rafmacalaba/datause-encoder-data huggingface dataset:
- 16 -

the stock of physical capital, and g+6 is assumed to be equal to 0.05 1/; _sk_ is the ratio of physical capital investment to GDP; _m_ is the ratio of military expenditures to GDP; _sh_ is a proxy for the ratio of human capital investment to GDP; f is a proxy for the degree of restrictiveness of the economy's international trade system; w is the proxy for the incidence of wars; (t represents time-specific factors; pi represents country-specific factors; and E is a white-noise error term.

In order to allow for the indirect effect of military spending on growth via its impact on productive investment, we extend the model of our earlier paper to include a second equation which specifies the ratio of investment in fixed capital as a function of the rate of investment in human capital, _sh;_ the restrictiveness of the trade system, f; the incidence of wars proxy, w; and the military spending ratio, m. The investment equation is: _l( ski, t) =_ 77n ln(ni,t _+g +6 ) +77mln(mi, t) +77h_ ln(shi) +r7f ln(f i) +i7wln(wi) _+ Hi_ + £i,t (2) As already noted, we use Hewitt's annual data on the ratios of military expenditure for all the countries in our sample. However, owing to limitations on the availability of data for some countries on the other 1/ The assumption that g+6=0.05 follows Mankiw, Romer, and Weil (1992). We found that although changes in this number affect the estimated _en,_ they do not significantly affect the other estimated coefficients.
**Table 13. Descriptive Statistics: Aggregate Consumption**

||**No Violent**<br>**Shock**|**Violent**<br>**Shock**|**Difference**<br>**between means**<br>**(t-test)**<br>**All**|
|---|---|---|---|
|Households annual consumption|1.454.108|1.604.235|***<br>1.518.754|
|expenditure per capita (in $COP))|(886.110)|(1.036.302)|(956.440)|
|Number of households|1.994|1.508|3.502|

Standard errors in parenthesis Source: CLS-WILL: First wave – household questionnaire.

43
These predicted consumption figures are then used to calculate poverty and inequality measures for every ward in the country. The poverty mapping data are missing for the islands of Zanzibar and Pemba, and so we restrict our analysis to mainland Tanzania.

Poverty maps are typically used by policy makers to distribute funding across large administrative units, such as districts or provinces, but not smaller ones, such as wards, because the standard errors for the poverty and inequality estimates become unacceptably large as the population size of the units decline. However, in this study, we are only using these estimates at the ward level to estimate the relationship between poverty and the likelihood of applying for and receiving TASAF II funds. In this sense, the larger standard errors at the ward level should only cause attenuation bias, which would drive the effects presented in Sections 5 and 6 to zero and therefore decrease our ability to draw inferences with confidence.

# _3.3.Electoral Data_

The final data used in the national analysis are from the 2005 presidential, parliamentary, and ward councilor elections. All data are available online at the website of the National Electoral Commission of Tanzania.<sup>7</sup> The presidential and parliamentary results are at the constituency level<sup>8</sup> , the councilor elections are at the ward level, and the electoral data is merged with the TASAF II institutional data and the poverty maps by ward. The elections took place prior to the announcement of the awards of TASAF II projects, and hence we take political outcomes as predetermined, and seek to understand how voter registration and turnout, as well as voting patterns, relate to application, approval, and funding patterns.

We calculate _voter turnout_ and _voter registration;_ the former given by the ratio of valid votes to registered voters in a ward, and the latter by the ratio of registered voters to population in a ward. Both of these variables are intended to measure the political activism of a ward, a feature which may make a unit more attractive as a target of pork-barreling, or may indicate a heightened level of collective action.<sup>9</sup> We also define independent variables based on these ward-level electoral outcomes that let us test several dimensions across which funding and political behavior may be linked. Given

> 7 Data available from http://www.nec.go.tz/

> 8 The constituency is a region defined for electoral purposes. The 232 constituencies in Tanzania are typically larger than a ward but smaller than a division.

