rafmacalaba/datause-encoder-data huggingface dataset:
13 commonly performed when estimating Translog cost functions, amounts to choosing the mean as the reference point for local approximation.

The set of control variables selected as extra explanatory factors in the cost function varies from one country to the other. In all cases, we keep the set of variables which yields the best fit to the data. Finally, the Translog cost function is estimated using data from 26 WSS utilities in Brazil (a total of 213 observations), 48 WSS utilities in Colombia (a total of 78 observations), 38 WSS utilities in Moldova (a total of 237 observations), and 49 water utilities in Vietnam (a total of 145 observations). Estimated parameters of the four Translog cost functions (except for the utility-specific effects) are shown in Appendix (tables A2, A3, A4, and A5, for Brazil, Colombia, Moldova, and Vietnam, respectively).

The goodness of fit of the Translog cost model is around 0.99 in the four models. The good fit of the model to the data is also reflected in the test of regulatory properties of the cost function. A well-behaved cost function should be: (i) monotonically increasing in input prices, (ii) monotonically increasing in output, and (iii) concave in input prices. In the case of a Translog cost function, conditions (i) and (ii) are satisfied if the estimated factor shares, _S_<sup>�</sup> _jt_ ( _j=c,e,l,o_ ), and the estimated elasticity of cost with respect to output are positive at all data points. A necessary and sufficient condition for a twice continuously differentiable cost function to be concave in input prices requires negative semi-definiteness of the matrix of second-order partial derivatives of the cost function with respect to input prices. We check that these properties are satisfied on our data.

# _Cost elasticities_

Since variable cost and the regressors are in natural logarithms and have been normalized (mean-scaling), the first-order coefficients are all interpretable as cost elasticities evaluated at the sample mean (see table 3).<sup>20</sup> The coefficients of the output variable and the input prices have the expected signs and are highly significant in the four countries. As one would expect, there is a strong positive relationship between total variable cost and output when all other factors are fixed. A one percent increase in volume of water produced leads to an increase in total variable cost which 20 By sample mean, we mean the service with the average characteristics in each country.
# **5. Conclusions**

**This paper aims to provide new information to support the Malaysian government in formulating evidence-based policies on foreign workers.** Anecdotal evidence suggests that the COVID-19 pandemic has had large negative effects on foreign workers in Malaysia. For example, foreign workers who are laid off because of contractions in business activities related to the MCO or the pandemic more broadly are not legally allowed to be employed by other companies while remaining in Malaysia. At the same time, many cannot go back to their home countries because of travel restrictions as a COVID-19 mitigation measure. Furthermore, irregular foreign workers have limited access to public medical facilities and treatment, posing health risks. Foreign worker estimates by nationality and by state thus could help formulate policies on how to reallocate existing foreign workers in the country and on how to adjust foreign worker levies and new inflows of foreign workers during the MCO phase and thereafter. Such policies would help the government to strengthen the economy in the recovery phase of the COVID-19 crisis.

**We demonstrate that the BNM remittance data set is a useful source to estimate the number of foreign workers on a monthly basis, using a 12-month moving average.** Our experience suggests that simple monthly data are too volatile for these purposes, as some foreign workers remit on an irregular basis. To smooth out fluctuations in monthly data and to capture an annual net stock of foreign workers, a 12month moving average would be an appropriate method. The initial work to try to identify individuals who transfer money on behalf of others is time-intensive, but once this model is set up, big hurdles would be lifted and thus it would be relatively straightforward to produce monthly estimates and future projections on a timely basis. In that context, our work contributes to making progress in the initial settingup stage. During this exercise, it is important to ensure data privacy of individual remitters. Bearing this in mind, we used MSP system-generated personal unique identification numbers as a variable for remitters.

**Our estimates suggest that the number of foreign workers in Malaysia was between 2.99 million and 3.16 million in 2017–18.** State and nationality distributions of foreign workers in our estimates are consistent with the MOHA data, lending support to the validity of our estimates. Nevertheless, we note that the BNM remittance data could potentially underestimate the number of workers in states with low access to MSPs, as well as nationalities that have access to alternative money transfer mechanisms such as commercial banking and informal transfer channels. This risk could be mitigated over time by BNM’s greater efforts to enhance the financial literacy of residents, including foreign workers, and increase the availability of online remittance services to ease accessibility and further reduce remittance costs. As a complementary measure, BNM could conduct annual foreign worker surveys to better understand their financial management behavior, leveraging the existing network of MSPs.

