rafmacalaba/datause-encoder-data huggingface dataset:
market policies in reducing labor-related risk and smoothing job-to-job transitions is mixed, partly because many of these programs are only able to reach people who have lost registered forms of work (cf. World Bank, 2012b). Still, Vietnam should explore opportunities for modestly and prudently increasing its commitment to active labor market policies. This might involve both the expansion of existing programs like public employment policies or policies to support employment changes for laborers in rural areas or the creation of new programs.

Vietnam should also renew its efforts to better integrate vulnerable groups into the labor market. In this context, it should strengthen its rules on equal employment opportunities in the Labor Code. Besides, it should explore other options for making sure that women, minorities, the disabled and other vulnerable groups can participate productively in the labor market. If this is successfully achieved, it will unleash the productive potential in terms of labor and human capital of these groups and promote overall growth. It will also have broader benefits for society by preventing discrimination and achieving equal pay for equal work. Enhanced economic opportunities for women might even help normalize the sex ratio at birth.

Finally, many of the challenges for Vietnam’s labor market policies can be traced back to weaknesses in the country’s labor market institutions, in particular its industrial relations institutions. These weaknesses have long been known and apparently fed into the 2012 revisions of the Labor Code and Trade Union Law. The revisions included many reforms to address weaknesses in the industrial relations system: First, formal procedures for settling industrial relations disputes were introduced. Second, the government of Vietnam was given a more active role in facilitating collective bargaining. Third, a National Wage Council as a new tripartite institution centrally involved in the setting of minimum wages was created. More generally, it seems that reforms of labor market regulations covering minimum wages and EPL (including more stringent regulations of labor leasing enterprises and more far-reaching restrictions of outsourcing contracts) were used as substitutes to bring about outcomes that would be ideally the result of firm- or sector-level collective bargaining agreements.

Today, Vietnam’s Labor Code appears to bear the weight of weak industrial relations institutions. Rapidly rising minimum wages and more stringent EPL have been used to address insufficient power of workers to negotiate adequate wages and assure fair and decent treatment by employers. This has resulted in a “one size fits all” set of regulatory parameters that fail to respond nimbly to the varying interests and demands of workers and employers and are not necessarily adequate across all sectors or geographies or types of work. Moreover, legislated parameters are difficult to alter once in place and

- 25 -
# **Table 8 Direct price impacts of Tunisia’s regulatory restrictions in selected services (percent)**

|_Sector_|_Dir_|_ect price impact_||
|---|---|---|---|
||_via markup_<br>|_s on_<br>|_via costs_|
||output|exports to<br>Tunisia||
|**_Currently_**||||
|Telecommunications – domestic providers<sup>**a**</sup>|6.8|||
|– foreign providers<sup>**a**</sup>|15.5|||
|Banking – domestic providers|7.5|||
|– foreign providers|17.5|||
|Professional services – domestic providers<sup>**b**</sup>|||6.0|
|– foreign providers<sup>**b**</sup>|19.1|19.1||
|**_After unilateral reform_**||||
|Telecommunications – domestic providers<sup>**a**</sup>|0.0|||
|– foreign providers<sup>**a**</sup>|0.0|||
|Banking – domestic providers|4.9|||
|– foreign providers|6.8|||
|Professional services – domestic providers<sup>**b**</sup>|||6.0|
|– foreign providers<sup>**b**</sup>|11.9|11.9||
|**_After further bilateral reform_**||||
|Professional services – domestic providers<sup>**b**</sup>|||6.0|
|– foreignproviders<sup>**b**</sup>|6.4|6.4||

> **a** A simple average of price impacts for fixed line and cellular services.

> **b** Simple average of estimates for legal, accounting, and engineering services.

_Source_ : Dee (2005), based on Warren (2000), Kalirajan et al. (2000) and Nguyen-Hong (2000).

38
and Kordufan were economically active in 2014. Compared to those living in Khartoum, women who live in Kordufan and Darfur are more likely than men to participate in the labor force. Living in rural areas increases the odds for women to be in the labor market and to be employed by 42 percent and 87 percent, respectively.<sup>6</sup> Yet despite the much lower participation rates, working-age women were about as likely as men to be unemployed. Conversely, youth labor force participation rates increased by varying degrees in all regions and in both urban and rural areas. For youth in rural areas, the odds to be employed and to be in the labor force increased by nearly 80 percent and by 32 percent, respectively. The labor force participation rate increased by 12 percentage points in the Northern region and only by 1.3 percentage points in Kordufan (Figure 4).

