rafmacalaba/datause-encoder-data huggingface dataset:
employment is driven largely by workers’ choices or by dysfunctions in the labor market is the number of involuntary part-time employees as a percentage of total part-time employees.

In 2013, 25 percent of the workforce in Sweden was employed part-time, a percentage similar to the averages in the EU15 (23 percent) and in other Nordic countries (22 percent). In Sweden, however, almost 30 percent of part-time employment was involuntary, compared with an average of 23 percent in other Nordic countries (Figure 14). Among the EU15 countries the Netherlands had the highest share of workers with part-time employment (50 percent). In this country, however, only 10 percent of part-time workers reported being in part-time employment involuntarily. The EU15 countries with the smallest shares of workers with part-time employment were Greece (8 percent) and Portugal (10 percent).

Figure 14: In Sweden growth in involuntary part-time employment was slower than the EU15 average but faster than the Nordic average <!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>EU15 average Sweden Nordic countries average<br>Netherlands United Kingdom Iceland<br>Involuntary part-time employees as<br>percentage of total part-time employees<br><!-- End of picture text --> Source: Eurostat, Labor Force Survey database, 2014 edition. Note: The Nordic average excludes Iceland and Sweden.

The evolution of the share of involuntary part-time employment between 2004 and 2013 is interesting in this regard. Involuntary part-time employment in Sweden grew at a remarkably slower pace (increasing from 23 percent of total part-time employment in 2004 to 30 percent in 2013) than the EU15 average (which grew from 18 percent to 29 percent) but faster than the average in other Nordic countries (which rose from 21 percent to 23 percent)—resulting in a wider gap between Sweden and the neighboring countries (see Figure 14). The share of involuntary part-time workers in Sweden is also higher than in comparator countries that experienced significant increases. In the 28
Economic Outlook (WEO) (available since 1980).<sup>47</sup> We then compute our spending variable as follows: ≡[G ) −G ]⁄ yy where iitt iitt is the 3-year moving average of NGDP and iitt tt iitt−1 iitt−1 iitt = ΔGG PPYY x NNGGDDMM /(1 + ππ PPYY x NNGGDDMM MMAANNGGDDMM ⁄ −1 is the inflation rate. iitt tt MMAANNGGDDMM ππ The primary data source to construct the commodity resource revenue variable, tt tt−1 CCMMII CCMMII , is the ICTD/UNU-WIDER Government Revenue Dataset 2018 (GRD), yy which provides data on “Total Natural Resource Revenue, General Government, iitt ΔCCRRC Percent of GDP” (CREVY), from 1980 to 2016. We also use data from the IMF World Commodity Exporters Dataset (WCED), which provides data on general government resource revenues (% of GDP) from 1980 to 2013.<sup>48</sup> In both datasets, resource revenue includes tax and non-tax receipts from natural resources that include a significant component of economic rent, primarily from oil, natural gas, and mining activities. For each country in the panel, we use the dataset (GRD or WCED) that provides the longest time series.<sup>49</sup> Like for the spending variable, we define the resource revenue variable as, yy iitt ⁄ ) ]⁄ ΔCCRRC iitt iitt tt iitt−1 iitt−1 iitt ≡[CCRRPPCCYY xNNGGDDMM (1 + ππ −CCRRPPCCYY x NNGGDDMM MMAANNGGDDMM 47 GGEY consists of general government total expenses and the net acquisition of nonfinancial assets (public investment). Conceptually, the measure should exclude public investment, but this decomposition is not available for many country-years in the WEO database.

48 GRP is a comprehensive dataset that contains data on resource revenue for 69 countries from five different sources: OECD Revenue Statistics, OECD Latin American Tax Statistics, IMF Government Finance Statistics, IMF Article IV Staff Reports, and CEPALSTAT Revenue Statistics in Latin America. The WCED is a collection of key macro-fiscal indicators covering 51 countries that are exporters of oil, gas, and metals (such as copper, gold, iron, and silver), where these commodities represent a large share of exports (20% or more of total exports) or fiscal revenues. The dataset is compiled from the following sources: International Financial Statistics (IFS), Balance of Payments Statistics, Direction of Trade Statistics, World Economic Outlook, and FAD’s fiscal rules database. The resource revenue data from the WCED is broadly consistent with GRD, although there are substantially divergent information for specific years and countries. Appendix Figure 9 displays the dispersion of resource revenues data across the WCED and GRD. In subsection I.B, we discuss the robustness of our main results to potential measurement errors present in the revenue dataset.