12
# **DO WE FACE A GLOBAL "CAPITAL SHORTAGE"?**

**Zia Qureshi World Bank**

Helpful comments and suggestions were received from Michael Bruno and Masood Ahmed. The paper draws heavily on global economic projections prepared by the Analysis and Prospects Division of the International Economics Department for _Global Economic Prospects and the Developing Countries 1995._ Special thanks are due to T.G. Srinivasan for assistance with the projections. Abdel Stambouli and Sarah Crow helped with the graphics. The paper also draws on recent analyses of global capital market prospects prepared by other organizations. An earlier draft of the paper served as a discussion document at the Aspen Institute's annual conference on The Future of the World Economy, Aspen, Colorado, August 19-23, 1995. The paper has benefitted from comments received from conference participants.
15 enforcement by the government and the provision of the subsidy would have to be credible even if foreign output rose so that a large subsidy would have to be paid. The key point is that the subsidy would have to depend on the reaction of the foreign firm.

We have here a kind of infant industry argument that should be analyzed in those terms. One might ask why a firm has to be underwritten by the government. Why cannot it go on the capital market and borrow the necessary funds or obtain credit lines to underwrite possible losses? Presumably the more financial resources it has available the more credible its threat or output decisions will be. This is an obvious and familiar point. And the US capital market, for which this theory is designed, is hardly - imperfect even though imperfection of the capital market is a familiar basis for an infant industry argument applying to developing countries.

The answer might be that strengthening the firm's financial resources would not give the firm complete credibility. It might be able to obtain the resources to sustain its output even if the foreign firm failed to respond by reducing its output, but it may not have the incentive to keep its output at the desired level (at S) at all costs. This is indeed correct. The argument is that, for some reason, the government has more credibility. The government would hang on, even when a firm on its own would not. It would hang on even when it becomes probable that there would be a net loss for the country. The firm gets the incentive
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE capacity, strengthen transmission networks, and boost electrification. The water and sanitation sector has the second highest spending needs: $774 million will be needed each year to meet the Millennium Development Goals, with capital expenditure accounting for 66 percent of that total. Transport spending needs are $341 million per year. Spending requirements for ICT are less than for other infrastructure sectors at $119 million a year (table 18).

**Table 18 Indicative infrastructure spending needs in Côte d’Ivoire for 2006 to 2015**

|$ million per year|Capital expenditure|Operations and<br>maintenance|Total needs|
|---|---|---|---|
|ICT|81|38|119|
|Irrigation|165|nav|165|
|Power (no trade)|724|239|963|
|Transport (basic)|201|140|341|
|Water supply and sanitation|511|264|774|
|Total|1,682|680|2,363|

Sources:Mayer and other 2009; Rosnes and Vennemo 2009; Carruthers and others 2009; You and others 2009. Derived from models that are available on-line at http://www.infrastructureafrica.org/aicd/tools/models.

Côte d’Ivoire’s infrastructure spending needs are high in absolute terms, but look relatively manageable when expressed in terms of GDP (figure 15). Relative to the size of Côte d’Ivoire’s economy, spending needs would amount to 10 percent of GDP in 2008. By comparison, many West African neighbors would need to spend around 20 percent of GDP to meet basic infrastructure targets. Investment would absorb around 7 percent of GDP; by comparison, China invested 15 percent of GDP in infrastructure during the mid-2000s.

31
marginal delay caused by one extra vehicle is  _Tf_<sup>_A_</sup>  _V_  1 . Multiplying by _V_ gives the added delay aggregated over all road mileage. Thus, we can see that the marginal delay is a constant multiple ( _θ =_ 4) of the average delay.

We assume a free flow travel speed of 25 miles per hour for cars, which implies a free flow time per mile (<sup>_T_</sup> _fA_<sup>) of 2.4 minutes (Parry and Strand 2010 estimate a free flow speed of 28 miles per hour</sup> based on simulating a model of the Santiago road network). And based on averaging over estimates for average auto travel speeds from IAPT (2007), 12.4 miles per hour, and for the representative 6<sup>th</sup> of October road, 15.5 miles per hour, we assume the current auto speed (averaged across the GCMA and time of day) is 14 miles per hour, implying a time per mile ( _T_<sup>_A_</sup> ) of 4.3. Thus, the average delay is 1.9 minutes per mile and the marginal delay is 7.5 minutes per mile, or 0.13 hours per mile.

