rafmacalaba/datause-encoder-data huggingface dataset:
Consequently, for those series in Table 4 that show a "YES" entry, we test for a structul break. As in Perron (1989), we consider three different models for each indicator series. The first allows for an exogenous change in the level of the series, the second permits an exogenous change in the growth rate of the series, and the third permits both. For indicator series y, these are: I) Yt = 1 + (2 -Il) DUMt +Ft 2) Yt =1 +I t + (P2 - PI) TDUMt +Et 3) Yt = _iL]_ + P t + (i2 -FL1<sup>) DUMt + (P2 - PI) TDUMt*+ct</sup> where DUMt = I if t > policy event date t*, 0 otherwise TDUMt = t - t* if t > policy event date t*, 0 otherwise TDUMt* = t if t > policy event date t*, 0 otherwise.

Tests for a structural break entail testing whether the coefficients on DUM, TDUM, and TDUM are significantly different from zero. However, these t-tests are only valid if the residuals from the above three models do not contain a unit root. Consequently, we (1) run regressions for the above three models, (2) test whether there is a one-time structural break at the policy event date for each stock market indicator, and (3) use Perron's calculated critical values to test whether the residuals from the regressions are stationary.

# _B. Results_

Table _5_ summarizes the evidence regarding the question of whether the policy event dates are associated with a structural break and a subsequent rise in stock market development for each stock market development indicator. Three types of entries are possible. First, if the original indicator rejected the unit root hypothesis, then Table _5_ reports the results of a t-test comparing the level of each indicator before and after the policy events. Using monthly data for each country, we compute the average of each indicator before the policy event date (period 1) and use 13
revenues are recycled via mode (c) as described in Section 3.3. All reported values are population-weighted averages and adjusted for behavioral responses to higher consumer prices (see also Section 3.2).

# **4.4.2 Incidence across economic sectors**

As economic activity transitions, there is a corresponding transition in the production structure, with fossil fuel-intensive sectors losing and energy-efficient or renewable-intensive sectors gaining. This may directly impact firms and workers to the extent that firms see cost increases—given more expensive after-tax energy inputs. To quantify these cost increases for non-fossil fuel/electricity sectors, we assume away full passthrough of producer cost increases onto consumer prices (i.e., producers bear the full incidence of the carbon tax<sup>38</sup> ) and use the same input-output data and methods outlined under Section 3.2 above.

The impacts of the US$25/ton CO2e carbon tax on industry input costs are moderate, except for a few sectors in a few economies (Figure 10). While the sectors with the highest cost increases in 2030 vary by South Asian economy, the general pattern is that the largest sectors - in terms of their share in total output - are the least likely to experience substantial cost increases. Cement and aviation see the highest average cost increases in 2030 (aviation is linked to tourism which is an important exporting sector for the smaller economies such as Maldives and Nepal).

In particular, the largest cost increases are seen in India, at around 11 to 12 percent (iron/steel and cement), given its heavy use of coal. Pakistan and Nepal also see relatively large cost increases in these sectors (at around 2-4 percent), while economic sectors in Bangladesh, Bhutan, Maldives, and Sri Lanka experience negligible cost increases overall. In fact, the 2030 average effective input cost increase for South Asia, weighted by the share of the sectors in regional output (and each economy’s share in regional GDP), would only be around 2 percent relative to BAU.

> 38 As discussed under Section 3.2 above, this assumption is particularly relevant for EITE (or heavy industry) sectors competing in world markets (and not being able to pass higher prices forward to households), since these sectors are a focal point of international competitiveness (as well as carbon leakage)-related concerns surrounding (and potentially impeding) the adoption of climate mitigation policies (Dolphin et al., 2020).

20
- Lu, Guangyu et al. (2014). Malaria outbreaks in China (19902013): a systematic review. Malar J 13, p. 269.

- Lucas, Adrienne M (2010). Malaria eradication and educational attainment: evidence from Paraguay and Sri Lanka. American Economic Journal: Applied Economics 2.2, pp. 46 71.

- Lynch, Caroline A et al. (2015). Association between recent internal travel and malaria in Ugandan highland and highland fringe areas. Tropical Medicine & International Health 20.6, pp. 773780.

- Macdonald, George et al. (1957). The epidemiology and control of malaria. The Epidemiology and Control of Malaria.