49 Columns (6)–(8) in Appendix Table 5 summarize the data source and sample size for expenditure and resource revenue. For nine countries, mixed data from the GRD and WCED are used.

35
<!-- Start of picture text --> Figure 2. Annual Exempt-Immediate-Relative Immigration to the United States in Fiscal Years 1972-85<br>200 Thousands<br>180<br>160<br>140<br>120<br>100<br>s<br>60<br>40'<br>20<br>0<br>1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 198, 1964 1965<br>Fial Year<br>1976 data<br>Note: These data do not include reugees. Fiscal year 1976 includes Juy-September<br>30 of Xt respecnw yw.<br>Since October 1, 1977, the data are for fiscal years ending September<br>Source: U.S. Immigradon and Naturalization Service, Immigrant Public Use Tapes, Wasungton<br>D.C., fiscaiyears 1972-85., General Accounting offfice, 1<br><!-- End of picture text -->
**Figure 1. Type of agreement and distance** _Average, 2005-2011_

<!-- Start of picture text --> 10<br>8<br>6<br>4<br>2<br>0<br>One Way Two Way FTA Customs Common Economic<br>PTA PTA Union Market Union<br>Bilateral log distance Abs. diff in log GDP cap<br><!-- End of picture text --> Source: CEPII and Baier and Bergstrand.

**Figure 2. No. of provisions and distance** _Average, 2005-2011_

<!-- Start of picture text --> 10<br>8<br>6<br>4<br>2<br>0<br>0 Prov. 1 Prov. 2 Prov. 3 Prov. 4 Prov. 5 Prov. 6 Prov. 7 Prov.<br>Bilateral log distance Abs. diff in log GDP cap<br><!-- End of picture text --> <!-- Start of picture text --> Source: CEPII and DESTA.<br><!-- End of picture text --> Taking this correlation between distance and depth into account, Table 3 presents results of equation (8), where the binary variables Depth_0ijt through Depth_7ijt correspond to those in Figure 2. Appendix 1 presents results of this same regression using binary variables corresponding to Figure 1 as controls.

Adding these controls strengthens our results: we continue to find evidence that the effectiveness of EIAs on bilateral aggregate trade flows decreases with distance (column 1a), and this finding is clearly led by the negative interaction between trade agreements and bilateral distance for intermediate goods (column 1b). These results further support that the principal channel through which EIAs and distance reinforce one another is through two-way trade in inputs, which likely are less differentiated and of more similar quality than other types of goods. Moreover, our variable of interest is both positive and statistically significant for other goods categories, lending evidence that EIAs help trading partners overcome the hurdle of distance when it comes to final goods for which the quality and variety is of paramount importance to consumers.

In terms of the coefficients on the controls for depth, we observe that—very much like in Figure 2—they tend to increase in magnitude until roughly four provisions, after which they decline to levels similar to zero provisions. This affirms our prior that certain provisions included in the DESTA depth index deter trade flows, and also that there might be an ordering to which provisions are added to agreements among proximate countries (i.e. agreements among proximate countries may include fewer provisions that negatively impact trade flows).

