rafmacalaba/datause-encoder-data huggingface dataset:
Whereby _u_ is the received unemployment benefits and _c_ is the fixed search costs born in the previous step. On (5), we add the present values of the returns to search, and obtain: Equating (6) and (4) and solving for w� gives: Equation (7) suggests that the reservation wage increases with the unemployment benefit and decreases with the search cost. It increases with the wage offer as well with the endowment of personal characteristics.

In the empirical literature, the corpus of research of the reservation wage has been mainly determined by data availability. Studies on the theme exist since the 1980s, but mainly on developed economies and frequently lacking extensive temporal or even empirical dimension. For example, Bloemen and Stancanelli (2001) only identify this basic elements of the job search theory through theoretical and auxiliary restrictions. Some empirically-grounded studies for developed countries include: Kiefer and Neumann (1979); Franz (1982); Lancaster and Chesher (1983); Feldstein and Poterba (1984); Maani and Studenmund (1986); Jones (1988); Blau (1991); Hui (1991); Gorter and Gorter (1993); Hogan (1999); Christensen (2001); Bloemen and Stancanelli (2001); Prasad (2003); Addison et al. (2004; 2009); Shimer and Werning (2007); Krueger and Mueller (2016); Schmieder et al. (2016); Le Barbanchon et al. (2019). In general, the empirical literature is positioned in two strands: i) the determinants of reservation wages; and ii) the nexus between reservation wages, unemployment duration and unemployment insurance. The first strand documents the significance of a set of personal and institutional characteristics which are relevant for the reservation wages in various setups; while the second strand documents that both unemployment duration and insurance potentially affect the reservation wage, despite the sign and the direction of the causality in case of the unemployment duration has been frequently contested.

# **3. Data**

We make use of two yearly waves of the Labor Force Survey (LFS), 2016 and 2017. We restrict the sample to working-age population, i.e. 15-64. The reservation wage, as survey question, has been asked for the first time in 2016. This is advantageous in the sense that this study is the first to use this quite new information, though disadvantageous in the sense that panel structure of our data cannot be imposed, despite LFS – for the rest of the variables – is on disposal since 2006. However, each year is composed of four quarterly survey waves, providing a total of eight periods for analysis. Further however, households are interviewed at most four times, every second quarter, and then they drop out.

4
In this case, GMM results suggest that the study’s IV model is robust. Previous studies have deployed GMM estimators in a dynamic panel setting to analyze the trade-growth nexus (Calderón, Cantú, and Zeufack 2020; Chang, Kaltani, and Loayza 2009; Easterly and Levine 2001; Felbermayr 2005; Lee, Ricci, and Rigobon 2004; Ramzan et al. 2019). Thus, similar results between this study’s IV approach and GMM shows that the study’s finding are robust. First, the coefficients on the lagged dependent variable in all the models are found to have a value of less than one and to be statistically significant at the 1% level, providing strong evidence of conditional convergence (Table 4). In addition to internal instruments used in the system GMM, the study has included the list of instruments used in the IV1, and IV2 strategies. The results confirm the finding of a statistically significant average trade effect on GDP per capita. Therefore, the results from the IV strategies are robust to model specification and estimation technique. The system GMM results show that a 1 percent increase in trade intensity increases GDP per capita by 0.60 percent (Table 4—column 1). This coefficient remains fairly stable when including instruments from IV1 and IV2 to the GMM specification. The estimation yields coefficients of 0.58, and 0.45 percent for GMM-IV1, and GMM-IV2 respectively (Table 4—columns 2 and 4). Following Brueckner and Lederman (2015), we also estimated a dynamic model using the IV approach (see Table A15). Given that the model includes country-fixed effects, the lagged dependent variable is instrumented by the second lag of GDP per capita to minimize the bias in the estimated coefficient for lagged GDP per capita. The findings suggest that GDP per capita increase by 0.52 and 0.44 percent (IV1 and IV2 respectively) following a 1 percent increase in adjusted trade share. GMM results provide coefficients that are within the confidence intervals to the IV approach.

