rafmacalaba/datause-encoder-data huggingface dataset:
Rivera-Monroy, V.H., Twilley R.R., Mancera-Pineda J.E., Madden C.J., Alcantara-Eguren A., Moser, Jonsson, B.F., Castañeda-Moya, E., Casas-Monroy, O., Reyes-Forero, P., and Restrepo, J. (2011). Salinity and Chlorophyll a as Performance Measures to Rehabilitate a Mangrove-Dominated Deltaic Coastal Region: the Ciénaga Grande de Santa Marta–Pajarales Lagoon Complex, Colombia. Estuaries and Coasts 34, pp. 1-19.

Rudnicki, M., Mitchell, S.J., Novak, M.D. (2004). Wind tunnel measurements of crown streamlining and drag relationships for three conifer species, Can. J. For. Res. 34. Pp. 666–676.

Saintilan, N., Khan, N.S., Ashe, E., Kelleway, J.J., Rogers, K., Woodroffe, C.D., Horton, B.P. (2020). Thresholds of mangrove survival under rapid sea level rise. Science 368.

Saenger, P., Siddiqi, N.A. (1993). Land from the sea: The mangrove afforestation program of Bangladesh. Ocean and Coastal Management, vol. 20, no. 1, pp. 23-39.

Sasmito, S., Murdiyarso, D., Friess, D., and Kurnianto, S. (2016). Wetlands Ecology and Management Simard, M., Fatoyinbo, L., Smetanka, C. et al. (2019). Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nature Geoscience 12, 40–45 Snedaker, S. C. 1982. Mangrove species zonation: Why?, Tasks for Vegetation Science, Vol. 2., p. 111-125.

Tanino, Y., Nepf, H. M. Laboratory investigation of mean drag in a random array of rigid, emergent cylinders. J. Hydraul. Eng. 134, 34–41. https://doi.org/10.1061/(asce)0733-9429(2008)134:1(34) (2008).

Trampanya, U. (2006). Mangroves and sediment Dynamics along the Coasts of Southern Thailand. Ph.D. Thesis, Wageningen University and Research, Wageningen, The Netherlands.

Uddin, M., Hossain, M.K. (2013). Growth performance of coastal plantations and land stabilization in an offshore Island of Hatiya, Noakhali, Bangladesh. Bangladesh Journal of Forest Science 32.

Van der Meer, J. W., Allsop, N. W. H., Bruce, T., De Rouck, J., Kortenhaus, A., Pullen, T., Schuttrumpf, H., Troch, P. & Zanuttigh, B. 2018. Manual on wave overtopping of sea defences and related structures. An overtopping manual largely based on European resreach, but for worldwide application. www.overtopping-manual.com: EurOtop.

33
that this pattern is largely reflecting the distribution of fiscal and growth events in the Middle East and Sub-Saharan Africa. Suppose then that the success of a fiscal event can indeed be measured by whether or not it is followed by a growth event. One is then tempted to add that these patterns could reflect the quality of underlying institutions. Indeed, according to many indicators, Sub-Saharan Africa and the Middle East have bad scores on several indicators of institutional quality.

Note that when we use the alternative definition of fiscal event allowing Lowincome countries to increase their average fiscal deficit during the fiscal spending growth period up to 4% of GDP, 3 additional fiscal events followed by a growth event are identified: Mali, Mauritius and Burkina Faso. Then, under this scenario, the probability that a fiscal event is followed by a growth event in Middle East and Africa increases from 11% to 21%.

