rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 6a Effect of mining on revenues and spending patterns (Indicators for mining communes and neighboring communes)**

||**Revenue**<br>||**Capita **<br>|**lexpenditures**<br>|||**Cur**<br>|**rent expenditu**<br>|**res**<br>|
|---|---|---|---|---|---|---|---|---|---|
||**(1)**<br>**Revenue**|**(2)**<br>**Total **|**(3)**<br>**Education **|**(4)**<br>**Health **|**(5)**<br>**Water **|**(6)**<br>**Others**|**(7)**<br>**Total **|**(8)**<br>**Salaries**|**(9)**<br>**Others**|
|Mining communes|8,480***|25,331**|14,450***|1,124|2,422|4,583|8,416***|1,092***|5,168***|
||(1,430)|(10,818)|(3,455)|(1,273)|(4,759)|(6,878)|(1,158)|(403)|(431)|
|Neighbors|-791|-2,484|-1,836|-669|188|-198|14|-61|-86|
||(676)|(5,117)|(1,634)|(602)|(2,251)|(3,253)|(548)|(190)|(204)|
|Poverty (%) 1998|-1|72|16|37***|46|-46|-7|1|-1|
||(12)|(94)|(30)|(11)|(41)|(59)|(10)|(3)|(4)|
|Enrollment (%) 98|-22|121|7|11|41|35|-8|7|-2|
||(18)|(138)|(44)|(16)|(61)|(88)|(15)|(5)|(6)|
|Rainfall (mm) 1998–|1*|-37***|-6***|-2***|-10***|-20***|0|0|-0**|
|2008||||||||||
||(1)|(5)|(1)|(1)|(2)|(3)|(0)|(0)|(0)|
|Urban|1,455**|8,064|-1,066|17|1,771|7,418**|2,287***|1,164***|530**|
||(703)|(5,316)|(1,698)|(626)|(2,338)|(3,380)|(569)|(198)|(212)|
|Observations|692|692|692|692|692|692|692|692|692|
|R-squared|0.063|0.109|0.050|0.041|0.043|0.089|0.102|0.087|0.185|

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

27
subject to substantial fluctuations, however, the following analysis aims at netting out the individual-specific component in how successfully each technology is operated by a given household. Moreover, we have to allow for the possibility of both individual and social learning when the new technology is used.

Learning by doing implies that individual’s catches trend upwards after adoption as the individual learns how to use the new technology more efficiently over time. Social learning (or learning from others), on the other hand, implies that an individual can use the expertise other individuals have acquired with the new technology to become more efficient himself. Quite generally, the latter implies that the “learning curve” of an individual, that is his success as a function of time since adoption, depends on the amount of information available at the time he adopts. More specifically, we expect the learning curve of later adopters to be flatter since they start out with relatively more information at the time of adoption. With monthly sales data from 43 fishermen who switched to a fibre boat before the date of the interview, a test for individual as well as social learning is thus facilitated by the regression specification _ysit_ denotes monthly sales (in Rupees) of fisherman _i_ in month _t_ who currently operates technology _s_ , where _s_ = _C_ for catamaran and _s_ = _F_ for a fibre boat. Also consistent with the notation in the previous section, _t_<sup>_∗_</sup> _i_<sup>denotesthetimeofadoptionbyindividual</sup><sup>_i_,and</sup> _τi_ denotes time since adoption, so that _t_ = _t_<sup>_∗_</sup> _i_<sup>+</sup><sup>_τi_.Variable</sup><sup>_μsi_isanindividual-specific,</sup> technology-dependent intercept term or fixed effect, while _δt_ is a month-specific dummy that picks up aggregate fishing conditions and shocks. Finally, _usit_ is an i.i.d. error term with _E_ [ _usit_ ] = 0.