**Figure 4. Labor force participation rate by locality and region**

<!-- Start of picture text --> (a) Youth labor force participation (b) Women labor force participation<br>60% 70%<br>50% 60%<br>40% 50%<br>40%<br>30%<br>30%<br>20%<br>20%<br>10%<br>10%<br>0%<br>0%<br>by rural/urban by region<br>by rural/urban by region<br>2009 2014<br>2009 2014<br>Sudan Rural Urban Northern Eastern Khartoum Central Kordofan Darfur Sudan Rural Urban Eastern Central Darfur<br>Northern Khartoum Kordofan<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on NHBPS 2014/15.

Many women do not even enter the labor force: women generally, and female youth in particular, represent the highest share of the population out of the labor force. For men it is more common to be out of the labor force before the age of 24, while women are likely to be out of the labor force across all age cohorts. Three in every four young women (75 percent) are not in the labor market, compared to just over half (54 percent) of young men. The percentage out of the labor force varies significantly across working-age men and women: only 3 to 13 percent for men aged 25 to 64 compared to 58 to 66 percent for women (Figure 5).

> 6 The pattern is the same for other categories (men and adults); compared to urban areas, living in rural areas increases the chances for men and adults to be employed by 35 percent and 41 percent, respectively. Data based on logistic regression model using the NHBPS 2014/15.

11
Proxy 3 definition of informal employment in 2016, new questions were also added to the questionnaire. In particular, book-keeping practices, types of benefits received from their employer, whether they have an employment contract, and the type of contract they have were all asked about. Additionally, the 2021 questionnaire asked respondents about whether, and when, the company or business in which they worked had been registered. Altogether, these changes imply that estimates of informal employment based on the SAKERNAS are not comparable over time, particularly for the pre-2016 and post-2016 periods. It is also unclear how the new question on registration status will affect estimates' comparability before and after 2021.

Given its comprehensive coverage of the population and the addition of new questions from 2016 onwards, SAKERNAS provides the most accurate estimates of informal employment among existing data sources. However, it also suffers from limitations. For example, information about main and secondary jobs is quite limited (Dong, 2016). This issue has been partly addressed by adding questions related to social insurance, employment benefits, and contract type, although these are only collected from employees. Consequently, data on employers, own-account workers, casual workers, and unpaid family workers remain unavailable, despite accounting for two-thirds of jobs in Indonesia (Wihardja & Cunningham, 2021). In addition, there is a wide range of jobrelated characteristics (e.g., work environment, nature of tasks, job satisfaction) that SAKERNAS does not capture. This hinders a comprehensive analysis of job quality in Indonesia. Finally, SAKERNAS was not designed to be a longitudinal survey. While a panel of households can theoretically be constructed from the rotating panel design, the period covered would be relatively short and not representative of the Indonesian population. Thus, surveys such as the Indonesian Family Life Survey, are needed to complement SAKERNAS.

