and Sheehy (I 996) by replacing the two components that relate to the degree of openness of the economy (i.e., trade and capital flows). The original IEF index emphasizes only the consequences of tariffs to evaluate the openness to international trade. We will replace this by considering not only the average level of tariffs, but also the share of industrial output affected by quantitative restrictions. Also, the original IEF evaluates a country's openness to capital flows by examining the degree to which each country interferes or not with foreign direct investment. This evaluation is mostly based on conclusions derived from an analysis of the legal and regulatory framework of the country. We assume a more market oriented, albeit still limited, methodology to measure capital movements by centering only on the existence of premiums in exchange rates as represented by the difference between the market and official exchange rate. Thus, our degree of openness of the economy has three components (i.e., tariffs, QRs, and exchange rate premiums), each with a weight of one third in the final index representing the degree of openness of the economy, and the period under study corresponds to the 1970s and 1980s. Several sources are used, among them Sachs and Warner (1995) and the IMF's International Financial Statistics. The main advantage of this modified IEF index is that it contains more "time series information" than the original series compiled by Johnson and Sheehy.

To construct the index, a value in the range I to 5 is assigned to each of the three indicators of openness previously mentioned and a weighted average of the mean of these three indexes and the remaining eighty percent of the IEF index is calculated. The table in this box represents how the index values were assigned for an economy's degree of openness. To summarize, the countries with a value between the mean and the
and 2012‐13). These are panel datasets in which considerable effort is made to recontact households which relocate between waves as well as to track the components of the households which break up or combine, generating in effect a panel of individuals. See <u>www.worldbank.org/lsms‐isa for details. The analysis is confined to the</u>

> 16 When using the dummy variable approach, the shares are about twice as high (22.6 and 15.2 percent for maize and rice respectively), overestimating the contribution of seasonality to price volatility by a factor two.

16
69

# Table I11.12

# Prices Charged for Contraceptives at Private Pharmacies in Selected Eastern Caribbean Countries (1986 U.S. Dollars)

|Method|Dominica|Grenada|St. Lucia|
|---|---|---|---|
|Orals||||
|Eugynon|$4.63/cycle||$3.06-4.56a/cycle|
|Microgyneni|2.93/cycle|$2.57/cycle|3.33/cycle|
|Logynon||2.46/cycle|3.70/cycle|
|Trinoridiol|2.78/cycle|2.41/cycle||
|Neogynon||2.57/cycle|3.37/cycle|
|Diane|8.13/cycle||8.13/cycle|
|Perle|||1.09/cycle|
|Condoms|.09@|.35@b|.04 or .12|
|Injection|7.22@|||
|Neo Sampoon|1.85 for 20|||
|Other Foam Tablets||2.07 for 12||

Source: Data collected by The Urban Institute.

- a. Price range reflects price of high-low dose pills.

- b. These were high quality Sheik condoms, which explains some of the price differential.
|-0.091|0.551|2,582|
|Height and weight measured|0.847|0.120**|0.037|2,582|
|Proportion of core physical exam<br>activities completed|0.341|0.107**|0.040|2,582|
|Average consultation time (minutes)|12.59|0.474|0.744|2,526|

Note: data from direct clinical observations conducted at the rural health centers. The PBF impact is estimated using a propensity score weighting specification as only follow-up observations are being included in the analysis. Standard errors are clustered at the district level.

a Data from consultations of adults aged 40 and above. Regressions include controls for facility’s exposure to the collaborative quality improvement and citizen scorecards interventions, Region, age and gender of patient, gender age and position of provider, and a binary indicator for experience of over 20 years. b Data from consultations of children under-5. Regressions include controls for facility’s exposure to the collaborative quality improvement and citizen scorecards interventions, Region, gender age and position of provider, and a binary indicator for experience of over 20 years. Level of statistical significance: *p<0.10; **p<0.05; ***p<0.01 27
By contrast, transfers from central government—often a vital component of municipal revenues—have had mixed outcomes following the crisis. While local governments in some nations have experienced a drop in transfers, others have seen a higher flow of capital as central governments take measures to combat declining economic output. European municipal associations illustrate these mixed outcomes, with a slight majority of 55% of respondents to an URBACT study indicating lower intergovernmental transfers from the national budget. The case of Flanders, Belgium demonstrates that real cuts in transfers also may take place in other forms. There, nominal transfers for police services have increased but they finance a lower share of costs than previously because of the rate of growth of wage costs. On the other hand, transfers to municipalities in several countries (Australia, Canada, Denmark, Finland, Norway, Spain, the USA, Colombia, Chile, Brazil, Mexico, etc), have increased to compensate for lower own tax revenues or additional resources have been made available as part of economic stimulus packages.

