rafmacalaba/datause-encoder-data huggingface dataset:
**Table** **<u>8 Proximity to mining and governance</u>**

||**(1)**<br>**Trust**|**(2)**<br>**Corruption **|**(3)**<br>**Accountability**|
|---|---|---|---|
|Cum. gold within 20km|-0.004**<br>(0.002)|-0.006***<br>(0.001)|0.135***<br>(0.015)|
|Education|-0.171***|0.265***|0.125*|
||(0.056)|(0.056)|(0.071)|
|Age|-0.008*|-0.001|0.025***|
||(0.004)|(0.004)|(0.006)|
|Male|-0.423***|-0.061|0.421***|
||(0.120)|(0.108)|(0.143)|
|Poverty|0.137**|-0.072|0.116*|
||(0.053)|(0.050)|(0.064)|
|Rural|0.498***|-0.402*|0.623**|
||(0.183)|(0.225)|(0.294)|
|Observations|2,170|2,097|1,160|

_Note:_ Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

**Table** **<u>9 Proximity to mining and local capacities</u>**

||**(1)**<br>**Road**|**(2)**<br>**Market**|**(3)**<br>**Health **|**(4)**<br>**Cleaning**|**(5)**<br>**Revenue**|**(6)**<br>**Spending**|
|---|---|---|---|---|---|---|
|Cum. gold within 20km|-0.066***|-0.103***|-0.119***|-0.076***|-0.694***|0.241***|
||(0.020)|(0.012)|(0.013)|(0.012)|(0.077)|(0.077)|
|Education|-0.015|0.044|0.133*|0.043|0.073|0.018|
||(0.055)|(0.077)|(0.077)|(0.063)|(0.133)|(0.092)|
|Age|-0.002|-0.002|0.010|0.005|-0.013**|-0.001|
||(0.003)|(0.007)|(0.007)|(0.005)|(0.006)|(0.005)|
|Male|-0.226*|-0.123|-0.159|-0.183|0.360*|0.414***|
||(0.129)|(0.185)|(0.192)|(0.115)|(0.217)|(0.158)|
|Poverty|0.057|0.160**|0.155*|0.106*|-0.214***|0.164*|
||(0.067)|(0.073)|(0.086)|(0.055)|(0.082)|(0.090)|
|Rural|-0.279|-0.490|-0.208|0.097|0.255|0.298|
||(0.190)|(0.367)|(0.329)|(0.207)|(0.388)|(0.305)|
|Observations|1,142|615|547|1,139|505|440|

_Note:_ Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

30
- 18 -

For dairy products, the size of Africa’s potential import market is defined as five percent of the total volume of production in the region.<sup>16</sup> The idea behind this is that dairy products are primarily expected to be consumed locally, and thus the potential market may be very small. Given that, each consumer in Africa is assumed to buy one unit of milk from other countries. Recall that the current regional milk import market is thin but _does_ exist. Some countries are importing dairy products from their neighboring countries. One of the critical assumptions for this definition to be valid is that the domestic and international markets are not completely separated. In principle, the domestic market is linked to the international market, because trade arbitrage would take place if there is a significant difference between the internal and external prices.

On the other hand, when consumers choose not to import dairy products, the outside option is to purchase domestic products. Dairy production data come from _FAOSTAT_ .<sup>17</sup> On the other hand, dairy exports data are collected from the _WITS COMTRADE_ database.

**Quantity and price** . Both market share and price variables take the five-year (from 2001 to 2005) average to avoid possible data fluctuation in the short run. Prices are the export value (millions of constant 2000 euro) divided by the volume of exports (millions of kilograms). Recall that the sample includes only one 5-year period.

**Infrastructure variables.** Four agriculture-related infrastructure proxies are available: rural access index, rural telecommunications adoption index, rural water access, and agricultural electricity consumption per capita. They are relevant to agriculture but not specific to it. The first is the rural access index, which measures the share of rural residents with access to major roads (Roberts _et al_ ., 2006). It is not panel but cross-sectional data from 178 countries for 1993-2003. Unfortunately, two-third of the data is associated with the period: 2001-05.

> 16 The amount of dairy exports is subtracted, though it is very small.