The gross hourly wage rate for Cairo is taken to be $1.55 (IMF 2007). Following US studies (e.g. see the review in Small and Verhoef 2007) we assume the value of travel time is one-half the wage rate, or $0.78 per hour. Multiplying by the marginal delay per mile gives a marginal congestion cost for autos of 9.7 cents per vehicle mile. Marginal congestion costs for other vehicles are obtained by scaling up according to their passenger car equivalents.

_Accidents_ . According to police-reported data, there were 730 road deaths in Cairo in 2006 (CAPMAS 2010).<sup>19</sup> Given the breakdown between pedestrian/cyclist versus vehicle occupant deaths is unavailable, we use the same share of pedestrian/cyclist deaths as reported in Parry and Strand (2010) for Santiago, namely 55 percent. We make the common assumption that all pedestrian/cyclist deaths are external. For single-vehicle accidents we make the usual assumption that fatality risks are internalized. To what extent injuries in multi-vehicle collisions are external is unsettled. All else constant, the presence of an extra vehicle on the road raises the likelihood that other vehicles will be involved in a collision, but a given collision will be less severe if people drive slower or more carefully in heavier traffic. To be conservative, we omit deaths in multi-vehicle collisions from external costs.

Multiplying external deaths (401.5) by the VSL ($600,000) gives a total cost of $241 million.<sup>20</sup>

> 19 We do not make any adjustment for possible under-reporting and in this regard are estimate of accident externalities may be conservative.

> 20 To be on the conservative side, we assume the same VSL for accident deaths as for local pollution deaths. In contrast, Small and Verhoef (2007) use a higher value for road deaths to account for the lower average age of someone killed by traffic as opposed to pollution (seniors are most at risk from local pollution).

33
suggesting that infection before marriage might explain some, but definitely not all, of the cases of couples where only the woman is infected. Certainly for the three other countries, and to a large extent for Ghana and Tanzania, HIV infection before the marriage or the union does not seem to be the main reason behind the substantial proportion of discordant female couples.

Polygyny is relatively frequent in Africa. 48.3 percent of married women in Burkina Faso are living in polygynous unions. That percentage is 30.5 in Cameroon, 22.7 in Ghana, 18.6 in Kenya and 9.7 in Tanzania. A priori, there is no particular reason to think that polygyny would be the reason behind the substantial portion of discordant female couples. The couple recodes used in the analysis are constructed starting from the interviewed women in the survey to whom the variables pertaining to her male partners are added. From the point of view of the woman, even in a polygynous union, she has only one regular male partner. The fact that this male partner has more than one wife might be a risk for HIV infection (the analysis in De Walque 2006, however, does not suggest so). But in the scenario where the male partner would be first infected by another wife and then would infect the wife surveyed, the couple formed by the latter wife and the husband will be considered as concordant positive. Another reason to investigate whether polygyny has an impact on the fraction of discordant female couples could be that women in a polygynous union are more likely to have extra-marital sex. De Walque (2006) reports results that suggest that this might be the case in some countries. Table 7 therefore reports discordance in HIV status among couples in non-polygamous unions. Excluding couples in polygamous unions does not modify the fraction of discordant female couples (between 30 and 40 percent in the five countries) among infected couples.

The existence of a bias in the coverage of HIV testing in the Demographic and Health Surveys could be another explanation for the substantial fraction of discordant female couples. Some individuals, who had been sampled for HIV testing have refused to be tested, were absent or there was a technical problem with the test (very rare). If the absence of a test is not random, this could be a source of bias. In an analysis at the level of the couple, this bias due to a less than complete coverage of the HIV test could be 13
# **Contents**

