rafmacalaba/datause-encoder-data huggingface dataset:
spatial pattern of background As levels has remained largely unchanged (BBS and UNICEF, 2010). Similarly, our groundwater risk maps represent an average level of salinity though it varies seasonally (Zahid et al., 2013). Our groundwater risk maps are based on multi-hazard indicators of various levels. Therefore, any area outside the mapped risk categories may or may not be free from a single hydrological hazard such as elevated arsenic, salinity or depletion of water storage. For example, an area with high salinity (>1500 μS/cm) but low arsenic (<10 μg/L) is not mapped as groundwater risk as it does not fulfill the set criteria of multi-hazard indicators but considered to be affected by individual hazards.

Our groundwater risk maps update the current, rather loosely constrained, estimates of population exposure to elevated As and salinity concentrations in Bangladesh. Our groundwater risk mapping reveals that an estimated 33 (23% of total population) and 64 (44%) million people in Bangladesh are currently exposed to As concentrations greater than 50 and 10 μg/L (WHO standard), respectively in shallow (<150 mbgl) groundwater. Using the groundwater electrical conductivity (EC) map, we estimate that approximately 26 million (18%) people, predominantly in the coastal region of Bangladesh are exposed to considerably high salinity (EC >1500 μS/cm) in groundwater. Our estimate of population exposure to high salinity in groundwater is substantially greater than a previous estimate of 20 million (Khan et al., 2011) people in coastal Bangladesh. Furthermore, the analysis of population exposure to various levels of groundwater risks reveals that approximately 37 million (26%) to 47 million (33%) people are currently at risk of elevated arsenic, salinity and declined groundwater storage combined that covers 31% to 44% of the land area of Bangladesh. Given the proven public-health risk to elevated arsenic and salinity in drinking water and considerable threat to food security associated with depleted water storage, our national-scale multi-hazard groundwater risk maps have the potential to guide current practices and policies towards achieving the UN’s sustainable development goals in Bangladesh.

# **5. Conclusions**

We draw the following main conclusions from this first-ever, national-scale multi-hazard groundwater risk mapping for Bangladesh: 12
POLICY RESEARCH W7ORKING PAPER 1916

# Summary findings

The explosion of informal entrepreneurial activity during Mongolia's transition to a market economy represents one of the most visible signs of change in this expansive but sparsely populated Asian country.

To deepen our understanding of Mongolia's informal sector during the transition, Anderson merges anecdotal experience from qualitative interviews with hard data from a survey of 770 informals in Ulaanbaatar, from a national household survey, and from official employment statistics.

Using varied sources, Anderson generates rudimentary estimates of the magnitude of, and trends in, informal actiity in Mongolia, estmates that are surprisingly consistent with each other.

He evaluates four types of reasons for the burst of informal activity in MIorgolia since 1990: * The crisis _oI_ tne early and mid-1990s, during which large pools of labor were released from formal employment.

- Rural to urba.n migration.

- The "market's" reallocation of resources toward areas neglected under the old system: services such as distribution and transportation.

**e** The institutional environments faced by the formal and informal sectors: hindering growth of the formal sector, facilitating entry for the informal sector.

Formal labor rrmarkets haven't absorbed the labor made available by the crisis and by migration and haven't fully responded to the demand for new services. The relative ease of entering the informal market explains that market's great expansion.

The relative difficulty of entering formal markets is not random but is driven by policy. Improving policies in the forrnal sector could afford the same ease of entry there as is currently being experienced in the informal sector.

_-_ This paper a product of the Development Research Group and the South East Asia and Mongolia Country Unit, East Asia and Pacific - is part of a larger program of research on the impact of institutional changes in Mongolia, and on the rule of law in transition economies. Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Paulina Sintim-Aboagye, room MC3-422, telephone 202-473-7656, fax 202-5221155, lnternet address psintimaboagyeCc@worldbank.org. The author may be contacted at janderson2@worldbank.org. May 1998. (66 pages) _The Policy Research 'Working Paper Series disseminates the findings of wvork in progress to encourage the exchange of ideas about develo pment_ **_issues._** _An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily repr-esenu the view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemnination Center
33 the eight years preceding the reforms and the period covered by labor productivity growth. We account for economic recovery using a dummy variable that takes 1 if a country was officially in crisis and 0 otherwise. The list of countries and the types of crisis are summarized in Table 6 below.

