rafmacalaba/datause-encoder-data huggingface dataset:
|Observations|688<br>688|688|
|---|---|---|

_Note:_ All coefficients refer to the mean marginal effects—that is, the effects for an average firm—based on the logistic regression model presented in Table D.6. Reference levels of categorical variables in parentheses. Standard errors are clustered on the ward level in parentheses: *** p<0.01 ** p<0.05 * p<0.1

|**Table D.8: Regression results for investmen**|**t volumes**<br>(1)|(2)|(3)|
|---|---|---|---|
||Log (total|Log (disaster related|Log (disaster|
|VARIABLES|investment)|investment)|unrelated investment)|
|**Region**(Dar es Salaam)||||
|Dodoma|0.604**|-0.465|0.626**|
||(0.272)|(0.320)|(0.273)|
|Tanga|1.091***|0.455|0.859**|
||(0.296)|(0.424)|(0.336)|
|**Direct disaster risk perception**(no risk)||||
|Low risk|0.341|-0.246|0.378|
||(0.256)|(0.382)|(0.334)|
|Moderate risk|0.757**|1.017**|0.752**|
||(0.296)|(0.482)|(0.325)|
|High risk|0.671*|0.052|0.712*|
||(0.396)|(0.520)|(0.421)|
|Very high risk|0.891**|-0.183|1.048**|
||(0.365)|(0.482)|(0.415)|
|**Indirect disaster risk perception**(no risk)||||
|Low risk|0.099|0.715*|-0.047|
||(0.352)|(0.387)|(0.430)|
|Moderate risk|0.431|0.731*|0.283|
||(0.380)|(0.398)|(0.481)|
|High risk|-0.091|0.310|-0.310|
||(0.381)|(0.473)|(0.494)|
|Very high risk|0.026|0.818*|-0.102|
||(0.393)|(0.485)|(0.469)|
|**Experienced flooding on business**||||
|**premises**: Yes|-0.037|-0.729*|0.032|
||(0.258)|(0.431)|(0.269)|
|Firm age (years)|0.018*|0.003|0.012|
||(0.009)|(0.008)|(0.013)|
|Number of employees|- 3.64e-04***|-3.79e-04***|-3.05e-04 **|
||(9.30e-05)|(8.86e-05)|(1.20e-4)|
|Log (total operational costs, $/month)|0.732***|0.585***|0.750***|
||(0.0504)|(0.0781)|(0.0554)|
|**Time since last serious disaster**||||
|(months)|-9.69e-04|7.19e-03**|-1.15e-03|
||(3.36e-03)|(3.22e-03)|(3.28e-03)|
|Constant|0.198|1.348**|0.148|
||(0.486)|(0.623)|(0.592)|
|Sector fixed effects|YES|YES|YES|
|Observations|361|168|309|
|R-squared|0.507|0.519|0.504|

47
**Figure 6: Smartphone and computer use requirements of the SDS’s 30 occupations**

_Share of occupations with at least half of workers reporting use of a smartphone or computer is required (%)_ <!-- Start of picture text --> 90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Use smartphone Use computer<br>Source: SDS.<br><!-- End of picture text -->

**Figure 7: Smartphone and computer use requirements of the SDS’s 30 occupations by skill level**

_Share of workers reporting the use of a smartphone, computer, or specialized software is required (%)_ <!-- Start of picture text --> Elementary occupations<br>Plant and machine operators, and assemblers<br>Craft and related trades workers<br>Service and sales workers<br>Technicians and associate professionals<br>Professionals<br>0% 20% 40% 60% 80% 100% 120%<br>Specific software is used Smartphone is used Computer is used<br>Source: SDS.<br>Low<br>Semi<br>High<br><!-- End of picture text -->

**<mark>Work from home</mark>** <mark>. Given the widespread use of computers in high-skilled occupations, workers in these occupations are also the most likely to be able to work from home if internet and computers are available (</mark> **<mark>Figure 8</mark>** <mark>). University, higher education, and secondary school teachers are the occupations with the largest share of workers saying that their jobs could be effectively carried out from home.</mark>

14
A Duration Analysis of CONALEP consultant firm which designed the sample process. The distribution of the 1998 CONALEP survey was as follows. Table 4 shows that the response rate is high.