> 9 Because the voter registration rate is also driven by demographics (in that one must be of age to vote in order to register), we control for the ward-level dependency ratio in our regression models that include voting variables.

8
**Figure 1: Foreign Direct and Portfolio Investment Inflows**

<!-- Start of picture text --> (USD, billions)<br>For the World, Portfolio Dominates FDI Inflows<br>5000<br>4000 Portfolio<br>Investment<br>3000<br>2000<br>1000 FDI<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>-1000<br>Bank<br>-2000 Lending<br>Source: BOP, IMF<br><!-- End of picture text --> <!-- Start of picture text --> (USD, billions)<br>For Developing Countries, FDI Dominates<br>1000<br>800<br>FDI<br>600<br>400 Portfolio<br>Investment<br>200<br>Bank<br>0 Lending<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>-200<br>Source: BOP, IMF<br><!-- End of picture text --> <!-- Start of picture text --> (USD, billions) For South Asia Region (SAR), FDI Dominates, but not by much<br>60<br>FDI<br>50<br>40<br>30 Portfolio<br>20<br>Bank<br>10 Lending<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>-10<br>-20<br>Source: BOP, IMF<br><!-- End of picture text --> 6
primarily reformed for the better. While many have followed the Basel guidelines and strengthened capital regulations and empowered supervisory agencies, existing evidence does not suggest that this will improve banking-system stability, enhance the efficiency of intermediation, or reduce corruption in lending. While some countries have reformed their regulations to empower private monitoring, consistent with the third pillar of Basel II, there are many exceptions and reversals along this dimension. Furthermore, many countries intensified restrictions on the non-lending activities, which existing evidence suggests hurts banking-system stability, lowers bank development, and reduces the efficiency of financial intermediation. Indeed, our simulations advertise the case in two countries. Malaysia empowered official supervision, reduced private monitoring regulations, and imposed greater restrictions on the nonlending activities of banks after its crisis. Mexico, while also strengthening official supervisory power, substantively increased regulations that enhance private monitoring and reduced restrictions on bank activities. While many other factors change in a country and many institutional characteristics shape the efficacy of bank regulations, our initial and preliminary estimates suggest greater optimism about Mexico’s reforms than Malaysia’s. In sum, our examination of the latest data on bank regulation around the world does not provide a uniformly positive view of recent reforms.

While our preliminary examination of the data challenges the confident proclamations of many observers about improvements in bank regulation and supervision, the qualifications associated with these results must be prominently and repeatedly explicated. We do not relate changes in bank regulations to changes in outcomes. Thus, we do not run any regressions of changes in bank fragility, development, efficiency, or corruption on changes in bank regulations. We leave that to future research. Rather, in this paper, we first document the responses in Survey III and illustrate changes in bank regulations that have taken place over the last decade. Then, based on our early estimates from Survey I, we simulate how changes in bank regulations may influence various outcomes. In sum, the conclusion of this paper is where the analytics begin. Given these new data on banking-system reforms, researchers must assess the direct impact of these reforms on national banking systems to be more confident about which regulatory changes are for the better and which for the worse.

