rafmacalaba/data-use-mentions-extended huggingface dataset:
# Index of Figures

|Figure 1. Analytical model for BNPP-funded surveys ............................................. ......._3_|
|---|
|Figure 2. Credible rules, credible policy and predictable resourcing constitute the<br>institutional environment within any perspective ...................... .<br>8|
|Figure 3 The main components of government employment .................... .................... 11|
|Figure 4. Officials say that the whole government is much more corrupt than their own<br>organization ............................................................<br>_15_|
|Figure_5._Construction of indicators for institutional environment in East Caribbean<br>states ...........................................................<br>17|
|Figure 6. Construction of indicators for performance in East Caribbean states .............. 20|
|Figure 7. Performance and Institutional Environment in sampled Bolivian public sector<br>
number of variables in each model as the number of observations decreases. No models were established for Harari, Dire Dawa, and Addis Ababa, as all woredas (or equivalent locations) were sampled in the survey. For those areas, the sample from EMDHS 2014 is used.

The Ethiopian census has both a short and a long form, and to increase model fit, the models use the long form, including enumeration area (EA) averages of all variables from the census whether found in the survey or not. Separate models for the long and short form are theoretically possible. Unfortunately, this is not possible using the PovMap software, as the program cannot handle the likely correlation between two such models, which would result in inaccurate estimates of undernutrition standard errors.

To mitigate issues arising from the time interval between the 2007 Population Census and EMDHS 2014, only variables whose distributions did not change much between the two are used in the z-score models. A detailed examination of the distribution of all variables found in both survey and census ensured that only similar variables were utilized in the models. Appendix Table A1 shows the survey and census means for variables included in the models. All models include variables that are based on EA or higher-level census means; these are not included in Table A1, as they are comparable by definition.

Variable selection into the estimation models was based on contribution to adjusted r-square, but also considered variable skewness (very skewed variables were excluded as it could result in overfitting to the sample) and correlation between variables (highly correlated variables were excluded). Further, all models include variables generated at levels above the household level, as too high spatial correlation in the error term relative to overall error term can jeopardize accuracy of estimated standard of error of undernutrition indicators [28,29] . The models also include variables as number of children in household and children close in range, which capture within household correlation. See also Jones and Haslett [22], as well as Fujii [16],
# **3.3 Firm Growth**

Without growth, the contribution of the manufacturing sector to economic development and poverty reduction will be modest. The firm surveys have taught us a lot about why some firms grow and other do not. From a policy point of view, a better understanding of the relationships between growth and certain firm characteristics is important, since it can offer guidance as to what type of firms are likely to be relatively successful and good at creating jobs in the future. Identification of such firms would clearly be informative to policy makers. The relationship between firm size and growth is of particular interest in the context of African manufacturing, since most firms in Africa are very small. How realistic is it to hope that some of these firms will grow and become successful large firms in the future? The relation between firm age and growth is also important. For example, if young firms grow quickly, policy measures aimed at encouraging entry may have significant growth effects in the short and medium term.

Ramachandran and Shah (1999) analyze firm growth (in terms of employment) in a RPED-sample of firms from Kenya, Zimbabwe, Zambia, and Tanzania. They draw on the learning model proposed by Jovanovic (1982), modeling firm growth as a function of firm age and firm size variables, plus various variables for entrepreneurial characteristics. They find that firm size and firm age are negatively correlated with firm growth. Sleuwaegen and Goedhuys (2002) obtain a similar result for firms in Côte d’Ivoire, as do Mazumdar and Mazaheri (2003) based on a pooled sample from Cameroon, Côte d’Ivoire, Ghana, Kenya, Tanzania, Zambia and Zimbabwe. Thus, the finding common across these studies is that growth is systematically higher among small and young firms than among large and old firms.

The negative relationship between size and growth suggests an important role for small firms in the development process, in that this is where we should expect to see most of the growth. It is
process for large _T_ . The test is not seriously affected by outliers for large samples ( _T_ > 3000).