**Table 4 Distribution of the 1998 Sample by Cohort**

|**Cohort**|**Interviewed**<br>**Number**|**Graduates**<br>**%**|**% Completed**<br>**Interviews**|
|---|---|---|---|
|90-93|779|14.0|59.0|
|91-94|951|17.1|72.0|
|92-95|1,127|20.2|85.4|
|93-96|1,268|22.7|96.1|
|94-97|1,449|26.0|109.8<sup>3</sup>|
|Total|5,574|100.0|84.5|

_Source_ : LEVANTA C.

# **5. Methodology**

Given that the CONALEP graduate tracer surveys of 1994 and 1998 are comparable, this paper analyzes the impact of modular courses implemented by CONALEP after 1992. The cohorts from the survey of 1994 are considered to be graduates of the pre-reform program including cohorts who graduated in 1991, 1992, and 1993. Additionally, cohorts from the survey of 1998 are considered to be from the post-reform program including cohorts who graduated in 1994, 1995, 1996, and 1997.

The Kaplan-Meier and the proportional hazard models were calculated to analyze whether graduates from the cohorts of the 1998 survey of the reformed program found a job faster than those from the cohorts of the 1994 survey of the pre-reform program. The variable of interest in this duration analysis is the length of time to find a job after graduating from CONALEP, conditional on being unemployed or searching for a job. The functions of interest are the survival and hazard functions: 3 This value, as listed in CONALEP data sets, appears to exceed 100% because the number of responding graduates exceeded the goal number.

10
_Additional References Online Appendix S6_

- Frölich, Markus, and Martin Huber. 2017. “Direct and Indirect Treatment Effects-Causal Chains and Mediation Analysis with Instrumental Variables.” _Journal of the Royal Statistical Society B_ 79 (5): 1645–66. https://doi.org/10.1111/rssb.12232.

- Hicks, Raymond, and Dustin Tingley. 2011. “Causal Mediation Analysis.” _The Stata Journal_ 11 (4): 605–19.

- Imai, Kosuke, Luke Keele, and Dustin Tingley. 2010. “A General Approach to Causal Mediation Analysis.” _Psychological Methods_ 15 (4): 309–34. https://doi.org/10.1037/a0020761.

- Imai, Kosuke, Luke Keele, Dustin Tingley, and Teppei Yamamoto. 2011. “Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies.” _American Political Science Review_ 105 (4): 765–89. https://doi.org/10.1017/S0003055411000414.

- Knowler, Duncan, and Ben Bradshaw. 2007. “Farmers’ Adoption of Conservation Agriculture: A Review and Synthesis of Recent Research.” _Food Policy_ 32 (1): 25–48. https://doi.org/10.1016/j.foodpol.2006.01.003.

- Laajaj, Rachid, and Karen Macours. 2017. “Measuring Skills in Developing Countries.” Policy Research Working Paper 8000. Washington D.C.: The World Bank. https://openknowledge.worldbank.org/bitstream/hadle/10986/26250/WPS8000.pdf?sequence=1&isAllowed=y

36
Table 7 Income Gains and Inequality Costs with Trade Policy Preference Reversals

|Trad<br>_ε∗_|e Policy Prefer<br>Lower Bound|ence Reversals<br>Upper bound|Potential <br>Lower Bound|Reversals<br>Upper Bound|
|---|---|---|---|---|
|A) Countries with Income Gains|||||
|Burkina Faso<br>0.6|0.5|0.7|||
|Bangladesh<br>0.6|0.5|0.7|||
|Gambia, The<br>1.2|1.1|1.3|||
|Togo<br>1.2|1.2|1.3|||
|Benin<br>1.5|1.4|1.6|||
|Nigeria<br>1.9|1.8|2.0|||
|Vietnam<br>2.0|1.8|2.1|||
|Kenya<br>2.5|2.4|2.7|||
|Ethiopia<br>3.1|2.7|3.6|||
|Mozambique<br>3.5|2.9|8.5|||
|Papua New Guinea<br>4.4|3.1|–|||
|Liberia<br>4.5|3.6|–|||
|Guatemala<br>7.0|5.2|–|||
|Malawi<br>7.1|4.1|–|||
|Sierra Leone|||4.0|–|
|Bolivia|||4.1|–|
|Niger|||6.0|–|
|Nicaragua|||6.3|–|
|Cˆote d’Ivoire|||7.0|–|
|Georgia|||7.1|–|
|Tanzania|||8.9|–|
|Nepal|||9.3|–|
|Ecuador|||9.9|–|
|B) Countries with Income Losses|||||
|Mali<br>0.4|0.2|0.5|||
|Mauritania<br>1.6|1.5|1.8|||
|C) Countries with multiple (potentia|l) reversals||||
|Countries with Income Gains|||||
|Burundi<br>0.1|0|0.2|5.6|7.1|
|Countries with Income Losses|||||
|Sri Lanka<br>0.3|0.2|0.4|||
|8.9|7.2|–|||