19
|**Table 4: Treatment Heterogeneity b**<br>Dependent Variable: Real MonthlyP|**y Randomizatio**<br>rofits(Cedi)<br>(1)|**n Strata**<br>(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||OLS|FE|OLS|FE|OLS|FE|OLS|FE|
|Interaction Category A|Single‐Se|x Industry|Low C|apture|Low C|apital|Low|Profits|
|Interaction Category B|Mixed I|ndustry|High C|apture|High C|apital|High|Profits|
|**Panel A: Females**|||||||||
|Cash Treatment*Category A|9.62|‐6.87|2.12|‐8.53|3.13|‐11.25|3.29|‐8.58|
||(10.08)|(10.57)|(12.40)|(13.55)|(10.62)|(11.75)|(7.15)|(9.65)|
|Cash Treatment*Category B|1.44|1.78|7.89|4.49|8.29|8.98|6.83|6.81|
||(13.37)|(13.47)|(12.00)|(11.35)|(14.05)|(13.06)|(20.59)|(17.01)|
|In‐kind Treatment*Category A|26.37*|25.39|28.30|35.41|15.96|14.25|2.21|4.58|
||(14.31)|(17.03)|(23.00)|(24.07)|(10.77)|(10.41)|(6.97)|(7.52)|
|In‐kind Treatment*Category B|48.26*|39.77**|46.66***|31.06**|65.06**|55.67**|96.18***|76.53**|
||(25.60)|(19.94)|(14.15)|(12.50)|(30.21)|(26.19)|(36.95)|(30.69)|
|Number of Observations|2604|2604|2604|2604|2604|2604|2604|2604|
|Number of Firms|474|474|474|474|474|474|474|474|
|P‐values for testing:|||||||||
|Cash Treatments equal|0.625|0.614|0.740|0.462|0.771|0.250|0.871|0.432|
|In‐kind Treatments equal|0.456|0.584|0.457|0.873|0.124|0.142|0.013|0.023|
|Cash=In‐kind|0.156|0.058|0.056|0.061|0.155|0.051|0.119|0.056|
|**Panel B: Males**|||||||||
|Cash Treatment*Category A|‐2.82|‐5.75|‐0.06|10.72|0.68|‐0.72|17.23|‐1.50|
||(16.42)|(21.54)|(19.55)|(23.92)|(18.06)|(20.14)|(12.99)|(12.76)|
|Cash Treatment*Category B|36.60*|17.00|25.13|0.77|30.16|8.66|15.43|9.50|
||(20.25)|(23.63)|(17.36)|(21.00)|(18.83)|(24.00)|(22.96)|(27.99)|
|In‐kind Treatment*Category A|44.85**|23.47|43.56|58.33|46.55**|26.33|35.08*|32.20|
||(21.72)|(31.46)|(27.06)|(35.74)|(19.24)|(25.52)|(18.00)|(23.07)|
|In‐kind Treatment*Category B|28.55|33.69|30.49*|8.94|25.78|28.51|34.88|21.99|
||(18.54)|(20.66)|(15.76)|(19.42)|(20.31)|(25.59)|(21.57)|(27.48)|
|Observations|1599|1599|1599|1599|1599|1599|1599|1599|
|Number of Firms|290|290|290|290|290|290|290|290|
|P‐values for testing:|||||||||
|Cash Treatments equal|0.132|0.477|0.337|0.755|0.260|0.765|0.946|0.721|
|In‐kind Treatments equal|0.569|0.786|0.677|0.226|0.458|0.952|0.994|0.776|
|Cash=In‐kind<br>Notes:<br>All estimation includes wave effects<br>Trimmed sample used. OLS estimatio<br>*, ** and *** denote significant at th|0.151<br>which vary by c<br>n includes dum<br>e 10%, 5% and|0.596<br>ategory. Sta<br>mies for th<br>1% levels.|0.312<br>ndard error<br>e matched q|0.349<br>s in parenth<br>uadruplets.|0.171<br>eses, cluste<br>|0.509<br>red at the f|0.563<br>irm level.|0.417|
- [29] Reichert, A.K., Impact of a toxic waste superfund site on property values, _Appraisal J._ 65:381-392 (1997).

- [30] Thayer, M., H. Albers and M. Rahmatian, The benefits of reducing exposure to waste disposal sites: a hedonic housing value approach.” _J. Real Estate Res._ 7:265-282 (1992).

- [31] Tietenberg, T., Disclosure strategies for pollution control, _Environ. Res. Econ._ 11:587602 (1998).

- [32] Wang, H., J. Bi, D. Wheeler, J. Wang, D. Cao, G. Lu, and Y. Wang, Environmental performance rating and disclosure: China’s Green Watch program, _J. Environ. Manage._ 71:123-133 (2004).