- Milusheva, Sveta et al. (2017). Understanding the Relationship between Short and Long Term Mobility. AFD Research Paper Series 2017.69.

- Montalvo, Jose G and Marta Reynal-Querol (2007). Fighting against malaria: prevent wars while waiting for the miraculous vaccine. The Review of Economics and Statistics 89.1, pp. 165177.

- Montjoye, Yves-Alexandre de et al. (2014). D4D-Senegal: The Second Mobile Phone Data for Development Challenge.

- Ndiath, Mamadou O et al. (2012). Low and seasonal malaria transmission in the middle Senegal River basin: identication and characteristics of Anopheles vectors. Parasites & vectors 5.1, p. 1.

- Nickell, Stephen (1981). Biases in dynamic models with xed eects. Econometrica: Journal of the Econometric Society, pp. 14171426.

- Nosten, François and Nicholas J White (2007). Artemisinin-based combination treatment of falciparum malaria. The American journal of tropical medicine and hygiene 77.6 Suppl, pp. 181192.

44
15 overall average of 0.2916 for all employees legally entitled to severance payments. Similar to the IFLS (2007), both proportions suggests a high degree of non-compliance.

The results of the analysis highlight two important findings about the compliance of employers with the severance pay regulations. First, employees who are more vulnerable are more affected by non-compliance. Second, while _de jure_ severance costs may be high for covered wage-and-salary workers, firms frequently do not actually pay the legally entitled amount to eligible employees. The required payments may not be delivered when the payment is due, however, if a firm enters bankruptcy because other creditors may have higher priority claims to the assets of the firm. If the rate of noncompliance due to bankruptcy is high, then an employer-based severance program would be ineffective in providing income security to wage-and-salary employees. A sound company, on the other hand, may face above-average costs when the volume of severance payments is high.

# **4.  Conclusions**

The current impasse in reforming severance regulations reflects objections from both employers and employees. While recent negotiations have either collapsed or ended in a stalemate, renewed efforts are needed to find a workable compromise between the interests of employers and those of employee representatives. In addition, the reform options need to consider the voice of vulnerable workers who have been excluded from the debate. While it is not possible to assess the degree of hardship wage-and-salary workers face following a job separation, it is possible to infer that workers experience two significant disadvantages. First, severance payments are received by only a minority of eligible employees. Second, the severance payments to recipients are only a fraction of the legally-entitled amounts. Approximately one-third of legally-eligible employees actually receive severance pay after a job separation. On average, the ratio of the severance pay received to the legally-entitled amount is below 40 percent. The product of these two ratios yields a wage-loss protection share of between 10 to 14 percent of eligible severed workers’ monthly wages.

Indonesia lacks an administrative structure to pool the risk of a job separation between employers and employees. While the statutory severance pay regulations appear
# continued

|SYMBOL|VARIABLE|
|---|---|
|INFL|inflation rate (average ) in %|
|1NTAX|indirect taxes in % of taxes, fees and contributions (INDIRECT/TAXES* 100)|
|LIFE|life expectancy at birth in years|
|LIQUID|liquidity (quasi money for 1992 divided by GDP in current prices for 1992)|
|PATENTS|total patent applications per 100.000 of population|
|POPULATION|total population in thousands|
|REVGDP|total public revenues in % of GDP|
|RFTIREES|POPULATION-labour force (employed+unemployed) - young (age group of 0-14)|
|SOCEXP|social transfers in % of total public expenditure (SOCGDP/EXPGDP* 100)|
|SOCGDP|social transfers in % of GDP|
|SOCIAL|social security contributions in % of GDP|
|SOCTAX|social security contributions in % of taxes, fees and contributions<br>(SOCIAL/TAXES* 100)|
|SOCWAGE|replacement rate or pension-wage rate (SOCGDP/WAGEGDP* 100)|
|SUBSIDY|share of subsidies in % of GDP|
|TAXES|Taxes, fees and contributions in % of GDP (TAXES =indirect taxes + direct taxes +<br>social security contributions + compulsory fees, fines and penalties)|
|TAXGDP|total tax receipts & social security contributions in % of GDP|
|TELEPHONES|telephone mainlines per 1000 of inhabitants|
|TERTIARY|university education rate (proportion of age-group concerned per thousand of age-group<br>entering tertiary education)|
|TRANSFER|subsidies and current transfers in % of total expenditure and lending minus repayments|
|TTAX|turnover tax (taxes on goods and services) & tariffs in % of TAXES|
|TTAXGDP|turnover tax (& tariffs) in % of GDP (TTAX * TAXGDP /100)|
|TTAXREV|turnover tax (& tariffs) in % of total public revenues<br>(TTAXGDP/REVGDP * 100)|
|UNEMPLOY|unemployment rate|
|WAGESEXP|share of wages in government expenditure|
|$PPP|the dollar price of one unit of purchasing power parities (GDP$WB/GDPPPWB)|