**Table 4.** <u>GMM - Impact of trade intensity on GDP per capita</u>

|VARIABLES|(1)<br>GMM<br>Log GDP/Cap|(2)<br>GMM-IV1<br>Log GDP/Cap|(3)<br>GMM -IV2<br>Log GDP/Cap|
|---|---|---|---|
|Long Term log Adj Trade(<sup>β</sup><br>1 −γ<br>⁄<br>)|0.6047***|0.5814***|0.4514***|
||(0.1329)|(0.1406)|(0.0901)|
|log GDP/Capt−1(γ)|0.909***|0.916***|0.889***|
||(0.0234)|(0.0228)|(0.0216)|
|Log Adj Trade (β)|0.0549***|0.0486***|0.0503***|
||(0.0162)|(0.0147)|(0.0131)|
|Invest|0.0104***|0.0109***|0.0102***|
||(0.00248)|(0.00260)|(0.00257)|
|Human capital|0.00113|0.00136|0.00260**|
||(0.000987)|(0.00101)|(0.00101)|
|Inflation|-0.000152***|-0.000156***|-0.000155***|
||(4.22e-05)|(4.26e-05)|(3.58e-05)|
|Observations|1,308|1,305|1,102|
|Number of id|165|165|150|
|Number of Inst|136|138|140|
|N-IV < N-Groups|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|
|F stat|46475|57500|70270|
|F stat p-value|0.000|0.000|0.000|
|Hansen J stat|134.1|135.5|127.4|

18
1. Dr. Timnothy Smeeding and Cheikh Kane provided valuable help and comments and should have been listed in the section entitled, "Acknowledgments".

2. Data on pension spending as a share of GDP which appears in several places in the book have been revised for several countries. These changes are explained in Section II.A of this Annex.

3. In Box Table 4.6 on page 135, the units were omitted and should have read millions of Colombian Pesos.

4. Zambian rate of return in table 6.4 on page on page 224 should have been -23.4 rather than -55%. Rates of return for Singapore and Malaysia should read 3.0 and 4.6 respectively. In the same table, Iglesias and Acuna were mistakenly omitted from the sources.

5. On page 247, first sentence of the first full paragraph should read, "The two mandatorv pillars together might aim to replace about 60 percent of gross average lifetime wage..." 6. On page 250, figure 7.1, the data on non-wage income sources refer to heads-of-households over age 65 in the various OECD countries.

|7. On page 294, Issue Brief table 1.1 under the section "Mandatory Target", the last two columns should read as follows:|
|---|
|Middle income<br>High-income|
|74<br>74|
|57<br>57|
|50<br>_50_|
|40<br>40|
|40<br>50|

- Also, the second note to the table should read: "The mandatory pension target is 40 percent of gross final year earnings,..."

8. On page 295, the first sentence in the last paragraph should have read, "For example, the government might require saving or contributions that would replace about 40 percent of the worker's gross wage, with a floor at about a third of the gross economy-wide average wage..." 9. On page 312, first full paragraph, the last 2 sentences, "negatively" should read "positively" and "positively" should read "negatively". The reference is to Mitchell et. al., (1994), not to Sunden and Mitchell.

10. In Appendix Table A.6 of the book on page 362, the Hungarian share of total revenues from payroll taxes, investments and general revenues should have read, 89.4, .01 and 10.5 percent, respectively.

I1. Payroll tax rates found in Appendix A.7, pp. 364-368, include errors described in Section II.F of this Annex.

12. In Appendix Table A.9 on page 370, informnation received after publication suggests that Chinese administrative costs are not comparable to other countries because a greater-than-average share of administration costs are borne by firms, in particular, state-owned enterprises.

13. In Figure 1.6, the year in which China is projected to reach 18 percent of the population over 60 is 2023, not 2026 as shown in the parentheses next to the bar.

14. South Africa was mistakenly listed as a contribution-related scheme. The main scheme is means-tested.

125
However, the Census is the only representative source of data available in many countries. In addition, extrapolating the entry age structure from surveys (such as NIS – 4\% of immigrants - or NSIP - a sample of 150,000 persons out of more than 25,000,000 adult immigrants - for the US) can be misleading. The number of observations can be very small for countries with few emigrants; this is typically the case of small countries which, on the other hand, are precisely the ones most affected by the brain drain in relative terms.

# **4.2. Results for Small States**

Table 3 presents the results for the brain drain from small states for all those who migrated, irrespective of their age (as in Table 2), for those who migrated before age 12, 18 and 22, and also the ratio of the latter to all those who migrated (22+/0+). Focusing on that ratio, we see for 2000 that it is equal to 70.1% for small states as a whole, with little variation across the three population groups (Groups 1 to 3), and about the same as that for developing as a whole. It is about 8% smaller for high-income countries and 2% smaller for the world average.