Table 2. Fiscal event probabilities _a/_

||**Nu**<br>Total<br>fiscal<br>events|**mber of f**<br>followed<br>by a<br>growth<br>event|**iscal eve**<br>NOT<br>followed<br>by a<br>growth<br>event|**nts:**<br> <br>obs.<br>_b/_|**Prob**<br>Total<br>fiscal<br>events|**ability of oc**<br>Fiscal<br>events<br>followed by<br>a growth<br>event|**currence :**<br>Fiscal<br>events NOT<br>followed by<br>a growth<br>event|Probability<br>that a fiscal<br>event is<br>followed by<br>a growth<br>event|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)=1/4|(6)=2/4|(7)=3/4|(8)=6/5|
|Total|95|25|70|977|9.7%|2.6%|7.2%|26.3%|
|First GDP pc Quartilec/|26|4|22|233|11.2%|1.7%|9.4%|15.4%|
|Second GDP pc Quartile|27|8|19|228|11.8%|3.5%|8.3%|29.6%|
|Third GDP pc Quartile|19|9|10|268|7.1%|3.4%|3.7%|47.4%|
|Fourth GDP pc Quartile|23|4|19|248|9.3%|1.6%|7.7%|17.4%|
|High Income OECD|||||||||
|countries|21|6|15|297|7.6%|2.2%|5.4%|28.6%|
|Developing countries|74|19|55|680|12.2%|3.1%|9.1%|25.7%|
|_Asia_|17|5|12|171|11.0%|3.3%|7.8%|29.4%|
|_Middle East & Africa_|28|3|25|198|14.1%|1.5%|12.6%|10.7%|
|_Latin America & Carib._|20|9|11|235|8.5%|3.8%|4.7%|45.0%|
|_Europe & Central Asia_|9|2|7|76|11.8%|2.6%|9.2%|22.2%|

Notes: See annex A.1.3, table A.3 for country classification in the sample.

a/ Computations based on benchmark set of parameters from table A.4, row 1 (58 growth events and 95 fiscal events).

b/ Obs. in which a fiscal event could have occurred.

c/ “First GDP pc Quartile” corresponds to “low income” and some “lower middle income” countries.

“Second GDP pc Quartile”: “lower middle income" and some "upper middle income” countries. “Third GDG pc Quartile”: “high income” countries.

“Fourth GDP pc Quartile”: “upper middle income” countries.

_Source: Authors’ computation from GFS and PWT 6.2 data, see Annex A.2_ 15
six different target variable combinations as outlined in Table 1 and explained in more detail in what follows.

Table 1: Target variables

|Target variable|Approach for estimating poverty|
|---|---|
|(1) Poverty rates|Predicted directly|
|(2) Changes in<br>poverty rates|Apply the predicted change to the most recent poverty rate|
|(3) Mean welfare|Scale the past distribution to the predicted mean welfare|
|(4) Growth in mean<br>welfare|Scale the past distribution by 1+the predicted growth in mean<br>welfare|
|(5) Mean welfare<br>and Gini coeffcient|(i) Assume the distribution is log-normal or log-logistic or (ii)<br>apply a growth incidence curve from the past distribution to<br>match the predicted mean and Gini|
|(6) Growth in mean<br>welfare and Gini<br>coeffcient|(i) Apply the predicted growth in the mean and Gini to the<br>past mean and Gini and assume that the distribution is log-<br>normal or log-logistic or (ii) apply a growth incidence curve<br>to the past distribution with the predicted growth in the mean<br>and Gini|

_Notes:_ The table lists the six different combinations of target variables we will use for the predictions and how poverty rates are backed out based on the target variable(s).

_(1) Poverty rates and (2) changes in poverty rates._ Predicting the poverty rate directly is the most intuitive and straightforward option; the poverty rates are the ultimate objective of this paper. Predicting poverty rates directly at the nowcasting year, _tn_ , has the advantage that it can yield estimates for countries without any previous poverty estimates at all. This is particularly a concern for countries that do not produce or share household survey data. Predicting changes in poverty from the past survey conducted at time _ts_ , in contrast, needs some assumptions about poverty levels in countries without data to arrive at global poverty rates. Yet, by utilizing the past survey, one can exploit that there is a past estimate to anchor the analysis around. When we predict changes in poverty, _c_ ˆ _poverty_ , _tn_ , _ts_ , the nowcasted poverty rates are given by When predicting changes from the past survey, we look at the annualized change in poverty rates in percentage points. This avoids having to predict extreme and undefined values, which often occur when predicting annualized growth in poverty rates, due to countries with poverty rates close to or at zero percent.