29
- 16 -

The upper and lower percent changes in mortality have coefficients of 0.130 and 0.062, respectively. The central estimate for the number of cases of premature mortality, can be expressed as: Change ir. mortality = 0.096 * change in PM1O * (1/100) crude mortality rate * exped population The crude mortality rate in the U.S. is approximately 0.007, while in Indonesia the rate is 0.0095. However, since Jakarta is expected to have a lower mortality rate than the rest of Indonesia, we assume a _rae_ of 0.007. Therefore, the range of changes in mortality (per person) is: Upper change in mortality = 9.10 * 10 * change in PM10 Central change in mortality= 672 *10 * change in PMIO Lower change in mortality =4.47* _1( *_ change in PM10 ResuiaMor Hospital Admissions. As described abdve, Plagiannakos and Parker'<sup>8</sup> used pooled cross-sectional and time-series data for nime counties in their study for the period 1976 to 1982 Soluthern Ontario, Canada. A sttistcaliy significant relationship was found betwen the incidence of hospital admissions due- to respiratory diseases qRHA) and ambient sumiate levels- Additional evidence for an effect of partimlates on hospital admissions is provided by a study by Pope.' In this study a sttistcally significant assocition was found between monthly RHA, including admissions for pneumonia, asthma and bronchitis, and monthly average PM1O in two valleys in Utah studied between 1985 and 1989. Ozone concentrations were close to baseline during the winter seasons when both PM1O and RHA were elevated so the effect appears to be mosdy related to particles. After analyzing, the results suggest the following fiuctions: Upper change in RHA per 100,000= 1.56 * change in PM10 Central change in RHA per [00,000 1.20 * change in PM1O Lower change in RHA per 100,000 = 0.657 * change in PM1O
may be due to its links to other parts of the legal and financial infrastructure, and thus may not work well, or indeed, at all, in a different environment.

# **A.  BACKGROUND**

According to the Bond Market Association (BMA)<sup>1</sup> , the United States supported a $20.2 trillion market in outstanding debt securities at the end of 2002. This was nearly double the $11.7 trillion market for US stocks. The market in outstanding debt securities was divided roughly as shown in Table 1.

**Table 1:** **_US Debt Market Data, 2002_**

||Amount o|utstanding|New Issua|nce|Average<br>TradingV|Daily<br>olume|Annual Tr<br>Volume|ading|Turnover<br>/3|
|---|---|---|---|---|---|---|---|---|---|
||USD<br>Billion|%-of<br>GDP|USD<br>Billion|%-of<br>GDP|USD<br>Billion|%-of<br>GDP|USD<br>Billion|%-of<br>GDP|%|
|US<br>Treasuries<br>/1|3205|30.7|572|5.5|366|3.5|92,333|883.9|2,881|
|Federal<br>Agency<br>Debt|2359|22.6|1710|16.4|82|0.8|20,614|197.3|874|
|Municipal<br>Securities|1784|17.1|431|4.1|11|0.1|2,696.4|25.8|151|
|Corporate<br>Debt|3997|38.3|594|5.7|20|0.2|5,141|49.2|129|
|Mortgage<br>Related<br>Securities /2|4705|45.0|2310|22.1|155|1.5|38,934|372.7|828|
|Asset-<br>Backed<br>securities|1543|14.8|485|4.6|NA||NA|||
|Total|17593|168.4|6102|58.4|634|6.1|159718|1529.0|908|

Memo: GDP USD 10446 bn; No of trading days in 2002: 252 Notes: /1: Gross coupon issuance /2: Includes agency and private-label pass-throughs and CMOs /3: Turnover measured as: Annual Trading volume / amount outstanding Particularly notable during 2002 was the tremendous growth in the mortgage-related securities market.