_Indonesian Family Life Survey_ The Indonesian Family Life Survey (IFLS) is a multi-topic, longitudinal survey that was first conducted in 1993 by RAND Corporation alongside a number of Indonesian organizations. The pilot wave was conducted in 13 provinces, specifically: North Sumatra, West Sumatra, South 9
tracked. Figure 2 displays the data. Thresholds for each indicator were then fine-tuned to best front run <!-- Start of picture text --> 350<br>300<br>w 250<br>Fd 200<br>%5<br>5150150<br>E<br>2 100<br>50<br>0<br>SEEEEsdassasasasasgasgasasasagageaELLEsdassasasasasgasgasasasagageaELLEELLE EEL LEE EEE ELLE ELLE<br>Date<br>Targetindicator mO m1<br><!-- End of picture text --> <!-- Start of picture text --> 350<br>300<br>g 250<br>E 200<br>5<br>$ 150<br>E<br>2 100<br>50<br>0<br>Messsssssssssssssssasssssssss CETL ET EEEsssssssssssssssssasssssssss CETL ET EEE CETL ET EEE ET EEE EEE EETEEEEEE<br>Date<br>Target indicator MO M1<br><!-- End of picture text --> 350 350 300 300 w 250 g 250 Fd 200 E 200 %5 5150150 $ 150 E E 2 100 2 100 50 50 0 0 SEEEEsdassasasasasgasgasasasagageaELLEsdassasasasasgasgasasasagageaELLEELLE EEL LEE EEE ELLE ELLE Messsssssssssssssssasssssssss CETL ET EEEsssssssssssssssssasssssssss CETL ET EEE CETL ET EEE ET EEE EEE EETEEEEEE Date Date Targetindicator mO m1 Target indicator MO M1 Notation-wise, this can be detailed as follows. Specifically, the target variable takes the value 1 if a food security emergency is observed, defined as IPC categories 4, or 5, and 0 otherwise. For convenience, 0 was defined as a column vect r contain ng the class labels, defined as a corresponding vector withi dicator values used to generate arnings. was defined as the is defined as a numeric threshol . Warnings for time to front run tra sition into emergencies by validating a insta specific subset of data: district/month specifically, let be defined as the sub-vectors of ~~( *# a~~
pooled 10-year periods.<sup>8</sup> In doing so, they place specific emphasis on SSA in both motivating their analysis and interpreting their results. They note that including neighbor growth in their regressions eliminates the significance of the dummy variable for the region which has traditionally been found to be important in empirical growth regressions ( _ibid._ , 1998, p. 133). They furthermore find large neighborhood multiplier effects, contending that these effects might have locked SSA into a slow growth pattern because they imply that slow growth in neighboring countries becomes mutually reinforcing. However, as they note, such effects also have a potentially positive upside for SSA. They imply that, if pursued in unison, a large policy change will not only have directly beneficial effects on the growth of each country in the region, but also reinforcing indirect effects. They calculate a neighborhood multiplier of 2.2, which is the factor by which the direct effects on domestic growth of a policy change are amplified if that policy change is pursued in unison ( _ibid._ , pp 136-137).

All of the above studies are concerned with long-run growth spillovers. There also exist several studies which instead focus on short-run growth spillovers, using annual growth data within a panel framework. Weinhold (2002) models a country's growth as being dependent on both contemporaneous and lagged values of a neighboring growth variable. She interprets the former as capturing spatial dependence that might arise through common shocks and the latter as capturing genuine growth spillovers. In doing so, she allows for parameter heterogeneity between industrialized and developing countries, finding that significant growth spillovers exist only for the latter.

> 8 Contrary to our analysis in section 5, however, they do not take advantage of the panel structure of their data set to control for time-invariant unobservable determinants of growth that might be correlated with their neighboring growth variable.

8
**Table 5.3b.** Sources **of Finance of HDB Procurement**

|**Goods and Services Procured**|**ALSM**|MOn|Ministry**of**<br><br>**Publie Works**|**Other**|
|---|---|---|---|---|
|**_S_**<br>**__l_**<br>**_ries andbonuses_**|432,395,50|0<br>258,184,40|6||
|**_Food items_**|5,993,895|<br>28,500,000|||
|**_Fuels_**|4,012,235|<br>19,273,240|||
|**_'gen and anesthetic products_**||14,670,000|||
|_tA4enance_**_ofpremises_**||6,851,600|<br>6,000,000||
|**_Equipmentmaintenance_**|14,339,085|<br>2,147,770|||
|**_i*nu*ure_**||3,000,000|||
|**_onery'_**<br>~4,976,3|20||||
|**_Awflcaneaussupplies_**||64,650,000||4,536,000|
|**_rug._**|115,394,900|<br>146,337,495||6,341,562|
|**_K-raJilm_**|956,710|<br>19,923,120|||
|**_Labsuppies_**|9,994,245|<br>14,665,490|||
|**_Maintenance products & parts_**|1,449,555|<br>12,097,250|||
|**_Water transport_**|9,315,000||||
|**_MIscellaneous_**|13,444,215||||
|**_Taal_**|612,271,660|<br>590,300,371|<br>6,000,000|10,877,562|
|**_Grand Total Expenditures_**||1,219,4|49,593||
|**_%af total DR expenditures_**|**_50.2%_**|**_1_**<br>**_48.4%_**|**_1_**<br>**_0.5%_**|**_|_**<br>**_0.9%_**|

Source: Author's construction, based on report produced by hospital admninistrator, Elias Nasr in 1994. All figures are in Lebanese Lira, unadjusted for inflation.

