rafmacalaba/datause-encoder-data huggingface dataset:
**The picture that emerges in the case of Tanzania** is that improvements in public investment management capacity (as measured by the PIMa) have allowed the Tanzanian public sector to play a supportive role in the private sector’s development with little measurable crowding out effects.  Overall gross fixed capital formation has increased alongside gross fixed capital formation by the private sector, while the public sector has accompanied this increase at a distance, ensuring the provision of infrastructure services (transport and energy),<sup>33</sup> as measured by the trend improvement in gross fixed capital formation (Figure 4).

**Figure 4** : Tanzania - Gross Fixed Capital Formation, total, private and public, 2000-2011 (% of GDP)

<!-- Start of picture text --> 40.0<br>Gross fixed capital formation (% of GDP)<br>35.0<br>Private sector (% of GDP)<br>30.0<br>Public sector (% of GDP)<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>2000' 2001' 2002' 2003' 2004' 2005' 2006' 2007' 2008' 2009' 2010' 2011'<br><!-- End of picture text --> _Source_ : World Development Indicators.

**Ghana provides a different example of the impact of public investment on the private sector** -- one where gross fixed capital formation has been trending downward over the period 2004-2011 (Figure 5).  The downward trend in gross fixed capital formation during this period appears to be the result of up and down shifts in fixed capital formation in the private and the public sectors.  While public capital formation was trending up from 2006 to 2008, private capital formation was shifting downwards.  In the aftermath of the Global Financial Crisis, public capital formation declined and stabilized at a lower level of around 7 percent of GDP. Meanwhile, private capital formation had a brief recovery in 2009 before declining in 2010 and 2011 bringing alongside overall capital formation down.  The trends in public capital formation appear to reflect a ‘crowding out’ effect, whereby increased government expenditure on debt service obligations comes at the expense of the fiscal space for public investment, as shown in Figure 2 above.

> value of land improvement is included in the GFCF measure as a net addition to wealth.  In special cases, such as land reclamation from the sea, a river or a lake (e.g. a polder), new land can indeed be created and sold where it did not exist before, adding to fixed assets.  The GFCF measure always applies to the resident enterprises of a national territory, and thus if e.g. oil exploration occurs in the open seas, the associated new fixed investment is allocated to the national territory in which the relevant enterprises are resident.

> 33 The improvements in the provision of public services in energy and transport are captured by Tanzania’s improved ratings in the World Bank Doing Business Indicators on access to energy and trading across borders.

20
Experts also faced a final debriefing section where they were asked, among other things, to suggest reformulations of the survey; possible problems in implementing the population surveys; and possible perceived differences between the “preservation” plans, which we used, versus an alternative “loss prevention” scenario for forest protection.

# **3. Main Survey Results**

# **3.1 Experts’ Estimates of Public’s WTP for Amazon Forest Preservation**

Key results from the survey are presented in Tables 3-4. Table 3 provides an overview of experts’ answers for (the more comprehensive) Plan A, while Table 4 presents results for (the less comprehensive) Plan B. For both plans, experts provided estimates of both mean and median WTP for their home countries’ populations.<sup>16</sup> Such figures are here presented for all experts in each category, given separately by world region (and within Asia, distinguishing between low/lower-middle income countries and upper-middle-income countries, with China, Malaysia and Thailand in the latter group). Only overview numbers for each of the regions are included in these tables; for more detailed figures consult the authors.

The average figures across experts mask high variability, varying by expert from a low of zero to a high of $500 per household per year. Overall, experts’ evaluations of WTP levels in their own countries are higher for means than medians. This is reasonable as these distributions are (viewed by most experts as) skewed with some high WTP households that pull averages up.

Another interesting feature is that average expert answers are lower in round 2 than in round 1. This holds for all regions and for both mean and median WTP answers, but more so for means. This follows (discussed more in section 4 below) from many experts changing their valuations from round 1 to round 2, and more, absolutely speaking, down than up.

We see that overall mean stated WTP is highest in North America and lowest in Asia, with Oceania and Europe in between at similar levels. Within Asia, WTP levels are substantially lower for low/lower-middle-income than for upper-middle-income countries. There were also considerable differences by country within each of the specified groups (not shown). Overall, the

> 16 Note that the European data (and only these) have already been documented in Navrud and Strand (2013).

10
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# **3 Describing the Distribution of Managerial Quality**

## **3.1 Measuring Management Practices**

The sampling methodology is taken from Bloom and Van Reenen (2007) and the reader is referred to that work for detail.<sup>9</sup> For most countries in the global sample, the sample of firms to be interviewed is drawn from BvD Orbis which collects comparable information on firms worldwide. The sample is further restricted to manufacturing companies between 50 and 5000 employees. Hence, the generally low poor country scores are not driven, for example, by an exaggerated informal sector.<sup>10</sup> A random sample is then extracted to be interviewed with attention paid to ensuring that both small and large firms are interviewed within the size band. The sample is thus representative of medium-sized manufacturing firms.