Notes: Authors’ calculations. The table presents estimates of the trade- _ε_<sup>_∗_</sup> , the cut-off value of inequality aversion at which there is a reversal of trade policy preference in terms of social welfare. The standard errors are bootstrapped using 1000 replications.

51
# Table 4

## Comparison of Costs of Norms and Actual Expenditures for Diagnostic Tests of Selected Diagnoses (DR$)

|Nu<br>Tests<br>Diagnoses<br>in|mber of<br>Specified<br>Norms|Expected<br>Cost of<br>Meeting<br>Norms|Actual<br>Expenditure on<br>Tests Specified<br>in Norms|Total<br>Expenditure<br>on<br>Norm-Specified<br>&Unspecified<br>(Other) Tests|
|---|---|---|---|---|
|Hernia|9|$606.73|39.05|$44.35|
|Acute Tonsilitis|1|231.29|47.47|38.93|
|Appendicitis|5|121.60|37.09|55.63|
|Cataract Surgerya|6|216.07|26.90|69.70|
|Hypertensionb|13|3,591.95|217.87|200.16|
|Diarrhea|0|0.00|19.75|n.a.|
|Total/Average|7|4,767.64|388.18|409.20|

## Source: The Urban Institute

- a. Outpatient only. No emergency cases. Unclear when inpatient tests complet-I The data suggest it may be prior to admittance.

- b. Outpatient and emergency only.
of the loans in my dataset. Naturally, doing so leads to a somewhat weaker first-stage relationship between changes in government spending and changes in predicted disbursements, with first-stage F- statistics ranging from 9.8 to 19.5 (as opposed to 12.6 to 28.1 using the default instrument). However, the first-stage fit remains respectable, and the point estimates of the multiplier change only slightly to around 0.5 as compared with around 0.4 in the default specification. This robustness check suggests that over-fitting of the predicted disbursement instrument is not a major concern in my benchmark results.

Another possible objection to the predicted disbursements instrument is it indirectly includes some information on future country-specific shocks, as it is based on typical disbursement rates averaging across all loans within the creditor/decade/region bins, including future loans to the country in question. This potential violation of the exclusion restriction is unlikely to be very important given the large number of loans within each bin (recall that the median bin includes 65 loans). However, as a further robustness check, I reconstruct the instrument, but now excluding all other loans to the country in question when calculating average disbursement rates. This eliminates any potential country-specific information in the predicted disbursement instrument that comes through the inclusion of the country in question in the calculation of average disbursement rates. The fourth set of results in Table 5 show that this robustness check has only minimal effects on my benchmark estimates. The first-stage F- statistics are actually slightly higher than in the benchmark results in Table 4, and the estimates of the multiplier are slightly smaller (ranging from 0.33 to 0.37).

In summary, the benchmark results in this section suggest that the one-year government spending multiplier is in the vicinity of 0.4, and moreover is reasonably precisely estimated. Specifically, I find that the multiplier is in most cases significantly different from zero and also significantly less than one. The statistical identification of these multipliers comes primarily from a strong first-stage relationship between fluctuations in the predetermined component of disbursements on loans from multilateral, as opposed to bilateral, creditors. These findings are robust to variants on the instrument designed to address possible concerns about over-fitting of predicted disbursements, and the possible incorporation of future information country-specific information in the calculation of typical disbursement profiles.