The ratio declined from 1990 to 2000 for small states as a whole (from 74.0 to 70.1%) as well as for _all_ small states groups (with the major decline for the largest group and for East Asia and Pacific), implying that a larger share of migrants obtained their degrees at home. On the other hand, the ratio increased for _all_ other country groups.

Table 4 shows the brain drain by small state for the various age-of-entry groups. The countries are ranked by ascending population size. Table 4a presents the same figures, but organized in descending order of brain drain rates for all migrants, irrespective of their age (BD 0+). The correlation between that group (BD 0+) and the last group (BD 22+) is over 95%, with identical ranking of the top six countries (Guyana, Grenada, Saint Vincent and the Grenadines, Trinidad and Tobago, Saint Kitts and Nevis, and Samoa), with 9 of the top 10 countries for BD 22+ also in the top 10 countries for BD 0+, and with the same top 20 countries in both groups.

The average brain drain for the 46 small states is 39.8%. It is 31.9% for those university educated individuals who migrated at age 22 or later. In other words, for every 100 individuals these countries educate at the university level, 32 live outside their home country and 68 stay. Another way of putting it is that these countries need to educate 147 individuals to have 100 working in their home country.

23
Table 12: Impact of Heat Exposure on Different Outcomes

||Dep|endent Variab|le|
|---|---|---|---|
|S|tandardised|Relative|Average|
||Score|Score (%)|Score (%)|
||(1)|(2)|(3)|
|T_>_33C|_−_0_._009<sup>_∗∗∗_</sup>|_−_0_._104<sup>_∗∗∗_</sup>|_−_0_._090<sup>_∗∗∗_</sup>|
||(0_._002)|(0_._024)|(0_._021)|
|T 30-33C|_−_0_._004<sup>_∗∗∗_</sup>|_−_0_._040<sup>_∗∗_</sup>|_−_0_._035<sup>_∗∗_</sup>|
||(0_._001)|(0_._016)|(0_._014)|
|T 27-30C|_−_0_._001|_−_0_._006|_−_0_._005|
||(0_._001)|(0_._012)|(0_._010)|
|T 24-27C|_−_0_._002<sup>_∗∗∗_</sup>|_−_0_._024<sup>_∗∗∗_</sup>|_−_0_._021<sup>_∗∗∗_</sup>|
||(0_._001)|(0_._009)|(0_._008)|
|T 21-24C|_−_0_._002<sup>_∗∗∗_</sup>|_−_0_._026<sup>_∗∗∗_</sup>|_−_0_._023<sup>_∗∗∗_</sup>|
||(0_._001)|(0_._007)|(0_._007)|
|T_<_18C|_−_0_._003<sup>_∗∗_</sup>|_−_0_._031<sup>_∗_</sup>|_−_0_._029<sup>_∗_</sup>|
||(0_._001)|(0_._017)|(0_._015)|
|Outcome Mean:|0.01|55.21|49.07|
|Female Control|X|X|X|
|**Fixed Effects**||||
|Year|X|X|X|
|School|X|X|X|
|Stream|X|X|X|
|Observations|2,132,635|2,132,635|2,132,635|
|R<sup>2</sup>|0.247|0.287|0.320|

Notes. Estimates are from linear regressions using panel fixed effects. The dependent variables are listed as column headers. All regressors include the number of days in the temperature bin for the school-year cycle: June-May. All regressions control for the number of days in the precipitation bins. Errors are clustered at the school level. *p _<_ 0.1 **p _<_ 0.05 ***p _<_ 0.01.

31
25 administrative districts. At the primary level there are 62,681 cooperative societies, with a total membership of nearly 3.5 million (Table 2.6).

**Table 2.6** Cooperative Membership (1994)

|PROVINCE|COOPERATIVES|MEMBERS|
|---|---|---|
|Punjab|46,729|2,234,453|
|Sindh|3,998|747,670|
|NWFP|7,242|405,472|
|Baluchistan|752|36,856|
|Azad Kashmir|3,757|41,327|
|Northern areas|383|16,466|
|Total|62,861|3,482,244|

Source: Federal Bank for Cooperatives (1994).