10
Table 2

# Exports, Engineers and Blue-Collar Workers IV Results

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|A) First Stage Results|||||
|average real exchange rate|–0.149***<br>(0.023)|–0.148***<br>(0.023)|–0.149***<br>(0.022)|–0.149***<br>(0.022)|
|average real exchange rate * initial sales|–0.0029*<br>(0.0017)|–0.0030*<br>(0.0017)|–0.0029*<br>(0.0016)|–0.0029*<br>(0.0016)|
|_R_<sup>2</sup>|0.4129|0.4147|0.4144|0.4146|
|_F_-statistic|888.09|882.96|888.42|884.31|
|Prob _> F_|0.0000|0.0000|0.0000|0.0000|
|B) Tasks|||||
|share managers|–0.01**|–0.01|–0.01|–0.009|
||(0.006)|(0.005)|(0.005)|(0.005)|
|share engineers|0.09***|0.10***|0.10***|0.10***|
||(0.031)|(0.031)|(0.032)|(0.032)|
|share services|–0.009|0.009|0.009|–0.009|
||(0.008)|(0.007)|(0.007)|(0.007)|
|share blue-collar|–0.07**<br>|–0.08**<br>|–0.08**<br>|–0.08**<br>|
||(0.031)|(0.033)|(0.034)|(0.034)|
|share maintenance|–0.01**|–0.01*|–0.01*|–0.01*|
||(0.005)|(0.004)|(0.004)|(0.004)|
|ratio engineers to blue-collar|14.42**|13.54**|13.67**|13.58**|
||(6.34)|(6.13)|(6.37)|(6.33)|
|frm and year FE|Yes|Yes|Yes|yes|
|log employment|No|Yes|Yes|Yes|
|industry-specifc trends|No|No|Yes|Yes|
|frm-specifc trends|No|No|No|Yes|

Notes: IV-FE regressions of employment shares on export intensity (exports/sales). The instruments are the weighted average the real exchange rate of a firm export partners and the weighted average of the real gdp of a firm export destinations. Column (1): firm fixed-effects and year fixed-effects; column (2): adds log total employment (firm size); column (3): adds controls for industry-specific trends (i.e., interactions between year dummies and industry dummies); column (4): adds initial conditions to control for firm-specific trends. Data are from the Encuesta Nacional Industrial Anual (National Annual Industrial Survey), Chile 2001-2005.

14
|**Guinea**|documents that constitute a deed of gift. Further<br>duty is payable on other documents.||ng.htm|
|---|---|---|---|
|**Philippines**|**Subject of Stamp Tax:**<br>SEC. 173. Stamp Taxes Upon Documents, Loan<br>Agreements, Instruments and Papers. That<br>whenever one party to the taxable document enjoys<br>exemption from the tax herein imposed, the other<br>party who is not exempt shall be the one directly<br>liable for the tax.<br>SEC. 174. Stamp Tax on Debentures and<br>Certificates of Indebtedness. Rate is one peso and<br>fifty centavos (P1.50) on each Two hundred pesos<br>(P200), or fractional part thereof, of the face value of<br>such documents.<br>SEC. 175. Stamp Tax on Original Issue of Shares of<br>Stock. – Tax rate is two pesos (P2.00) on each Two<br>hundred pesos (P200), or fractional part thereof, of<br>the par value, of such shares of stock: Provided,<br>That in the case of the original issue of shares of<br>stock without par value the amount of the<br>documentary stamp tax herein prescribed shall be<br>based upon the actual consideration for the<br>issuance of such shares of stock: provided, further,<br>That in the case of stock dividends, on the actual<br>value represented by each share.<br>SEC. 176. Stamp Tax on Sales, Agreements to Sell,<br>Memoranda of Sales, Deliveries or Transfer of Due-<br>bills, Certificates of Obligation, or Shares of<br>Certificates of Stock. Tax rate is one peso and fifty<br>centavos (P1.50) on each Two hundred pesos<br>(P200) or fractional part thereof, of the par value of<br>such due-bill, certificate of obligation or stock;<br>Provided, That only one tax shall be collected on<br>each sale or transfer of stock or securities from one<br>person to another, regardless of whether or not a<br>certificate of stock or obligation is issued, indorsed,<br>or delivered in pursuance of such sale or transfer:<br>and Provided, further, That in the case of stock<br>without par value the amount of documentary stamp<br>tax herein prescribed shall be equivalent to twenty-<br>five percent (25%) of the documentary stamp tax<br>paid upon the original issue of said stock.<br>SEC. 177. Stamp Tax on Bonds, Debentures,<br>Certificate of Stock or Indebtedness Issued in<br>Foreign Countries.<br>SEC. 178. Stamp Tax on Certificates of Profits or<br>Interest in Property or Accumulations. Tax rate is|**19 March 2003**<br>Lawmaker calls for documentary<br>stamp tax reforms. The clamor<br>of the Philippine Stock Exchange<br>(PSE) to rationalize the<br>imposition of documentary stamp<br>tax on the issuance of shares<br>and stock certificates has<br>obtained popular support from<br>Sen. Ralph G. Recto. Recto has<br>sponsored Senate Bill 2518,<br>otherwise known as The<br>Documentary Stamp Tax Bill,<br>which he said “aims to break<br>investment barriers, remove<br>distortions from the domestic<br>capital market, develop it,<br>mobilize savings, and hopefully<br>alleviate poverty.”<sup>2)</sup>|1)http://www.bir.gov.ph/nirc/nir_tl07_<br>ch00.html<br>2)http://www.manilatimes.net/nationa<br>l/2003/mar/19/business/20030319bu<br>s13.html|

