rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 4: Tests of the Calvo-Mendoza Hypothesis,1987-1995

|**Sample:**|**All**|Europe|Latin<br>**I**America|Asia+|
|---|---|---|---|---|
|||||_|
||M1||||
|**Log(R/Ml)**E-03|-2.03|-0.439|-17.0|-2.04|
||0,004|0.039|0.070|0.478|
|VJLog(RIMI)] E-02|4.77|11.2|-3.76|-10.8|
||0.003|0.000|0.001|0.646|
|Constant E-03|-0.258|_-1.05_|-6.41|-0.649|
||0.001|0.142|0.106|0.446|
|**R2**|0.018|0.049|0.080|0.000|
|Observations|1 577|960|232|289|
|Overall Significance|3.39|49.06|2.67|0.69|
||0.000|0 000|0.002|0.763|
||M2||||
|Log(RlM2) E-03|-2.47|-0.764|-3.16|0.044|
||0.0)01|0.037|0.150|0.894|
|V[Log(R/MZ)J E-02|7,43|15.4|13.7|-1.82|
||0.000|0.000|0.000|0.591|
|
physical appearance of the enterprise structure, access to electricity; (v) the nature of the good or service being produced; (vi) the level of competition faced by enterprises; (vii) the level of technology, the labor-intensity of production, or the level of skills necessary for production; (viii) whether the participants have coverage under the social security system; (ix) whether the participants adhere to tax, health, labor, and other regulations. See Ahmad (1990) and Lubell (1991) for reviews of empirical studies.

5.None of the activities are reflected explicitly in national accounts statistics. An _ad hoc_ adjustment for taxicabs is included in statistics on the transport sector, simply by assuming every vehicle is a taxicab for 100 km per year and multiplying by the average price per kilometer. The estimates of value added derived in this report are much higher than those obtained through this _ad hoc_ adjustment. Informal activity, in general, may be reflected in official statistics to some degree, as official statistics are based, in part, on the expenditure side of the SSO Household Survey of Income and Expenditure. To the degree that households spend money in the informal sector, and to the degree that this is reflected in the household survey, some informal activity is reflected in the aggregates.
**Implications of public investment efficiency.** The efficiency of public investment plays a crucial role in driving its growth effects.<sup>26</sup> The estimates suggest a greater effect on GDP in response to public investment shocks in EMDEs with the highest efficiency, culminating in an increase in output of about 1.6 percent after five years—one-half percentage point higher than the effect of public investment in EMDEs with the lowest efficiency (figure 5). In countries with the lowest efficiency, the effects of public investment are lower and not statistically significant (albeit still positive).

**Figure 5. Effects of public investment on output conditional on public investment efficiency**

# **A. High investment efficiency B. Low investment efficiency**

<!-- Start of picture text --> Percent Baseline response Percent Baseline response<br>3.0 3.0<br>2.5 2.5<br>2.0 2.0<br>1.5 1.5<br>1.0 1.0<br>0.5 0.5<br>0.0 0.0<br>-0.5 -0.5<br>-1 0 1 2 3 4 5 -1 0 1 2 3 4 5<br>Year Year<br><!-- End of picture text --> _Source:_ Authors’ estimates.