The KL test has good power only for large and non-monotonic changes in the GARCH parameters for any data generating processes for the absolute returns rather than the squared. The KL (2000) test shows good power for detecting changes in the variance of the error terms in the GARCH process and appears fully robust to outliers. Finally, as the sample size ( _T_ ) increases the performance of the test improves even for small change points.

We use both monthly and daily data on individual commodities in order to account fully for differences across commodity markets since both the mean and volatility in individual prices exhibits different break points. Additionally, the use of individual prices allows us to examine whether the relationship between prices of different commodities has changed over time. Further, a measure of how this relationship has changed permits an examination of the role of financial market integration in determining price dispersion or synchronization. Finally, we introduce robustness checks given the scale-dependence of measured volatility.

# **5. Results**

Both tests highlight the heterogeneity across individual commodities. Even in periods where volatility breaks are more common it is far from the case that all, or even most commodities, exhibit the same regime shifts. This can be illustrated by Chart 2 below, which shows the percentage of commodities in our sample for which we detect a break in any given decade. As Chart 2 highlights, in most decades the proportion of commodities that experience a break is relatively low (below 10-15 percent using the KL test and less than 50 percent using the IT test). Even in decades of very high volatility, like the 1910s, 1940s, and 1970s there is a number of commodity prices which did not exhibit breaks in volatility.

13
12 A third objective is to enhance the transparency of cross-subsidies embedded in pricing schemes. The HCPM is likely to be the most accurate of the approaches, since it incorporates economic optimization and thus will calculate more accurate estimates of the costs of networks delivering subsets of the services provided by a complete network (stand-alone costs). Such computations are necessary for determining the presence and amount of cross-subsidies. Once again, we present a table where both the HCPM and the accounting methodologies are shown together.

|**Policy**<br>**Objectives**|**Accounting**<br>**Approach**|**HCPM**|
|---|---|---|
|Benchmarking|Accounting models do<br>not set independent<br>benchmarks.|All sides can<br>recognize<br>independent<br>benchmarks.|
|Information<br>Control|Determined by<br>accounting rules.|Determined by public<br>data inputs.|
|Cross-Subsidy<br>Transparency|Accounting models do<br>not enable calculation<br>of stand-alone costs;<br>thus, subsidies cannot<br>be calculated.|Greater optimization<br>improves estimate of<br>cross-subsidies.|

# **Theoretical validity**

The credibility of any model proposed for policymaking is determined by the adherence of the model to theoretical precepts. Such precepts include the realism of the model's treatment of customer locations, the incorporation of economic optimization, and the usefulness of the model for calculating true long-run incremental costs.

The HCPM accepts fully disaggregated customer location data and "builds" plant to individual customer locations, as mentioned in earlier sections of this paper. This characteristic helps in convincing skeptics who feel that cost models build "fantasy" networks, and is likely to result in more realistic cost estimates. Accounting models have no provision for handling customer locations on a prospective basis, since
> <mark>All variables described above are based on farmer self-reports. In addition, we include key geospatial variables, which are provided as part of the publicly available data files. These include agro-ecological zones, distance of the household from the nearest market and population center as well distance of the plot from the household (in kilometers), plot slope, elevation, a wetness index and soil quality indicators. The soil quality indicators contain binary variables for nutrient availability, nutrient retention, rooting conditions, oxygen availability, excess salts, toxicity, and workability. These variables are equal to one if there is no or a slight constraint to the soil in each dimension. To summarize these variables into a single dimension, we compute a soil fertility index using a principal component analysis (PCA), which also employs a regression method to predict factor scores</mark> (Rencher, 2002) <mark>. All the geospatial variables outlined above are obtained from remote sensing products and integrated via the geocoordinates of the interviews (without offset). They are provided alongside the raw survey data, and their integration was therefore conducted prior to the creation of the HP. More information, along with their origin, can be found in the surveys’ online documentation (see Table 1).</mark> <mark>A few crucial processing steps, undertaken while creating the HP, must be highlighted:</mark>

- A fraction of plot areas is self-reported, and not measured with handheld GPS devices. Due to the importance of that variable to calculate key agricultural variables such as yields, and known inaccuracies of self-reported plot areas, we impute missing GPS area values. We employ a predictive mean matching method with five nearest neighbors, using self-reported areas along with administrative divisions to obtain linear predictions which serve as our distance variable (Little, 1988).