This parametrization assumes that shocks affect sales generated through the old and new technology identically in a proportional sense. This is strictly true as far as price fluctuations (per kg of fish) are concerned as the price indices faced by catamaran and fibre boat fishermen are the same. Whether it is also an appropriate assumption for weather shocks remains an open question. It is to be expected, however, that at least the 15
**Table XIV Probability of Exit to Employment in the Private** Sector **(Marginal Effects)**

|**Variables**|**Baseline**|**I**|**11**<br>**it1**||
|---|---|---|---|---|
|**Receivesunemployment**<br>**insurance(UI)**||0.087<br>(0.006)|-0.413<br>(0.004)||
|**Durationofspell**||-0.006<br>(0.000)|-0.009<br>**_0._**_0003)_|-0.009<br>(0.0003)|
|**UIw/highreplacementratio**||||-0.417"<br>(0.004)|
|**UI w/lowreplacementratio**||||**-0.387**<br>(0.003)|
|**EverreceivedUI**|||0.351<br>(0.005)|0.477<br>(0.005)|
|**Female(=1)**|0.016'<br>(0.006)|0.022'<br>(0.006)|0.014<br>(0.006)__(0.0_|0.010<br>_05)_|
|**Married(=1)**|0.051<br>(0.007)|0.049"<br>(0.007)|0.040<br>(0.007)|0.030"<br>(0.006)|
|**26-35**|0.043<br>(0.008)|0.047<br>(0.008)|_0.055_<br>(0.008)|0.052<br>(0,007)|
|**36-45**|0.075"<br>(0.009)|0.077<br>(0.009)|_0.080_<br>(0.009)|**0.051**<br>(0.008)|
|**46-55**|0.066"|0.063|0.064|0.027|
||(0.011)|(0.011)|(0.011)|(0.010)|
|**56-65**|0.018|0.018|0.025|-0.002|
||(0.018)|(0.018)|(0.018)|(0.016)|
|**Primary**|0.135"<br>(0.026)|0.11•'<br>(0.027)|0.094<br>(0.031)|0.092<br>(0.031)|
|**Apprenticeship**|0.321<br>(0.025)|0.20.208<br>(0.027)|(0.030)|0.185<br>(0.029)|
|**Secondary**|0.310<br>(0.023)|0.246<br>(0.025)|0.205<br>(0.030)|0.188-<br>(0.030)|
|**Coltege(+)**|0.339"<br>(0.014)|0.296<br>(0.017)|0.313"<br>(0.027)<br>_|0.316<br>_<br>(0.031)|
|**Nopreviousexp.**|0.029"<br>(0.012)|0.003<br>(0.012)|-0.040<br>__(0o.012|-0.059<br>(0.011)|
|**Occupation**|**YES**|YES|YES|YES|
|**Sector**|YES|YES|YES|YES|
|**Region**|**Y:ES**|YES|YES|-YES|
|**Year**|**YES**|YES|YES|YES|
|**Quarter**|**YES**|YES|YES<br>_|<br>YES|
|**Numberifindividuals**|18,141|18,141|18,141|18,141|
|**Numberofspells**|**30,741**|30,741|30,741|30,741|
|**Numberof pseudo-spells**<br>|30,741|30,741|39,711|46,577|
|**Prob> Chi**<sup>**2**</sup>|**0.000**|**0.000**|0.000|0.000|

**Source:** Unemployment Registry, 1990-2000.

Coefficients represent marginal effects evaluated at sample means.

Robust standard errors in parentheses (errors clustered at the individual level).

Significant at the 5 percent level. Significant at the 10 percent level.

Baseline comparison: Illiterate single male, 16-25 years old, with previous employment experience. but with no recorded employment prior to current unemployment spell.

Only exit to employment in the private sector was considered as a successful exit. Qualitatively and quantitatively equivalent results are obtained when exit to both employment in the private sector and employment in a public employment program are considered instead.

_55_
Finally, our paper sheds new light on the implications of multiple, overlapping disasters and the challenges they pose to disaster risk management. The COVID-19 pandemic had major implications for the financial wellbeing of households, particularly those whose income was earned in close-contact professions. We document that deteriorating financial circumstances during the COVID-19 pandemic continued to result in reduced disaster risk preparedness even after the peak of the pandemic. The findings underline that addressing disasters will become increasingly complex as climate change will lead to a more volatile and riskier environment.

# **Acknowledgments**

We thank the World Bank’s LAC High Frequency Phone Survey team for leading the design of the survey, coordination and helpful advice. We thank Sergio Rivera for research assistance. We thank _Sistemas Integrales_ and their national partners for implementing the survey and all respondents for the time they took to answer our questions. We thank our development partners for their inputs on the disaster risk data collection module developed for this project. We gratefully acknowledge financial support from the European Union in the framework of the Caribbean Regional Resilience Building Facility, managed by the Global Facility for Disaster Reduction and Recovery (GFDRR) and from the Canada-Caribbean Resilience Facility (CRF). The sole responsibility of this publication lies with the authors. The European Union, Canada, and the World Bank are not responsible for any use that may be made of the information contained therein.