> 17 For milk production, it includes all kinds of milk (i.e., cow (SITC4-0882), buffalo (0951), sheep (0982), goat (1020), and camel milk (1130)). For butter products, butter and ghee (do. 1811) data are used. All kinds of cheese (do. 1745) are used for the cheese item.
- Aterido, R., M. Hallward-Driemeier, and C. Pages. 2007. “Investment Climate and Employment Growth: The Impact of Access to Finance, Corruption and Regulations across Firms.” IZA Discussion Papers 3138, Institute for the Study of Labor.

- Atje, R., and B. Jovanovic. 1993. “Stock Markets and Development.” _European Economic Review_ 37 (2–3): 632–40.

- Avram, K., Y. Nguyen, and M. Skully. 2010. “Insurance and Economic Growth: A Cross-Country Examination.” Unpublished manuscript.

- Ayyagari, M., A. Demirguc-Kunt, and V. Maksimovic. 2008. “How Important Are Financing Constraints? The Role of Finance in the Business Environment.” _World Bank Economic Review_ 22 (3): 483–516.

- Barajas, A., R. Chami, and S. Yousefi. 2012. “The Finance and Growth Nexus Reexamined: Do All Countries Benefit Equally?” Paper presented at the Financial Deepening, Macro-Stability, and Growth in Developing Countries conference, held by the International Monetary Fund, the World Bank, the Consortium on Financial Systems and Poverty, and the U.K. Department for International Development, September 24.

- Barajas, A., R. Steiner, and N. Salazar. 2000. “The Impact of Liberalization and Foreign Investment in Colombia’s Financial Sector.” _Journal of Development Economics_ 63 (1): 157–96.

- Barros, A. R. 2009. “Finance and Growth in Brazil.” Working Papers 47, Datametrica Consultoria Economica, Pernambuco, Brazil.

- Barth, J. R., G. Caprio, and R. Levine. 2000. “Banking Systems Around the Globe: Do Regulation and Ownership Affect the Performance and Stability?” Policy Research Working Paper Series 2325, World Bank, Washington, DC.

- ———. 2004. “Bank Regulation and Supervision: What Works Best?” _Journal of Financial Intermediation_ 13 (2): 205–48.

- ———. 2012. “The Evolution and Impact of Bank Regulations.” Policy Research Working Paper Series 6288, World Bank, Washington, DC.

- ———. 2013. “Bank Regulation and Supervision in 180 Countries from 1999 to 2011.” NBER Working Papers 18733, National Bureau of Economic Research, Cambridge, MA.

- Barth, J. R., C. Lin, P. Lin, and F. M. Song. 2009. “Corruption in Bank Lending to Firms: Cross-Country Micro Evidence on the Beneficial Role of Competition and Information Sharing.” _Journal of Financial Economics_ 91 (3): 361–88.

- Bayraktar, N., and Y. Wang. 2004. “Foreign Bank Entry, Performance of Domestic Banks and Sequence of Financial Liberalization.” Policy Research Working Paper Series 3416, World Bank, Washington, DC.

- ———. 2006. “Banking Sector Openness and Economic Growth.” Policy Research Working Paper Series 4019, World Bank, Washington, DC.

- Basel Committee on Banking Supervision. 2010. “An Assessment of the Long-Term Economic Impact of Stronger Capital and Liquidity Requirements.” Technical report, Bank for International Settlements, Basel.

- Becchetti, L., and N. Ciampoli. 2012. “What Is New in the Finance-Growth Nexus: OTC Derivatives, Bank Assets and Growth.” AICCON Working Papers 110-2012, Associazione Italiana per la Cultura della Cooperazione e del Non Profit, Forli

28
growth relative to the wages of unskilled workers, gradual reduction in farm employment, pronounced decline in capital rental rates during the second period, and a reduction in the share of formal activities during the first period followed by increased formalization in the second. The increasing skill premium can be largely explained by the differences in labor supply and labor demand. The scenarios considered in this paper do not incorporate increases in the average educational attainment over time, which means that the stock of both skilled and unskilled workers grows at the same rate as the working age population. On the other hand, demand for skills rises over time as Panama shifts out of unskilledintensive activities like agriculture and into more skill-intensive manufacturing and services.<sup>15</sup> This transition is consistent with econometric evidence that food income elasticities tend to be below one; it also results in a relative increase in demand for skilled labor and widening of the skilled wage premium.