45
The general conclusion from the interest spread regressions is that reductions in overheads led to lower spreads for all banks during this period, but the results were especially pronounced for the foreign-owned banks. Other factors such as the reduction in the T-bill rate and lower inflation also contributed to lower spreads during this period, but those reductions were only significant for the foreign banks. The regressions therefore indicate that the lion’s share of the sectoral reduction in interest spreads that we began this analysis with (Figure 1) is attributable to the foreign banks. Moreover, the lack of significant results for the other ownership types suggests either that they are competing in a separate market niche (as seems likely for many of the private domestic banks) or that they are pursuing some of the same clients as foreign banks but pricing loans in a less rationale way (as seems likely for the government-influenced banks).

# **5. Access to Financial Services**

FinAccess 2006 and FinAccess 2009, two household surveys, conducted by the Financial Sector Development Trust Kenya jointly with the Central Bank of Kenya, confirm three previouslyassumed conclusions about access to financial services: (a) a large proportion of the Kenyan population has no access to financial services, whether formal or informal; (b) there is a general tendency for access to services from formal and semi-formal providers (banks, SACCOs, and MFIs) to decline as one goes from urban to rural, from high-income to low-income, and from better-educated to not educated; and, (c) although the percentage of the population that is served is similar in urban and rural districts, the mix of those services is different.<sup>16</sup> In urban areas, respondents rely more heavily on services from banks and semi-formal sources (SACCOs and MFIs) while in rural districts, there is greater reliance on services provided via informal groups.

Table 11 shows that the use of formal financial services in Kenya is at similar levels as in other East African countries, but below that in several countries in Southern Africa. As the surveys are not completely consistent across countries, we follow Porteous (2007) in his definition of formal bank and bank-like financial services, which varies from the definition to be

> 16 The discussion of the findings of FinAccess 2006 and 2009 is based on Beck (2009).

24
**_In the surveyed cities, citizens generally and the poor and migrants in particular appeared to be unwilling to submit a formal complaint and seek redress_** .  Generally the citizens in the richest quintile and officially registered urban residents demonstrated a higher willingness to complain formally, that is through official channels.  Across sectors, the respondents in Shenzhen reported the highest rates of formal complaints (above 3% compared to less than 1% in the other surveyed cities).  Analysis suggests that the willingness to complain formally may be somewhat related to the younger age of citizens in Shenzhen compared to the other surveyed cities.

**_In education, about half of respondents admitted to have complained informally, but only 8% in the richest quintile and 2% in the poorest quintile had ever complained formally_** . Chengdu had the highest rate of parents complaining about education informally at 68%, and also the highest rate of official complaints at 18% among the richest quintile (Figure 20). This finding may raise questions about the openness of citizens in Chengdu when reporting their satisfaction in the citizen scorecard.  Compared to the other surveyed cities, citizens in Chengdu reported higher levels of satisfaction in the scorecard while admitting to have complained more often (especially informally).

