rafmacalaba/datause-encoder-data huggingface dataset:
to agricultural income or social security contributions paid by the employee. Self-employed income can either be included in the global income or taxed separately at the flat rate of 19%.<sup>5</sup>

**Table 1. Poland: Tax Revenue Structure 2014**

||Fiscal data<br>(Million<br>PLN)|% of<br>GDP|Included<br>in<br>analysis|Derived<br>in<br>survey<br>(Million<br>PLN)|Ratio of total<br>amounts in the<br>survey and<br>external<br>statistics, %|
|---|---|---|---|---|---|
|**Total revenue**|667,698|38.8||398,917|60%|
|Total tax receipts|339,851|19.8||119,416|35%|
|_Direct taxes_|108,657|6.3||45,840|42%|
|Personal income tax|78,617|4.6||45,840|42%|
|Employees & self-employed paying 18% or 32%|55,567|3.2|Yes|43,293|55%|
|Self-employed who choose 19% flat tax|16,972|1|No|||
|Capital tax|836|0.0|Yes|67|8%|
|Agricultural tax|2,090|0.1|Yes|2,479|119%|
|Other income taxes on households|3,152|0.2|No|||
|Corporate profit tax|30,040|1.7|No|||
|_Indirect taxes_|185,833|10.8||73,576|40%|
|VAT|124,262|7.2||54,833|44%|
|from household consumption 1/|83,043|4.8|Yes|54,833|66%|
|from other consumption|41,219|2.4|No|||
|Excise duties and consumption taxes|61,570|3.6|Yes|18,743|30%|
|_Other taxes_|45,361<br>|2.6|No|||
|Net social contributions|227,548|13.2||205,926|90%|
|Old-age pension contribution|121608|71|Yes|86,164|100%|
|Disability pension contribution|,|.|Yes|35,313||
|Accident insurance contribution|6,212<br>|0.4|Yes|8,540|137%|
|Sickness insurance contribution|10,849<br>|0.6|Yes|9,593|88%|
|Labor Fund|9,569<br>|0.6|Yes|10,841|113%|
|Fund of Guaranteed Employee Benefits|390<br>|0.0|Yes|392|100%|
|Farmers old-age and disability contribution|1,518<br>|0.1|Yes|1,503|99%|
|Farmers other contributions|701<br>|0.0|Yes|713|102%|
|Health insurance contributions|61,204|3.6|Yes|52,869|86%|
|Other contributions|15,496<br>|0.9|No|||
|Other revenue<br>Sources: Eurostat, GUS, and own estimates based on HBS 20<br>1/ We assume that the share of VAT paid by households is e<br>consumption according National Accounts and total gross v|100,299<br>14.<br>quivalent to t<br>alue added in|5.8<br>he ratio<br>the econ|No<br>between to<br>omy.|tal house|hold|

Similarly, capital gains and investment income, such as dividends, interest and proceeds from sale of shares, are subject to withholding tax at a flat rate of 19%. Income from renting property can be either included in global income, or individuals can choose the flat tax on revenues of 8.5%. Households can deduct costs of obtaining income, a basic tax-free allowance of PLN 3,000 per

> 5 If a self-employed person decides to use the flat tax, they may not file jointly with their spouse or deduct child tax credit.

5
# Table 6

Loans and Discounts of Financial Inmtitutioni (trillion yen)

||Total|Banks|Trust<br>Fund<br>Bureau|Govt<br>Fin<br>Inst|Insur<br>Comp|Agric<br>Coop|Other|
|---|---|---|---|---|---|---|---|
|1945|0.1|0.1|..|..||||
|1950|1.5|1.1|0.1|0.1|0.01|<br>0.03|<br>0.05|
|1960|15.9|10.6|1.5|1.5|0.5|0.3|0.4|
|1970|91.1|56.5|10.3|7.8|4.0|3.1|2.8|
|1980|385.5|201.8|76.2|42.0|16.8|1±.1|11.5|
|1988|779.7|442.8|145.8|76.6|36.7|12.7|20.5|

- Notes: Total includes overlapping accounts, i.e., inter-financial institutions loans.