- [33] Wheeler, D., H. Hettige, S. Pargal, and M. Singh, Formal and informal regulation of industrial pollution: evidence from Indonesia and the U.S.” _World Bank Econ. Rev._ 11:433450 (1997).
Our data on days worked by hired labor, off-farm work, and animal husbandry employment are left-censored as they have many zero values (around 35% for hired labor and 90% for off-farm and If we the the effect of our husbandry). ignore censoring, explanatory variables would be underestimated. Therefore, we use the semiparametric method for censored estimation developed by (Honoré, 1992), which, unlike typical maximum likelihood estimators for censored variables, accounts for fixed effects in a consistent way. We estimate the following censored model: with _Qhijt_ “ _max_ t0, _Q_<sup> ̊</sup> _hijt_<sup>u, and where</sup><sup>_Qhijt_represents days worked by hired labor for</sup> household _h_ , as well as days worked by household members in off-farm work, or in animal husbandry, _Q_<sup> ̊</sup> _hijt_<sup>represents their unobserved latent variables,and the explanatory</sup> 2. variables are the same as in Equation Finally, to test the hypothesis that polygyny increases child marriage during or after the drought, we specify the following model: where _Mghit_ is a binary variable coded as 1 if a girl _g_ born in cohort _h_ living in EA _i_ (located in commune _j_ ) entered her first marriage at age _t_ and 0 otherwise. 1r.s is an indicator function equal to 1 if the inequality in brackets holds. _Drougthit_ , _Drougthit_ ́1 and _Drougthit_ ́2 are time-varying variables measuring the occurrence of drought at EA _i_ in year _t_ , _t_ ́ 1 and _t_ ́ 2 ; _Polyj_ measures the prevalence of polygyny in commune _j_ . _λg_ , _αh_ and _ηt_ are individual fixed effects, cohort fixed effects and year fixed effects respectively; _ε_ is a zero-mean error. _ghijt_ 28
Table 2. Growth of Gross Agricultural output since 1970 (annualized percentage change in volume)

||1971-75|1976-80|1981-85|1986-88|1989|1990|
|---|---|---|---|---|---|---|
|Bulgaria|2.2|2.1|1.2|-0.3|0.3|-1.4|
|Czechoslovakia|3.8|2.7|3.4|3.8|0.0|1.3|
|GDR|2.2|1.4|1.6|2.2|-2.7|2.5|
|Hungary|3.9|3.9|2.4|0.2|-0.2|9.0|
|Poland|3.2|0.6|-0.5|2.8|1.3|-9.0|
|Romania|4.7|4.7|2.0|9.9a|-4.9|2.4|
|USSR|2.5|1.7|1.1|2.9|1.3|-2.3|

_Note:_ Figures indicate annualized changes between periods shown and previous five-year period.

a. 1986-87.

_Source:_ UN/ECE Common Data Base.
Pae

# Table of Contents

|Roles of the Manpower Planner ..............................................<br>|1|
|---|---|
|Guiding Market Training Decisions .<br>........................................<br>|2|
|Strengthening the Management of Training Systems .......<br>.....<br>|................<br>3|
|Improving Labor Market Efficiency .<br>.........................................<br>|3|
|Planning Public Investments in Training .....**.**<br>.............................|<br>3|
|Abandoning Manpower Requirements Forecasting .,...|4|
|Introducing Labor Market Signalling .............................................<br>|7|
|Focusing on Education and Training Qualifications ........<br>......<br>|...............<br>8|
|Producing Labor Market Signals ...........................................<br>|9|
|Market Training Decisions .<br>........................................<br>|9|
|Managing Training Systems ........................................<br>|11|
|Labor Market Efficiency ...........................................<br>|14|
|Public Investments in Training .<br>......................................<br>|18|
|Possible Objections ..............................................|21|
|Sources of Labor Market Information ............................................|23|
|Conclusions ...............................................|25|
|References ...............................................|28|
|Tet Tables<br>Table 1 .............................................|26|
|Text Boxes<br>Box 1 ........<br>..........<br>|6..................<br>.......<br>.<br>6|
|Box 2 .................<br>|.<br>12|
|Box3 ..................<br>Box 4 .................|15<br>27|

