rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 5: Demand and credit sensitivity on employment**

|||Cha|nge in full-|time perma|nent employm|ent||||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Demand Sensitivity|-0.127||||-1.259*|-0.974|0.764|-1.097|-1.794**|
||(0.63)||||(0.67)|(0.71)|(0.89)|(1.08)|(0.78)|
|Dependence on External Finance||-0.145|||0.642|||2.075***||
|||(0.22)|||(0.44)|||(0.54)||
|Cash Conversion Cycle|||-0.085|||-0.012||-0.200||
||||(0.12)|||(0.15)||(0.20)||
|% of investment from own fund 2007||||0.000|||0.001|-0.001||
|||||(0.00)|||(0.00)|(0.00)||
|Firm age||||||||-0.000<br>(0.00)||
|Small firm||||||||0.230<br>(0.14)||
|Foreign owned||||||||0.136<br>(0.12)||
|Small*DemandSensitivity|||||||||2.702***<br>(1.00)|
|Small*DependExtFin||||||||||
|Small*CashConCycle||||||||||
|Small*Ownfund07||||||||||
|Constant|-0.389***|-0.454***|-0.376**|-0.342***|-0.401***|-0.315*|-0.313*|-0.528**|-0.391***|
||(0.12)|(0.11)|(0.16)|(0.11)|(0.13)|(0.16)|(0.17)|(0.24)|(0.12)|
|Country fixed effects|√|√|√|√|√|√|√|√|√|
|Observations|1091|907|677|1021|479|505|684|254|1039|
|R-squared|0.03|0.03|0.05|0.04|0.07|0.06|0.06|0.24|0.04|

Nevertheless, we see severe across-firm declines in employment: our calculation shows that on average, a firm sheds 20.4 percent of its full-time employees between 2007 and 2009. This is quite interesting. We observe across the board declines in employment but they do not seem to clearly correlate with firms’ demand or credit sensitivities. One potential explanation is that employment decisions, particularly those about permanent full-time employment are more forward-looking. Since investing and training permanent workers is costly, firms only let the workers go when they do not see good prospect for firms’ products and services in the future. In other words, the employment decision today is driven by the expectation about the future demand for the firms’ products and services. Indeed we see some evidence of that the expected change in sales one year from June 2009 is significantly correlated with the change in employment (see table A8). In other words, if a firm expected sales to improve, they significantly retain more full-time permanent workers.

16
Given that we do not model factor prices (wages and rents) at the sector level, we make the strong assumption that factor markets equalize across the sectors. While the transition dynamics of each sector price will be different, the balanced growth path will be the same and will grow in line with inflation expectations. Note that aggregate marginal costs grow at the rate of the inflation target in the long run.

Finally, it is now easy to solve for the level of our sector variables. For sectoral valueadded at constant prices simply multiply the sector shares with total value-added:

# 3 Mitigation channels

We provide an update on the modeling of the power sector in MFMod. Previously all renewables were lumped into a single category. Now, different renewable sources are included. Further changes reflect the soft-linking of MFMod and EPM in cases where a power planning model exists.

First, we describe the endogenous demand for energy, the import and production decisions. In the second part of this section, we describe the soft-linking to power planning models.

Electricity production, imports and consumption data are typically available for energy quantities (e.g., terajoules, or kilowatts). The description in PAKMod converted energy units into national account equivalents. While this was useful, it needlessly complicated matters. It was also assumed that supply met demand at any point in time, an unreasonable assumption for countries struggling with energy generation. Consequently, this section simplifies the energy representation and introduces supply constraints into the model.