# **B.  SNAPSHOT OF FIXED INCOME MARKET – TYPES OF ISSUERS**

The four key segments of the fixed income market in the United States are the federal government, the federal agencies and government sponsored enterprises, the municipalities, and US domestic corporations.

> 1 The statistics included in this section of the Report are taken from the Bond Market Association

> (www.bondmarkets.com), unless otherwise noted.  Figures have been rounded as well as estimated so may not total 100 percent.

2
area they lived. In the analysis, we combined the exit interview and household survey data for first prenatal care visits to assess the impact of P4P on prenatal care quality, and we control for the observation’s source in the analysis with an indicator variable for whether the observation was from the exit interview or household survey.

In addition to the prenatal care process quality index, we measure whether women reported receiving the tetanus vaccine during prenatal care visits, as this enters the payment formula as one of the 14 P4P output indicators and as one of the process quality of care measures in the quality of care index.

**Child Preventive Care:** We define preventive care as whether the mother reported taking her child for a preventive care visit in the last four weeks. Child preventive services cover immunization, vitamin A, distribution of mosquito nets and child growth monitoring with referral of malnourished children to hospitals for treatment. We analyze preventive visits separately for children 0-23 and 24-59 months old as the younger group is expected to have more visits than the older group.

We also examine the impact of P4P on full immunization, measured by an indicator variable for 12-23 month olds, coded 1 if the child received all vaccines required by the national protocol and 0 otherwise. Vaccination status was assessed based on the vaccination card. Less than 4 percent of the mothers could not produce the child’s vaccination card and they were dropped from the analysis.

# **_Statistical Methods_**

As a result of the reassignment of some districts, we view the evaluation design as quasiexperimental and use difference-in-differences to estimate program impact. This method 7
13 increase production, due to subutilization, by more than 30 percent ... [A]ccording to recent studies ... unutilized capacity has reached, in the last few years, 61 percent in the cloth industry, 50 percent in the baking industry ... 74 percent in the shoe industry, etc. (As reproduced in Garcia, 1972, p. 72) An important corollary of the unutilized capacity hypothesis was that firms in many sectors faced decreasing average costs so that, under proper administrative price cortrols, demand increases would not generate inflationary pressure. Moreover, to the extent that many of the larger firms were indeed nationalized, as called for by the UP program, output could be greatly increased even with price reductions. In a document prepared by the Planning Office (ODEPLAN) in 1971, it was stated that by "combining the increase in production derived from a higher use installed capacity ... the former monopolies will be able to absorb, without problems, the required wage increases, while maintaining, or even reducing, prices and still generating the same surplus".

The lack of "full" utilization was, in turn, attributed to two fundamental factors: The previously mentioned monopolistic nature of the manufacturing industry and, the structure of income distribution. According to this interpretation, the unequal income distribution resulted in a highly diversified pattern of consumption. Vuskovic stated this view as follows: "[There is] ... an obvious association between income distribution and the composition of demand and, consequently, the structure of production ... Given current income distribution ... the effective market is limited to higher income groups ... [I]t is a restricted ... and highly diversified demand ... [A] broad range of industries producing nonessential consumption goods operate an insufficient scale ... (Vuskovic, 1970)
migrants (Dinat and Peberdy (2007)).

Table A.1 provides summary statistics for the gumtree domestic work sample divided by the main nationality groups. As the website does not systematically collect information of job seekers, all data reported are derived from the profile text visible in the search result. For example, the age mean is calculated from the 28.3% of Gumtree job seekers that provide information on their age. There is thus the possibility of reporting bias - an issue I will discuss below.<sup>16</sup> For a first descriptive summary of employer demand across population groups, Figure A.1 plots the distribution of profile clicks for South Africans, Malawians, Zimbabweans and people who do not state their nationality. The figure shows that the profile clicks follow a normal distribution for each population group. South Africans get more profile clicks than profiles without nationality information which in turn get more clicks than profiles of Zimbabweans and Malawians. The order of employer preference seems robust to outliers; in fact, the ranking of distributions (almost) follows a pattern of statistical dominace. However, these differences may be due to differences in profile characteristics between population groups documented in Table A.1. The next section offers two empirical strategies to estimate the causal effect of nationality on employer demand.