## Tet Tables

## Text Boxes
The host country investment cost is insignificant in all equations. However, the improvements in institutions significantly increase aggregate and manufacturing FEI into Singapore. Finally, there is one major difference between the results for the aggregate sample with Singapore GDP presented in Tables 1 and 2 and the aggregate inbound industrialized country sample with ASEAN GDP presented in Table 5. Distance has a positive and significant impact on inbound aggregate and manufacturing stocks when ASEAN GDP is used suggesting that countries further away invest more heavily in Singapore than those that are nearer. This may indicate that MNEs from distant countries such as the US and the UK are more likely to invest heavily in Singapore to have an easier access to markets in the ASEAN region by avoiding higher transport costs.

Regarding skill endowments, the direct coefficients on Singapore’s relative skill differences are negative and significant at the 5 percent level for both the 1984-2003 and 1994-2003 samples. They are positive but insignificant for the manufacturing inbound samples. This may be because Singapore is attracting both high end and low end manufacturing investment. While high end manufacturing investment stays on in Singapore, the low end investment passes through Singapore on its way to neighboring countries. During 2003 manufacturing accounted for about forty percent of all outbound investment (manufacturing plus non-manufacturing investment) by foreign MNEs with regional headquarters in Singapore, mostly to neighboring countries<sup>13</sup> . However, although nonmanufacturing investment to neighboring countries by foreign MNEs accounted for 60 percent of all investment, inbound non-manufacturing investment stocks from industrialized countries were nearly three times the manufacturing investment. This clearly indicates that a

> 13 Singapore Department of Statistics, 2003

28
counteracted by other factors. By excluding alternative explanations, and in line with an emergent literature, we speculate that the widening of migrants’ horizon (i.e., additional information or value change) may be the main driver of the reduction in petty corruption.

Incorporating school-level budget data in our analysis, we show that there is no strong correlation between public school funding and petty corruption. Thus, the most socially accepted justification for informal payments to teachers—the need for school maintenance and wage supplements for motivation—is not a good predictor of differences between schools. Within schools, additional analysis suggests that reductions in payments to teachers quickly spill over to non-migrants. This is in line with qualitative evidence according to which per capita payments to teachers cease once a few parents in a class refuse to pay due to only weak enforcement devices in the hands of teachers or other paying parents. Our results fit with novel research that shows how participation in corruption often depends on people perceiving it as widespread. This is a prevalent phenomenon in low- and middle-income countries. In such a setting, simply increasing teachers’ salaries and school resources might decrease the perceived legitimacy of informal payments. If budget constraints made this impossible and these payments continued to exist, structures such as teacher-parent committees should formalize them as donations. The available funds should then be focused on making teachers wages sufficient while stepping up enforcement of laws against individual corruption. This way, transparency and accountability would be improved while providing solutions for underfunding that do not distort incentives. Both the opportunity to siphon off part of the payments for private use and the necessity to do so would thus decrease. For bribes which are used to get one’s own child ahead of the competition, other measures are likely to be more effective. In a bold move, the Moldovan government recently introduced videotaping of the most important high school exam to put an end to teachers, motivated by informal payments, telling answers to their classes or, worse, individual students. As such laudable reforms reduce the scope for corruption they may also make it easier for both migrants and non-migrants to resist corruption. Focusing reform efforts on increasing awareness that petty corruption in education is a problem, stoking demand for educational achievement rather than for good grades, and creating incentives to deviate from the social norm of participating in petty corruption hold promise. This paper thus provides evidence of a petty corruption channel through which the all too often forgotten positive effects of emigration on 19
22 Table 3: Poverty patterns under a national and a context-specific poverty line, Mozambique 1997-2003-2009 and Ghana 2013