|**T**|**able 6. List of countries and typ**<br>|**es of crisis**<br>||
|---|---|---|---|
||**Types of crisis**|**Crisis years**|**Period of reforms**|
|The Democratic Republic of Congo|Currency crisis|2009|2009-2012|
|Dominican Republic|Currency crisis<br>Sovereign debt restructuring|2003<br>2005|2005-2008|
|Ghana|Currencycrisis|2009|2009-2012|
|Madagascar|Currency crisis|2004|2006-2009|
|Moldavia|Sovereign Debt Restructuring|2002|2006-2009|
|Mongolia|SystemicBanking crisis|2008|2009-2010|
|Ukraine|Systemic Bankingcrisis|2008|2010-2012|

We include then the dummy variable in equation (5) and re-estimate the model using the same methodology as previously. The findings reported in Panel A of Table 7 show that the increase in productivity is due to reforms implemented and not to economic recovery after the crisis. All coefficients are positive and statistically significant at the 1 percent level.<sup>28</sup> The sample balance analysis discussed earlier and presented in the appendices corroborates that the estimated effects of reforms are not driven by economic recovery. Tables 10 and 11 in the appendix show that countries under IMF reforms are not statistically different from those without reform programs regarding the level of debt, the current and overall balance, the inflation and exchange rate, the GDP growth, and interest payment on the external debt.

**_Additional control variables._** By definition, labor productivity is affected by the stock of capital and investment. Consequently, both variables may affect labor productivity growth. Failing to account for those variables could weaken our findings or be a case of missing variable bias. We check in this session the robustness of the findings controlling for both the net book value of capital and the investment in equipment and land. The findings reported in Panel B of Table 7 confirm the positive of reforms on labor productivity growth. As previously,

> 28 The same evidence is obtained by excluding countries officially in crisis from the sample and re-estimating equation (5).
2 adults were illiterate (table 1)21; only one child in three was enrolled<sup>in</sup> primary or lower secondary schoo.1 (table 2). Moreover, raising school attendance was particularly challenging since two-thirds<sup>of all Peruvians were</sup> scattered in rural areas, and 35 percent of the population<sup>spoke only Quechua</sup> or Aymara--the two main Indian languages--while the language of the schools was Spanish (Government of Peru 1981).

Table 1: Education of Males and Females, Aged 15 and Over, 1940-81

||1940<br>1961|1972|1981|
|---|---|---|---|
|Percent Literate|42<br>61|73|82|
|Males|55<br>74|83|90|
|Females|31<br>48|62|75|
|Urban|82|88|92|
|Rural|41|49|62|
|Mean Years of Schooling|1.9<br>3.1|4.4|6.0|
|Males|2.4<br>3.8|5.1|6.7|
|Females|1.4<br>2.4|3.6|5.4|
|Hiehest Level of Education|Attended (percentage)|||
|No school|58<br>39|27|16|
|Males|45<br>26|16|9|
|Females|69<br>52|37|23|
|Primary|34<br>48|48|42|
|Males|47<br>58|54|44|
|Females|27<br>38|42|40|
|Secondary|1<br>2|5|10|
|<br>Males|6<br>14|24|35|
|Females|3<br>9|17|28|
|Postsecondary|1<br>2|5|10|
|<br>Males|2<br>3|6|12|
|Females|0.3<br>1|3|9|

Sources: Literacy Rates: Government of Peru 1981, Fernandez 1986.

/ Among Latin American countries only El Salvador, Nicaragua, Honduras, Bolivia, Guatemala, and the Dominican Republic had<sup>higher illiteracy</sup> rates than Peru in the early 1940s (Drysdale and Meyer 1975).
16 above mentioned set. Hungary is an exception in transition economies insofar as the female unemployment rate is lower than the rate for males. This is entirely due to a lower inflow rate into unemployment (0.80% versus 1.14%). This lower inflow rate can in turn be explained to a great degree by the different occupational structure of male and female employment, with half of all females in non-manual jobs and only a quarter of all males in such jobs. As manual workers have on average an inflow rate which is about 3.5 times larger than for non-manual workers, more nonmanual jobs for women implies a lower female inflow rate into unemployment.14 The inflow rates disaggregated by age and education show expected patterns. Younger groups tend to have higher inflow rates than older ones, a finding observed in Western economies as well as in transition economies.'<sup>5</sup> For women the variation by age is, however, not as strong as for men. Educational attainment and inflow rates into unemployment are inversely related, with the special finding that persons with a vocational background have the same high inflow rate as persons with only primary education. Vocational training before the regime switch seems to have led to skills which are required less than other skill groups as the implied higher separation rate indicates. The often stated notion that there has been overinvestment in vocational education in Eastern Europe receives further confirmation by the transition probabilities of the Polish labour market disaggregated by educational attainment which are presented in section IV.3.2.1.16 Finally, Micklewright and Nagy show that regional inflow and unemployment rates are strongly positively correlated (correlation coefficient of 0.81). Besides Budapest, the regions bordering Austria have the lowest unemployment and inflow rates while regions in the East of Hungary have the highest unemployment and inflow rates.