# **4 and Results**

## **4.1 Selection on Observables**

### **4.1.1 Domestic Work Sector**

As discussed in section 3, I observe exactly the same information as the employer at the time she makes the decision whether to screen an applicant’s profile. While employment studies using observational data often suffer from the omitted variable problem, this study design guarantees that the observable data are orthogonal to the error term (assuming that I correctly codify the profile

> 16While this study focuses on domestic workers, I will also compare results to the nanny and general work sector (which includes gardening) to test how employer preferences vary between sectors. Table A.2 compares summary statistics across sectors. 75% and 80% of job seekers revealing their gender in the housekeeper and nanny sector, respectively, are women, whereas the general work sector is dominated by men. The share of Malawians is disproportionately high in the general work sector and Zimbabweans are overrepresented in the nanny sector. While job seeker characteristics such as age, experience, and the share holding work permits are relatively similar across sectors, I can reject a test of equal means for almost all variables.

9
# **Living on the Edge**

Risk, Protection, Behavior, and Outcomes of Argentine Youth By

## **Michael Justesen**

World Bank<sup>1</sup>

**JEL Classification:** I10, I20, J12, J13, J15, J16, J24.

**Keywords:** Youth, risk and protective factors, outcomes and behaviors, cluster analysis, Argentina.

> 1 I am extremely grateful to Emilie Bagby for help with the cluster analysis. Comments from reviewers Niels-Hugo Blunch, assistant professor at Washington and Lee University, and Jasmin Chakeri, Argentina country economist in the Latin America and Caribbean Region in the World Bank, are gratefully acknowledged. I am also thankful for comments from Rafael Rofman, to Dorte Verner and LCSEO for inviting me to undertake the analysis, and to Jean Fares, Milan Vodopivec, and HDNSP for supporting the completion and publication of it. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author.
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 17. Hidden costs of water supply utilities in Cameroon and Central Africa countries**

<!-- Start of picture text --> a. CDE b. Selected water utilities in Central Africa<br>140 160<br>120 140<br>100 120<br>100<br>80<br>80<br>60<br>60<br>40<br>40<br>20 20<br>0 0<br>CAR (2009) Congo, Rwanda DRC Cameroon<br>Rep. Congo (2010)<br>Collection inefficiencies Underpricing Losses<br>Collection Inefficiencies Underpricing Losses<br>Percentage of revenues Percentage of revenues<br><!-- End of picture text --> Source: Derived from Briceño-Garmendia and others 2009. DRC = Democratic Republic of Congo; CAR = Central African Republic.

# **Challenges**

The institutional setup for the provision of water supply and sanitation services in Cameroon needs to be completed and clarified. Cameroon has made efforts to decentralize the provision of services but so far has failed to implement the policy. The inability of local authorities to manage water and sanitation services as stated in the laws on decentralization is noticeable (Global Water Partnership 2010). The sanitation sector is very poorly organized, lacking goals, a specific strategy, and a dedicated institutional body (AMCOW 2010b).

There is a wide disparity between rural and urban populations in access to sources of drinking water. At 69 percent of the population, urban access to household piped water and public stand posts is 6 times higher than rural access; at 28 percent, urban reliance on wells and boreholes is one-third of the level in rural areas (figure 18a). As of 2006, 72 percent of the rural population got their water from boreholes equipped with hand pumps, compared with 34 percent on 1998.

Sewerage is virtually nonexistent in Cameroon, and no major investment has been made in recent years. A tiny 5 km sewerage network in Douala dates from before independence and is no longer operational. Since then, SIC<sup>11</sup> —a public real estate development agency—has built some small sewer systems as part of housing construction projects for civil servants. In theory the SIC systems serve 60,000 people (or 0.6 percent of the urban population), but in practice many of the systems are no longer functioning. Some small-scale pilot projects have recently been initiated. The private sector has invested in approximately 70 sludge collectors in Douala and Yaoundé, and donors have financed small condominial systems in Douala, Bertoua, and Edea, and as part of the Community-Led Total Sanitation campaign in selected rural areas (World Bank 2011d).

> 11Société Immobilière du Cameroun.

29