|||**MOZAMBIQUE**||**GHANA**|
|---|---|---|---|---|
||1997|2003|2009|2013|
|National headcount|||||
|-<br>National line|69.6|60.5|56.6|25.2|
|-<br>Rural-urban line|69.8|61.0|56.9|24.0|
|-<br>Spatial domains|68.6|53.1|53.7|25.6|
|Rural headcount|||||
|-<br>National line|76.9|70.6|64.4|40.1|
|-<br>Rural-urban line|71.5|63.1|59.6|30.7|
|-<br>Spatial domains|70.7|54.2|55.7|29.2|
|Urban headcount|||||
|-<br>National line|43.0|39.3|38.8|10.5|
|-<br>Rural-urban line|63.7|56.6|50.9|17.5|
|-<br>Spatial domains|60.8|50.9|49.1|22.1|
|**Poverty correlates**|||||
|Food in total consumption|||||
|-<br>National line|0.24|0.29|0.24|0.27|
|-<br>Rural-urban line|0.18|0.21|0.19|0.21|
|-<br>Spatial domains|0.14|0.11|0.11|0.17|
|% home grown foods|||||
|-<br>National line|n.a|0.32|0.21|0.31|
|-<br>Rural-urban line|n.a|0.17|0.12|0.22|
|-<br>Spatial domains|n.a|0.03|0.05|0.12|
|Underfives stunted|||||
|-<br>National line|0.15|n.a|0.08|n.a|
|-<br>Rural-urban line|0.07|n.a|0.06|n.a|
|-<br>Spatial domains|0.06|n.a|0.02|n.a|
|Literacy|||||
|-<br>National line|-0.19|-0.21|-0.18|-0.28|
|-<br>Rural-urban line|-0.12|-0.14|-0.14|-0.26|
|-<br>Spatial domains|-0.10|-0.08|-0.10|-0.20|
|Safe drinking water|||||
|-<br>National line|-0.25|-0.25|-0.28|-0.35|
|-<br>Rural-urban line|-0.17|-0.16|-0.21|-0.22|
|-<br>Spatial domains|-0.17|-0.12|-0.14|-0.14|
|<br>Sanitation|||||
|-<br>National line|-0.24|-0.28|-0.27|-0.20|
|-<br>Rural-urban line|-0.16|-0.16|-0.21|-0.17|
|-<br>Spatial domains|-0.10|-0.00*|-0.17|-0.11|
|<br>Solid roof|||||
|-<br>National line|-0.37|-0.36|-0.29|-0.18|
|-<br>Rural-urban line|-0.23|-0.23|-0.22|-0.14|
|-<br>Spatial domains|-0.15|-0.03|-0.11|-0.08|

* All Pearson correlation coefficients are significant at 95%, with the exception of sanitation in Mozambique, 2003, when the spatial domain poverty line is applied.

** The CSPL is listed in Annex 3. In Mozambique the CSPL is based on 13 spatial domains, following MPD-DENEAP (2010). For Ghana, we define 20 levels and distinct a rural and an urban domain in each of the 10 regions.
# **6 Employment multipliers over the business cycle**

This extension examines employment multipliers during the 2002-2007 boom, the Great Recession (2008-2010) and the Recovery period (2011-2015). This is a topic of great interest. Economists and policy makers are interested in the quantitative spillover effects of job creations and more broadly, stimulus policies, over the business cycle. Some argue that when the economy is slack, stimulus programs have stronger multiplying effects. Auerbach and Gorodnichenko (2012, 2013) test this hypothesis and find larger fiscal multipliers during recessions in both quarterly post-World War II US data and in annual cross-country panel data since 1985. Their findings suggest fiscal multipliers near zero during expansions but between 1.5 and 2 during recessions. However, Owyang et al (2013) do not find this to be the case with U.S. historical data.

Since exogenous job creations bring exogenous additional income in a local area, they could be considered a form of exogenous stimulus. Estimating employment multipliers over the business cycle thus provides insights into the debate about the effects of stimulus over the business cycle. Fortunately, with the identification approach employed in this paper, estimating employment multipliers over the business cycle is relatively straight-forward. We just have to break the time series into periods corresponding to booms and busts in the U.S. economy. In particular, we break the time series into three periods: boom (2002-2007), the Great Recession (2008-2010), and recovery (2011-2015).

21