rafmacalaba/datause-encoder-data huggingface dataset:
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special financial treatment, caregivers to paraplegic and tetraplegic and women who retire for the period of accomplishing the higher education, is equal to the minimum wage.

# **_Contribution rates:_**

The contribution rate for health care insurance is 3.4% of the gross salary, according to the payroll, where the employer’s part is 1.7% and the employee’s part is 1.7%, but not less than the minimum wage.

|**Contributions**|**2011**|**2012**|**2013**|**2014**|**2015**|
|---|---|---|---|---|---|
|Contributions for health insurance<br>(employee)|1.7%|1.7%|1.7%|1.7%|1.7%|
|Contributions for health insurance<br>(employer)|1.7%|1.7%|1.7%|1.7%|1.7%|
|Self-employed in commercial or<br>service activities, ambulant||||1 496|1 496|
|For maintenance workers, the<br>employer should declare and pay the<br>monthly minimum contribution||||748|748|
|The self-employed and maintenance<br>workers with the exception of self-<br>employed in agriculture, persons over<br>the age of 18, with a regular income<br>from movable property and real estate,<br>self-employed in agriculture||||3.4%|3.4%|

46
The administrative records of the BPS included two databases, which were collected to identify the target groups from each experiment:

- _Contributors' Administrative Records, from the Tax and Collection Advisory:_ This database included possible

- future subsidy beneficiaries who made up the sample for experiment 1. Separate strata of men and women were randomly assigned to receive the information message (T1) or none (C).

- _Records of Beneficiaries of leave programs:_ From this registry, we identified participants for experiment 2 as BPS contributors who had requested or were benefiting from maternity or paternity leave five months before messages were sent. A sample of men and women using or with an approved request for paternity or maternity leave was taken. Men and women strata were randomly assigned to receive one of the three messages: information (T1), testimony (T2), or planning (T3).

Finally, we rely on additional administrative records to analyze the medium-term effects of the intervention on effective use of the parental leave. For all surveyed individuals, we obtained subsidy applications and access for the period up to nine months after the intervention from the BPS databases.<sup>10</sup>

# _3.4. Randomization checks_

The estimation sample consists of 2,463 individuals, 858 men, and 1,605 women. 1,221 correspond to Experiment 1, from the sample of formal employees of reproductive age. The remaining 1,242 corresponds to Experiment 2, from the sample of recent parents benefiting from maternal or paternal leave. Table B.1 in the Appendix details the sample size for each experiment and treatment arm by sex.

We use individuals' characteristics to check the sample balance across treatment arms. Tables B.2 to B.4 in the Appendix show the balance test results for each sample. For Experiment 1, we found no statistically significant differences in covariates by treatment arm. For Experiment 2, we tested 15 mean differences (5 variables between 3 treatment groups). We found a lack of balance for men and women in one variable (tertiary education), within the tolerance level of 10%. All variables are included in the regression as controls to account for potential imbalances.

# _3.5. Descriptive statistics_

This section presents descriptive statistics from the impact variables collected in the post-intervention survey. Figure 2 shows responses on recalling receiving the message by experiment. On average, 38.5% of

> 10 We consider individuals who applied for the benefit between September 1, 2021, and June 6, 2022.

9
Table 1: Effects on Adoption

||(1)<br>Any Action|(2)<br>Spray|(3)<br>Pour Ash|(4)<br>Crush<br>Eggs/Worm|
|---|---|---|---|---|
|Trained Farmer|0.15|0.13|-0.01|0.03|
||(0_._08)<sup>_∗_</sup>|(0_._09)|(0_._03)|(0_._03)|
|Spillover Farmer|0.14|0.12|-0.00|0.01|
||(0_._06)<sup>_∗∗_</sup>|(0_._06)<sup>_∗_</sup>|(0_._02)|(0_._01)|
|Control Mean|0.42|0.40|0.04|0.01|
|Observations|739|739|739|739|