_(3) Mean welfare and (4) growth in mean welfare._ While predicting changes in poverty rates from the past survey exploits some past information available, it 6
_Table 11. Estimation results for “firms” with 1-3 employees_

||||ln(C|A)|||||ln(rev|enue)|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||OLS||Rand|om eff|ects||OLS||Ran|dom eff|ects|
||Coef.|SE|P>|z||Coef.|SE|P>|z||Coef.|SE|P>|z||Coef.|SE|P>|z||
|woman|-0.18|0.13|0.15|-0.17|0.17|0.30|-0.13|0.16|0.42|-0.22|0.20|0.29|
|can read|0.26|0.17|0.13|0.25|0.23|0.28|0.08|0.22|0.71|0.09|0.28|0.74|
|education>2|0.43|0.18|0.02|0.42|0.24|0.08|0.27|0.23|0.23|0.27|0.29|0.36|
|age 30-39|-0.10|0.10|0.35|-0.10|0.14|0.45|-0.21|0.13|0.11|-0.23|0.17|0.18|
|age 40-49|0.04|0.18|0.83|0.04|0.24|0.88|-0.05|0.22|0.83|-0.09|0.28|0.74|
|age>49|-0.14|0.21|0.50|-0.17|0.28|0.56|-0.19|0.28|0.49|-0.28|0.35|0.42|
|age unknown|0.28|0.15|0.07|0.28|0.21|0.17|0.19|0.19|0.33|0.24|0.25|0.34|
|exp. 5-10|0.37|0.15|0.01|0.38|0.20|0.06|0.52|0.19|0.01|0.57|0.24|0.02|
|exp.>10|0.40|0.18|0.03|0.41|0.25|0.10|0.63|0.23|0.01|0.66|0.30|0.03|
|couture|1.79|0.23|0.00|1.80|0.31|0.00|1.73|0.31|0.00|1.61|0.39|0.00|
|electronics|1.12|0.24|0.00|1.14|0.33|0.00|0.92|0.33|0.01|0.77|0.41|0.06|
|Abidjan|0.37|0.16|0.02|0.36|0.22|0.10|0.45|0.20|0.03|0.43|0.26|0.10|
|PB|0.29|0.11|0.01|0.29|0.11|0.01|0.09|0.14|0.52|0.15|0.15|0.32|
|PA|0.34|0.11|0.00|0.33|0.11|0.00|0.31|0.14|0.03|0.34|0.15|0.02|
|NPB|0.13|0.10|0.18|0.13|0.05|0.01|-0.20|0.12|0.10|-0.16|0.08|0.06|
|constant|9.31|0.29|0.00|9.30|0.38|0.00|9.09|0.37|0.00|9.16|0.46|0.00|
|PB-NPB=0|2.15||0.14|2.17||0.14|4.59||0.03|4.44||0.04|
|PA-PB=0|0.20||0.65|0.52||0.47|2.55||0.11|4.36||0.04|
|PA-PB=-NPB|1.52||0.22|5.45||0.02|0.01||0.92|0.08||0.77|
|B&P LM-test||||149.3||0.00||||55.29||0.00|
|Hausman-test||||0.00||1.00||||11.09||0.00|
|N|510|||510|||510|||510|||

From the random effects model of ln(CA) for firms with 1-3 employees in table 11 it is seen that educational level, experience, working in the tailoring or electronics sector, and living in Abidjan all have positive significant impact on the level of CA. With respect to the training indicators we see that the level of CA is the same for nonparticipants and participants before the training program, whereas afterwards the participants have maintained their CA, but the CA of the nonparticipants has declined. This leads to the conclusion that participants have gained from participation in PAFPA, which is confirmed by the formal test.