_Note:_ Responses of real GDP (cumulative change in year _t_ relative to year _t_ = -1, in percent) to a public investment shock equivalent to 1 percent of GDP, conditional on fiscal space. _t_ = 0 is the year of the shock. Dashed line indicates the baseline unconditional response. Shaded area denotes 90 percent confidence bands, based on standard errors clustered at the country level. High-efficiency and low-efficiency samples are based on the top and bottom quartiles of the IMF (2021b) public infrastructure efficiency index,
Among the non-SAM data, labor, population and school enrollment stocks were readily available (World Bank 2011a; World Bank 2011b). Private capital stocks were estimated on the basis of capital value-added in the SAM, a net profit rate of 16 percent (Nehru and Dhareshwar 1993, p. 53), and a depreciation rate of 4.5 percent based on a review of the literature (Goldman-Sachs 2006, p. 5; Nehru and Dhareshwar 1993, p. 46). Government capital stocks were estimated on the basis of the assumption that base-year investment levels (in the SAM) matched what was required to ensure that these capital stocks grow at the same rate as GDP, after adjustment for depreciation, compared to cross-country data to verify aggregate plausibility (Arslanalp _et al_ . 2010).<sup>23</sup> Drawing on surveys of econometric evidence, standard values for developing countries were used for elasticities in trade and production (Annabi _et al_ . 2006). Income elasticities for household consumption (entering a linear expenditure system) are from the database of the ENVISAGE model; for details see van der Mensbrugghe (2010). For the poverty module, initial rural and urban consumption poverty rates and Gini coefficients are medians for LICs from the World Bank (2011b) and World Bank (2012).<sup>24</sup> MAMS was parameterized to replicate _base_ -run projections for MDGs 4, 5, 7s, and 7w (underfive mortality, maternal mortality, water access, and sanitation access, each of which was disaggregated into rural and urban) as well as a wide range of education indicators (with data available for a wider range of indicators the lower the level of education), generated on the basis of constant-elasticity regressions of these indicators on gross national expenditure (or absorption) per capita and the simulated evolution of GNE per capita under the _base_ run (Quijada 2012). The definitions of individual elasticities, which drive the relative importance of the different determinants in Table 2
|(0.021)|(0.014)|
|Ethiopia-Nigeria|0.205|0.000|0.000|0.224|0.000|0.002|
|Ethiopia-Uganda|0.000|0.236|0.026|0.187|0.000|0.000|
|Nigeria-Uganda|0.000|0.000|0.000|0.008|0.955|0.002|
|Observations|5,595|5,595|5,595|5,595|5,595|8,719|
|R-Squared|0.015|0.025|0.217|0.012|0.031|0.011|

_Note_ : Each column reports results from a single regression of a binary variable equal to 1 if the respondent stated that the household relied on that strategy to cope with the loss of income and 0 if they did not. The binary variable is regressed on indicators for each country, with Malawi as the base case. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country indicator equals the coefficient on an indicator for a different country. Data come from the most recent round of phone survey data available. This is round 3 in Ethiopia and Nigeria, round 2 in Malawi, and round 1 in Uganda. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

50
<!-- Start of picture text --> Model Data 95% CI Model Data 95% CI<br>300 0.4<br>0.35<br>250<br>0.3<br>200<br>0.25<br>150 0.2<br>0.15<br>100<br>0.1<br>50<br>0.05<br>0 0<br>06-07 07-08 08-09 09-10 10-11 11-12 2006 2007 2008 2009 2010 2011 2012<br>Number of Attacks Success Rate<br>(a) Total number of attacks (b) Success rate<br>Model Data 95% CI Model Data 95% CI<br>5 4<br>3.5<br>4<br>3<br>2.5<br>3<br>2<br>2<br>1.5<br>1<br>1<br>0.5<br>0 0<br>2007 2008 2009 2010 2011 2012 2007 2008 2009 2010 2011 2012<br>Average Size Average Conditional Size<br>(c) Size of attacked ships (d) Size of hijacked ships<br>Model Data 95% CI Model Data 95% CI<br>6 0.7<br>5 0.6<br>4 0.5<br>3 0.4<br>2 0.3<br>1 0.2<br>0 0.1<br>-1 0<br>2006 2007 2008 2009 2010 2006 2007 2008 2009 2010<br>Average Ransom Average Delay<br>(e) Ransoms (f) Length of captivity<br><!-- End of picture text --> Figure 6: Model fit of first moments Note: The figures plot the average number of attacks, success rates, size
ones.<sup>6</sup> This recent literature has also brought forward new TFP measures such as the one by Ackerberg _et al_ . (2006;

> 4 A study by Bourlès _et al_ . (2013) and Barone and Cingano (2011) do seek to explain downstream TFP through services linkages as explained above in a multiple developed country setting. However, both papers use industry‐ level data as opposed to firm‐level data which our paper takes as a starting point.

> 5 Apart from Fernades and Paunov (2012) which show the results of three different types of TFPs.

> 6 Duggan _et al_ . (2013) use a TFP computation following Aw _et al_ . (2001).

4
# **4.1 Political Aid Cycle**

If mission areas attract more aid because of stronger political connections, these connections might be expected to break down in the event of government turnover. Turnover is a relatively rare phenomenon in African countries, in both autocratic and democratic societies (in the data, the median country has two turnovers in 20 years). Against this backdrop, political connections are both durable and valuable. In order to test for the existence of a differential political aid cycle, an annual balanced panel of cells covering the period 1995–2014 is constructed, and data on national elections are compared with the timings of cell-level aid commitments. The equation to be estimated by OLS is where _i_ , _k_ , and _t_ are indices for cell, country, and year, respectively.