**Notes:** The table reports estimates based on specification (10). _Trained Farmer_ is a dummy variable equal to 1 if a farmer has received training on the Fall Armyworm as part of the experiment. _Spillover Farmer_ is a dummy variable equal to 1 if a farmer lives in a village where training was provided to another farmer. The dependent variable in Column (1) is a dummy equal to 1 if the farmer reported having adopted any of the following recommended practices: spraying their field, pouring ash (or sand or soil) on infected plants, crushing the eggs or caterpillars. In the remaining columns the dependent variable is a dummy for each recommended practice. All specifications include fixed effects for the willingness to pay of the Decision Maker for the potential trainee in the village. Robust standard errors are clustered at the village level and given in round brackets; *** (**) (*) indicates significance at the 1% (5%) (10%) level.

mind with respect to estimates in Table 1. The training is randomized at the village level among 103 villages and takes place in 64 villages (translating into 64 trained farmers). Under standard assumptions, this sample size is not adequate to detect effects of the training on adoption. Powering this part of my experiment was not a first-order concern since adoption is not the main outcome of my analysis, but an identical program could fail to replicate these results.

The first column of Table 1 shows adoption of any of the three recommended practices, and the remaining columns show the contribution of each individual practice to overall adoption. The omitted comparison group in this case is that of spillover farmers in villages where training did not take place. Adoption increases by 14% among spillover farmers in villages in which training took place, while it increases by 15% among farmers who received training themselves. This result is driven by higher take-up of pesticides, which increases by a similar magnitude among trained and spillovers farmers. The fourth column shows that trained farmers are much more likely to adopt the practice of crushing eggs and caterpillars by hand, but the practice does not transfer to spillover farmers (their adoption rate is identical to farmers in control villages). These results suggest that only the practice of spraying pesticides diffuses, while the practice of crushing eggs and worms does not diffuse, a pattern that is qualitatively confirmed by two metrics I collected at follow-up. First, when spillovers farmers are asked which practices the trained farmer recommended, most of them mention spraying pesticides and few of them mention any other practice (see top panel of Figure A.9). Second, in July 2019 I administered an 17
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**TABLE 2 Small states’ fiscal reaction functions: Results**

||Ba|seline|Exten|ded model|
|---|---|---|---|---|
||FE|Panel FE-IV|FE|Panel FE-IV|
|Lagged primary balance ratio|0.476***|0.461***|0.474***|0.459***|
||(0.035)|(0.069)|(0.035)|(0.069)|
|Lagged debt ratio|0.0146**|0.0202**|0.0140**|0.0191**|
||(0.005)|(0.009)|(0.006)|(0.009)|
|Inflation|0.0768|0.0534|0.0779|0.0553|
||(0.048)|(0.049)|(0.048)|(0.049)|
|Real GDP growth|0.062|0.0614*|0.0624|0.0617*|
||(0.042)|(0.035)|(0.042)|(0.036)|
|Elections|||-0.439|-0.501|
||||(0.400)|(0.483)|
|Fiscal rule|||0.243|0.389|
||||(0.657)|(0.745)|
|Number of observations|494|455|494|455|
|Number of countries|22|22|22|22|

Note: Dependent variable is primary balance as a percent of GDP. Lagged values (first and second lags) of inflation and growth are used as instruments in the panel fixed effects instrumental variable (FE-IV) estimator. Elections and fiscal rules are dummy variables. Time fixed effects are included in each regression. Robust standard errors in parentheses. *** indicates statistical significance at the 1 percent level, ** at the 5 percent level, and * at the 10 percent level. These results are robust to other approaches, including the bias-corrected least-squares dummy variable, Prais-Winsten, and Driscoll-Kraay estimators.

**TABLE 3 Variables, Definitions, and Data Sources for Fiscal Reaction Function Analysis**

|Variables|Definition|Sources|
|---|---|---|
|Primary balance|Primary balance as a share of GDP|World Economic Outlook, IMF|
|Debt to GDP|General government debt as a share of GDP|World Economic Outlook, IMF|
|Economic growth|Real GDP growth|World Economic Outlook, IMF|
|Inflation|Inflation, average consumer prices|World Economic Outlook, IMF|
|National elections|Dummy variable equals 1 for the year of and prior<br>to general elections in countries, and 0 otherwise|Cruz, Keefer, and Scartascini (2018).|
|Fiscal rule|Dummy variable equal to 1 for the period of time<br>countries had a fiscal rule in place and 0 otherwise.|Davoodi et al. (2022b).|

Note: IMF = International Monetary Fund.