# _Explaining HDB s decision rights complementarities in procurement_

All results from the Decision Rights Analysis Interview Framework point in the same direction. Decision rights over procurement reached a quick equilibrium after the first year of the ALSM's operation and were not contested much after that. In the areas of medical and other consumables decision rights were exclusively held by the director, whose decisions the ALSM would discuss and disburse on _ex post._ These decision rights were essential to improving HDB's allocative efficiency and they complemented decision rights in finance by giving HDB the flexibility to respond to demand. The results in service delivery were clear. In the area of major medical and other fixed equipment, the decision right was held by the ALSM, which would explore alternative investments and seek prices based on recommendations made by the director for expansions in HDB's fulfilled and the amount of LL18,000,000 (USD13,891) was paid off (ALSM Minutes of Meetings, 28.VII.95).

30
**Table A. 5: Sensitivity Analysis of Poverty Incidence under changing Economies of Scale Across Socioeconomic Groups in the late 1990s**

||**HB**<br>|**S 2000(**<br>θ<br>|1<br>=<br><br>**)**<br>|**HB**<br>|**S 2000(**<br>θ<br>|8.<br>=<br><br>**)**<br>|
|---|---|---|---|---|---|---|
|**Socioeconomic Groups**|**P0**|**P1**|**P2**|**P0**|**P1**|**P2**|
|Wage Earners|12.29|3.30|1.35|10.87|2.81|1.14|
|Self-employed|9.54|2.67|0.787|9.54|1.83|0.415|
|Pension|13.08|3.73|1.53|14.82|4.02|1.63|
|Other Social Benefits|41.42|16.52|8.79|41.06|14.23|7.16|
|Other Income|22.24|7.37|3.65|22.69|7.79|3.79|
|Urban|9.15|2.51|1.03|8.91|2.36|0.09|
|Rural|25.86|7.93|3.56|26.27|7.63|3.31|
|**Latvia**|**13.99**|**4.09**|**1.76**|**13.95**|**3.89**|**1.65**|

**Table A. 6: Sensitivity Analysis of Poverty Incidence across Geographical Regions under changing poverty lines in absence of economies of scale (** θ = 1 **)**

||**HB**|**S 2000(**<br>_z_|30<br>=<br>**)**|**HB**|**S 2000(**<br>|32<br>=<br>_z_<br>**)**|
|---|---|---|---|---|---|---|
|**Geographical Regions**|**P0**|**P1**|**P2**|**P0**|**P1**|**P2**|
|Riga|6.76|1.93|0.82|8.79|2.30|0.976|
|Kurzeme|16.61|4.50|1.79|19.32|5.33|2.17|
|Vidzeme|23.31|7.14|3.41|27.01|8.29|3.93|
|Zemgale|15.14|4.36|1.85|17.92|5.14|2.20|
|Latgale|24.17|7.24|3.11|27.02|8.36|3.68|
|Urban|9.15|2.51|1.03|11.28|2.98|1.23|
|Rural|25.86|7.93|3.56|29.44|9.16|4.17|
|**Latvia**|**13.99**|**4.09**|**1.76**|**16.54**|**4.78**|**2.08**|

**Table A. 7: Sensitivity Analysis of Poverty Incidence across Geographical Regions under changing poverty lines in absence of economies of scale (** θ = 1 **)**

||**HB**|**S 2000(**<br>_z_|30<br>=<br>**)**|**HB**|**S 2000(**<br>_z_|35<br>=<br><br>**)**|
|---|---|---|---|---|---|---|
|**Geographical Regions**|**P0**|**P1**|**P2**|**P0**|**P1**|**P2**|
|Riga|6.76|1.93|0.82|11.77|2.98|1.24|
|Kurzeme|16.61|4.50|1.79|22.51|6.64|2.80|
|Vidzeme|23.31|7.14|3.41|29.97|10.00|4.79|
|Zemgale|15.14|4.36|1.85|21.74|6.39|2.78|
|Latgale|24.17|7.24|3.11|32.45|10.20|4.60|
|Urban|9.15|2.51|1.03|14.09|3.80|1.59|
|Rural|25.86|7.93|3.56|34.66|11.13|5.15|
|**Latvia**|**13.99**|**4.09**|**1.76**|**20.06**|**5.93**|**2.62**|