The variable Aid _ikt_ is an indicator that equals 1 if at least one aid project is committed in the corresponding calendar year with at least one project location in cell _i_ ; Election _kt_ is a binary indicator that equals 1 if there is a national election for the office of head of state; and Turnover _kt_ is an indicator that equals 1 if the election results in a change in the person holding office. Turnover is defined as a change in the head of state, irrespective of party affiliation.<sup>31</sup> The data on elections come from the Varieties of Democracy (VDEM) dataset, version 7.1 (Coppedge, Gerring, Lindberg, Skaaning, Teorell, Altman, Bernhard, Fish, Glynn, Hicken, et al., 2017). Fixed effects at the country-year level ( _λkt_ ) and at the cell level ( _μi_ ) are included in all regressions.

Note that the inclusion of country-year fixed effects means that the specification cannot identify a general political aid cycle at the country level, which is beyond the scope of this article. It does, however, allow credible testing for the presence of a political aid cycle specific to mission cells. The coefficient _β_ captures
records of the distribution companies may be fairly accurate with respect to the number of households connected. Hence, to obtain a yearly estimate of access it is necessary to interpolate between successive census years, and to extrapolate beyond the most recent census year. These considerations are important in the case of Pakistan.

The World Bank’s World Development Indicators shows access increasing steadily from 59 percent in 1990 to 98 percent in 2014, although a study of South Asia pointed out that, while access rates are nearly 100 percent in urban Pakistan, in certain parts of rural Pakistan rates are still very low.<sup>31</sup> However, a summary of the 2017 population census has just been released and this points to a rather different access rate.<sup>32</sup> According to previous estimates the population in 2016 was 198 million, with an average household size of 6.45. The power system statistics indicated that 22.8 million households were connected to the grid, giving an access rate of 74 percent. The new census (the first for 19 years) indicated that in 2017 the population was 207 million with 32.2 million households (average household size of 6.43). If the number of connections were 22.8 million then the access rate would be 71 percent. A breakdown by region indicated that in Punjab the access rate was 92 percent, in Sindh 37 percent, in Balochistan 24 percent, and in Khyber Pakhtunkhwa 71 percent. Some households have solar home systems or other forms of off-grid supply and the access to electricity from all forms of supply will be higher than the grid connected values.

The State of Industry Report for 2017, published by NEPRA, also provides some related information through statistics for each DISCO for each of the years from 2012 to 2016 of the percentage of villages in their area that have not yet been electrified. These statistics seem questionable in several cases. The percentages of total electrified villages fell in some cases or rose and then
241,700|277,900|
|60-64|194,600|241,600|
|65-69|140,400|171,100|
|70-74|114,900|158,600|
|75-79|71,500|90,700|
|80+|96,400|140,600|
|Total|17,344,000|18,158,100|

- Note: UBOS official projections for 2015 and based on the population census of 2014 available at <u>https://www.ubos.org/wp content/uploads/statistics/Population_Projections_2018.xlsx, downloaded June 28, 2021.</u>

We use published data and reports from the National Population and Housing Census (NPHC) 2014 (UBOS 2016; UBOS 2017) and the Uganda National Household Survey (UNHS) 2016/17 as main sources of official statistics. The former is conducted by UBOS about every 10 years with the aim of collecting benchmark demographic and socio-economic data of the Uganda population. The latter is the sixth follow-up survey of the UNHS, a cross-sectional survey implemented by UBOS starting in 1999/20, which aims to collect data on demographic and socioeconomic characteristics, with a sample of 15,636 households. The UNPS is a follow-up to its 2005/06 survey and largely implements the same methodology.

The NPHC 2014 and UNHS 2016/17 are the official source of data with the closest collection period to the UNPS 2015/16 we found. The reference period for the UNPS 2015/16 is March 26
or exercise books available at home, there are no significant gains in program schools (Panel B), although all six point estimates in the panel are positive, providing suggestive evidence in favor of some increased parental investments. Further evidence is provided by a specification that pools all cohort 1 and 2 girls and boys in Busia district, and finds an increase of 0.26 additional new exercise books or textbooks at home in program schools (standard error 0.19, regression not shown).