11
# 1 **Introduction**

The causes of the sharp decline in credit to the private sector in the crisisstricken countries of East Asia (as documented, for instance, by Alba et al. (1999)) continues to generate much controversy among economists. Some observers have argued that the fall in bank credit resulted from a credit crunch, that is, increased incidence of credit rationing. Others have suggested that the decline in bank loans may have resulted instead from the reduction in the demand for credit, itself a consequence of the contraction in aggregate demand and the fall in output that accompanied the crisis.

Whether a contraction in domestic credit is due to supply or demand factors has, of course, important implications for monetary and fiscal policies. If banks are _unwilling_ to lend because, for instance, of a perceived increase in the risk of default that cannot be internalized by raising the cost of borrowing, attempts to increase liquidity and force interest rates down to stimulate aggregate demand and the demand for loans will prove largely ineffective. By contrast, if banks are _unable_ to lend (because firms are curtailing their demand for credit as a result, for instance, of a perceived weakening in future demand and thus lower profits), easing the fiscal stance may help expand aggregate demand and generate an expansion in credit.

Recent attempts at determining whether the fall in bank credit in Asia's crisis-stricken countries resulted from supply or demand factors have provided mixed results. Several papers have used survey evidence on changes in bank lending and firm borrowing during the crisis to assess the incidence of a credit crunch. Some of the better-known studies include those of Domac and Ferri (1999a), Dollar and Hallward-Driemeier (1998), Dwor-Frecaut, HallwardDriemeier, and Colaco (1999), and Ito and Pereira da Silva (1999). The latter study, in particular, used survey data for commercial banks to argue in favor of a credit crunch in Thailand during the period extending from mid-1997 2
Ebenstein, Avraham, Ann Harrison, Margaret McMillan, and Shannon Phillips, “Estimating the Impact of Trade and Offshoring on American Workers Using the Current Population Surveys”, NBER Working Paper 15107 (2009).

- Feenstra, Robert, and Gordon Hanson, “Ownership and Control in Outsourcing to China: Estimating the Property Rights Theory of the Firm”, Quarterly Journal of Economics 120:2 (2005), 729-762.

- Fisman, Raymond, Daniel Paravisini, and Vikrant Vig, “Cultural Proximity and Loan Outcomes”, Working Paper (2012).

- Foley, Fritz, and William Kerr, “Ethnic Innovation and U.S. Multinational Firm Activity”, Management Science, forthcoming (2012).

- Forman, Chris, Anindya Ghose, and Avi Goldfarb, “Competition Between Local and Electronic Markets: How the Benefit of Buying Online Depends on Where You Live”, Management Science 55:1 (2009), 47-57.

- Freedman, Seth, and Ginger Zhe Jin, “Do Social Networks Solve Information Problems for Peer-to-Peer Lending? Evidence from Prosper.com”, Working Paper (2008).

- Freeman, Richard, “People Flows in Globalization”, Journal of Economic Perspectives 20:2 (2006), 145170.

Ghani, Ejaz (ed.) The Services Revolution (Oxford, UK: Oxford University Press, 2010).

- Gibson, John, and David McKenzie, “Eight Questions about Brain Drain”, Journal of Economic Perspectives 25:3 (2011), 107-128.

- Gould, David, “Immigrant Links to the Home Country: Empirical Implications for U.S. Bilateral Trade Flows”, Review of Economics and Statistics 76 (1994), 302-316.

- Harrison, Ann, and Margaret McMillan, “Offshoring Jobs? Multinationals and U.S. Manufacturing Employment,” Review of Economics and Statistics 93:3 (2011), 857-875.

- Hatzigeorgiou, Andreas, and Magnus Lodefalk, “Trade and Migration: Firm-Level Evidence”,
This approach allows us to link Peri and Sparber’s conceptual framework directly with measures of labor market vulnerabilities created by COVID-19 related risks.