25
**Table 1. Serbia general government revenue, 2016**

||Fiscal data|<br>|Included<br>(SILC|in analy<br>and HBS<br>|sis<br> )<br>|
|---|---|---|---|---|---|
||bn RSD|%<br>GDP|Yes/No|bn<br>RSD|%<br>GDP|
|**Total revenue**|1,765.90|42.5||||
|**_Direct taxes_**|235.5|5.7||||
|Personal income tax|155.1|3.8|Yes|101.5|2.5|
|Wages/Salaries|113.0|2.8|Yes|89.7|2.2|
|Self-employment income|6.5|0.2|Yes|9.0|0.19|
|Capital income|11.9|0.3|Yes|0.2|0.0|
|Rental income|3.8|0.1|Yes|2.2|0.1|
|Capital gains|0.7|0.0|No|||
|Other income|13.0|0.3|No|||
|Annual income tax|6.0|0.1|Yes|0.4|0.0|
|Corporate income tax|80.4|1.9|No|||
|**_Social contributions_**|527.5|13.0|Yes|455.1|11.3|
|Pension and disability insurance|354.7|8.8|Yes|313.2|7.7|
|Health insurance|132.7|3.3|Yes|124.1|3.0|
|Unemployment insurance|19.3|0.5|Yes|17.8|0.5|
|**_Indirect taxes_**|755.5|17.7|Yes|363.0|8.9|
|VAT|453.5|11.2|Yes|267.7|6.6|
|Excise taxes|265.6|6.5|Yes|95.3|2.4|
|Other indirect taxes (customs)|36.4|0.9|No|||
|Other revenues|247.4|6.1|No|||

Sources: Authors' calculations based on Ministry of Finance _Note: “Included in analysis” refers to the items that can be identified in the household data for this analysis and the amount that can be estimated and reflected in the analysis._ Social security contributions (SSC) represent a large source of public revenues in Serbia, at 13 percent of GDP (pension contributions – 9.15 percent of GDP, health contributions - 3.4 percent of GDP and unemployment contributions – 0.6 percent of GDP). The social security system in Serbia consists of three components: pension and disability insurance, health insurance, and unemployment insurance. This is a Bismarckian social security system financed 6
# **Tables and Figures**

**Figure 1. Sample selection**

<!-- Start of picture text --> Citations identified<br>1. Jstor (n = 450)<br>Rexer and<br>2. Scopus (n = 80) CCDRs<br>Sharma(2024)<br>3. Core.ac.uk (n = 320) n = 37<br>n = 324<br>4. Index studies (n = 3093)<br>Excluded:<br>1. Duplicates (n = 2640)<br>Abstract review<br>Excluded:<br>n = 1303<br>1. Reviewer<br>Excluded: screening for<br>outcomes of<br>1. Field and journal<br>interest (n = 304)<br>screening (n = 518)<br>2. AI screening (n = 424)<br>3. Reviewer screening<br>for outcomes of<br>interest (n = 278)<br>Full text review Full text review Full text review<br>n = 89 n = 20 n = 37<br>Excluded: Excluded: Excluded:<br>1. Reviewer 1. Reviewer 1. Reviewer<br>screening for screening for screening for<br>outcomes of outcomes of outcomes of<br>interest (n = 64) interest (n = 12) interest (n = 0)<br>Studies Studies Studies<br>included n = 25 included n = 8 included n = 37<br>Included in the meta-analysis: 70 Studies and 701 estimates<br><!-- End of picture text --> _Note_ : CCDR = _Country Climate and Development Report_ . Rexer and Sharma (2024) document 324 relevant climate change adaptation studies. Among 324 relevant climate change adaptation studies, eight document either the poor’s exposure to climate shocks or impact of climate shocks on the poor.