|**1**|**Intr**|**oduction**|**3**|
|---|---|---|---|
|**2**|**Lite**|**rature Review**|**4**|
||2.1|Subjective Well-Being and Labor Market Status<br>. . . . . . . . . . . . . . .|4|
||2.2|The Social Norm Efect of Unemployment . . . . . . . . . . . . . . . . . . .|5|
||2.3|Unemployment Duration and Subjective Well-Being<br>. . . . . . . . . . . . .|6|
|**3**|**Dat**|**a Description**|**7**|
|**4**|**Det**|**erminants of Subjective Well-Being**|**9**|
||4.1|Labor Market Status, Age, Income, Civic Status, Education and Health . .|11|
||4.2|The Social Norm Efect of Unemployment . . . . . . . . . . . . . . . . . . .|13|
|||4.2.1<br>Theory : a binary choice model with externalities . . . . . . . . . . .|13|
|||4.2.2<br>Empirical evidence : pooled data regressions<br>. . . . . . . . . . . . .|14|
|||4.2.3<br>Empirical evidence : results with panel data specifcation<br>. . . . . .|16|
||4.3|A note on the use of Pooled data vs Fixed Efect . . . . . . . . . . . . . . .|17|
|||4.3.1<br>Pooled Data<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|17|
|||4.3.2<br>Fixed Efects Regressions : individual fxed-efects<br>. . . . . . . . . .|18|
|**5**|**Un**|**employment Duration**|**20**|
||5.1|Determinants of duration<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .|20|
|||5.1.1<br>Unemployment duration and well-being: how to fnd the causality<br>direction ? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|21|
||5.2|The role of social norms in duration<br>. . . . . . . . . . . . . . . . . . . . . .|24|
|||5.2.1<br>Social norms and duration : empirical evidence from the duration<br>model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|27|
||5.3|Is search intensity related to change in GHQ ?<br>. . . . . . . . . . . . . . . .|28|
||5.4|Conclusion on unemployment duration . . . . . . . . . . . . . . . . . . . . .|29|
|**6**|**Con**|**clusion**|**30**|
|**7**|**Ref**|**erences**|**31**|
|**8**|**Ap**|**pendix**|**34**|
||8.1|Is the GHQ-12 a good measure of Well-Being?<br>. . . . . . . . . . . . . . . .|34|
||8.2|Duration model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|35|
|||8.2.1<br>Cox Proportional Hazard<br>. . . . . . . . . . . . . . . . . . . . . . . .|36|
||8.3|Regression Results and tables . . . . . . . . . . . . . . . . . . . . . . . . . .|37|

2
Table 4: Characteristics of the villages surrounding FIP and non-FIP subforests.

||(1)|(2)|(3)|
|---|---|---|---|
|Variable|Overall village<br>characteristics<br>Mean (SD)|Villages surrounding<br>FIP forests<br>Mean (SD)|Villages surrounding<br>non-FIP forests<br>Mean (SD)|
|Number of households|173.413|177.517|172.430|
||(83.996)|(63.819)|(88.346)|
|Share of farmer households|0.980|0.981|0.979|
||(0.072)|(0.045)|(0.077)|
|Share of households using organic fertilizers|0.552|0.479|0.569|
||(0.269)|(0.301)|(0.259)|
|Share of households using inorganic fertilizers|0.582|0.595|0.579|
||(0.297)|(0.316)|(0.294)|
|Share of households heads with primary education or better|0.149|0.118|0.156|
||(0.150)|(0.101)|(0.159)|
|Value of household assets (FCFA, in natural logarithms)|12.445|12.060|12.537|
||(0.987)|(1.014)|(0.962)|
|Distance to the main road (in km)|2.792|3.422|2.641|
||(1.171)|(1.280)|(1.096)|
|Distance to the village’s subforest (in km)|1.573|1.055|1.697|
||(1.422)|(0.640)|(1.528)|
|Distance from village’s subforest to the main road (in km)|8.977|9.286|8.903|
||(6.293)|(3.867)|(6.756)|
|N|150|29|121|

_Note:_ Means and standard deviations of the socio-economic characteristics of the sample of villages surrounding all forests are presented in Column (1); those for the subsamples of intervention and nonintervention villages are presented in Columns (2) and (3), respectively.

occurrences before the treatment on a set of village characteristics. The results of the cross-sectional OLS regression are shown in Figure 6. We find that forest fires tend to occur more frequently the poorer the village, the more intensively it makes use of chemical fertilizers and the less it relies on organic fertilizers. And we also find that forest fires tend to occur more frequently in subforests of villages that are better connected to the road network. These results are in line with the findings of Bandiera et al. (2017), Balboni et al. (2021) and Oliveira et al. (2007), who also document the relevance of agricultural dependence and access to markets as drivers of forest fires.