39
19 Table 7 presents results from a set of regressions based on application of the GLM and POISSON procedures that examine the relationship between the number of treaties in a river and the several independent variables identified in our empirical specification section. Observations used for this analysis include rivers with and without treaties. In this case a GLM and Poisson regression procedures are applied as the dependent variable ( _Number of treaties_ ) assumes a discrete values 0, 1,..., 10 of treaties (Maddala 1983). The results strongly suggest that the relationship between scarcity and cooperation takes the shape of an inverted U curve, that governance in the basin countries is an important factor, that trade and treaty cooperation are complementary, and that geography is not conclusive in explaining differences in level of cooperation, as only the _Through-border_ geography has several significant coefficients. Since the IMF-based and the UN-based trade variables provide similar results, we present selected regressions for various specifications of IMF and for UN trade variables.

Table 8 presents results from a set of regressions based on application of the OLS procedure that examines the relationship between the number of treaties in a river and the several independent variables identified in our empirical specification section. Observations used for this analysis include rivers with treaties only. All variables are with expected signs, and among the geography variables the _Through-border_ is significant in only one equation.

Table 9 presents results from an analysis of those observations where only rivers with water allocation treaties are included. We used two sets of dependent variables: _Number of treaties_ , and _Share of water allocation issues_ . A GLM estimation procedure was applied to regressions with _Number of treaties_ as a dependent variable, and an OLS estimation procedure was applied to regressions with _Share of water allocation issues_ as a dependent variable. The estimated coefficients are significant, and all the variables of interest had the expected signs. The results suggest that water scarcity and trade are the major explanatory variables of cooperation between basin riparian states. In the case of regression (3) the _Through-border_ was found to be significant in explaining lower levels of cooperation compared to the other geographies. However it is significant only in the OLS equation. Although this paper argued that the results stemming from the geography variable may be ambiguous, the findings here may indicate that given the reciprocal nature of the _Border-creator_ configuration, states might voluntarily abate pollution and informal cooperation would replace formal cooperation.

The two scarcity variables that were used— _Water scarcity intercept_ and _Water scarcity slope_ (with the variations related to the various data sets), support our hypotheses regarding the _scarperation_
Department, Government of NCT of Delhi.

Greenstone, M., and R. Hanna. 2014. “Environmental Regulations, Air and Water Pollution, and Infant Mortality in India.” _American Economic Review_ 104 (10): 3038–72.

Gurjar, B. R., J. A. Aardenne, J. Lelievelda, and M. Mohan. 2004. “Emission Estimates and Trends (1990–2000) for Megacity Delhi and Implications.” Atmospheric Environment 38 (33): 5663–81.

Guttikunda, S., and R. Goel. 2013. “Health Impacts of Particulate Pollution in a Megacity- Delhi, India.” _Environmental Development_ 6 (1): 8–20.

Guttikunda, S., and G. Calori. 2012. “Multi-Pollutant Emissions Inventory for the National Capital Region of Delhi.” SIM-air Working Paper Series, New Delhi, No. 38.

Kennedy, P. E. 1980. “Estimation with Correctly Interpreted Dummy Variables in Semilogarithmic Equations.” _American Economic Review_ 71 (4), 801.

Lee, D., and T. Lemieux. 2010. “Regression Discontinuity Designs in Economics.” _Journal of Economic Literature_ 48:281–355.

Litman, T. 2014. “Evaluating Rail Transit Criticism.” Victoria Transport Policy Institute, Victoria, BC.

Lleras-Muney, A. 2010. “The Needs of the Army: Using Compulsory Relocation in the Military to Estimate the Effect of Air Pollutants on Children’s Health.” _Journal of Human Resources_ 45 (3): 549–90. Ministry of Urban Development. 2008. “Study on Traffic and Transportation Policies and Strategies in Urban Areas in India: Final Report.” Ministry of Urban Development, Delhi.

Moretti, E., and M. J. Neidell. 2011. “Pollution, Health, and Avoidance Behavior: Evidence from the Ports of Los Angeles.” _Journal of Human Resources_ 46 (1): 154–75.

34