There is no convincing evidence of any positive or negative impacts on attitudes toward education, for instance, thinking of oneself as a “good student”, or preferring school activities to nonschool activities, based on survey responses (Tab le 8, Panel C).

Overall, then, there is evidence of increased student and teacher effort (reflected in school attendance, and increased textbook use at home), perhaps suggestive evidence of increased parental educational investments, but no evidence of the changes in attitudes emphasized by psychologists.

# **<u>5.3 Regression Discontinuity Estimates of the Impact of Winning the Scholarship</u>**

We explore within-school impacts of the program in 2002 using a regression discontinuity method. This method, in effect, compares the 2002 outcomes of girls who barely won the scholarship (their 2001 test score was slightly above the winning threshold) to girls who barely lost out, in order to estimate the impact of winning. In practice, 2001 test score polynomials (linear, quadratic, and cubic terms) are included to control for any smooth underlying relationship between the 2001 test score and 2002 outcomes, and an indicator variable for having a 2001 test score above the threshold then captures the impact of winning the scholarship. By including students in both program and comparison schools, we are able to estimate impacts on both scholarship winners and non-winners in program schools, in addition to estimating differences between non-winners across program and comparison schools.

The coefficient estimate on the interaction term between the program school indicator
# **2.1 Measuring Plant-Level Automation**

Unlike some other studies in high-income countries (e.g. Acemoglu et al. (2020) and Stapleton and Webb (2020)), we do not directly observe plants’ use of robots. Instead, we match data on Indonesian imports of industrial robots by industry with plants’ observable characteristics to build various plausible measures of plant level exposure to automation.

The key assumption underlying our proxies of plant exposure is that routine taskintensive occupations are the most likely to be automated (Autor et al., 2003). As we do not observe the tasks performed by workers in each plant, our baseline measure of exposure proxies routine-intensive tasks with the educational level of each plant’s workforce, which we observe in 2006 when our manufacturing plants’ data becomes a census.

Specifically, we identify the production occupations most exposed to being automated using two alternative definitions of occupations’ “replaceability”. The first is the definition used in Graetz and Michaels (2018) (GM). GM exploit information on the applications for which robots are used. They look at 3-digit occupational classifications in the United States and assign a replaceability value of 1 to an occupation if its title corresponds to at least one of the IFR application categories and 0 otherwise. For instance, if robots perform the task “handling materials”, they deem an occupation to be replaceable if the occupational title includes the word “handling”. The second is the probability of computerization calculated by Frey and Osborne (2017) (FO henceforth), which update the probability of computerization by occupation originally computed by Autor et al. (2003) and David and Dorn (2013). Compared to these studies, FO has the advantage of accounting for the more recent technological progress.

Using Indonesian labor force survey data (described in Section 4.3), we compute the distribution of workers in occupations at high risk of automation by their educational attainments in 2007, the first year such information is available
# Macroeconomic Modeling of Managing Hurricane Damage in the Caribbean: The Case of Jamaica

Andrew Burns<sup>♣</sup> Charl Jooste Gregor Schwerhoff JEL Classification: C54, Q54 Keywords: Hurricanes, Economic Modeling, Disaster Risk Management

> ♣ Global Practice for Macroeconomics, Trade and Investment, The World Bank. The fi <mark>ndings, interpretations, and conclusions are the authors’ own and should not be attributed to the World Bank, its Executive Board of Directors, or any of its member countries</mark> . This paper was written under the guidance of Stefano Curto. The authors would like to express their thanks for various contributions and comments from Stefano Curto, James Sampi, Rohan Longmore, Stephane Hallegatte, Jose Angel Villalobos, Vasileios Tsiropoulos, Abha Prasad, Karlene Collette Francis, Doerte Doemeland and Diego Rivetti. The paper has also benefitted from data on hurricane damages and a discussion on disaster risk management from Rashmin Gunasekera and Thibaut Humbert.
credit spreads, and the ratio of equity prices to the 10-year<br>average of earnings per share, as explained in Hatzius and Stehn (2018). Higher index numbers reflect<br>tighter financial conditions. Data are at a monthly frequency and used as annual averages of year-on-<br>year growth of the index. Growth in aggregated groups is computed with market-weighted output as a<br>weight.|Bloomberg, Goldman<br>Sachs, World Bank.|
|Inflation|Change in CPI in percent. Data are at a quarterly frequency and used as annual averages of year-on-<br>year percent changes of CPI. The series is taken from the Bank for International Settlements, Haver<br>Analytics, and the IMF. Inflation in aggregated groups is computed with market-weighted output as a<br>weight. In order to eliminate an effect of countries with a history of high inflation, countries whose<br>average inflation over the entire sample period (based on quarterly data) is above the top 20th<br>percentile,are excluded.|Bank for International<br>Settlements, Haver<br>Analytics, IMF, World<br>Bank.|