Second, we test for heterogeneous treatment effects, to explore whether the FIP intervention proved to be more effective in changing the behavior of some villages than of others. We use the SCM to estimate _αsrt_<sup>November</sup> , the subforest-specific treatment effect for subforest _s_ in region _r_ in post-intervention year _t_ ( _t > T_ 0). To construct synthetic con27
**<u>Table 6: Characteristics associated with total hours worked in the previous week for male workers</u>**

|||Male Youth|||Male Adults||
|---|---|---|---|---|---|---|
||1|2|3|4|5|6|
|Weekly earnings|1.04<br>[6.30]***|||0.2<br>[3.13]***|||
|Actual or predicted weekly<br>|||0.99<br>|||0.18<br>|
|earnings|||[5.32]***|||[2.68]***|
|||-015|-013||117|137|
|Own account workers||.<br>[0.43]|.<br>[0.37]||.<br>[5.73]***|.<br>[6.50]***|
|||-563|-562||-156|-142|
|Unpaid family workers||.<br>[16.05]***|.<br>[15.91]***||.<br>[4.77]***|.<br>[4.29]***|
|||-0.92|-0.78||0.4|0.64|
|Casual wage workers||[2.65]***|[2.23]**||[1.51]|[2.37]**|
|Age|2.28|4.2|4.05|-0.27|0.29|0.27|
||[2.78]***|[7.18]***|[6.90]***|[2.95]***|[5.15]***|[4.80]***|
|Age –squared|-0.05|-0.09|-0.09|0|0|0|
||[2.61]***|[6.16]***|[5.93]***|[2.66]***|[6.94]***|[6.67]***|
|Married|0.23|-0.46|-0.39|0.68|0.71|0.61|
||[0.51]|[1.37]|[1.16]|[1.99]**|[2.94]***|[2.53]**|
|Had training|-2.13|-1.94|-1.82|1.51|0.86|0.96|
||[1.87]*|[1.99]**|[1.85]*|[2.54]**|[1.72]*|[1.91]*|
|Below Primary Education|1.34|0.7|0.5|0.69|0.79|0.74|
||[2.22]**|[1.57]|[1.13]|[1.31]|[2.28]**|[2.13]**|
|Primary Education|<br>0.79|<br>0.33|<br>0.28|<br>-0.03|<br>0.78|<br>0.77|
||[2.01]**|[1.13]|[0.96]|[0.08]|[3.74]***|[3.67]***|
|Middle|-0.14|-1.23|-1.27|-0.05|0.59|0.54|
||[0.33]|[3.95]***|[4.04]***|[0.13]|[2.48]**|[2.24]**|
|Matric|<br>-1.17|<br>-1.38|<br>-1.5|<br>-2.3|<br>-0.07|<br>-0.16|
||[2.53]**|[4.14]***|[4.46]***|[7.20]***|[0.34]|[0.72]|
|Inter|-4.1|-3.71|-3.98|-4.93|-2|-2.11|
||[5.50]***|[6.61]***|[6.96]***|[12.01]***|[6.52]***|[6.65]***|
|Bachelors or Higher|-8.76|-7.06|-8.06|-7.44|-4.48|-5.07|
||[9.08]***|[9.04]***|[10.00]***|[19.79]***|[15.95]***|[15.50]***|
||2.28|1.82|1.82|1.61|0.65|0.72|
|Migration within 10 years|[3.71]***|[3.42]***|[3.42]***|[4.32]***|[2.18]**|[2.38]**|
|Manufacture|-2.51|1.17|1.09|-0.72|2.54|2.39|
||[4.15]***|[3.03]***|[2.80]***|[1.57]|[9.22]***|[8.50]***|
|Construction|-7.63|-3.4|-3.58|-6.8|-2.58|-2.73|
||[12.46]***|[7.11]***|[7.43]***|[15.13]***|[7.53]***|[7.88]***|
|Wholesale and retail|<br>2.51|<br>5.68|<br>5.68|<br>3.86|<br>6.92|<br>6.79|
||[3.72]***|[16.49]***|[16.45]***|[7.00]***|[31.57]***|[30.73]***|
|Transportation|3.62|6.33|6.19|3.62|7.12|7.16|
||[5.13]***|[13.32]***|[12.91]***|[7.52]***|[24.55]***|[24.49]***|
|Service|<br>-2.11|<br>1.54|<br>1.53|<br>-4.49|<br>-0.33|<br>-0.25|
||[3.26]***|[3.70]***|[3.65]***|[10.20]***|[1.30]|[0.98]|
|Other industry|-2.12|0.63|0.39|-3.39|0.71|0.57|

Note: 1. Absolute t statistics in bracket. 2. * significant at 10%; ** significant at 5%; *** significant at 1%. 3. Other variables in the model but not reported include: household size, household demographic structure, household head information, and province dummies.

27
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