Table 1 presents the summary statistics by country. Because our later analysis will require standardization of the management variables, the _z_ statistic is presented below the raw management score in the first line. Table 2 offers a more compact presentation of the statistics for high management score ( _z >_ 0) and low management ( _z <_ 0) countries which maps broadly to rich and poor countries.

In terms of the size of firms in the sample, poor country firms in the sample tend to be smaller than in richer countries, although there is a fair amount of dispersion. The largest mean firms sizes are found in the US (1,254), Germany (937), China (960), Australia (847) and the UK and France (828). In a middle group of between 400 to 700 we find all the Latin American countries, Greece, India, Italy, Japan, Poland, the Republic of Ireland and Sweden. Among the smallest average size we find New Zealand (265) and Portugal (339).

Whether or not a firm is a subsidiary of a foreign or domestic multinational is not known until the end of the survey when the firm is asked for ownership information.

> 9The additional Latin American data financed by the present authors was collected by their survey team and is hence fully consistent.

> 10Orbis does not provide a good sampling frame for some countries. The UK data, for example, comes from Companies House. Chile is also not drawn from Orbis.

7
Table A.11: Trends in son preference by firstborn sex

||Outcome variable:|Ideal fraction of sons|
|---|---|---|
|**A. By survey round**|(1)|(2)|
|Round 2|-0.0169***|-0.0134**|
||(0.00586)|(0.00635)|
|Round 2 _×_ Firstborn girl|0.00374<br>(0.00720)|0.00407<br>(0.00733)|
|Round 3|-0.0441***|-0.0381***|
||(0.00763)|(0.00771)|
|Round 3 _×_ Firstborn girl|0.00770<br>(0.00774)|0.00881<br>(0.00756)|
|**B. By ultrasound availability**|(1)|(2)|
|Post2|-0.0288***|-0.0226***|
||(0.00560)|(0.00617)|
|Post2 _×_ Firstborn girl|0.00510|0.00605|
||(0.00638)|(0.00641)|
|N|10,160|10,160|
|Firstborn girl|x|x|
|State FE|x|x|
|Households controls||x|

NOTES: The data pools the three waves of the NFHS, conducted in 1991/2, 1998/9 and 2005/6. For each survey round, we keep women who had their first birth within a calendar year from their survey data, so that their preferences at the survey date reflect their preferences around the birth of their first child. We then compare mother’s reported son preference between firstborn-girl and firstborn-boy families and across the 3 survey rounds, that span about 15 years. In Panel A, we compare rounds 2 and 3 with round 1; the omitted group is the first NFHS round. In Panel B, we compare the two rounds (NFHS-2,3) in the late diffusion period of ultrasound availability with one round (NFHS-1) in the early diffusion period. Standard errors are clustered by state. *** 1%, ** 5%, * 10%.

58
dummy variables that are endogenously determined by a stochastic property test: dummy variables for changes in drift and trend slopes.

Overall, the Pedroni and Kao cointegration tests strongly conclude to the existence of a cointegration relationship between variables in the _Augmented Models 1 and 2_ in respectively CAMU and WAMU. In CAMU and WAMU, the Kao and Pedroni tests are not conclusive for respectively Augmented Model 1 (Table 3).<sup>2</sup> _Table 3: Results of Panel Cointegration Tests in the CAMU and WAMU regions._

|**Regions**|**Demeaned?**|**Augmented**<br>**Model**|**Kao- ADF statistics**|**Kao test**<br>**p-value**|**Pedroni-modified**<br>**Philips Perron**<br>**statistics**|**Pedroni-**<br>**test p-value**|
|---|---|---|---|---|---|---|
||No|1|1.491|0.068|1.172|0.121|
||No|2|1.363|0.086|1.548|0.061|
|**WAMU**|Yes|1|1.976|0.024|1.675|0.047|
||Yes|2|1.838|0.033|1.922|0.027|
||No|1|-0.994|0.160|1.777|0.038|
|**CAMU**|No|2|-1.460|0.072|1.895|0.029|
||Yes|1|-2.348|0.009|2.511|0.006|
||Yes|2|-1.397|0.081|3.374|0.000|

We tested both initial equations [1] and [2] in a panel cointegration form according to the (Kao & Chiang, 2000) model. Table 4 contains results of estimated equations that adequately explain the inflation-growth nexus in the CAMU and WAMU regions. At the panel level, equation [2] better explains this nexus in CAMU and equation [1] best fits the WAMU. As the difference between the Augmented Model 1 and Augmented Model 2 is the inclusion the natural resource. As estimated equations suggest that natural resource rent significantly affect growth in both region (Appendix 5 and Appendix 6), the related thresholds could be reference points. For CAMU, Equatorial Guinea does not have data on natural resource rent. Thus, result from the augmented model 2 in CAMU cannot be considered as a reference point. This means that by considering a homogeneous cointegration relationship, the inflation threshold above which

> 2 Cointegration tests for the _Basic Model_ also conclude to the existence of a cointegration relationship.