19
Liu, M., and H. Yamagata (2000), “Trends Among Foreign Graduate Faculty at US Medical Schools”, American Association of Medical Colleges analysis brief, Vol.3, No. 1, June 2000, available at http://www.aamc.org/data/aib/aibissues/aibvol3_no1.pdf .

Market Watch Optical Sector Report (2002), “Cataract Market: New Extraction Technology and Under-Penetrated International Markets Hold Promise for Growth”, December 2002, also available at <u>http://www.optistock.com/mw/2002_12all.htm</u> .

Mattoo, Aaditya (2001), “Can No Competition Policy be Better than Some Competition Policy?”, International Journal of Industrial Organization 19: 55-77.

Medicare Payment Advisory Commission (2001), “Medicare Physician Payment Rates Compared to Rates Paid by the Average Private Insurer, 1999-2001”, study by Direct Research, LLC for the MEDPAC, also available at <u>http://www.medpac.gov/publications/.</u> McMahon, Graham T. (2004), “Coming to America – International Medical Graduates in the US”, New England Journal of Medicine, 350(24): 2435-37.

Pauly, Mark V. (1968), “The Economics of Moral Hazard: Comment”, American Economic Review 58(3): 531-37.

R (Watts) v Secretary of State for Health (2003), European Law Reports of Cases in the U.K. and Ireland (EULR), Vol. 8, Issue 1, January 2004.

Robinson, James C. (2004), “Consolidation and the Transformation of Competition in Health Insurance”, Health Affairs 23(6): 11-24.

Robinson, James C. (2003), “Hospital Tiers in Health Insurance: Balancing Consumer Choice with Financial Incentives”, Health Affairs web exclusive W3-135.

Srinivasan, T.N. (2005), “Information Technology Enabled Services and India’s Growth Prospects”, forthcoming in Brainard and Collins (eds) Offshoring White-Collar Work – The Issues and Implications, Brookings Trade Forum 2005.

The Guardian (2004), “Sun, sea, sand ... and surgery”, May 11, 2004.

Warner, David & L. R. Jahnke (2001), “Toward Better Access to Health Insurance Coverage for U.S. Retirees in Mexico”, Salud Publica de Mexico, Vol. 43(1).

Warner, David (1999), “Getting What You Paid For: Extending Medicare to Eligible Beneficiaries in Mexico”, US-Mexican Policy Studies Program Policy Report No. 10, LBJ School of Public Affairs, University of Texas.

28
**TABLE A10: Capital market openness and development relationships without country fixed effects.** Table 8 is replicated without country fixed effects. Constant, R- squared, and number of observations not reported for brevity. Standard errors are clustered at the country-year. *** p<0.01, ** p<0.05, * p<0.1

||(1)|(2)|(3)<br>Qua|(4)<br>ntile Reg|(5)<br>ression|(6)|
|---|---|---|---|---|---|---|
||OLS|0.1|0.25|0.5|0.75|0.9|
||PME|PME|PME|PME|PME|PME|
|Panel A of Table 8|||||||
|De-jure fnancial openness index|0.36|0.00|0.02|0.06|0.05|-0.07|
||(0.47)|(0.02)|(0.02)|(0.04)|(0.06)|(0.12)|
|Private sector credit (% of GDP)|-0.78*|0.00|0.02|0.01|0.03|-0.26*|
||(0.41)|(0.02)|(0.02)|(0.04)|(0.05)|(0.14)|
|Panel B of Table 8|||||||
|De-jure fnancial openness index|-0.03|-0.00|-0.04|-0.11*|-0.20**|-0.60**|
||(0.33)|(0.04)|(0.06)|(0.06)|(0.09)|(0.26)|
|Private sector credit (% of GDP)|0.41|0.05|0.02|0.04|0.22|0.23|
||(0.42)|(0.05)|(0.07)|(0.09)|(0.18)|(0.35)|
|Country fxed efects|No|No|No|No|No|No|

55
B are estimated to have a lower probability of being earners, participating in the labor force, and working by 17.8, 16.0, and 16.2 percentage points, respectively. Further, women in Group C face a penalty that is nearly twice as large, with a lower probability of being earners, participating in the labor force, and working by 30.4, 28.3, and 30.6 percentage points, respectively.