Banks include city banks, regional banks, trust banks, long-term credit banks, sogo banks and shinkin banks.

# Other includes the Shokochukin and Norinchukin banks.

The series is discontinued after 1988.

- Source: Bank of Japan, _Economic Statistics_ Annual, 1989 (pp. 167-168) and 1988 (pp. 165-166).

37
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29
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30
|Waste Disposal|21.3|0.052|17.2|0.037|4.1|
|---|---|---|---|---|---|
|Drinking water|9.1|0.055|5.7|0.028|3.4|
|Electricity|20.4|0.053|7.8|0.036|12.7*|
|Cooking fuel|22.5|0.051|18.4|0.041|4.1|
|Housing|14.2|0.061|5.4|0.026|8.8|
|Sanitation|17.4|0.032|17.0|0.037|0.5|
|Unemployment|7.1|0.038|3.0|0.016|4.2|
|Legal id|4.5|0.013|9.4|0.046|-4.9|
|Bank account|22.4|0.051|18.1|0.040|4.3|

Notes: h0 is the proportion of male- and female-headed households deprived in various indicators, SE- standard errors. Asterisks indicate statistical significance at 1% ***, 5% **, and 10% * levels.

_Table A4: Censored headcounts of each indicators in the MPI, by the gender of the household head in Somalia (2017)_

|Somalia|Male-heade|d HH|Female-head|ed HH|Difference|
|---|---|---|---|---|---|
||h0|SE|h0|SE||
|Years of Schooling|49.6|0.033|32.7|0.030|16.9***|
|School attendance|45.7|0.028|33.3|0.030|12.5***|
|Food Security|33.6|0.030|23.1|0.027|10.6***|
|Pregnancy care|26.9|0.025|14.6|0.022|12.3***|
|Physical safety|7.6|0.016|6.9|0.016|0.8|
|Early marriage|33.0|0.027|26.8|0.032|6.3*|
|Waste Disposal|55.7|0.030|38.9|0.032|16.9***|
|Drinking water|43.2|0.033|22.2|0.023|20.9***|
|Electricity|49.6|0.034|28.5|0.028|21.1***|
|Cooking fuel|55.5|0.036|39.3|0.033|16.1***|
|Housing|54.6|0.031|34.6|0.032|20.0***|
|Sanitation|49.8|0.035|26.7|0.029|23.0***|
|Unemployment|5.2|0.011|5.0|0.012|0.2|
|Legal id|46.3|0.034|32.1|0.032|14.1***|
|Bank account|25.8|0.038|20.0|0.023|5.9|

Notes: h0 is the proportion of male- and female-headed households deprived in various indicators, SE- standard errors. Asterisks indicate statistical significance at 1% ***, 5% **, and 10% * levels.

_Table A5: Censored headcounts of each indicators in the MPI, by the gender of the household head in South Sudan (2017)_

|South Sudan|Male-heade|d HH|Female-head|ed HH|Difference|
|---|---|---|---|---|---|
||h0|SE|h0|SE||
|Years of Schooling|7.9|0.018|17.9|0.033|-10.0***|
|School attendance|10.0|0.026|16.7|0.036|-6.7|
|Food Security|13.3|0.023|30.3|0.044|-17.0***|
|Pregnancy care|4.6|0.016|7.3|0.024|-2.7|
|Physical safety|12.8|0.025|30.0|0.043|-17.2***|

35
the LSMS-ISA surveys. Ethiopia also presented a rare case where information on a key productive tree species—eucalyptus—was not collected.