Finally, many governments lost streams of revenue derived from capital holdings and other assets. For example, Belgian municipalities experienced major losses from the bankruptcy of Dexia bank, in which they owned shares (because Dexia was originally a bank providing governments and local public finance operators with banking and other financial services). Local governments in a number of other European countries, including Iceland, the UK and the Netherlands, lost substantial assets deposited in banks that failed during the crisis.

# **Impact on Municipal Expenditures**

Municipalities in many countries have been forced to reduce current expenditures not only for maintenance and operation but also for some categories that have been relatively unaffected in past crises, such as public sector employment and social services. Making matters worse, these cutbacks are of course not without social and political repercussions. For example, Argentine President Cristina Fernandez has recently made severe cuts to Buenos Aires’ budget, impacting spending on everything from public transportation to hospitals to the electricity system. As a
We next quantify at the state-industry level how changes in the size and industrial organization of the organized sector relate to changes in the employment share of the unorganized sector for the state-industry. Our outcome measures consider both changes in the fraction of the state-industry’s employment in the unorganized sector and raw changes in the unorganized sector’s size. Our metrics on industrial organization are indicator variables for where in the establishment size distribution the employment growth occurs and the degree of initial entrepreneurship. We control for state and industry fixed effects.

Three important themes emerge from this empirical analysis:

1. _In both manufacturing and services, the most important factor for reducing the unorganized sector share is growing the overall size of the organized sector_ – Our primary estimations separate the overall growth of the organized sector from its establishment size distribution and entrepreneurship rates. Growth in the sector size, versus the particulars of where in the establishment size distribution growth occurs, is central to reducing the share of stateindustry workers in the unorganized sector. Demonstrating the strong persistence of the unorganized economy, however, this overall organized sector growth has limited effect on the absolute levels of unorganized sector employment. Its effect comes through increase in the overall state-industry size, and thereby reducing the unorganized sector’s share.

2. _Within manufacturing, the most consistent force for reducing the unorganized sector share is growth in employment in large establishments in the organized sector. High rates of initial entrepreneurship in the organized sector are also important since 2000_ – Our manufacturing data allow us to observe three time periods: 1989-1994, 1994-2000, and 2000-2005. Across these three periods, state-industries with their strongest relative employment growth in large establishments with more than 100 workers have the greatest declines in unorganized sector shares. Moreover, these state-industries experience absolute reductions in unorganized sector employment levels. In 2000-2005, high initial entrepreneurship rates in the organized sector also closely links with declines in the unorganized sector’s employment share and level. This role is exclusive to entrepreneurs and is not evident in small manufacturing establishments generally.

3
.|Hours last week|43.39|1.13|0.10|43.05|-1.05|-0.5|
|||(18.15)|(1.08)|(0.73)|(18.53)|(1.30)|(0.85)|
||Controls|||||||
||Municipality fixed effects||Yes|Yes||Yes|Yes|
||Timefixed effect||Yes|Yes||Yes|Yes|

_Notes:_ Each entry in panels A–B, rows a–c, columns 2–5 represents a coefficient from a different regression. All the regression include municipality fixed effects, year fixed effects, a vector of maternal background, and a vector of municipality characteristics. All the regressions control for cluster by municipality, and standard error are consistent for heteroskedasticity and error correlation within municipalities over time.

*** _p_ < .01   ** _p_ < .05  * _p_ < .1

_Source:_ Underlying data are from the Chilean socioeconomic household survey (CASEN) and administrative data from the Ministry of Education for 1990–2011.

34
## 5.3 Endogeneity Concerns

The initial papers using the growth regressions undertaken above (Glaeser et al, 1992, Henderson et al 1995 and Combes, 2000) did not address possible endogeneity issues. It is likely that in a cross section there are unobserved factors correlated with 2003 characteristics and employment or that there is a problem with reverse causality (Combes and Gobillion, 2015). For example local amenities, such as transport infrastructure, that we have not controlled for might attract firms and increase employment, but also lead to increased density. Using employment growth rather than its level, as the dependent variable, which has been undertaken in this paper, partly mitigates these concerns as it is more plausible to think that amenities are not correlated both with density and _growth_ in employment than that they are not correlated with both density and the level of employment.