**Table 3. Summary of the robustness tests results: Marginal effects of the correlation between the availability of childcare and labor market outcomes**

||(1)|(2)|(3)|
|---|---|---|---|
||Earner|LFP in last 30|Worked in last|
|||days|30 days|
|**1.**<br>**Limit the age of women to prime age: 25 to 49 years**<sup>**a**</sup>||||
|_Ref: A. No child or youngest child is 6 years and older_||||
|B. Youngest child is 0–5 years and childcare available|-0.178**|-0.160**|-0.162**|
||(0.074)|(0.073)|(0.073)|
|C. Youngest child is 0–5 years and no childcare available|-0.304***|-0.283***|-0.306***|
||(0.073)|(0.074)|(0.073)|
|Observations|<br>829|<br>829|<br>829|
|Individual controls|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|
|Regional controls|Yes|Yes|Yes|
||Yes|Yes|Yes|
|**2.**<br>**Limit the age of child to 0–12 years**<sup>b</sup>||||
|_Ref: A. Youngest child is 6–12 years_||||
|B. Youngest child is 0–5 years and childcare available|-0.250***<br>(0.066)|-0.240***<br>(0.065)|-0.233***<br>(0.066)|
|C. Youngest child is 0–5 years and no childcare available|-0.376***|-0.353***|-0.388***|
||(0.066)|(0.066)|(0.066)|
|Observations|<br>758|<br>758|<br>758|
|Individual controls|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|
|Regional controls|Yes|Yes|Yes|
|**3.**<br>**Limit the age of child to 0–8 years**<sup>**c**</sup>||||
|_Ref: A. Youngest child is 6–8 years_||||
|B. Youngest child is 0–5 years and childcare available|-0.205***<br>(0.076)|-0.226***<br>(0.076)|-0.230***<br>(0.074)|
|C. Youngest child is 0–5 years and no childcare available|<br>-0.358***|<br>-0.348***|<br>-0.337***|
||(0.076)|(0.077)|(0.076)|
|Observations|<br>599|<br>599|<br>599|
|Individual controls|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|
|Regional controls|Yes|Yes|Yes|

Note:<sup>a</sup> Full table provided in Appendix B, Table B3.<sup>b</sup> Full table is in Appendix B, Table B4.<sup>b</sup> Full table is in Appendix B, Table B5.<sup>d.</sup> *** 1%, ** 5% and * 10% level of significance.<sup>e.</sup> Standard errors are in parentheses.<sup>f.</sup> Survey weight applied.

Next, instead of including all women in the analysis, we limit the sample to those women whose children are 0–12 years (Subsample 2) and 0–8 years (Subsample 3). Generally speaking, the younger the children, the more intense the childcare need and the harder it is for the mother to participate in the labor market in the absence of any childcare support. By excluding the mothers of older children from the 17
activities. As shown in Hallward-Driemeier and Gajigo (2010), higher levels of empowerment can influence the economic opportunities women pursue. The increase in legal age of marriage is particularly relevant here. When marriage age is delayed, the future bargaining position of these young women would be significantly improved through higher education and/or an increase in own resources accumulated in the intervening years.

Thus, supporting hypotheses that are tested are that the reform would also give relatively greater encouragement for women to work full-time and that as their economic opportunities increased, so too would investments in their education such that their participation in higher educated occupations would rise. The effects are likely to be larger for younger women who are better positioned to adapt to the change than older women, particularly if in longstanding marriages, that have already made choices that make shifting occupations more difficult.

We use two rounds of DHS data that were collected in 2000 and 2005. In 2000, no region or chartered city in the country had implemented the law, but by 2005, five regions and chartered cities<sup>4</sup> had done so. The reforms now apply across all regions and chartered cities. These surveys contain detailed demographic and labor market information at the individual level that makes them ideal for this topic. Using the difference-in-difference estimation technique, we found that this reform in family code has a strong effect in increasing women’s share in occupations that are non-home based, paid work, year-round employment, and those with average higher educational requirement, though the strength of the law’s effect is heterogeneous across different age and marital categories. The effect of the reform is particularly strong for young, unmarried women. It appears that the provision of the law that increases the legal age of

> 4 To include both regions and chartered cities together, we refer to ‘geographic areas’.