# 3. Data and Descriptive Statistics

## 3.1. Data

The primary data source used throughout this paper is the European Labour Force Survey (EULFS), which is a harmonized dataset composed of nationally representative labor force surveys conducted in all of the 28 EU countries, in addition to Switzerland and Norway. The sample size of the EU-LFS ranges from 4.5 million to 5.2 million observations depending on the year. Data are available on a yearly basis between 2001 and 2018, the latest available year prior to COVD19. The data provide information on whether the individuals in the sample were born in the same country in which they work. While the specific origin countries if migrant workers are not available in the data, broad world regions of origin are observed. These are as follows: the 15 original member states of the European Union (EU15)<sup>4</sup> , the 13 new member states of the EU as of 2013 (NMS13)<sup>5</sup> , non-EU European countries, Latin America, Asia, the Middle East and North Africa, Sub-Saharan Africa, and other non-European high—income countries that are members of the Organisation for Economic Co-operation and Development (OECD).<sup>6</sup> The dataset includes detailed information on various labor market outcomes of the individuals sampled, along with their demographic characteristics such as gender, age, and educational attainment. In terms of labor market status, observations are labelled as (i) out of the labor force, (ii) seeking employment, or (iii) currently employed. Detailed information on the characteristics of the jobs of employed workers are classified according to (1) the sector of activity at the one-

> 4 EU15 comprises the higher-income EU countries that were members before accession of the 10 new members in 2004. These are
parity in 2011 international dollars). 2012 was selected because it is the last year for which access rates are available in the Global Tracking Framework report, three other years being 1990, 2000, and 2010. 1990 was selected as an alternative because the poverty gap in South Asia, the region with the second lowest rate of access to electricity, was close to that in Africa in 2012. Unlike GDP, the availability of the poverty gap data is sporadic, reducing the sample size by more than half. For access in 2012, corresponding poverty gap data were taken from 2012 whenever they were available, and otherwise from 2011 or 2013, and if the data were not available from any of these years, they were taken from 2010 (the poverty gap in 2014 was not available for any country). For 1990, in order to capture India, which had the poverty gap data in 1987 and 1993 but no other year in between, the range of years was extended to 1987–1992. For regression equations, in addition to access, per capita GDP, and the poverty gap, 1/0 regional dummies for Africa, South Asia, East Asia and Pacific, Latin American and the Caribbean, and “other” for the countries in the remaining regions were also used.

The next section entitled “Cross‐Regional Perspective” is the only one that does not use data from recent household surveys in the 22 countries covered in the rest of the paper. It is also the only section that defines the poverty gap according to the poverty lines of $3.10 and $1.90 per person per day. Elsewhere, the poverty gap refers to the grid‐electricity poverty gap defined in the equation above. To distinguish different measures of the poverty gap, “poverty gap” is followed by threshold values: $3.10, $1.90, 30 kWh, 50 kWh, 100 kWh, and 250 kWh.

> 7 For the probit for the dependent variable that excludes
000|98.23|9.76%|
|2008|9,411|3,619|2.60|910,165|9,411,000|96.71|9.67%|
|2009|14,765|7,567|1.95|1,421,190|14,749,000|96.45|9.64%|

Note: In some cases BASIX sold combined policies covering all three monsoon phases. These are counted as three policies (one per phase). Figures are also adjusted for 2003-2004. Source: BASIX administrative data.

1
>2,000<br>0<br>1986 87 1988 89 1990 91 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06<br><!-- End of picture text --> _Source:_ Kawakami (forthcoming) from the Internet Information Search System, Department of Statistics, Ministry of Economic Affairs, China. _Note:_ PCs = personal computers.

The co-evolution of lead firms, suppliers, and platform leaders outlined here reveals a recursive dynamic of outsourcing, upgrading, and further outsourcing; the enabling role of open standards and modular product architecture in the PC sector; the intense competition and rapid product life cycles that drove lead firms to seek to spread risk and lower costs through 29
an unfair advantage to small farmers who rely mainly on the latter. We therefore also tried running our estimates without subtracting hired labor costs from gross revenues and the results (not reported) did not change to any significant extent.