But we do acknowledge that there are endogeneity concerns. The concern that has been addressed in the literature is instrumenting current population density with historical values (Combes and Gobillion 2015, Collier et al 2018). We have obtained district population totals from the 1970 census (Møller‐Jensen and Knudsen, 2008). Table 7 shows the IV regressions when we instrument population density for 2000 using the 1970 values. The instrument is always very strong, but the estimated coefficients are, like in OLS, fairly small and never statistically significant. It would be better to use population values from further back in time, but we currently do not have access to such data by district in Ghana.

Other endogeneity concerns have been addressed in other papers using system GMM or firm‐level panel data. System GMM requires at least 3 time periods. We could use the 1987 firm census data, but we have concerns about the coverage of firms in the more rural districts because it is clear from the data that the coverage of firms in rural areas was limited (Kerr and McDougall, 2019).

# 6 Conclusion

In most African economies manufacturing output and employment is growing, but
# **2.2 Context: The Indonesian Crisis**

The East Asian crisis struck after an extended period of industrialization and economic growth, which was in part driven by expansion of labor-intensive exports (Dwor-Frecault et al., 1999). After a reduction in FDI flows in response to the depreciation of the Thai Baht, the Rupiah depreciated dramatically, precipitating a sharp increase in inflation rates. Interest rates were raised to defend the currency, which exacerbated the decline in demand. GDP growth contracted severely in 1997, and fell in absolute terms by over 13% in 1998. Manufacturing was one of the first and hardest hit sectors due to its greater reliance on imported inputs, exposure to changes in foreign demand (particularly given the importance of intra-regional trade) and greater reliance on external financing, often in foreign currency, which became an enormous burden post-devaluation. The drop in manufacturing output both preceded and exceeded the drop in aggregate GDP. In fact, the census data suggest that manufacturing suffered its largest decline in 1997 already.

The crisis also led to the end of the Suharto regime, which exposed firms with ownership connections to Suharto to greater competitive pressure (Fisman, 2001; Mobarak & Purbasari, 2008). To the extent that such firms had been able to generate high profits (and consequently record high productivity) during the Suharto era by virtue of such connections, the removal of Suharto might attenuate the relationship between observed productivity and firm survival, even though the likely concomitant reduction of cronyism associated with connectedness is efficiencyenhancing rather than scarring. By virtue of having detailed information on political connectedness with the Suharto regime at the firm-level compiled by Mobaraq and Purbasari (2008) we are able to explore this issue.

The fall of Suharto also sparked pro-labor pressures and precipitated the introduction of more stringent labor market regulation. During the New Order government minimum wages had been low and enforcement was fairly lax. In real terms, they collapsed during the crisis, yet they 7
To overcome discrepancies in temporal coverage and currency terms (constant and current), and to keep the data consistent and comparable for countries across the world, shares from sub-national statistics are calculated and then applied to a national total to derive a calibrated number at the sub-national level. The national totals are obtained from the World Development Indicators (World Bank, 2019) and averaged over three years around 2010. For a few countries, which do not report their national agricultural GDP in the WDI database, sums of all agricultural GDP components are used as proxies.

The calibrated statistics are then linked to grids through a shapefile of the Global Administrative Unit Layers (GAUL) that maintains global geographic layers with a consistent and comprehensively unified coding system (FAO, 2015). Then, we overlay the GAUL administrative boundaries on the grid network to assign the corresponding codes of the administrative units to each grid. For areas where sub-national AgGDPs have different administrative areas than GAUL, the GAUL areas are merged or split to match the sub-national AgGDP area.

# **2.3 Spatial Allocation Model**

After constructing all the components, we define a spatial allocation model in a crossentropy framework following (You, Wood, et al., 2014) to allocate administrative statistics to 5 arc-minute pixels.<sup>8</sup> National and sub-national AgGDP values are used as a constraint, while the distribution of crop, livestock, fishery, and forestry production (hunting is included in non-timber products of forestry) is used to create priors for estimating pixel-level AgGDP. Measurement units are unified using deflators and exchange rates.<sup>9</sup> The first step is to transform all real-value parameters into corresponding probabilities. Let _Si_ be the share of the total agricultural GDP allocated to pixel _i_ within a country x. _AgGDPi,x_ is the agricultural GDP allocated to pixel _i_ in country _x_ and X is a set including all pixels that fall within the boundary of a nation. Therefore: Let _PreAgGDPi_ be the pre-prior allocation
# Table 13. OLS