By June 1994 the FBC had lent Rs. 30.8 billion, of which only Rs. 458 million was for the medium term. Cooperative credit is essentially for short-term purposes, mainly to finance agricultural inputs. The annual level of lending has been about Rs. 3 billion in the last three years. The disbursement of medium-term credit during this period has been less than Rs. 20 million on average.

In principle, recovery of loans should not pose a problem, because loans are guaranteed by provincial governments. Yet lending in Sindh was suspended for five years, and in Baluchistan for ten years because of persistent default. The NWFP also defaulted in 1994, and loan operations had to be suspended. The recovery rate in the Punjab (which receives 90 percent of loans) and the Northern Areas, however, has been satisfactory (about 90 percent in 1993/94 after scheduling), which has helped to sustain FBC lending operations. Loans amounting to Rs. 1058 million outstanding against Sindh (Rs 419 million), NWFP (Rs 560 million), Azad Kashmir (Rs 56 million) and Baluchistan (Rs 23 million) had to be rescheduled.
**TABLE 17:  ANALYSIS OF DRIVERS OF FIRM LEVEL AVERAGE DIRECT COST (US CENTS PER TON-KILOMETER)**

|Independent Variable|Coefficient|Robust Standard Error|
|---|---|---|
|Fleet size|-0.03554***|(0.01252)|
|Fleet size squared|1.36e-04***|(4.14e-05)|
|Driver salary|0.29581*|(0.15651)|
|Other worker salaries|0.00346*|(0.00204)|
|Percentage used tires|-0.00643|(0.00589)|
|Use of FE practices|-0.36015|(0.43747)|
|Use of FE technologies|-0.29203|(0.44764)|
|Percentage of workers temporary|-.2.19701**|(1.03832)|
|Utilization per truck (1,000 km)|-0.09916***|(0.02312)|
|Utilization squared|4.35e-04***|(1.22e-04)|
|Interest rate on bank loans|0.01705|(0.02243)|
|Average wait time per ton-km.|147.3966**|(58.11952)|
|Mean overload weight|-0.15855***|(0.0501)|
|Mean empty backhaul|-0.00623|(0.0043)|
|Mean informal payment per ton-km|0.8096***|(0.1068771)|
|Quality/training requirements=1|0.84421|(0.57148)|
|Panama|0.46891|(0.56898)|
|Costa Rica|-1.07811*|(0.57373)|
|El Salvador|1.35624|(0.86242)|
|Guatemala|1.26533|(0.72399)|
|Nicaragua|0.82172|(0.97676)|
|Percentage fleet truck|-1.57503|(3.68608)|
|Percentage fleet tractor|-3.72497|(3.4932)|
|Percentage of volume on international routes|n.s./excluded||
|Mean kilometer length of routes|n.s./excluded||
|Age of firm|n.s./excluded||
|Fleet age|n.s./excluded||
|Mean quality road used|n.s./excluded||
|Average speed|n.s./excluded||
|Bank primary source of finance=1|n.s./excluded||
|Constant|10.54082***|(3.30802)|
|Observations|225||
|R-squared|0.5961||
|*** p<0.01, ** p<0.05, * p<0.1|||

25
## where

# _VG = CES[CES(SG,UG),KG]._

(8) Employment levels of both categories of workers are treated as predetermined policy variables. This specification of the production process of the public sector good makes it possible to analyze the effects of changes in government employment. Public sector layoffs, for instance, will influence the rest of the economy both through quantity effects on the labor market as well as a reduction in public sector output.

Note also that, as indicated below, public sector wages for skilled workers are set equal to the efficiency wage paid to that category of workers in the private formal sector. Wages for unskilled workers in the public sector are, by contrast, set equal to the minimum wage; unskilled workers are therefore "off" their supply of labor. We will assume that "excess" employment (or disguised unemployment) of unskilled labor prevails in the public sector (so that output is never constrained), as documented in some studies of the labor market in developing countries.<sup>8</sup>

# **3.1.4 Urban Formal Private Production**

As with the production of the public good, private formal production also employs skilled and unskilled labor. In addition, physical capital is also included as an input in the production process. An important step in specifying the production technology consists of defining the degree of substitutability among inputs. We assume-as suggested by some of the empirical evidence-that skilled labor and private physical capital have a higher degree of complementarity (lower degree of substitution) than physical capital and unskilled workers. In order to account explicitly for these differences in the degree of substitutability among inputs, we adopt a nested CES production structure. Specifically, gross production of the private formal-urban sector, _Xp,_ is taken to be given by the sum of value added, Vp, and intermediate consumption: where 8See Agenor (1996), who discusses the role of political factors in the determination of public sector employment.