# **Declaration of interest**

The authors are not aware of any financial or personal relationships with other people or organizations that could inappropriately influence or bias this work.

# **Data availability statement**

The microdata used in this paper is publicly available through the World Bank Microdata <u>Catalogue</u> for four of the five countries: Belize (https://doi.org/10.48529/6zwy-a603), Dominica <u>(https://doi.org/10.48529/3sf6-h006), Haiti (https://doi.org/10.48529/dj1x-q178), and Saint Lucia (https://doi.org/10.48529/ynvs-3h73). At the time of writing this statement, the team was in the</u> process of publishing the data for Suriname and the Do files to replicate the analysis.

18
# **Annex 2 – Methodology to Select Illustrative Rules for PSNP Scalability**

## **Selecting Historic Poverty Data**

Underlying poverty data (poverty numbers by zone, 2001-2015, rural Ethiopia) was provided by Hill/Porter and is shown in Figure A2.1 below.

## **Figure A2.1:  Hill/Porter data showing the population in poverty based on in year WRSI and 2010/11 consumption data.**

<!-- Start of picture text --> 25<br> 20<br> 15<br> 10<br> 5<br> ‐<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>Year of crop loss<br>100% poverty line 75% poverty line<br>Total population (Millions)<br><!-- End of picture text --> The underlying poverty estimates were based on the following methodology:

- Regress 2010/11 household survey consumption data on household data and 2010/11 Water Requirement Satisfaction Index (‘WRSI’) data to estimate a predictive formula for poverty, as a function of household data and WRSI.

- Use this predictive formula to generate estimates of the number of people in poverty in each zone (each zone consisting of multiple woreda) for 2001-2015.

The following assumptions were made:

- The poverty line was defined as the cost of 2,200 calories per adult equivalent per day, plus very basic non-food items (e.g. cooking fuel).

- The data is based on modelling historical rainfall/crop loss data over the past 15 years with a baseline assumption for consumption, to estimate the population falling below the poverty line in each historic year.

- There are a number of caveats related to the simulation of these population estimates. For example, there is no adjustment for consumption growth, and the model has not been adapted to account for any factors other than the difference in rainfall from one year to the next.

19
normal distribution and _f_ ( _._ ) is a polynomial of the variables. I also include a full set of year and industry effects in the equation. The panel dataset allows me to implement a Differencein-Differences (DID) matching estimator as suggested by Heckman et al. (1997). Blundell and Costa Dias (2000) note that combining matching with DID allows for the possibility of unobservables affecting participation if they can be represented by separable firm specific and /or time specific components.

Matching treated and control groups on propensity scores requires two assumptions. The common support assumption requires the possibility of a nonparticipant analogue for each participant. It is given by _P_ ( _W_ = 1) _|P <_ 1, where _P_ is the propensity score.

The conditional mean assumption requires that the expected change in outcome of the treated firm had it not been treated should be the same as the expected change in outcome of the control firm i.e., _E_ ( _Yt_<sup>0</sup><sup>_′−Y_</sup> _t_<sup>0</sup> _−_ 1<sup>_|P, W_=1)=</sup><sup>_E_(</sup><sup>_Y_</sup> _t_<sup>0</sup><sup>_′−Y_</sup> _t_<sup>0</sup> _−_ 1<sup>_|P, W_=0).Here,</sup><sup>_t −_1denotesthetimeperiod</sup> just prior to export entry, _t_<sup>_′_</sup> = _t_ + _i_ where _i_ = 1 _,_ 2 _,_ 3 _,_ 4 _..._ denote time periods after export entry and ( _Yt_<sup>0</sup><sup>_′−Y_</sup> _t_<sup>0</sup> _−_ 1<sup>) denotes the change in the outcome of interest between the two periods in the untreated</sup> state.

The treatment on the treated is given by Here _N_ refers to the number of firms that start to export, _T_ refers to the treated group i.e., the export starters and _C_ refers to the control group of never exporters. Each firm _i ∈ T_ is matched to firms in the control group and _wij_ is the weight on each firm _j_ in the control group as a match for firm _i_ .<sup>18</sup> _Yit_<sup>1</sup><sup>_′−Y_</sup> _it_<sup>1</sup> _−_ 1<sup>isthechangeintheoutcomevariablefortheexportentrantbetween</sup><sup>_t′_</sup> and _t −_ 1 and _Yjt_<sup>_c′−Y_</sup> _jt_<sup>_c_</sup> _−_ 1<sup>isthechangeintheoutcomevariableforthecontrolfirmsbetween</sup><sup>_t′_</sup> and _t −_ 1.