**Figure 20 Complaints about education, 2006** (% of population)

<!-- Start of picture text --> 70%<br>60%<br><!-- End of picture text --> <!-- Start of picture text --> Poorest quintile Informal Complaints Poorest quintile Formal Complaints<br>Richest quintile Informal Complaints Richest quintile Formal Complaints<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Shenzhen Xi'an Shanghai Dalian Chengdu<br><!-- End of picture text -->

**_Among the parents who complained, only 38% received response, and the response was considered satisfactory by 21% of recipients_** .  Among those who did not complain, nearly 30% of parents, including 41% percent of parents in the poorest quintile, considered complaining useless; and nearly 20% of parents worried about a possible negative consequence of complaining on their child’s schooling.

**_With respect to healthcare, among the respondents, 58% admitted that they shared their discontent with family or friends, but only 1% submitted a formal complaint_** .  Among those who complained formally, less than one third received any response from the responsible agency and the response was considered satisfactory by 30% of recipients.  Among those who did not complain formally, one third thought it would be useless to complain and one quarter thought that the problem in question has become common practice.

**_Regarding water, nearly 50% of respondents said that they shared their dissatisfaction about some aspects of their city’s water services with their friends and relatives, but only 3% complained formally._** Among those who did not complain, 60% reported that it is not useful to complain, 15% worried about the monetary and time cost of complaining, and 3% worried about a possible revenge.

27
12

# VI. The Results

# A. Aggregate Estimates

We first estimated the value of X, through estimation of the probability of belonging to group 1 (employed) or to group 0 (seeking inactives and unemployed). We used a probit model, the results of which are presented in Table 1. In general, the estimates are statistically acceptable, and the parameters are very stable! for 1977, 1980 and 1985. However, the observed change between 1968 and 1977 appears very significant, which may reflect change in other labor market conditions, such as total unemployment levels, government intervention and industrial composition of employment.

Table 1 Probit Participation Functions

||1968|1977|1980|1985|
|---|---|---|---|---|
|Schooling|.0766|.1169|.131|4<br>.1273|
||(8.04)*|(11.03)*|(10.73)|*<br>(11.18)*|
|Experience|.0694|.0952|.102|3<br>.1096|
||(9.41)*|(11.30)*|(10.68)|*<br>(12.09)*|
|Experience Squared|-.0011|-.0013|-.001|4<br>-.0014|
||(-10.59)<br>*|(-11.44)*|S-10.62|)*<br>(-11.78)*|
|(School)x(Experience)|<br>-.0013|-.0015|-.002|5<br>-.0031|
||(-3.36)*|(-3.43)*|(-4.81,|*<br>(-6.75)*|
|Constant|- 4926|-1.2402|-1.33Q|3<br>-1.591|
||(-4.26)*|(-8.91)*|(-8.44)|*<br>(-10.38)*|

# Notes:

- -2 Log likelihood ratio 207.96 336.13 254.79 257.71 t ratios appear in parentheses * Significant with a 952 confidence interval.
27 Table 4. Pqpulation nd Percentage Distribution by World Bank Region and Country Department, 1985-2100