In table 12 the findings for the somewhat larger firms are presented, namely the firms having 4-6 individuals employed. Generally we do not see much evidence of variation in earnings due to the observable variables included. Actually the only variables significantly different from zero in the appropriate specification of ln(CA) is the indicator variable for the tailoring sector. The participation variables are not significant at all, and hence we cannot identify any effects from program participation. In the random effects model of ln(revenue) the same conclusion is drawn.

23
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<!-- Start of picture text --> G9 RISE index HM Summation index HG Composite index<br><!-- End of picture text -->
# **rv TAlU. FKIU,** AwD **BoxEs**

|**Figure AM**|Pomeatge of Total Fertility Assigned to Different Ages,<sup>1</sup>vIevel of<br>TOlFertility ..........................|l.......<br>Ixxxvi|
|---|---|---|
|Figur A6|<br>Assumed Total FetiUity Trend, Less Developed Countries, and U.N.<br>**and**Previous World Bank Trends<br>I.lxxxvii||
|Fipgu A7|Assumed Total Fertility Trend, More Developed Countries, and U.N.<br>and Previous World Bank Trends<br>.lxxxvii||
|Box 1|Illustrative Table with Population Trends.|x|
|Box 2|<br>Illustrative Table with Population Projections .xii||
|Box 3|<br>Trends in Urbanization .xvii||
|Box 4|Contraceptive Prevalence Levels and Trends .xxi||
|Box S|<br>Countries and Economies Classified by Income Group (Based on||
||GNP per Capita in 1988 U.S. Dollars) .xxx||
|Box 6|<br>Countries and Economies Classified by World Bank Region and<br>Country Depgrtment .|xxxii|
|Box 7|<br>Government Assessments of Population Growth .|xxxv|
GDP in 2008. While remittances are rarely seen as manifestations of successful policy actions (often they are ways in which people in countries with large conflicts or weak policy fight for the economic survival of their families), the impressive performance in FDI figures tell a different story. FDI rose from $2.85 billion in 2003 to $6.42 billion in 2007. These figures suggest that foreign investment into Nigeria had increased significantly, thus strengthening the notion that the business environment, at least for foreign investors, has become friendlier.

Although this growth performance looks strong at face value, we need to create a suitable comparator unit in order to really understand whether Nigeria’s growth record is extraordinary or not. The critical question at hand is; had the reforms during 2001-2004 not happened, how would Nigeria have grown till date? We attempt this exercise next.

# **4.4 Running Synthetic Controls**

The synthetic controls procedure for Nigeria creates a synthetic control where countries with a positive weight on the control are Bolivia, Tajikistan and Zambia. Table 4.1 shows the nations with their weights.

Table 4.1: Specific Country Weights in Nigeria’s Synthetic Control

|Country|Weight|
|---|---|
|Bolivia|0.18|
|Tajikistan|0.055|
|Zambia|0.764|

The method works well in creating a synthetic unit that matches Nigeria in its pre-GDP trends before between 1995 and 2004. The RMSPE is 12.53, and as Table 4.2 shows, the characteristics of the synthetic unit are very similar to Nigeria between 1995 and 2003. There is one caveat. The investment rate differs between the synthetic and the real unit. When we look qualitatively at the two countries that have significant weights in the synthetic control unit, we can see that both Zambia and Bolivia are economies with strong natural resource bases. Zambia is well know for its copper exports, and the price of copper has increased in the post-intervention period, similar to the price of oil. Similarly, Bolivia is well known for natural gas, and energy prices have risen all around. As a further check, none of the two countries have had conflicts. While this period has coincided with Evo Morales’ rule in Bolivia, the economy has hummed along due to the rising price of commodities in the international market. Finally neither of the two countries are Nigeria’s largest trading partners, which helps us believe in the non-interference assumption.