The outcomes of interest are ( _α_ � _χχit_ + � _ωit_ ) as well as the individual components _α_ � _χχit_ and _ω_ � _it_ , to examine whether overall productivity improves on export entry as well as the relative importance of the two channels.

# **5 Data**

The firm-level variables are from the Prowess dataset provided by the Center for Monitoring of the Indian Economy (CMIE). This is a panel data set of Indian firms in the organized sector. It includes both listed and unlisted firms. The dataset has been used in several papers including Goldberg et al. (2010) and Topalova and Khandelwal (2011).

> 18For example, a nearest neighbor matching with one neighbor would mean that for firm _i ∈ T_ , the firm most similar to firm _i_ in the control group _C_ would have _wij_ = 1 and all the remaining firms in _C_ would have _wij_ = 0.

16
# **_3.1.4 Pensions Spending_**

The public pension system in Argentina is fragmented into various sub-systems and constitutes the second most significant expense, ranking among the highest globally. Among a sample of 33 advanced, emerging, and regional peers, Argentina's estimated pension spending in 2022, accounting for 10 percent of GDP, ranks within the top five countries (see the Annex). At the regional level, Argentina performs similarly to Uruguay and Brazil, surpassing the rest of its neighboring countries.

Argentina has conducted its pension spending in one of the most procyclical manners during the past decades (

**Figure 10** ), and this has significantly contributed to overall fiscal procyclicality. During the period of 19952021, nearly 80 percent of the countries in the sample pursued either counter-cyclical or acyclical pension spending policies. In contrast, the correlation between the cyclical component of pension spending and real GDP in Argentina stood at +0.53, which is statistically significant (at 5 percent level) and the highest in the region, significantly surpassing OECD figures (-0.422, statistically significant). Therefore, instead of remaining neutral to the economic cycle, as theory suggests, the Argentine public spending on pensions has reinforced economic fluctuations.

**Figure 10. Pension spending cyclicality (1995-2021 or closest year)**

<!-- Start of picture text --> 1,0<br>OECD: -0,422*<br>0,8 Latin America: +0,037<br>Argentina: +0,53**<br>0,6<br>0,4<br>0,2<br>0,0<br>-0,2<br>-0,4<br>-0,6<br>-0,8<br>-1,0<br>OECD Latin America<br>Procyclical<br>spending<br>Countercyclical<br>Correlation between cyclical components of GDP and pensions<br>Latvia United States Ireland France Finland United Kingdom Netherlands Korea Portugal Austria Italy Japan New Zealand Estonia Norway Sweden Spain Slovenia Canada Luxembourg Belgium Czechia Slovak Republic Poland Türkiye Denmark Australia Lithuania Israel Hungary Germany Iceland Greece Peru Costa Rica Mexico Chile Brasil Colombia Uruguay<br><!-- End of picture text --> _Source:_ OECD, CEPAL, Ministry of Economy, INDEC, ANSES. _Notes:_ the cyclical components of GDP and public spending have been estimated using the Hodrick-Prescott filter. Public spending is considered in real terms, deflated by CPI. *, ** and *** indicate statistical significance at the 15%, 10% and 5% levels, respectively.
<!-- Start of picture text --> Figure 3c. Algeria and Tunisia ‐ Inflation (Annual %)<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999<br>‐5 ‐4 ‐3 ‐2 ‐1 0 1 2 3 4 5<br>Algeria Tunisia Military coup Onset<br>Annual inflation (%)<br><!-- End of picture text --> Source: Authors’ calculations based on data from Penn World Tables 9.0, International Financial Statistics Database (IMF), and Historical Public Debt Database (IMF)/Abbas et al. (2011).

The two comparisons between conflict‐afflicted and non‐conflict neighboring countries provide both commonalities as well as contrasts. In both, the conflict‐afflicted economies (Nicaragua and Algeria) were characterized by rising current public expenditures and public debt as a share of GDP relative to their non‐ conflict neighbors (Costa Rica and Tunisia, respectively). However, in one case, inflation shot up in Algeria while inflation was gradually falling in Tunisia, whereas the contrast in the inflation dynamics of Nicaragua and Costa Rica was less clear. These differences could be due to regional or global trends of economic adjustment and reforms that coincided with the onset of armed conflict. The following section discusses our econometric strategy, which aims to disentangle the contrasting macroeconomic trends around the onset of conflict by systematically comparing conflict economies with non‐conflict economies for as many episodes of conflict as possible.