Overall, the results suggest that trees on farms should be given more attention in agriculture, food security and poverty-related policy debates in Sub-Saharan Africa, particularly as the need to tackle climate change becomes more urgent. The data and analysis presented here provide a baseline for future benchmarking as well as building blocks for improving the information base relating to privately owned trees in Africa, including through improved data collection in future surveys. The open access LSMSISA surveys and other national survey data provide an important opportunity to do so. They deserve full support and could be strengthened in at least two ways: 1) by capturing the full range of relevant trees on farms, including those that may not have an immediate productive function, and 2) by including cost information in a way that facilitates comparison of income across LSMS-ISA countries. We were unable to include Niger in our analysis, despite its status as an LSMS-ISA country and success in farmer managed natural regeneration and agroforestry (Garrity et al., 2010; Sendzimir et al., 2011) because income from trees on farms did not cover in-kind income from these trees, only income from sales.

The findings also point to several new avenues for exploring the interaction of agriculture, trees, and forests to better understand the dynamics of rural livelihoods in Africa and beyond. One area ripe for further exploration is the relationship between trees on farms and forest areas. Are trees on farms associated with more or less forest clearing? Why are households with trees on farms more likely to be located near forests? Panel data from LSMS-ISA surveys combined with newly available, highresolution forest cover data make it possible to shed new light on these dynamics. LSMS-ISA panel data also enable study of the economic contribution of trees on farms over time so as to understand the extent to which such trees can provide a means for farmers to escape poverty or achieve more enduring prosperity. Finally, we see significant scope for future research to collect and analyze information on the economic contribution not only of trees on farms, but also from forests and other wildlands to gain a more complete picture of the dynamics of rural livelihoods in Africa over time at the national scale. This would require adding some specific questions to the LSMS-ISA questionnaires.

16
successive primary deficits. The same holds true for the aggregate performance of states; figure 6 shows that the fiscal correction phase in states also coincides with a higher GDP growth rate and lower rate of interest paid on state debt. This was also in part the result of fiscal correction, which led to a reduction in government dis-savings and debt. This, however, masks the varied fiscal performance of individual states, which has been mixed over the period, causing concern in the context of the global crisis.

<!-- Start of picture text --> Figure 6: Differential between GDP growth rate and interest rate on state debt<br>25.0<br>Onset of deterioration<br>where g<r Period of correction<br>20.0 with g>r<br>15.0<br>10.0<br>5.0<br>0.0<br>Nominal GDP Growth (g) Average Interest on Debt (r )<br><!-- End of picture text --> Source: authors‘ calculation with base data from RBI

# **Section 5: Impact of the Global Financial Crisis and Going Forward**

Although, the immediate impact of the global financial crisis on state finances was somewhat subdued, there are implications going forward. The challenges will be more on the resource side through reduced central transfers, accentuated by the low-cost recovery by states. These may well render difficult the achievement of the Thirteenth FC road map. Going forward, sustainable finances require states to undertake reforms to maintain solvency via, among other things, increases in own taxes, implementing the goods and services tax (GST), and revising tariffs.

Assessing the impact of the crisis on the center, the immediate impact was relatively ―muted‖ (Milan 2011<sup>44</sup> ; RBI 2011). Although there was a setback in the growth of the economy,

> 44 Milan et al. (2011), ―Fiscal Policy for Growth and Development in India: A Review‖ (work in progress).

28
for all _j_ = _t − J, . . . , t_ and _ρ_ 1 _,_ 2 the weighted correlation coefficient between the Google Mobility Index and the normalized series.