15
9 each member per activity domain. This greatly reduces the potential risk of bias (such as overstating male and understating female agricultural labor participation), which might occur if there were a lack of probing about labor engagement in agriculture across household members.

The assumption of gender neutrality in _level_ reporting is likely also somewhat tenuous when it comes to self- versus proxy reporting.<sup>14</sup> Men may systematically over- or underreport the labor contribution of women, and vice versa. As Bardasi et al. show, however, the direction of potential bias is not clear a priori. The authors find that response by proxy has no effect on female labor statistics (compared with self-reports), but that it yields substantially lower male employment rates, mostly due to underreporting of the agricultural activity. They further find that the large impacts of proxy responses on male employment rates are attenuated when proxy informants are spouses or individuals with some schooling.

This highlights a second important feature of the respondent (in addition to their gender), i.e. their familiarity with each member’s agricultural labor input. In the surveys supported under the LSMS-ISA initiative, when possible, plot-level modules are administered to the manager of the plot, who is arguably the most knowledgeable individual about the household labor input on the plot.

To assess the robustness of the findings, the sensitivity of the estimates of the female labor share in crop production is assessed in accordance with the gender and knowledge profile of the respondents across plots in a given household, in particular whether the information was provided by a female respondent in the majority of the plots,<sup>15</sup> and whether the share of labor hours provided

> 14 See Blair, Menon and Bickart (1991) for a review of the reasons why there may be discrepancies between proxy and self-reports. However, their experiments do not relate to employment issues.

> 15 In Malawi, 47 percent of the sample households have only male respondents, while 52 percent have only female respondents. In Nigeria, 79 percent of the sample households have only male respondents, and 19 percent have only female respondents.
Table 2: Severe Depression by 4Ps Status and Survey Wave

||(1)<br>Severe<br>Depression|(2)<br>Severe<br>Depression|(3)<br>Severe<br>Depression|(4)<br>Severe<br>Depression|
|---|---|---|---|---|
|4Ps|0.0036||||
||(0.010)||||
|Wave=3|0.33***|0.33***|0.33***|0.33***|
||(0.037)|(0.037)|(0.038)|(0.037)|
|Wave=3 x 4Ps|-0.099**|-0.098**|-0.094**|-0.093**|
||(0.044)|(0.044)|(0.045)|(0.044)|
|Observations|2906|2906|2906|858|
|Baseline Mean Outcome|0.01|0.01|0.01|0.02|
|Fixed Efects|None|Household|Household|Individual|
|Controls|None|None|Age/Gender|N/A|
|Sample|All|All|All|Panel<br>indiv.|

Notes: Standard errors (clustered at the municipality level) are in parentheses * p _<_ 0 _._ 1, ** p _<_ 0 _._ 05, *** p _<_ 0 _._ 01.

The coefficient on the wave 3 dummy is 0.33 ( _p <_ 0 _._ 01), indicating that non-4Ps households experienced a large increase in severe depression relative to the baseline mean of 0.01. The coefficient on the interaction term indicates this increase was 10 percentage points smaller ( _p <_ 0 _._ 05) for 4Ps beneficiaries. In other words, consistent with Figure 2, these regressions reveal that a gap in depression rates only emerges during the pandemic, with the non-4Ps suffering from higher rates of depression than the 4Ps.

Results remain similar when we add household fixed effects (column 2) and subsequently, age and gender fixed effects (column 3). In column 4, we include individual fixed effects, which means we restrict to panel respondents who responded to the depression question in both waves. Despite the much smaller sample and controls for time-invariant, individual-specific unobservables, our conclusions remain the same. In Appendix Table A3, we show similar patterns for moderate depression and any depression, but none of the interaction terms are statistically significant. In Appendix Table A4, we show that results are almost identical when we drop any non-4Ps households who did not receive the SAP payment (27% of the non-4Ps sample). This implies that the lower likelihood of SAP receipt by non-4Ps is not driving the results reported in Table 2.

# **4.2 Interpretation of Results**

What do these findings tell us about our main research question (that is, whether the impact of 4Ps on mental health differs in normal and crisis times)? To provide a concrete interpretation of _β_ 3 in the above 12