## 3.1 Determining energy production and consumption

### 3.1.1 Aggregate production

This section describes the implementation of a power sector module without recourse to a technoeconomic energy model. Four distinct features are important: (i) Modeling electricity 7
**<u>Table 5. Predictive Power of MAPS I Yields</u>**

|_Dependent Variable:_<br>_Sample:_|**C**<br>_All_|**C Yield MAPS**<br>_Parcel Panel_|**II**<br>_Not Parcel_<br>_Panel_|**S**<br>_All_|**R Yield MAPS II**<br>_Parcel Panel_|**‡**<br>_Not Parcel_<br>_Panel_|
|---|---|---|---|---|---|---|
|CC Yield MAPS I|0.121**|0.143***|0.088||||
|SR Yield MAPS I||||-0.002|0.007|-0.092|
|Constant|582.775***|545.015***|647.650***|1849.667***|1566.281***|2504.644***|
|**N**|240|158|82|240|158|82|
|**R**<sup>**2**</sup>|0.034|0.051|0.016|0.000|0.000|0.001|

**Notes:** Robust standard errors; ‡ MAPS II cross-sectional regressions exclude plots subject to full-plot crop cutting.

# **Correlation coefficients:**

_Parcel Panel Only; excludes households with full plot crop-cut in MAPS II_

||CCI|CCII|SR I|SR II|
|---|---|---|---|---|
|CC MAPS I|1||||
|CC MAPS II|0.2263|1|||
|SR MAPS I|-0.0085|-0.0330|1||
|SR MAPS II|0.0029|0.0251|0.0116|1|

34
**Table 1.  TTB Policy-Imposing Economies and Affected Imports in 2011**

|||**_Product lin_**<br>**_imp_**|**_e share of_**<br>**_orts_**||**_2011 Tra_**|**_de-weighted_**<br>**...****_from_**|**_share of imports_**<br>**...from****_other_**|**...from**<br>**_high-_**|
|---|---|---|---|---|---|---|---|---|
|||**...subject**<br>**to all TTBs**<br>**_in 2011_**<br>**(1)**|**...subject**<br>**to all TTBs**<br>**_in 2001_**<br>**(2)**|**...subject**<br>**to****_all_**<br>**_TTBs_**<br> **(3)**|**...subject**<br>**to****_AD_**<br>**_only_**<br>**(4)**|<br>**_China_**<br>**subject**<br>**to**<br>**all TTBs**<br>**(5)**|**_emerging_**<br>**_economies_**<br>**subject to**<br>**all TTBs**<br>**(6)**|**_income_**<br>**_economies_**<br>**subject to**<br>**all TTBs**<br> **(7)**|
|**_G20_**|**_emerging_**|3.2*|1.5*|3.3*|3.0*|10.8†|2.3†|1.7†|
|**_ec_**|**_onomies_**||||||||
|1.|India|6.9|2.8|6.3|5.8|21.9|3.0|2.7|
|2.|Turkey|6.9|1.5|4.3|2.6|16.0|4.2|2.1|
|3.|China|1.4|0.3|3.2|3.2|.|1.1|3.9|
|4.|Argentina|3.3|2.4|2.5|2.5|6.8|1.9|1.2|
|5.|Brazil|1.9|1.2|1.7|1.7|4.4|1.0|1.2|
|6.|Indonesia|1.8|0.9|1.2|0.8|2.4|1.5|0.8|
|7.|South Africa|0.6|1.3|0.3|0.3|0.4|0.3|0.3|
|8.|Mexico|1.1|23.4|0.3|0.3|0.5|0.7|0.2|
|**_Othe_**|**_r emerging_**||||||||
|**_econ_**|**_omies_**||||||||
|1.|Pakistan|0.3|.|1.4|1.4|2.5|2.0|0.7|
|2.|Peru|2.5|0.8|1.3|1.3|3.6|1.3|0.3|
|3.|Thailand|0.6|<0.1|1.2|1.2|1.2|0.8|1.3|
|4.|Colombia|0.9|0.4|0.5|0.5|3.4|0.1|<0.1|
|5.|Malaysia|<0.1|<0.1|0.2|0.2|<0.1|0.4|0.1|
|6.|Philippines|0.1|0.3|<0.1|<0.1|0.1|<0.1|<0.1|
|7.|Chile|<0.1|0.8|<0.1|<0.1|0.0|<0.1|<0.1|
|**_G20_**|**_high-income_**|1.9|1.8|2.2|2.1|4.7|1.9|1.0|
|**_econ_**|**_omies_**||||||||
|1.|United States|5.8|4.6|4.0|3.9|8.3|3.6|2.1|
|2.|European Union|3.1|2.3|1.8|1.7|4.2|1.2|0.8|
|3.|Canada|1.1|2.2|0.7|0.7|2.1|0.5|0.4|
|4.|Australia|0.7|0.6|0.5|0.4|1.2|0.1|0.3|
|5.|South Korea|0.6|0.6|0.4|0.4|0.6|0.9|0.1|
|6.|Japan|<0.1|0.1|<0.1|<0.1|<0.1|<0.1|<0.1|
|**_Othe_**<br>**_econ_**|**_r high-income_**<br>**_omies_**||||||||
|1.|New Zealand|0.4|0.1|1.1|1.1|0.4|0.3|1.4|
|2.|Israel|0.3|0.1|0.3|0.3|1.7|0.1|0.1|
|3.|Taiwan, China|0.4|0.1|0.2|0.2|0.8|<0.1|<0.1|