Regarding the terms on the right hand side, we expect productivity to be positively related to the use of variable farm inputs, including hired labor. Given the supportive evidence provided by several of the contributions to the inverse farm size productivity debate (Lau and Yotopoulos 1971; Berry and Cline 1979; Kutcher and Scandizzo 1981; Carter 1984; Eswaran and Kotwal 1985; 1986; Barrett 1996; Udry 1996; Benjamin and Brandt 2002), we expect labor market imperfections to give an advantage to farms able to rely on family labor, and hence a positive sign on the coefficient on this variable.

To control for various aspects of land quality, variables on farmer-assessed soil quality, steepness and irrigation of the plots are included as regressors. The soil quality variable is computed as the share of total cultivated land the farmer reports being in plots with good soil quality. The share of steep land is similarly computed using plot level data, and taking the sum of plot size with steep slope as a share of the total landholding. In the same fashion the share of land irrigated is constructed 9
Table 3. Number of households (HH) in the sample

||Host<br>Community|Settled Migrant<br>HH<br>(>4 years in<br>Turkey)|Recent Migrant<br>HH<br>(<=4 years in<br>Turkey)|Settled Migrant<br>HH<br>(>3 years in<br>Turkey)|Recent Migrant HH<br>(<=3 years in<br>Turkey)|
|---|---|---|---|---|---|
|2009|133,710|2,127|54|2,142|39|
|2010|141,597|2,216|58|2,223|51|
|2011|142,203|2,101|57|2,111|47|
|2012|143,756|2,086|68|2,098|56|
|2013|143,572|2,363|120|2,374|109|
|2014|147,175|2.523|359|2.553|329|

_Source_ : LFS 2009-2014. World Bank author’s calculations.

The rate of increase in the recent migrant population of labor force surveys between 2011 and 2013 is parallel to observed trends in the government and UNHCR data regarding the SUTPs (Figure 2 in the annex). At the level, the LFS is not comparable to the government and UNHCR data since the sampling structure was not planned to represent the entire SUTP population. However, Figure 2 shows that the change between 2012 and 2013 in the recent migrant population of the LFS has a similar slope to the increase in the SUTP population from administrative data. The trend in the recent migrant population near the Syrian border is almost identical to the movement in the UNHCR and government numbers.

A caveat to the analysis in this
2014 censuses that would, without addressing them, make it impossible to estimate employment growth in Ghana over this period (Kerr and McDougall, 2019). The main issues we identified are differences in coverage of rural and urban areas, differences in the enumeration of associations and differences in coverage of the smallest firms.

In Kerr and McDougall (2019) we showed that the 2014 IBES enumerated a total number of own account workers that was around 5% of own account workers enumerated in the 2010 population census. The number is “only” 5% because the smallest firms did not qualify for the firm census (which excluded household‐based firms unless there was a sign indicating the presence of a firm in the household dwelling). But this same percentage was only 0.5% in the 2003 NIC. This means that the 2014 IBES covered a dramatically higher fraction of own account workers than 2003. The fraction of small firms covered also increased (Kerr and McDougall, 2019). To ameliorate this issue, we limit our main analysis to firms with 5 or more persons engaged. Persons engaged includes working proprietors, employees, unpaid family workers and apprentices, as a large fraction of Ghanaians working in manufacturing are not employees in the sense that the word is used in developed countries.

The 2014 census covered rural areas much better than the 2003 census (Kerr and McDougall, 2019). The 2003 NIC survey documentation notes that in rural areas only firms on the initial list of firms obtained prior to the census (likely obtained from tax records or the 1987 census) were enumerated, although enquiries were made of other firms in the vicinity. It is thus likely that rural areas would experience higher “employment” growth between 2003 and 2014, simply because coverage of rural firms was better in 2014. We ameliorate this issue in two ways. Firstly, we limit our analysis to firms with 5 or more persons engaged, as discussed above, since we think that the enumeration of larger firms was more comparable between 2003