# Outflow and hazard rates from insured unemployment

Micklewright and Nagy first report the exit states for spells of unemployment insurance (Ul) starting in March 1992. Most importantly the largest fraction of exits from Ul occurs because of exhaustion of benefits (41.9% and 46% for men and women respectively). This hints at the large share of long-term unemployed among

> 140nce controlled for occupational status the differences by sex nearly disappear; manual female workers have a monthly inflow rate of 1.24% (men 1.41%) while non-manual female workers a monthly inflow rate of 0.36% (men 0.38%).

> 15The same pattern is observed for eastern Germany in the period 1990-1991 by Bellmann et al. (1995) and for Poland in the period 1992-1994 by G6ra and Lehmann (1995).

16 1n Poland the transition probability PEU is for both men and women with vocational background much larger than for any other educational group.
Xie, W., L. Yu, S. Xu, and S. Wang. 2006. “A New Method for Crude Oil Price Forecasting Based on Support Vector Machines.” Conference proceedings, Computational Science–ICCS 2006: 6th International Conference, Reading, United Kingdom, May 28–31, 2006.

Xiong, T., Y. Bao, and Z. Hu. 2013. “Beyond One-Step-Ahead Forecasting: Evaluation of Alternative Multi-Step-Ahead Forecasting Models for Crude Oil Prices.” _Energy Economics_ 40 (C): 405–415.

Ye, M., J. Zyren, and J. Shore. 2002. “Forecasting Crude Oil Spot Price Using OECD Petroleum Inventory Levels.” _International Advances in Economic Research_ 8 (2002): 324–333.

Ye, M., J. Zyren, and J. Shore. 2005. “A Monthly Crude Oil Spot Price Forecasting Model Using Relative Inventories.” _International Journal of Forecasting_ 21 (3): 491–501.

Ye, M., J. Zyren, and J. Shore. 2006. “Forecasting Short-Run Crude Oil Price Using High-And Low Inventory Variables.” _Energy Policy_ 34 (17): 2736–2743.

Yousefi, S., I. Weinreich, and D. Reinarz. 2005. “Wavelet-Based Prediction of Oil Prices.” _Chaos, Solitons and Fractals_ 25 (2): 265-275.

Yu, L., S. Wang, and K. K. Lai. 2008. “Forecasting Crude Oil Price with an EMD-Based Neural Network Ensemble Learning Paradigm.” _Energy Economics_ 30 (5): 2623–2635.

Yu, L., Y. Zhao, and L. Tang. 2014. “A Compressed Sensing-Based AI Learning Paradigm for Crude Oil Price Forecasting.” _Energy Economics_ 46 (C): 236–245.

Zamani, M. 2004. “An Econometrics Forecasting Model of Short-Term Oil Spot Price.” Conference proceedings, 6th International Association of Energy Economics European Conference, Tehran, 2004.

Zeng, T., and N. Swanson. 1998. “Predictive Evaluation of Econometric Forecasting Models in Commodity Future Markets.” _Studies in Nonlinear Dynamics and Econometrics_ 2 (4): 159–177.

<mark>Zhao, Y., J. Li, and L. Yu. 2017. “A Deep Learning Ensemble Approach for Crude Oil Price Forecasting.”</mark> _<mark>Energy Economics</mark>_ <mark>66 (C): 9-16.</mark> Zhang, J., Y-J. Zhang, and L. Zhang. 2015. “A Novel Hybrid Method for Crude Oil Price Forecasting.” _Energy Economics_ 49 (C): 649-659.