14
Figure B2. Papers that Produce Publication Bias Affect Weights for Meta Impact Estimations

|ID<br>Study||**ES (95% CI)**|**Weight**<br>**%**|
|---|---|---|---|
|04AUT01||<br>**4.06 (3.54, 4.65)**|**0.56**|
|05DEU02||**1.43 (1.13, 1.80)**|**0.19**|
|08SVK03||**1.31 (0.95, 1.81)**|**0.10**|
|10WBC04||**1.19 (1.08, 1.30)**|**1.19**|
|04BEL05||**1.03 (1.01, 1.05)**|**22.73**|
|11EUC06||**1.05 (1.03, 1.08)**|**16.53**|
|11ECA07||**1.03 (0.94, 1.14)**|**1.10**|
|10RUS08||**1.11 (1.06, 1.16)**|**4.77**|
|10USA09||**1.14 (0.35, 3.66)**|**0.01**|
|10ARG10||**1.36 (1.00, 1.87)**|**0.11**|
|10TUR11||**1.26 (0.99, 1.59)**|**0.19**|
|10DNK12||**1.33 (0.98, 1.80)**|**0.11**|
|10CAN13||**1.10 (1.07, 1.13)**|**16.27**|
|10EUC14||**1.23 (1.05, 1.44)**|**0.43**|
|10TUR15||**1.22 (1.18, 1.25)**|**12.22**|
|10AUT16||**1.35 (1.25, 1.47)**|**1.64**|
|10ITA17||**1.37 (1.09, 1.71)**|**0.21**|
|10SER18||**1.15 (1.01, 1.30)**|**0.69**|
|10UKR19||**1.18 (1.00, 1.39)**|**0.41**|
|10JAP20||**1.05 (0.99, 1.11)**|**3.51**|
|10CEE21||**. (0.00, .)**|**0.00**|
|10EUC22||**3.00 (1.11, 8.16)**|**0.01**|
|09CEE23||**1.67 (1.09, 2.58)**|**0.06**|
|09EUC24||**. (0.00, .)**|**0.00**|
|09FIN25||**1.28 (0.96, 1.72)**|**0.12**|
|09ECA26||**1.27 (1.12, 1.44)**|**0.67**|
|08TUR27||**1.36 (0.96, 1.94)**|**0.08**|
|08FIN28||**1.45 (0.94, 2.24)**|**0.06**|
|08GER29||**1.35 (1.22, 1.50)**|**0.98**|
|06EUC30||**2.00 (0.98, 4.10)**|**0.02**|
|07HRV31||**. (0.00, .)**|**0.00**|
|07MUC32||**1.35 (0.93, 1.97)**|**0.07**|
|06BGR33||**. (0.00, .)**|**0.00**|
|10MEX34||**1.26 (0.99, 1.59)**|**0.19**|
|05FIN35||**2.47 (2.36, 2.59)**|**5.10**|
|10ESP36||**1.36 (1.32, 1.41)**|**9.52**|
|11ITA37||**1.21 (0.93, 1.58)**|**0.15**|
|Overall (I-squared = 97.9%, p = 0.000)||**1.18 (1.17, 1.19)**|**100.00**|
|1.0e-08|**1**|1.0e+08||

Source: Own estimations 35
The growth of revenues under the stochastic scenario is defined as: _SS t_ = _baseline REVENUE growth ratet_ + .0105 * ε _tP_ COFFEE_ .

# **_Rainfall shocks_**

The impact of rainfall shocks on the agricultural GDP is estimated using a simple reduced-form time series regression. Annual rainfall and civil war are treated as purely exogenous variables. We tried several models, including models using REVNEUE and GDP as a dependent variables, respectively. The model using AG_GDP as a dependent variable was chosen as it offered the best fit with the data. Including lagged values of rainfall and civil war did not significantly improve the estimation. The estimation results imply: The impact of shocks to rainfall on growth rate of agricultural GDP is given by: We assume that a 1 percent shock to agricultural GDP will lead to 0.50 percent shock in GDP— somewhat higher than the share of agriculture in GDP (0.34) but lower than the total elasticity implied by the IMF’s (share of agriculture in GDP + 0.71).