63
despite not having a contractual and regulated relationship with an employer (e.g., independent professionals, business persons, renters).

The subsidized regime applies to poor and vulnerable persons—in order to enter this regime, individuals are required to undertake the SISBEN survey.

The associated regime applies to non-insured poor persons.

The entire health insurance system is financed through the Solidarity and Guarantee Fund (FOSYGA, or “social security bank”), which is supported by the insurance contributions from the contributory regime. The required contribution is 12.5% of the basic salary: In the case of salaried workers, the worker assumes 4% and the employer assumes 8.5%.

In the case of self-employed workers, the worker assumes the total amount.

The FOSYGA manages four types of funds: (i) compensation; (ii) solidarity; (iii) promotion and prevention; and (iv) those for catastrophic events and traffic accidents.

The EPSs are entities that perform the following functions, as delegated by the FOSYGA: (i) managing the affiliation and registering of persons; and (ii) organizing and ensuring the provision of the basket of basic services – the Plan Obligatorio de Salud (POS) – that are required in all EPSs, and to which all affiliated members have the right to access. It includes the integral protection of families, maternity care, health information, health promotion, diagnostics, treatment and rehabilitation for all diseases. Those persons affiliated with the contributory and subsidized regimes freely choose their EPSs to register with the beneficiary.

The EPS organizes the network of services (hospitals, clinics, laboratories, etc.) in order to guarantee a plural offer of providers to affiliated persons. To that end, the EPS enters into contractual arrangements with provider institutions (IPSs). The EPS-s also enters into contractual arrangements with traditional public hospitals—renamed as Social State Enterprises. The affiliated persons can freely choose an IPS from the list of providers offered by their EPS.

26
>Asia: What Have We Learned?|Rashid Faruqee<br>Kevin Carey|April 1997|C. Anbiah<br>81275|
|WPS1755 Survey Responses from Women<br>Workers in Indonesia's Textile,<br>Garment, and Footwear Industries|Mari Pangestu<br>Medelina K. Hendytio|April 1997|J. Israel<br>85117|
|WPS1756 World Crude Oil Resources:<br>Evidence from Estimating Supply<br>Functions for 41 Countries|G. C. Watkins<br>Shane Streifel|April 1997|J. Jacobson<br>33710|
|WPS1757 Using Economic Policy to Improve<br>Environmental protection in Pakistan|Rashid Faruqee<br>|April 1997|C. Anbiah<br>81275|
|WPS1758 The Restructuring of Large Firms<br>in Slovakia|Simeon Djankov<br>Gernard Pohl|April 1997|F. Hatab<br>35835|
|WPS1759 institutional Obstacles to Doing<br>Business: Region-by-Region Results<br>from a Worldwide Survey of the<br>Private Sector|Aymo Brunetti<br>Gregory Kisunko<br>Beatrice Weder|April 1997|M. Geller<br>31393|
Conference, Washington DC.

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- Hahn, R., and C. Cecot. 2009. “The Benefits and Costs of Ethanol: An Evaluation of the Government‟s Analysis”. _Journal of Regulatory Economics_ . 35 (3): 275-295.

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IMF Commodity Prices. (http://www.imf.org/external/np/res/commod/index.asp)

- International Food Policy Research Institute. 2008. “Higher Food Prices: The What, Who, and How Proposed Action”.IFPRI Policy Brief, IFPRI, Washington D.C.

- Ivanic, M. and Martin, W. 2008. “Implications for Higher Global Food Prices for Poverty in Low Income Countries”. Development Research Group, Trade Group. World Bank, Washington D.C

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