Zheng, J., J. Cheng, and Y. Yang. 2014. “Partly Ensemble Empirical Mode Decomposition: An Improved Noise-Assisted Method for Eliminating Mode Mixing.” _Signal Processing_ 96 (B): 362374.

69
Table 2: Summary statistics : labor market conditions at the governorate level

||Mean/Sd|N|p25|p50|p75|
|---|---|---|---|---|---|
|Employed|0.643<br>(0.192)|1470|0.548|0.671|0.774|
|Employed with social security coverage|0.162<br>(0.210)|1440|0.000|0.060|0.267|
|Employed full time|0.553<br>(0.208)|1440|0.426|0.568|0.695|
|Employed with a contract|0.195<br>(0.236)|1422|0.009|0.083|0.333|
|Employed in public sector|0.074<br>(0.124)|1440|0.000|0.009|0.115|
|Monthly wage|1626.489<br>(586.129)|1377|1279.949|1584.927|1875.000|
|Hours worked per week|29.144<br>(9.352)|1470|24.386|30.143|35.000|
|wage worker|0.283<br>(0.176)|1470|0.146|0.265|0.407|
|Number of workers in establishment<br>of the main job|398.810<br>(177.858)|1305|275.022|393.637|509.667|
|Working in agriculture sector|0.261<br>(0.224)|1440|0.071|0.222|0.423|

Notes: The variables are constructed based on the Egyptian Labor Force Survey waves between 2006-2019. Individuals in the sample are those who graduated between 2006-2019 and are aged 15 years of age and older. Data are collapsed on the cohort of graduation, governorate of residence and educational attainment.

Table 3: Summary statistics: economic condition indicators at the national level

||Mean/Sd|N|
|---|---|---|
|Youth employment rate, males<br>aged between 15-24|34.764<br>(4.322)|29|
|Oil price (US $ per barrel)|48.362<br>(31.787)|29|
|GDP growth|4.381<br>(1.598)|29|
|Wheat price (Us $ per bushel)|4.529<br>(1.549)|29|

Notes: The table provides the mean for each of the 4 variables on the country level (Egypt) over 29 years (1991-2019). Data source: World Bank data.

9
pact of inflation can be obtained (Houthakker 1975).

Annual data for 11 non‐energy indices covering the period from 1960 to 2008 are used in the analysis, giving a total of 49 observations. The Manufacture Unit Value (MUV) is used as an inflation proxy. The MUV, often considered as a developed coun‐ try deflator indicator, represents the unit value index in dollar terms of manufactures exported from industrial countries weighted proportionally to the countries’ exports to the developing countries.

Data on all indices are collected and reported by the World Bank (World Bank, various issues) and are defined as follows. **Energy** [world trade‐based weights in square brackets] consists of crude oil [84.6%], natural gas [10.8%], and coal [4.6%]. **Non‐energy** consists of metals [31.6%], fertilizers [3.6%], and agriculture [64.8%]. **Agriculture** con‐ sists of beverages [8.4%], raw materials [16.5%], and food [39.9%] while **Food** consists of cereals [11.3%], edible oils [16.3%], and other food [12.3%]. The commodity‐composition of the sub‐indices is as follows: **Beverages** : cocoa, coffee (arabica and robusta), tea; **Ce‐ reals** : maize, rice, sorghum, wheat; **Edible oils** : coconut oil, groundnut oil, palm oil, soybean meal, soybean oil, soybeans; **Other food** : bananas, meat (beef and chicken), oranges, shrimp, sugar; **Raw materials** : cotton, rubber, timber (logs and sawnwood); **Fertilizers** : DAP, phosphate rock, potassium chloride, TSP, urea; **Metals** : aluminum, copper, iron ore, lead, nickel, tin, zinc. Finally, a **Precious metal** index was included in the analysis—not part of the non‐energy index—consisting of gold [89.7%] and silver [10.3%].

The use of low frequency data was motivated by the desire to circumvent the noise typically embedded in high frequency data and also compare the effect of energy prices on the prices of all primary commodity groups. Because most agricultural com‐ modities are subject to crop cycles, annual frequency is, perhaps, more relevant. For ex‐ ample, the decision of how much land to allocate to each commodity and how much in‐

**_— 5 —_**
- 10 -

# **Figure 2: Size of public debt and share in pension funds portfolio – select countries**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>0 20 40 60 80 100 120 140 160<br>Public debt as % of GDP<br>Argentina Bolivia Chile Colombia Costa Rica El Salvador Mexico Peru Uruguay<br>Public debt as % of portfolio<br><!-- End of picture text --> Source: Otermin (2004).