# **Table 4 Factor markets**

||BaU|B|aU|Can|al|
|---|---|---|---|---|---|
||2003|2003-14|2014-20|2003-14|2014-20|
||Levels||Annual grow|th rates (%)||
|Unskilled wage|2.70|3.51|2.1|3.57|3.3|
|Non-farm unskilled wage|3.07|2.84|1.7|2.90|3.0|
|Farm unskilled wage|1.95|4.91|3.3|4.96|3.9|
|Skilled wage|8.68|4.28|5.7|4.33|8.2|
|Formal capital real rent (index)|1.00|-0.36|-2.2|-0.35|-2.6|
|Informal capital real rent (index)|1.00|0.85|-3.8|0.87|-5.8|
|Canal capital real rent(index)|1.00|-0.11|-1.1|-0.20|0.3|
|Total labor supply|1,178|1.6|1.3|1.6|1.3|
|Unskilled labor|713|1.6|1.3|1.6|1.3|
|Unskilled farm labor|236|-0.4|-0.4|-0.4|-0.5|
|Unskilled non-farm labor|477|2.5|1.9|2.5|2.0|
|Unskilled formal non-farm labor|217|2.2|3.2|2.3|3.5|
|Unskilled informal non-farm labor|260|2.7|0.8|2.6|0.6|
|Unskilled migration|5.2|-2.0|-1.6|-2.0|-0.8|
|Skilled labor|465|1.6|1.3|1.6|1.3|
|Skilled formal non-farm labor|365|1.7|1.8|1.7|1.8|
|Skilled informal non-farm labor|84|1.3|-0.9|1.2|-1.0|

The structural shift out of agriculture is also driven by falling farm employment, which declines at an average rate of 0.4 percentage points per year. This is consistent with the experience of Panama between 1997 and 2003, when the rates of worker migration to non-agriculture activities outpaced the growth rate of farm labor force. As a result of relative labor scarcity in agriculture, farm wages actually grow faster than nonfarm wages in the BaU scenario, reducing the non-agriculture wage premium to 15 percent in 2020 from 58 percent in 2003.

The changes in consumer prices (reported in the bottom part of Table 3) are mainly determined by the trends in agriculture production and demand for food products.

> 15 The contribution of agriculture to total output declines from 7.3 percent in 2003 to 6.2 percent in 2014 and further to 5.6 percent by 2020.

12
Policy Research Working Paper 5910

# **Abstract**

This paper presents the results of an attempt to use the combined results of the dedicated survey performed by CASE in 2007 and Polish LFS data in order to: (a) analyze the development of the shadow employment in Poland in years 2003–2008 and, (b) analyze the transition probabilities in and out of shadow employment. The estimated share of shadow workers in total employment in Poland in years 2003–2008 was increasing until 2006 and then started to decrease in the years 2007 and 2008. Other results are in line with one of the main conclusions of the CASE study from 2007 suggesting that shadow employment is more a way of coping with lack of other employment opportunities than an equivalent or even superior alternative to any legal employment contracts. On the other hand those who enter shadow employment are more active part of the group having problems with finding full time/open term employment. They are much more inclined to cope with their situation by entering some form of self-employment than to stay passive and depend on social assistance.

This paper is a product of the Human Development Economics Unit, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at Mateusz.Walewski@case-research.eu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **3.2 Weather Data**

We match our daily arrest counts with daily weather data from the PRISM Climate Group’s gridded re-analysis product. The PRISM product provides daily information on minimum and maximum temperature, minimum and maximum vapor pressure deficit, dew point, and precipitation on a 4km by 4km grid for the continental United States. We aggregate these measures to the county level by taking the average across the grid points within the county. We assign daily maximum temperature to one of 12 5<sup>_◦_</sup> F temperature bins from 40<sup>_◦_</sup> F up to 100<sup>_◦_</sup> F. Days below 40<sup>_◦_</sup> F and above 100<sup>_◦_</sup> F are included in separate bins. We also bin daily precipitation to control for the impacts of particularly rainy days. We assign days to four exclusive precipitation bins: no precipitation, less than half an inch, one half to one inch, and more than one inch.