||Blue Collar<br>|Households<br>||Households<br>|with Low Edu<br>|cation<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(1)|(2)|(3)|
|Proportion of Foreign-Born in the Population|1.123*|-0.128|-0.0154|1.098*|-0.108|0.0367|
|Constant|(0.512)<br>0.279***|(0.0857)<br>0.190***|(0.0763)<br>-0.724|(0.517)<br>0.258***|(0.0890)<br>0.161***|(0.0707)<br>0.238|
||(0.0447)|(0.00121)|(0.553)|(0.0465)|(0.00126)|(0.459)|
|Observations|52|52|52|52|52|52|
|R-squared|0.095|0.959|0.995|0.086|0.959|0.994|
|Year Controls||X|X||X|X|
|Regional Controls||X|X||X|X|
|Labor and Demographic Controls|||X|||X|

Notes: * p<0.10, ** p<0.05, *** p<0.01. _Source_ : LFS 2011 and 2013. World Bank author’s calculations.

# Table 14. OLS

|
crowding out effect (on the intensive margin) is not diminishing, and possibly getting larger, over time. It is possible to conduct the same analysis on informal loans exchanged with other households. Appendix Table S1.8 presents the treatment effects on interhousehold loans received/given by ultra poor households. The program lowers the value of informal loans received by ultra poor households, while it increases both the likelihood of giving loans and the value of loans given by ultra poor to other households. As a result, in net, value of loans received by ultra poor household goes down, by nearly 100% relative to the control group. This is true for both loans within and outside the village. These findings complement the results reported in Bandiera et al. (2017) where I analyzed the treatment effects on likelihood of receiving and giving loans (see Table IV). I showed that the program increased the likelihood of both receiving and giving loans by the ultra-poor. The key difference between the results in Bandiera et al. (2017) and those reported here is the fact that loans received in Bandiera et al. (2017) include formal loans, whereas the results in Table S1.8 are limited to interhousehold loans alone.

> 23For food transfers, data on the intensive margin (value of transfers) was not collected. However, at midline and endline surveys, respondents were asked to report whether their households’ frequency of borrowing (lending) from (to) households reported as sources (recipients) of food transfers at baseline had increased, decreased or remained the same. In Appendix Table S1.5 I use this information to test if the program affected the frequency of borrowing or lending food to baseline network partners. Overall, I fail to find a consistent effect. Ultra-poor in treated communities were (relative to control) less likely to say their borrowing from baseline sources of food transfers had increased, but they were no more likely to say it had decreased; and there was no significant change in
Rosenzweig, M. And K. Wolpin. 1982. "Govermnent Interventions and Household Behavior in a Developing Country: Anticipating the Unanticipated Consequences of Social Programs." Joumal of Development Economics 10:209-225.

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Thomas
also seen usage in the economics and disaster literature. Firstly, Chen and Nordhaus’s (2015) analysis finds promising results for VIIRS as an economic and population indicator, also when comparing to the DMSP product. Secondly, Zhao, et al. (2018) used the underlying NPP‐VIIRS DNB Daily Data to analyze selected natural disasters. They found that the images were useful for detecting damages and power outages, but that cloud coverage was a major limitation in the assessment.

More recent research utilizing the VIIRS nightlights also points to the limitation of monthly VIIRS in the detection of GDP and urban markets. Hu and Yao (2019) show how monthly VIIRS data have very low correlation for low income countries.<sup>4</sup> The overall correlation is significantly better for middle income countries and annual, corrected data. Even when using the annual mean of monthly data, the results are worse than for annual VIIRS and DMSP.

The data output consists of two variables: the average light radiance from DNB and the number of cloud free days. An important note regarding cloud free days is that for the tropical areas in Southeast Asia one will have months where there are no cloud free days and hence no radiance measurement. To account for months with no radiance value, this paper has interpolated between the month before and after. Furthermore, for cells with little light, one often finds negative light values due to airglow contamination (Uprety, et al. 2017). There is no established standard for how to interpret these measurements, but we did any analysis

> 4 If one includes all the low income countries, the correlation is negative (‐0.02), but it is heavily skewed by the poorest African nations. With the exclusion of the Central African Republic, the correlation improves to 0.12.

7