<u>Table 4.2: Predictor Balance</u>

|Variables|Treated|Synthetic|
|---|---|---|
|Population Growth Rate|2.44|2.50|
|Secondary School Enrollment Ratio|26.15|36.62|
|Investment Rate|2.79|11.41|
|Initial GDP per capita|1414.9|1404.9|

Figure 4.1 below shows the GDP per capita trends for Nigeria and the Synthetic control unit. The synthetic control unit trends are the counterfactuals which show us how Nigeria would have performed if not for the reforms in 2003-04.

18
27 5). Our conclusion then is that overall indices of inequality can mask important changes in distribution-particularly across and within geographic regions.

Geography is even more important in explaining poverty trends. In some countries the decline in poverty is observed in both the rural and urban areas (Uganda, Mauritania, Ghana-Table 13). In others, the change is confined mainly to rural areas (Zambia between 1993-1996). It is clear from the case studies that both within the rural and the urban sectors, poverty changes have varied considerably depending on geographical location. Some geographical areas have not benefited as much as others from growth, and some have even lost ground during the period of recovery. The different experience in the evolution of poverty seems closely related to the extent to which the region or village is integrated within the overall economy. The experiences of Ghana and Madagascar are illustrative.

**Table 13: Headcount poverty trends in rural and urban areas of six African countries**

|||**_Rural_**||||**_Urban_**||
|---|---|---|---|---|---|---|---|
||_Population._<br>_sharein_<br>_yearI ('Y)_|_YearI _<br>_(%/)_|_Year2_<br>_(M)_|_Change_<br>N<br>_points)_|**_Yearl_**<br>_(N)_|**_Year2_**<br>_(%)_|_Change_<br>C/<br>_points)_|
|Ghana<br>1992-1998|67|64|49|-15|28|19|-9|
|Madagascar<br>1993-1999|81|74.5|76.7|2.2|50.1|52.1|2|
|Mauritania<br>1987-1995|56|68|48|-20|**45**|17|-28|
|**Nigeria**<br>1992-1996|62|46|72|+26|37|59|22|
|**Uganda**<br>1992-1997|88|59|48|-11|28|16|-12|
|Zambia<br>1993-1996|62|92|83|-9|**45**|46|+1|
|1996-1998|62|83|83|0|**46**|**_55_**|+9|
|Zimbabwe<br>1991-1996|63|36|48|+12|3|8|_+5_|

Sources: Studies under the _Poverty Dynamics_ program.

From Figure 4, we see that poverty in Accra fell sharply, but not in other urban areas. In the Savannah zone poverty _increased_ in both urban and rural areas, and especially in the Northern Region and among subsistence farmers." The fact that growth in Ghana saw the Gini ratio 18 This finding was confirmed by the repeated cross-sectional multivariate analysis (Coulombe and McKay, 2001).
contribution record more slowly, but even so the great majority reach the minimum level by age 65. Although never-married women have more years of employment (and so of contribution) than ever- married women, the home responsibility protection condition allows iLtore ever-married women to meet the necessary minimum number of contributions than never-married women at any age. The combination of mninimum duration and minimum level of contribution required under _Condition_ 2 is more difficult to meet, particularly for women because much of their employment is low-paid and part-time. Fewer than two-thirds of all women now meet the minimumrr pension qualification requirements. Ever-married women are affected more because they are more likely than never-married women to work part-time, and therefore many of them fail to make an adequate level of contribution despite satisfying the duration conidition.

# joint contributions

So far we have only examined PAYG systems that are based exclusively on individuals' own contribution records. However, in many countries there is provision for joint treatment of married or cohabiting couples. Under our joint contribution rule, we assume that the recorded duration and level of pension contribution by both partners is split equally, as is the HRP protection if received by a non-working mother. Table 4 reports the outcomes for ever-married individuals (never-married individuals cannot, by definition, experience contribution sharing).