Notes: Shares of nonoil imports. Ranked by column (3) within each category of policy-imposing economy. TTB=temporary trade barriers, AD=antidumping. *Aggregation does not include Mexico as policy-imposing economy, for reasons discussed in the text. †Aggregation does not include China or Mexico as policy-imposing economies.

26
|DUMwood||(0.008)**<br>0.397|(0.008)**<br>0.390||(0.008)**<br>0.389||(0.007)**<br>0.385|
|---|---|---|---|---|---|---|---|
|||(0.020)**|(0.020)**||(0.021)**||(0.025)**|
|DUMmineral||-0.045|-0.038||-0.047||-0.003|
|||(0.055)|(0.054)||(0.054)||(0.051)|
|DUMclothing||0.231|0.237||0.242||0.194|
|||(0.012)**|(0.012)**||(0.012)**||(0.013)**|
|Observations|23631|23631|23631<br>23631|23631|23631|23631|23631|

Marginal effects are presented in the table. Standard errors in parentheses, significant at 5%, ** significant at 1% Table 9 presents the results of the Heckman regression without using the results of the threshold estimation. The dependent variable of the selection equation takes the value of 1 when preferences were requested and zero otherwise. The results are different from previous results when the threshold value of the preferential tariff reduction was taken into account. The coefficient of duty difference becomes higher and significant. Furthermore, the coefficient of distance is slightly smaller. Overall, these results show that if low levels of duty reduction are included in the analysis, it is more the magnitude of duty reduction that matters and less other factors. Thus previous empirical work emphasising the importance of the magnitude of the preferences is somewhat misleading since once the costs of requesting preferences are covered the preferential margin does not significantly influence the amount of preferences requested any more.

# **Conclusions**

This chapter investigated the impact of the preferential scheme on exports of non least-developed ACP countries to the EU. It also examined the importance of tariff reduction on requesting preferences by undertaking endogenous threshold estimation. While the analysis was limited to EU preferences towards non-least developed ACP countries, the main results are likely to be relevant for most preferential schemes.

Our results showed that the magnitude of preferences offered has a significant impact on the uptake of preferences. As intuitively expected, the higher the value of preferences offered, the higher the probability that preferences are requested. However, once the trader decided to request preferences, the preference margin (the difference between third country and preferential tariffs) does not influence the magnitude of preferential trade volumes.

28
Snilstveit, Birte, Jennifer Stevenson, Daniel Phillips, Martina Vojtkova, Emma Gallagher, Tanja Schmidt, Hannah Jobse, Maisie Geelen, Maria Grazia Pastorello, and John Eyers. 2015. “Interventions for Improving Learning Outcomes and Access to Education in Low- and Middle-Income Countries: A Systematic Review.” London: International Initiative for Impact Evaluation (3ie).