|Region and||Po|pulation|(millio|ns)||Pe|rcenta|ge of|world p|opula|tion|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|country<br>department|1985|1990|2000|2025|2050|2100|1985|1990|2000|2025|2050|2100|
|World total|4,843|5,282|6.205|8,262|9,639|10,560|100.0|100.0|100.0|100.0|100.0|100.0|
|Borrowers and non-borrowers|||||||||||||
|SLb-Saharan Africa|457|<br>535|<br>724|1,287|1,774|2,168|<br>9.4|10.1|11.7|15.|6 18.|4 20.5|
|Asia and Oceania|2,573|2,803|3,272|4,185|4,713|4,997|53.1|53.1|52.7|50.|7 48.9|47.3|
|Europe,<br>Middle<br>East,|||||||||||||
|nid<br>North<br>Africa|1,147|1,224|1,,8|3 1,75|0 2.003|2,190|23.7|23.2|22.3|21.2|20.|8 20.7|
|America|667|<br>721|<br>825|1,04|0 1,150|1,205|13.8|13.7|13.3|12.|6 11.|9 11.4|
|Borrowers<br>only|||||||||||||
|Africa Region|415|<br>487|<br>664|1,19|5 1,658|2,035|<br>8.6|9.2|10.7|14.|5 17.|2 19.3|
|1Occidental<br>and Central|<br>40|<br>47|<br>64|<br>113|155|<br>187|<br>0.8|0.9|1.0|1.4|1.6|1.8|
|2 Eastern|106|<br>124|<br>169|303|<br>422|<br>520|<br>2.2|2.3|2.7|3.7|4.4|4.9|
|3 South-Central<br>and|||||||||||||
|Indfan<br>Ocean|52|<br>61|<br>83|<br>149|205|<br>251|<br>1.1|1.2|1.3|1.8|2.1|2.4|
|.Western|119|<br>141|<br>194|351|<br>486|<br>593|<br>2.5|2.7|3.1|4.2|5.0|5.6|
|5 Sahelian|36|<br>41|<br>55|<br>98|<br>138|173|<br>0.7|0.8|0.9|1.2|1.4|1.6|
|6Southern|62|<br>73|<br>100|<br>181|253|310|<br>1.3|1.4|1.6|2.2|2.6|2.9|
|Asia Region|2,382|2,603|3,056|3,948|4,473|4,756|49.2|49.3|49.3|47.8|46.4|45.0|
|1other<br>South|134|<br>151|<br>190|291|<br>367|423|<br>2.8|2.9|3.1|3.5|3.8|4.0|
|2Southeast|274|<br>304|367|<br>500|<br>581|<br>626|5.7|5.8|5.9|6.1|6.0|5.9|
|3China|1,040|1,117|1,279|1,544|1,648|1,695|21.5|21.1|20.6|18.7|17.1|16.1|
|4India|765|844|1;002|1,323|1,542|1,655|15.8|16.0|16.1|16.0|16.0|15.7|
|5Indonesia and Pacific|168|<br>184|<br>218|<br>290|<br>335|<br>358|3.5|3.5|3.5|3.5|3.5|3.4|
|EMN Region|476|<br>536|<br>667|<br>997|1,249|1,425|9.8|10.1|10.7|12.1|13.0|13.5|
|1 Pakistan<br>andTurkey|147|<br>169|<br>217|<br>343|443|<br>519|3.0|3.2|3.5|4.2|4.6|4.9|
|2 orth Africa|55|<br>63|81|123|<br>154|<br>172|1.1|1.2|1.3|1.5|1.6|1.6|
|3Middle East|170|<br>197|<br>258|<br>411|<br>530|<br>609|3.5|'3.7|4.2|5.0|5.5|5.8|
|4 Europe|105|<br>107|<br>112|<br>120|<br>122|<br>125|2.2|2.0|1.8|1.5|1.3|1.2|
|LAC Region|388|430|<br>517|<br>706|821|<br>879|<br>8.0|8.1|8.3|8.5|8.5|8.3|
|1 Brazil|136|<br>150|<br>180|242|278|<br>287|<br>2.8|2.8|2.9|2.9|2.9|2.7|
|2 Central<br>America|104|<br>116|<br>1L3|<br>205|<br>244|263|<br>2.1|2.2|2.3|2.5|2.5|2.5|
|3CaribbeanondOther|70|78|95|130|<br>151|<br>159|1.4|1.5|1.5|1.6|1.6|1.5|
|4TemperateandTropical|78|85|100|<br>130|<br>148|<br>158|<br>1.6|<br>1.6|1.6|<br>1.6|<br>1.5|<br>1.5|

Note: World BaMM regions and country departments are defined in Table 14.
**Table** **<u>5: Current yield relative to estimated potential yield</u>**

|**Country/ region**|**Maize**|**Oil palm**|**Soybean**|**Sugarcane**|
|---|---|---|---|---|
|Asia (excluding West Asia)|0.62|0.74|0.47|0.68|
|North Africa and West Asia|0.62|n.a.|0.91|0.95|
|South America|0.65|0.87|0.67|0.93|
|Sub-Saharan Africa|0.20|0.32|0.32|0.54|
|Europe|0.81|n.a.|0.84|n.a.|
|North America|0.89|n.a.|0.77|0.72|
|Oceania|1.02|0.6|1.05|0.91|

_Source:_ Deininger et al., 2011 28