21
Table 9: How did obstacles affect your business? Choose 3 options Fraction of owners that chose the following options:

||ICT|non ICT|
|---|---|---|
|Reduced demand from costumers/clients|13%|12%|
|Delays from suppliers|12%|19%|
|Transportation cost|12%|9%|
|Access to costumers/clients|5%|5%|
|Workers delays to get to work/reach their homes|5%|6%|
|Delays to supply product to clients|3%|9%|
|Difculty to obtain supplies|1%|4%|
|None of the above|48%|31%|

_Notes:_ The options available were: access to clients; reduced demand from clients; difficulty to obtain supplies; delays from suppliers; delays to supply product to clients; workers delays to get to work/reach their homes; transportation cost; Other; None of the above. _Sources:_ Authors’ survey.

_x_ ˆ _≡ x_<sup>_′_</sup> _/x_ . The advantage of this approach is that it allows us to obtain estimates for the productivity levels for each industry and each location. In particular, the following holds:

**Proposition.** Given parameter values for the productivity dispersion _θ_<sup>_j_</sup> , the expenditure shares _α_<sup>_j_</sup> , and the input shares _β_<sup>_j_</sup> , and given values or estimates for the bilateral trade shares _π_<sup>_j_</sup> _ni_<sup>, employment by industry and location,</sup><sup>_Lj_</sup> _i_<sup>, and wage rates in each location</sup><sup>_wi_, and given</sup> exogenous changes in trade costs _τni_<sup>_j_,weareabletocomputecounterfactualsforwages,</sup><sup>_w_ˆ</sup><sup>_i_,</sup> employment _L_<sup>ˆ</sup><sup>_j_</sup> _i_<sup>,tradeshares</sup><sup>_π_ˆ</sup> _ni_<sup>_j_,expenditure</sup><sup>_X_ˆ</sup> _i_<sup>_j_andprices.</sup>

The parameters needed to solve the model are reported in table 10. The table also reports the data used to calibrate the parameters and compute the counterfactuals. In the next section we describe more in detail how we use the data to produce estimates for the model.

# **6.2 Estimation of Trade Costs and Trade Shares**

For the West Bank, estimates of the trade flows between towns and villages are not available. However, we rely on our novel data collected with the survey instrument to obtain estimates for these flows. In this section, we describe how we proceed.

In the survey, we ask firms detailed information on the location of the trading partners. We combine these data with data from the Establishment Census and data on wages from the Labor Force Survey to estimate the gravity equation stemming from the model. This approach requires to obtain an estimate of the elasticity of trade flows to distance and an estimate of location- and industry-specific productivities. This section proceeds as follows. First, for the estimation of the elasticity of trade flows to distance, we estimate a logit of the probability that a firm interviewed in the survey, with establishment in location _o_ has a 25
# Figure 11: Capital income tax revenue increased after 2016

- (a) Number of taxpayers subject to capital income tax

<!-- Start of picture text --> Tax Returns<br>2016<br>50,000<br>Amnesty<br>40,000<br>30,000 Capital income<br>(taxpayers)<br>20,000<br>10,000<br>0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text -->

- (b) Capital income tax base

<!-- Start of picture text --> Tax base<br>(2015=100)<br>2016<br>600<br>Amnesty<br>Capital income<br>(tax base)<br>400<br>200<br>Rental income<br>Wage<br>income<br>Business<br>0 income<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text --> _Notes:_ this figure compares the number of taxpayers subject to the capital income tax and the capital income tax base in panels (a) and (b), respectively. There is a meaningful increase in reported capital income starting in 2016 when disclosers are required to register income: the number of taxpayers reporting some capital income doubled, and the capital income tax base tripled. By contrast, none of the other three sources of income (wage income, business income, rental income) changed after 2016. These patterns are consistent with foreign and domestic assets, which generated taxable income and were left undeclared before the amnesty, becoming more truthfully reported after the program. Critically, these improvements persisted years after the amnesty program ended.

_Source:_ authors’ calculations using data from AFIP statistical yearbooks.

34