Page | 18
Household attrition was very low between the baseline and one-year follow-up survey (1.26 percent), and increases slightly when comparing the baseline and two-year follow-up survey (3.56 percent). In Appendix Table 1, we explore the relative differences between attritor and non-attritor households. Column 1 presents means of individual and household-level characteristics from the baseline survey for the sample of households that were followed from the baseline to the two-year follow-up survey (non-attritors). Column 2 presents means for the sample of attritor households, and column 3 presents the average difference in characteristics between attritors and non-attritors, as well as a test of whether the difference is statistically significant. For the main child education and health outcomes, there are no significant differences between attritor and non-attritor households. Attritor households do appear to be smaller in size, more likely to be female-headed, and with a younger household head. To address any potential bias due to attrition, in Section 4.8 we conduct additional robustness checks related to attrition and sample selection. These include estimating inverse probability weighted regressions to determine if any of the results for the main outcomes are affected when explicating addressing the issue of attrition.

In the subsequent tables, we report the results of the impact evaluation. Our tables are structured in 4 horizontal panels. Panels A and B report results from round 3, 2 years after the start of the intervention while Panels C and D report results from round 2, one year into the experiment. Panels A and C focus on the comparison between cash transfers given to the mothers versus fathers. The results displayed are from Equation 1. In those panels, the coefficient on the variable treatment can be interpreted as the impact of cash transfers given to mothers. The coefficient on CTF indicates whether giving the transfers to fathers yields a (statistically) different impact from giving them to mothers. Below the table, we present the p-value for “Treatment + CTF” indicating whether the impact of cash transfers given to fathers is statistically different from zero. Similarly, Panels B and D focus on the comparison between conditional and unconditional cash transfers. The results displayed are from Equation 2. In those panels, the coefficient on the variable treatment can be interpreted as the impact of conditional cash transfers. The coefficient on UCT indicates whether giving the transfers unconditionally yields a (statistically) different impact from imposing conditions on the transfers. The p-value for “Treatment + UCT” indicates whether unconditional cash transfers have an impact statistically different from zero.