Sobel, J. (2005) “Interdependent preferences and reciprocity,” _Journal of Economic Literature_ , 43(2): 392-436.

<mark>Subrahmanyam, Kaveri, Robert E. Kraut, Patricia M. Greenfield, and Elisheva F. Gross. 2000. “The Impact of Home Computer Use on Children's Activities and Development.”</mark> _<mark>The Future of Children</mark>_ <mark>10(2): 123-144. https://doi.org/10.2307/1602692</mark> <mark>Tauchmann, Harald. 2014. “Lee (2009) treatment-effect bounds for nonrandom sample selection.”</mark> _<mark>The Stata Journal</mark>_ <mark>14(4): 884-894.</mark> <mark>Vigdor, Jacob L., and Helen F. Ladd. 2010. “Scaling the Digital Divide: Home Computer Technology and Student Achievement.” NBER Working Paper No. 16078. Washington, DC: National Bureau of Economic Research.</mark> <mark>Wartella, Ellen A., and Nancy Jennings. 2000. “Children and Computers: New Technology—Old Concerns.”</mark> _<mark>The Future of Children</mark>_ <mark>10(2): 31-43. http://psycnet.apa.org/doi/10.2307/1602688</mark> World Bank. 2003. _World Development Report 2004: Making Services Work for Poor People_ . World Bank Publications.

World Bank. 2017. _World Development Report 2018: Learning to Realize Education’s Promise_ . World Bank Publications.

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Disabilities, Indigenous Peoples, and Sexual and Gender Minorities (English). Washington, D.C. : World Bank Group. <u>http://documents.worldbank.org/curated/en/590781562905434693/Equity-and-Inclusion-inEducation-in-World-Bank-Projects-Persons-with-Disabilities-Indigenous-Peoples-andSexual-and-Gender-Minorities</u>

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38
15 reported daily wage in agriculture for month t, St is a set of monthly dummies, **Pt** is the number of works in progress in month t, and ut is an i.i.d. disturbance term.<sup>19</sup> The lagged dependent variable was included to preclude a spurious estimate of bs as a result of serial correlation in both the L and P series. One would generally expect that the notional demand for EGS employment depends positively on W. Since agricultural employment is an alternative to EGS employment, we would expect L to depend negatively on WA under the null. And the monthly dummies are included to capture the seasonal nature of agricultural activity.

Since April 1987, EGS authorities have been keeping more detailed monthly records of employment, wages paid, and works in progress. Our employment data after April 1987 will be estimates of actual person-days of employment, rather than attendances, as were used in the previous section. At the time this study began, these data were available for the period from April, 1987 to February, 1990. For the period up to December 1989 monthly data on agricultural wage rates were also available from the Director of Economics and Statistics, Government of Maharashtra.20 OLS estimates of the parameters of interest are given in Column 1 of Table 5. The estimated coefficient on P is positive and significant. The estimate of the coefficient on W is significant, but does not have the sign one would expect of the notional demand function for EGS work. The elasticity w.r.t. the agricultural wage rate is not significantly different from zero.

While these results are suggestive of rationing, they do not warrant an immediate rejection of the null. There may be sources of bias in the OLS estimates which would invalidate any inference based on them.
quality indicators have the expected positive effects but agricultural assets affect child education negatively. We also demonstrate that the negative effect of agricultural assets is more pronounced among rural children and children of crop producers, which we argue stems from the higher opportunity cost of their schooling.

In what follows, section 2 sets out our theoretical model, which builds on the Basu et al. (2010) model of child labor and landholding, which in turn adopts the agricultural household model of Singh et al., (1986). Our primary extension is to introduce an education production function that constrains the household’s utility maximization problem. In section 3, we describe our data – three waves of the Tanzania National Panel Survey (NPS)<sup>3</sup> -- and empirical model. In section 4, we discuss both the descriptive and the empirical results. Rather than assuming undifferentiated effects of assets, we categorize assets into three types – household durables, agricultural, and housing quality – and estimate how each type affects children's educational outcomes. The findings demonstrate that different types of assets have differential effects. Section 5 discusses the policy implications of our conclusions.