# **3.3 Socio-economic and Neighborhood Data**

We collect socio-economic data at the census block group level from the 5-year American Community Survey (ACS) for every year in our sample. We use the geocoded addresses from the arrest data to assign individuals to census block groups and categorize arrests based on the characteristics of the block group in which the defendant lives at the time of the arrest. We focus in particular on median income, the poverty rate, and the median housing age. These allow us to classify arrests as occurring in block groups based on income or poverty rates and by the age of the housing stock, which we take as a proxy for the presence of air conditioning. We also use the share of residents in a block group living in an urban environment to classify block groups as urban or rural. We define block groups as urban if at least 80% of their residents live in an environment defined as urban by the ACS.

12
- 122 -

# **Comparison of Default Rates on Cat Bonds versus Other Bonds**

||Bond Default Probabilities<br>(or equiv. Cat Event Prob.)|Spread above LIBOR|
|---|---|---|
|_Below Investment_**_Grade Bonds_**|||
|Ba2|0.60%|1.10%|
|Ba3|2.70%|1.36%|
|B I|3.80%|1.84%|
|B2|6.79%|2.00%|
|**_Principal at Risk Cat Bonds_**|||
|Res Re '97|1.00%|5.76%|
|Parametric|1.02%|4.30%|
|Trinity|1.53%|4.36%|
|<br>Res Re**'98**|0.87%|4.00%|
|**_Principal Protected Cat Bonds_**|||
|Res Re ' 97|1.00%|2.73%|
|Parametric|1.02%|2.06%|
|Trinity|1.53%|1.49%|

Source: Goldman Sachs In addition to the higher return/risk profile that many catastrophe bonds can offer investors, they also improve the investors overall portfolio risk given that catastrophe bond performance is not linked to global financial market prices since they are based on natural event triggers versus economic/financial factors. Thus, besides the benefits of higher returns, the lack of correlation of cat bond performance with the broader financial markets, improve the efficient investment frontier:

# **Change in Efficient Investment Frontier from Inclusion of Cat Bonds in Portfolio**

<!-- Start of picture text --> A<br>B<br>Risk<br><!-- End of picture text --> The addition of cat bonds to a portfolio of assets with significant global price correlation, would reduce expected portfolio volatility due to the risk diversification aspect of cat bond returns versus global financial market returns (e.g.: U.S. markets,
categorized non‐work activities as “work” (Campanelli, Rothgeb, and Martin, 1989).<sup>12</sup> A 1991 experiment to evaluate the CPS questionnaire revision used direct screening questions and vignettes for unreported work and found that both the sequence of questions as well as their wording influenced respondent interpretation of work and affected the employment statistics generated (Martin and Polivka, 1995). Specifically, and of interest for our setting, the study found that using direct screening questions helped in detecting under‐reporting of work related to household business or farm, as well as underreporting of teenage work. These concerns are highly relevant for developing countries, where much work activity is informal and risks to go undetected in a survey. This may be particularly true for female work, and several scholars have expressed concerns about the underreporting and undervaluing of women’s work when using common survey methods to collect employment data (Anker, 1983; Dixon‐Mueller and Anker, 1988; Charmes, 1998; Mata Greenwood, 2000).<sup>13</sup> In previous work, we illustrate that this is also relevant in the Tanzanian setting when obtaining labor statistics. Using a short questionnaire, without screening questions, seems to induce individuals to adopt a broad definition of employment, which tends to include domestic duties. But even after reclassifying these – to obtain the correct ILO classification, the short module results in lower female employment rates, higher working hours for both men and women who are working, and lower rates of wage work (Bardasi et al 2011).

Surveys with a labor content can be categorized into two groups: those making use of multiple detailed questions to find out whether the respondent is economically active and participating in the labor force during the reference period (typically the last seven days), and those using one question only to determine this. To illustrate the importance of this survey design feature in recently implemented surveys, Table 1 provides an overview of surveys with a labor content in sub‐Sahara Africa for 2009‐2012. Of the 21 surveys, 12 (57%) used a long and detailed questionnaire, while the remaining 9 (43%) used a short questionnaire. Our survey experiment implements short versus detailed questionnaires to reflect this variation.

Another dimension of survey design which may have implications on structural parameter estimation is the type of respondent. Different surveys adopt distinct approaches to choosing

> 12 See Esposito et al. (1991) for methods to obtain diagnostic information in order to evaluate the effect of questionnaire revisions.

> 13 Women often play a dominant role in household, domestic and agricultural activities, which may go unregistered as this is culturally often not considered as work (Mata Greenwood, 2000). The 1991 CPS study for the US also found gender dimensions of the survey effects, with the revised questionnaire trying to better capture unpaid work in a household business and farm activities, which increased the female employment rate.