# **_III. D. Empirical strategy_**

The purpose of our estimation strategy is to identify the potential differential effects of armed conflict on the three macroeconomic variables of interest. The econometric strategy aims to identify these potential effects with a difference‐in‐difference estimator: The subscripts c and t denote countries and event years respectively. yc,t represents the dependent macroeconomic variable. The country fixed effects μ c capture country‐specific and time‐invariant factors that could be correlated with the explanatory variables. The time fixed effects μ t accounts for common 12
64 based on the “nominal rate of protection at farm gate”, which is defined as the difference between actual and expected farm-gate fertilizer prices, with expected prices computed based on international prices and the cost of transportation from port to farm. Since there is no available subsidy data for cinnamon, cloves, and groundnuts, we impute their fertilizer subsidy rates with the average rate across potato, maize, tea, and onions.

# **A.F Potential Crop Yields**

To estimate the elasticity of substitution across crops of our model’s demand system, we use an IV approach that leverages variation in agricultural productivity across space due to exogenous geographic factors (such as climate and geology). To do so, we use data on district-level potential attainable yields for each of seven crops, which we obtain from the FAO-GAEZ project. The crops are: coconut, groundnut, maize, onion, rice, tea, and white potato. Unfortunately, cinnamon potential yields are not available in GAEZ. We extract potential yield data (in kg/ha) at the grid cell level and aggregate up to the district level. GAEZ offers different yield estimates depending on the irrigation system (rainfed or irrigated) and climate conditions (e.g. historical conditions, various climate change scenarios). To reflect the actual technologies commonly used in Sri Lankan agriculture, we choose the rainfed option for all crops except rice, which is irrigated. We choose the climate conditions of 1981-2010 without climate change. Section VI.A.1 describes how this data is used in estimation.

# **A.G Labor Share in Value Added**

To estimate labor and land coefficients in our model’s Cobb-Douglas agricultural production functions, we use data from Sri Lanka’s IO table for 2000, produced by the Institute of Policy Studies (Amarasinghe and Bandara, 2005). We extract information on employee compensation and value added (at factor cost prices) for a variety of agricultural sectors: “paddy”, “minor export crops”, “highland crops”, “tea growing” (separately for high, medium, and low elevations), and “potatoes”. Section VI.A.3 describes how this data is used in estimation.

# **A.H Wages, Household Characteristics, Occupational Distribution, and Land Distribution**

We use the 2016 and 2019 editions of the HIES for data on wages, employment, land ownership, land distribution, income, and consumption patterns. The HIES is a yearlong survey conducted in consecutive monthly rounds, with a nationally representative sample enumerated in each round to capture seasonal and regional pattern variation. The HIES provides household-level statistical
> - 47 -

private investments through tax deductions for -chases coupled with taxation of receipts. Such treatment is likely to al.er business decisions regarding risky investments. To the extent that the business tax is a flat rate tax and firms can use losses to offset other income, the primary result of such government risk-sharing is likely to be an increase in private risk-taking. If business investors are risk-averse, such a taxinduced increase in risk-taking may be desirable from a social standpoint.34 D. Consistency With Economic Growth Although the question is far from decided, many consumption tax proponents argue that eliminating the taxation of capital income would result in significantly greater economic growth. In the developing country context, this may occur for a variety of reasons. First, domestic saving may increase in response to higher after-tax returns to saving. The response of saving to changes in the net return is theoretically ambiguous.35 Moreover, it would clearly vary across developing countries. Nevertheless, recent empirical research for the United States suggests that a positive response is certainly possible, and arguably the likely outcome.<sup>36</sup> To the extent that increased domestic saving would lead to increased domestic investment (rather than simply replacing foreign-financed investment), a higher level of investment would be achieved.

Second, the amount of domestic savings invested domestically may increase. For example, suppose that domestic savings invested at home bear some income tax burden in the developing country. investors generally have

> 34/ For discussions of how taxation can increase private risk-taking and whether such an increase is socially desirable, see Atkinson and Stiglitz, 1980.

35/ See Feldstein, 1978.

36/ See Boskin (1978).