6
_L. Fox and A.M. Oviedo_ _Institutions and Labor Market Outcomes in Africa_ that the share of manufacturing in GDP between 2002 and 2006 in 17 countries does not exceed 14 percent (an exception is Swaziland, where it reaches 37 percent). At a first glance, the growth rate of manufacturing appears to have been higher than GDP growth in most countries over the period 2002-2006 (see Table 2); however, the average growth of manufacturing for the entire region is somewhat lower than GDP growth (weighting countries by their corresponding GDP). Figure 1 depicts the aggregate growth of GDP and manufacturing, showing that they have been similar over the period.

<!-- Start of picture text --> Figure 1<br>GDP and Manufacturing Growth (%)<br>6.0<br>5.0 4.8 4.5 5.1 4.7 5.14.5 4.8<br>4.0 3 . 9<br>3.3 3.4<br>3.0<br>2.0<br>1.0<br>0.0<br>2002 2003 2004 2005 2006<br>GDP Manufacturing<br><!-- End of picture text --> Note: GDP and manufacturing value added growth rates are aggregated using a weighted average, where each country is weighted by its total GDP in constant 2000 US Dollars. Source: Authors’ calculations using WDI (2007) data.

In terms of employment, manufacturing is also a very small sector, accounting for less than 10 percent of total employment on average.<sup>13</sup> For example, between 1998 and 2003 total wage and salary employment grew at 6 percent per year in Burkina Faso, however, its share in total employment went from 2.9 percent to 3.7 percent only.<sup>14</sup>

# INSERT TABLES 1-3

The good news for SSA is that growth in the manufacturing sector is bringing jobs. Despite having different economic conditions, in all countries (except Kenya) that were surveyed since 2003, more that 50 percent of firms survey reported a larger number of employees than they had three years ago, and most reported double-digit growth in employment (Table 3). Data from two countries suggest that this employment growth is a very recent trend - both Ugandan and Tanzanian firms show most of their expansion since 2003, and Senegal, also surveyed in 2003,

> 13 For example, the share of industry employment in total employment is about 7 percent in Madagascar and Uganda (World Bank, 2007).

> 14 See Fox and Gaal (2008).

8
7 16. The income effect of changes in the terms of trade (TTADJ) thus is the difference between the capacity to import and real exports as ordinarily calculated. It is written as: TTADJt - XTTADJt - Xt The terms of trade adjustment is simply a way of taking into account the effects of changes in export and import pr'ces upon potential real expenditures. If the export and import price indices are equal, the adjustment will be zero. In the base year, therefore, the terms of trade adjustment will always equal zero and GDP will equal GDY. If the terms of trade have improved (XPI > XPI) since the base year, then the adjustment will be positive (and vice versa).

17. Domestic saving must also be adjusted for the income effects of changes in the terms of trade. Domestic saving is properly defined as income less consumption rather than as production less consumption.

This is identical to investment less the resource gap (RG) where the resource gap is defined as exports adjusted for the terms of trade less imports.

GDSt - i RGt 15) RGt - Mt- XTTADJt 16) Note that consumption and investment are not affected by the introduction of the terms of trade adjustment. Equation 11, however, can be re-written as: ct - GDYt + RGt - It 11)

# The Constrained Savings Case

18. The RMSM is often run as a one-gap (trade gap) model. When this is done, the model is closed as indicated in paragraph 13 and equation 11: consumption is a residual. Savings is also a residual since it is defined as income less consumption. The RMSM, however, was originally intended to be, and can scill be used as, a two-gap model in which either the trade or the savings gap may prove to be the binding constraint. Particularly where there are socio-political constraints on consumption and spending, it is desirable to specify a realistic maximum marginal savings rate. This is the same as choosing a minimum marginal propensity to consume. When the savings rate binds, net imports must exceed the level required to fill the trade gap because it is necessary to have sufficient resources available to meet both required investment and minimum consumptio'x levels.

19. To see if the savings gap (rather than the trade gap) is binding, one must solve for the level of imports necessary to guarantee the