7
- Marshall, Alfred. 1920. Principles of Economics. _London: MacMillan_ .

- McPeak, John. 2004. Contrasting Income Shocks With Asset Shocks: Livestock Sales in Northern Kenya. _Oxford Economic Papers_ , **56** (2), 263–284.

- Miguel, Edward, Satyanath, Shanker, & Sergenti, Ernest. 2004. Economic Shocks and Civil Conflict: An Instrumental Variables Approach. _Journal of Political Economy_ , **112** (4), 725–753.

- Rosenzweig, Mark, & Wolpin, Kenneth. 1993. Credit Market Constraints, Consumption Smoothing, and the Accumulation of Durable Production Assets in Low-Income Countries: Investments in Bullocks in India. _Journal of Political Economy_ , **101** (2), 223–244.

- Skinner, Jonathan. 1988. Risky Income, Life Cycle Consumption, and Precautionary Savings. _Journal of Monetary Economics_ , **22** (2), 237–255.

- START. 2017. Global Terrorism Database [Data File]. _National Consortium for the Study of Terrorism and Responses to Terrorism (START), Retrieved from https://www.start.umd.edu/gtd_ .

- Swinton, Scott M. 1988. Drought Survival Tactics of Subsistence Farmers in Niger. _Human Ecology_ , **16** (2), 123–144.

- Udry, Christopher. 1995. Risk and Saving in Northern Nigeria. _American Economic Review_ , **85** (5), 1287–1300.

- Verpoorten, Marijke. 2009. Household Coping in War-and Peacetime: Cattle Sales in Rwanda, 1991–2001. _Journal of Development Economics_ , **88** (1), 67–86.

- Watts, Michael J. 1983. _Silent Violence: Food, Famine, and Peasantry in Northern Nigeria_ . University of Georgia Press.

- World Bank. 2014. World Development Report 2014: Risk and Opportunity – Managing Risk for Development. _World Bank, Washington D.C._

21
We identify structural shocks following broadly the identification approach proposed by Blanchard and Perotti (2002). This methodology relies on the assumption that, due to the institutional and economic constraints, governments cannot react to changes in economic activity within the same period. Originally, this approach was applied to advanced economies with quarterly data. Due to limited data availability, we have to implement it with annual data. While stronger assumptions are needed for the results to be valid in this case (Beetsma et al. 2014), there are also advantages from using annual data. First, shocks from annual data are more likely to capture actual fiscal shocks, since government spending decisions usually follow an annual cycle and new fiscal impulses typically do not appear at quarterly frequency (Beetsma et al. 2008, Beetsma and Giuliodori 2011). This is especially true for many developing countries with larger lags in implementing fiscal policy (Diop and Ben Abdallah 2009). In addition, anticipation effects are usually present in VAR models estimated with quarterly data (Ramey 2011). With annual data, the role of anticipation effects is less relevant as a given shock is less likely to be anticipated one year before. Third, with annual data there are no seasonality effects, which can be important in many developing countries. Beetsma et al. (2006) and Beetsma et al. (2009) compare the results from quarterly and annual data for several countries for which non-interpolated quarterly fiscal data are available and show that recursive identification restrictions for spending shocks in a VAR with annual data delivers plausible results. Born and Müller (2012) provide further evidence that the identification assumptions are not too restrictive for annual data.

The identification of the system is achieved by introducing restrictions that can be divided into four segments. First, we assume that there are no contemporary responses of discretionary measures of policy makers in response to each other (a��= a��= 0) and the previous matrix is hence equivalent to the following system of equations: The first equation states that unexpected changes in the government expenditure can be attributed to unexpected changes in GDP (a��ε���) and reactions to structural shocks in expenditures (b��u���) and taxes (b��u���). Unexpected changes in tax revenues are explained in a similar way in the third equation. Finally, unexpected changes in tax revenue, government expenditure and/or structural shocks in output account for unexpected GDP movements.

Second, parameters a�� and a�� can include in principle two types of effects of economic activity on tax revenue and expenditure: automatic responses within the existing fiscal rules, as well as discretionary fiscal policy reactions on unexpected events within the same period. However, as explained, we assume that it takes more than one period to react to a possible shock in economic activity 9