## **4.3 Nowcasting industrial production**

In this section we consider the Mixed Data Sampling (MIDAS) regression of Ghysels et al. (2004) for nowcasting industrial production growth rates (sourced from OECD Main Economic Indicators database), _xt_ . Industrial production is published on a monthly basis. _ft_<sup>(</sup><sup>_d_)</sup><sup>_,MLE_</sup> represents the daily ”Google Mobility Index”, which is observed _d_ days in a particular _M_ month. Specifically, we want to predict the variable _xt_ onto a history of lagged observations of _ft_<sup>(</sup> _−_<sup>_d_)</sup> _j_<sup>_,MLE_</sup> . The superscript ( _d_ ) denotes the higher frequency sampling and its exact timing lag is expressed as a fraction of the unit interval between months _M_ and _M −_ 1. The MIDAS regression is expressed as follows: for _t_ = 1 _, . . . , T_ , and where _B_ ( _L_<sup>1</sup><sup>_/d_</sup> ; Θ) =<sup>�</sup><sup>_K_</sup> _k_ =0<sup>_B_(</sup><sup>_k_; Θ)</sup><sup>_Lk/d_and</sup><sup>_L_1</sup><sup>_/d_isalagoperator</sup> such that _L_<sup>1</sup><sup>_/d_</sup> _ft_<sup>_d,MLE_</sup> = _ft_<sup>_d,MLE_</sup> _−_ 1 , and the lag coefficient in _B_ ( _k_ ; Θ) of the corresponding lag operator _L_<sup>_k/d_</sup> are parameterized as a function of a small-dimensional vector of parameters Θ. In order of addressing the parameter proliferation, in a MIDAS regression the coefficients of the polynomial in _L_<sup>1</sup><sup>_/d_</sup> are captured by a known function _B_ ( _L_<sup>1</sup><sup>_/d_</sup> ; Θ) of a few parameters summarized in a vector Θ, typically, polynomial specifications.

# **5 Results**

Figure 2 presents the results of the two-step estimator for extracting one common factor of the six Google Mobility indicators, the “Google Mobility Index”, for each Latin America and the Caribbean (LAC) country available in the Google database. The gray lines represent the non-smoothed indicators while the bold red line represents the smoothed Kalman filter 8
_Box 2. Egypt: Financial Infrastructure Improvements pave the way for G2P-led Financial Inclusion_ Egypt is building on its improved payments systems infrastructure to move government payroll and pensions through debit cards, using the automated clearing house (ACH). As a result, 12 million monthly payments are expected to pass through the ACH within three years. Around 350,000 card-based accounts have already been opened under this new initiative, with eight banks participating. The government also plans to conduct a major portion of tax and customs collections through the ACH, and even shift government vendors‟ payments through the ACH, using government certified digital signatures and the treasury single account.

The government makes 30,000-40,000 procurement-related transactions daily, mostly through checks sent to microenterprises and SMEs (for example small tradespersons). These will increasingly instead be made through ATMs and banks accounts. The „billers hub‟ that is currently used for bill payment to mobile phone providers, could also be extended to cover payments to government (thus making use of mobile phones), and payment of credit card bills using direct debit arrangements.

Further, a national inter-bank switch system for ATM, Electric Funds Transfer-Point of Sale and interbank mobile transactions has been established to support retail payments, and will be extended to cover the remaining four banks not yet included, and to handle local transactions in Egypt rather than abroad. Payment systems regulations would be issued to cover licensing of payment service providers and the oversight of payment services and systems.

# _2.6 Government Policy and Programs_

# **Financial inclusion is not yet a priority for financial regulators in most MENA countries.**

Financial regulators in MENA have the second lowest degree of involvement in promoting financial inclusion, ahead only of high income countries where financial exclusion is at much lower levels (figure 9). Almost half of all the countries covered by the CGAP and World Bank Financial Access 2010 report (48 percent) have a strategy document for the promotion of financial inclusion, with 90 percent of those introduced within the last 6 years.

**Figure 9. Index of Regulator’s involvement in Financial Inclusion**

<!-- Start of picture text -->
1.00  0.92<br>0.80  0.65  0.71<br>0.60<br>0.39  0.39  0.43<br>0.34<br>0.40<br>0.20<br>-<br>High IncomeMiddle East  Latin  Europe &  East Asia &  Sub-saharan  South Asia<br>& North  America &  Central Asia Pacific Africa<br>Africa Caribbean<br><!-- End of picture text -->

Source: CGAP and World Bank Financial Access 2010 24
suggest that gains from trade liberalization are maximized when all RTA parties have ratified Montreal Protocol Amendments thereby avoiding any trade frictions that might otherwise arise. Notably, we find that RTA linkage to the Montreal Protocol can dramatically increase the ratification rate of Montreal Protocol Amendments above and beyond the effect of trade liberalization itself.