# **2. Theoretical model and results**

While the hypothesis that different types of assets can have differential effects on child education is intuitively appealing and empirically testable, there has been significantly less consideration of the theory of this relationship. A handful of studies model the asset-education relationship using variants of the agricultural household model, but they do not differentiate between types of assets. Whenever the expected return to schooling is less than the return to child labor, providing households with more assets can have adverse effects on child education (Cockburn and Dostie, 2007). That child labor adversely affects child education is a common finding (Basu et al., 2010; Haile and Haile, 2012). Basu

> 3 The Tanzania NPS is part of the LSMS-ISA program which aims to marry complex consumption-based household surveys with plot-crop detailed agricultural surveys. The Tanzania NPS data, along with details on the sample and instrument design, are publicly available.

4
|Question|Average|Median|Variance|n|
|---|---|---|---|---|
|Please rate the key hindrances to strengthening your ag<br>supervision:|ency’s cap|acity to con|duct effective|systemic|
|a. Developing proper staff skills|3.56|4.00|1.49|27|
|b. Paying proper salaries|2.86|3.00|1.68|28|
|c. Rethinking the organization of supervision|3.07|3.00|1.70|28|
|d. Strengthening the powers, legal protection, and|||||
|<br>independence of the supervisor|3.48|4.00|1.49|27|
|e. Better coordination with the central bank and/or other|||||
|supervisory agencies|3.37|4.00|1.32|27|
|f. Better coordination with foreign supervisors|3.30|4.00|1.37|27|
|g. Better use of market data|3.26|3.00|0.89|27|
|h. Other|4.00|4.00||1|
|a. Analysis and monitoring of compliance and trends<br>Please rate the relative importance placed on the follow|ing superv|isory activiti|es:||
|observed from reported prudential reports|4.25|4.00|0.49|28|
|b. Reviewing the accuracy of reports of regulatory|||||
|<br>compliance|3.79|4.00|1.14|28|
|c. Assessment of risk profiles strategic directions financial|||||
|, ,<br>conditions and the adequacy of internal controls and risk<br>management given the overall characteristics (size,|||||
|complexity and risk profile) of the institution involved|4.46|5.00|0.41|28|
|<br>What in your view is the most important hindrance to th|e use of su|pervisory di|scretion?||
|a. The civil code|1.81|1.00|1.62|27|
|b. The agency’s legal mandate|2.89|3.00|1.95|27|
|c. Political pressures|2.11|2.00|1.10|27|
|d. Industry pressures|2.22|2.00|1.26|27|
|e. Lack of or weak legal protection of supervisory officials|2.37|2.00|1.86|27|
|f. Other|3.75|3.50|0.92|4|
|Please rate the relative importance of the following pot<br>regarding systemic oversight:|ential cross|-border sou|rces of conc|ern|
|International financial groups|4.06|4.00|0.64|18|
|Regional financial groups|3.63|4.00|1.36|19|
|Off-shore entities|2.89|3.00|2.10|18|
|Other|3.00|3.00|4.00|3|
|Please rate the effectiveness of the following mechanis<br>cross border exchanges of information:|ms, both fo|rmal and inf|ormal, for pr|oviding|
|Legally binding agreements|3.20|4.00|2.17|15|
|Memoranda of understanding|4.33|4.50|0.94|18|
|Letters of commitment|2.85|3.00|1.81|13|
|Ad hoc contacts|3.94|4.00|0.46|16|
|Others|5.00|5.00||1|