The stochastic rainfall shock to be applied to the baseline projection of GDP growth is calibrated − based on the historical rainfall data (1980 2008), and is assumed to be normally distributed with mean zero and standard deviation 0.266: The growth of real GDP under the stochastic scenario is defined as: _SS t_ = _baseline GDP growth ratet_ + .050 * .0013* ε _tRAIN_ .

31
last two survey rounds but who are involved in on-farm activities (including crop and livestock production), ranges in rural areas from 90 percent in Nigeria to 100 percent in Ethiopia, and in urban areas from 34 percent in Nigeria to 99 percent in Ethiopia. The share of people that exited off-farm employment and returned to / started with on-farm employment ranges in rural areas from 84 percent in Nigeria to 100 percent in Ethiopia, and in urban areas from 27 percent in Nigeria to 95 percent in Ethiopia. Consistently across and within countries, women are less likely to be onfarm employed than men.

# _5.2. Econometric models_

We analyze two econometric models to investigate people’s decisions to start or continue with offfarm employment. We study the probability of being employed in the last panel round, conditional on the employment status in the previous round. We estimate a separate logit model on two subsamples: those who were not employed in the previous round and those who were. The models take the following form: where _y_ is the employment status of individual _i_ in year _t_ , _X_ a vector of individual, household and community characteristics and _E_ a vector of job characteristics. The first model assesses the determinants of starting employment and explains why some people enter employment while others are never employed. The second model assesses the determinants of continued employment and explains why some people remain employed while others drop out of employment.

In line with the literature on determinants of employment, we include independent variables at individual-level (age, relation to household head, marital status and education), household-level (household size, dependency ratio, wealth, landholdings and livestock units), and community-level (distance to nearest road and market). _X_ also includes regional fixed effects. Further, we are interested in the effects of covariate and idiosyncratic shocks, and include dichotomous variables identifying whether the household recently faced a death or serious illness of a household member, extreme weather events (floods and droughts) and a sudden increase in food prices. The 16
have a stronger focus on all households’ members, rather than focusing only on women and young children. In addition, they provide more detailed information on labor market participation, such as exact profession (where, for example, being a miner is a possible outcome), hours worked, and a wage indicator. The data estimate household expenditure and household income. Wages, income, and expenditure can, however, be difficult to measure in economies where nonmonetary compensation for labor and subsistence farming are common practices.

# **4 Empirical Strategies**

# **4.1 Individual-level difference-in-differences**

Time-varying data on production and repeated survey data allow us to use a difference-indifferences approach.<sup>7</sup> However, due to the spatial nature of our data and the fact that some mines are spatially clustered, we use a strategy developed by Benshaul-Tolonen (2018). The difference-in-difference model compares the treatment group (close to mines) before and after the mine opening, while removing the change that happens in the control group (far away from mines) over time under the assumption that such changes reflect underlying temporal variation common to both treatment and control areas.

We limit the data to include households within 100 km of a mine location and estimate the following: where the outcome of an individual _i_ in cluster _v_ , and for year _t_ is regressed on district and year fixed effects, a dummy for whether the respondent lives within 20 km of a mine (which is a current or future mine<sup>8</sup> ), a dummy for whether the mine is active at the time of the survey (active), an interaction term between active mines and living close to a mine ( _<mark>active</mark> t_ _<mark>* mine</mark>_ <mark>)</mark> , and a vector of individual-level control variables. _Mine_ is the terminology chosen to capture a known gold resource in the ground, regardless of whether it is being extracted or not. In all regressions, we also control for living in an urban area, years of education, and age.

> 7 We have not done a synthetic control approach because of limited ability to explore pretreatment trends. 8 A current or future mine could also be called a deposit. We have chosen against this terminology as there may be known deposits in Ghana that never started actively producing. Such deposits are not included in our dataset and thus not in the analysis. In addition, deposits may be considered all existing geological deposits whether or not known to man, or those that have been discovered. The latter being truly exogenous, while known deposits and “mines” according to our definitions are not truly exogenous.

10