We turn our attention to enforcement linkages in Table 3 in which we report estimates of our triple-difference model in Equation (2). We find that trade liberalization significantly increases deviations from targets for Annex A gases. However, the mean level of our outcome variable is negative, indicating overcompliance in Montreal Protocol commitments. CFC deviation from target increases by approximately 70% after RTA signature, which corresponds to a reduction in overcompliance of approximately 980 tonnes of ODP. On average, this corresponds to an increase in 100-year global warming potential (GWP) equivalent to 7.7 million tons of carbon dioxide.<sup>12</sup> Halon deviation from target increases by approximately 37% after RTA signature, which corresponds to a reduction in overcompliance of approximately 97 tonnes of ODP (approximately equivalent to an increase in 100-year GWP of 25,000 tons of carbon dioxide).<sup>13</sup> We reiterate that, on average, countries are still in compliance with Montreal Protocol targets following signature of RTAs, the targets are simply less slack than they were before an RTA. We estimate positive coefficients on target deviations of Annex B (other fully halogenated CFCs, CTC, and TCA) gases after RTA signature, however none of these coefficients is statistically differentiable from zero. We estimate a negative responses in target deviations for HCFCs and Methyl Bromide after RTA signature, although only the HCFC effect is significant.

We find fairly broad evidence that the inclusion of ODS and Montreal Protocol provisions in RTAs lead to significantly negative response on target deviations for both Annex A gases (CFCs and halons) and from most Annex B gases (CTC and TCA). Negative point estimates here correspond to _increases_ in overcompliance, i.e. the Montreal Protocol targets become even more slack with the inclusion of the provisions. For CFCs, CTC, and TCA, the provision magnitude is such that in

> `12` UNEP CFC data does not differentiate CFC gases in ODS consumption data. Different CFC gases have different global warming potential. Our calculation here is based on the average GWP across all CFCs.

> `13` As above, individual halon gas consumption quantities are not separately reported in the UNEP data. This number is based on average ODP and GWP for halons and aggregated halon consumption data.

17
**Table 19: Role of baseline levels of human capital**

||Math|English|
|---|---|---|
|WSD|-0.50***|-0.31*|
||(0.17)|(0.17)|
|Grant|-0.13|0.01|
||(0.16)|(0.18)|
|Adult Literacy|0.54*|1.66***|
||(0.32)|(0.37)|
|WSD`×`Adult Literacy|1.12**|0.78*|
||(0.46)|(0.51)|
|Grant`×`Adult Literacy|0.07|-0.46|
||(0.43)|(0.54)|
|Constant|0.25|-0.10|
||(0.11)|(0.12)|
|Observations|2331|2331|

Notes: Robust Standard errors in parentheses. *** 1% Significance Level, **5% Significance Level,*10% Significance Level. Adult literacy is the district level percentage of adults who are literate. It is expressed in the range 0-1.

**Table 20: Role of human capital at the baseline**

||Math|English|
|---|---|---|
|WSD|0.36|0.38|
||(0.24)|(0.28)|
|Grant|0.17|0.20|
||(0.25)|(0.32)|
|SMC Literacy|0.02|-0.28|
||(0.21)|(0.24)|
|WSD×SMC Illiteracy|-0.65**|-0.57*|
||(0.29)|(0.34)|
|Grant×SMC Illiteracy|-0.36|-0.39|
||(0.30)|(0.39)|
|Constant|0.41|0.64|
||(0.17)|(0.21)|
|Observations|2035|2035|

Notes: Robust Standard errors in parentheses. *** 1% Significance Level, **5% Significance Level, *10% Significance Level. SMC illiteracy is the percentage of the School Management Committee members who have no formal education. It is expressed in the range 0-1.

36