35
||||||||Root-|mean|-squar|e erro|r|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||n=|400|n=|1,000|n=|3,000|n= 1|0,000|n= 3|0,000|
|pT|pY|∆ATE|∆LAT E<br>∆AT E|BP|IV|BP|IV|BP|IV|BP|IV|BP|IV|
|0.1|0.1|0.08|<br>2.60|0.40|3.06|0.29|2.23|0.16|0.24|0.07|0.16|0.04|0.14|
|0.1|0.3|0.15|1.12|0.41|4.07|0.34|3.47|0.25|0.33|0.15|0.17|0.09|0.10|
|0.1|0.5|0.16|0.46|0.40|4.25|0.39|3.95|0.33|0.38|0.24|0.21|0.16|0.14|
|0.1|0.7|0.12|0.15|0.25|3.57|0.29|3.02|0.35|0.37|0.37|0.21|0.29|0.15|
|0.1|0.9|0.05|0.02|0.09|2.93|0.09|2.18|0.10|0.23|0.15|0.13|0.21|0.09|
|0.3|0.1|0.06|1.34|0.26|1.10|0.17|0.23|0.08|0.11|0.04|0.06|0.02|0.04|
|0.3|0.3|0.14|1.54|0.30|1.61|0.21|0.31|0.12|0.17|0.07|0.11|0.04|0.09|
|0.3|0.5|0.16|1.11|0.31|2.65|0.24|0.33|0.15|0.17|0.09|0.09|0.05|0.06|
|0.3|0.7|0.12|0.60|0.32|2.93|0.28|0.34|0.18|0.17|0.11|0.10|0.06|0.07|
|0.3|0.9|0.05|0.16|0.17|1.17|0.19|0.22|0.25|0.12|0.28|0.07|0.21|0.06|
|0.5|0.1|0.06|0.53|0.23|0.50|0.22|0.19|0.17|0.10|0.08|0.06|0.04|0.04|
|0.5|0.3|0.13|1.16|0.27|0.76|0.20|0.26|0.12|0.14|0.06|0.08|0.04|0.05|
|0.5|0.5|0.16|1.39|0.28|0.87|0.20|0.26|0.12|0.15|0.07|0.10|0.04|0.08|
|0.5|0.7|0.13|1.16|0.26|0.70|0.19|0.24|0.11|0.14|0.06|0.07|0.04|0.05|
|0.5|0.9|0.06|0.53|0.23|0.48|0.22|0.18|0.17|0.10|0.08|0.06|0.04|0.04|
|0.7|0.1|0.05|0.16|0.18|0.81|0.20|0.23|0.24|0.12|0.28|0.07|0.20|0.06|
|0.7|0.3|0.12|0.60|0.32|1.40|0.28|0.32|0.18|0.17|0.11|0.10|0.06|0.07|
|0.7|0.5|0.16|1.11|0.31|1.53|0.23|0.33|0.15|0.17|0.08|0.09|0.05|0.06|
|0.7|0.7|0.14|1.54|0.29|1.32|0.20|0.29|0.12|0.17|0.07|0.11|0.04|0.09|
|0.7|0.9|0.06|1.34|0.26|0.67|0.16|0.20|0.08|0.10|0.04|0.06|0.02|0.04|
|0.9|0.1|0.05|0.02|0.09|2.43|0.08|0.72|0.09|0.24|0.14|0.13|0.22|0.08|
|0.9|0.3|0.12|0.15|0.24|4.51|0.29|0.94|0.35|0.36|0.39|0.20|0.30|0.14|
|0.9|0.5|0.16|0.46|0.40|4.53|0.39|1.37|0.33|0.38|0.23|0.21|0.16|0.14|
|0.9|0.7|0.15|1.12|0.42|4.73|0.34|1.66|0.25|0.35|0.16|0.17|0.09|0.10|
|0.9|0.9|0.08|2.60|0.39|3.45|0.27|0.92|0.15|0.23|0.06|0.16|0.03|0.14|

Table 5: Root-mean-square error of ∆<sup>ˆBP</sup> ATE<sup>andˆ∆IVfortrueATEasafunctionofpTand</sup> pY , in bivariate probit model simulations with no covariates and ρ = 0.7.

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