26. At the start of the reform, Hungary was expected to follow a mixed financing strategy that would result in some increase in national savings and open more room for investment in private instruments. This would be achieved through parametric reforms to the PAYG system, and observance of the Maastricht ceiling of 3 percent of GDP on fiscal deficits during the whole period of reform implementation.<sup>8</sup> However, fiscal deficits increased significantly during the early 2000s, as shown in Table 10. This increase in the fiscal deficit was due in good part to a sharp increase in public sector wages, and exceeded by a wide margin the revenue losses to the second pillar. As shown in Table 10, between 2000 and 2004, the annual losses to the second pillar increased by about 0.5 percent of GDP, while the primary balance worsened by 3 percent of GDP. In the intermediate years the difference was much larger. This increase in deficits led to an increase in the ratio of public debt to GDP above and beyond the increase that would result from a debt-financed transition with a neutral fiscal policy in other areas, and also contributed to the decline in national savings in the same period. Under these conditions, a large share of Government bonds in pension fund portfolios is not a surprising outcome.

**Table 10: Fiscal Policy and National Savings in the Transition, 2000 – 2004**

||**Unit**|**2000**|**2001**|**2002**|**2003**|**2004**|
|---|---|---|---|---|---|---|
|General Government Balance|% GDP|-2.8|-4.5|-9.4|-7.2|-5.4|
|Primary Balance|% GDP|2.1|-0.3|-5.6|-3.4|-1.2|
|Losses to the Second Pillar|% GDP|0.6|0.7|0.7|0.9|1.1|
|Government Debt|% GDP|55.4|53.5|57.1|59.1|60.7|
|Gross National Savings<sup>1</sup>|% GDP|22.3|20.6|18.1|16.6|15.3|
|<br>Gross Domestic Investment<sup>2</sup>|% GDP|30.9|26.8|25.2|25.3|24.1|

Notes: 1) consistent with the external current account; 2) includes changes in inventories. Source: Ministry of Finance, HFSA, IMF 8 Rocha and Vittas (2002).
In this context, it is important to keep in mind the distinction between the _statutory incidence_ of a tax and its _economic incidence_ . The former identifies the person who is obligated by law to remit a tax payment to the government while the latter refers to changes in economic well-being resulting from changes in behavior and equilibrium prices induced by taxation. Various incidence assumptions made in tax incidence analysis will be reviewed in this section, which also covers issues related to equity in the distribution of the tax burden.

The discussion of these equity issues will be organized around the concept of _progressivity_ and its connections to vertical and horizontal equity. In general, a progressive policy favors the poor relative to the non-poor. In the particular context of income taxation, a progressive tax is such that the average tax burden faced by an income unit is an increasing function of income. Thus a better-off taxpayer would not only have a bigger tax liability but also pay a bigger share of his income in tax. Given a pre-tax income distribution, a progressive tax shifts part of the tax burden from low to high incomes. In so doing it exerts a redistributive effect on the distribution of income. This redistributive effect can be viewed as a shift of part of total post-tax income from high to low income recipients (Lambert 2001). The shifting of the tax burden is known as the _disproportionality_ effect<sup>4</sup> . Both this and the redistributive effect underpin the measurement of progressivity. We will consider the links between progression, horizontal and vertical equity in the context of unequal treatment of pre-tax equals<sup>5</sup> . To the extent feasible, some of these concepts will also be translated in the context of nonincome tax.

Section 3 of this review focuses on the _incidence of public expenditure_ . One policy conclusion that emerges from tax incidence studies reveals the limited ability of tax policy to significantly change the distribution of income (Martinez-Vazquez 2008). This situation shifts the redistributive function of fiscal policy on public spending. The potential impact of public expenditure on the distribution of economic welfare depends on the level and type of spending as well as on the efficiency of such spending.

> 4 As income increases, the tax burden increases more than proportionately.

> 5 Lambert (2001) argues that progression is a meaningful feature of income taxation only in the case of social homogeneity, i.e. all income units are of the same type.  It therefore makes sense to model tax liability as a function of income alone.

4