11
21 Table 4: ESTIlWS OF MB AND (I-M) FROM EQllWOtt 9 a/ b/

|Explana|tory Variables|al<br>Cash ani<br>Securities|a2<br>Peso<br>Trade<br>Credit|a<br>Do1lar<br>Trade<br>Credit|a4<br>Peso<br>Las|aS<br>Dollar<br>tLoas<br>Ir|a6<br>ntories|a7<br>Fxed<br>Capital|
|---|---|---|---|---|---|---|---|---|
|xi<br>x2<br>X3<br>:4<br>XS<br>x.<br>X8<br>a1(-1)<br>2(-1)<br>a3(-1)<br>a4(-1)<br>a5(-l)<br>`6(-1)<br>a7(-1)|constant<br>inflation<br>peso interest<br>rate<br>LIIDR rate<br>tdevaltion<br>rate<br>sales growth<br>Xlgdsal<br>grceth<br>log of firm size<br>laed<br>cash<br>and securities<br>lagd<br>peso<br>trade credit<br>l^md dollar<br>trade credit<br>t4pd<br>peso<br>loam<br>Ltgpd dollar<br>lows<br>Tagd<br>inventories<br>Lamd f1md<br>capital|.51******<br>1 <sup>5.07)</sup><br>-.06 *<br>(-3.69)<br>-.00<br>(-0.25)<br>.05<br>(.73)<br>-.02<br>(-1.21)<br>.06<br>*<br>(6.34)<br>.04 *<br>(3.92)<br>-.04******<br>(-4.55)<br>.45******<br>(12.71)<br>-.01<br>(.47)<br>.02<br>(.80)<br>.02<br>(1.11)<br>.02<br>(1.41)<br>.04******<br>(4.29)<br>-. 01<br>(-0.97)|-.00<br>(-.02)<br>-.03<br>(-1.14)<br>.03<br>(1.11)<br>.05<br>(.48)<br>-.06 *<br>(-2.36)<br>-.05<br> <br>(-2.97)<br>-.03*****<br>(-1.94)<br>.01<br>(.82)<br>-o6*****<br>(-3.30)<br>.40<br>(9.90)<br>-.13******<br>(-2.35)<br>-.13 *<br>(-2.85)<br>-.10<br>(-2.58)<br>-.02<br>(-.68)<br>-.15 *<br>(-6.21)|-.11<br>(-1.53)<br>.00<br>(.31)<br>-.03*****<br>(-2.23)<br>.07<br>(1.18)<br>-. 03 *<br>(-2.58)<br>* -.00<br>(-.19)<br>-.00<br>(-.25)<br>.01<br>(1.88)<br>*****-.02<br>(-.84)<br>* -.02<br>(-1.21)<br> <br>.3 *<br>(5.16)<br> <br>.04 *<br>(2.20)<br>******-.01<br>(.59)<br>.01<br>(.62)<br> <br>-.01<br>(-1.19)|.05<br>(.70)<br>-.00<br>(-.07)<br>.00<br>(.37)<br>-.03<br>(-.78)<br>-.01<br>(-1.40)<br>-.00<br>(-.01)<br>.01<br>(.88)<br>-. 00<br>(-.66)<br>-.02<br>(-1.00)<br>-.02<br>(-.99)<br>.02<br>(.86)<br>.41**<br>(10.15)<br>-.03<br>(-1.41)<br>-.01<br>(-.53)<br>.00<br>(.17)|-.11<br>(-.88)<br>-.02<br>(-1.16)<br>-.03<br>(-1.42)<br>.12<br>(1.57)<br>.06**<br>(3.49)<br>-. 02**<br>(-2.51)<br>.00<br>(.38)<br>.01<br>(1.20)<br>-.05<br>(-1.45)<br>-.08**<br>(-3.21)<br>.10<br>(1.81)<br> <br>.03<br>(.89)<br>.51**<br>(13.29)<br>-.06**<br>(-2.84)<br>-. 06**<br>(-2.69)|.49**<br>(2.36)<br>.09**<br>(2.72)<br>.24**<br>(7.07)<br>-.01<br>(-0.09)<br>.14**<br>(4.96)<br> <br>.03<br>(1.60)<br>.05**<br>(2.60)<br>-. 02<br>(-.81)<br>-.16<br>(-3.07)<br>-. 19**<br>(-4.42)<br>-.16**<br>(-2.39)<br>-. 24**<br>(-4.60)<br> <br>-. 281*<br>(-610)<br> <br>.11**<br>(3.45)<br> <br>-.38**<br>(-12.12)|<br>.17<br>(1.27)<br> <br>.03<br>(1.36)<br> <br>-. 21**<br>(-8.80)<br>-.251*<br>(-2.88)<br> <br>-.08*<br>(-4.21)<br>-.02<br>(-1.45)<br> <br>-. 06**<br>(-5.31)<br>.02<br>(1.58)<br>-.O9*<br>(-1.51)<br> <br>-.09**<br>(-4.01)<br>-. 09**<br>(-2.49)<br> <br>-. 14**<br>(-4.85)<br> <br>-. 11**<br>(-4.51)<br> <br>-.07**<br>(-4.57)<br>.61**<br>(25.67)|

***** Significant at the 5Z levL

****** Siificant at the 1Z levl.

a/ The transpose of MB appas **as** the first 8 _rows;_ the traupoe of (141) appears as the last 7 rows.

b/ Estimted as a camtratzwd Zellner-efficient sysm after eVpres1sz data in firspedfic deviation form and correcting for tterosAaty.
experience contribute 20 percent more to the productivity of their plant than do workers without such experience, even after controlling for differences in unobservable worker characteristics. The difference between the private returns to mobility and the productivity effect at the plant level suggests that this type of labor mobility represents a knowledge externality. The same issue is examined by Poole (2013), though in a somewhat different manner. Poole also uses matched employeremployee data, but she focuses on Brazil and studies wage spillovers. She estimates wage equations for incumbent workers in domestic firms and finds that their wages are positively affected by the share of workers with prior work experience from multinationals.

Another set of studies examines knowledge spillovers from FDI pertaining to export markets. Based on panel data on 2,104 Mexican manufacturing plants from the 1986–90 period, Aitken, Hanson and Harrison (1997) demonstrate that the presence of exporting multinationals in the same region reduces the costs of exporting for Mexican firms. No such externalities are found for exporting firms in general. Using detailed Chinese trade statistics that identify the type of exporters and their location, Chen and Swenson (2008) find that the presence of foreign affiliates in the same sector is associated with more and higher unit value trade transactions by Chinese firms. Using the same data set, Swenson (2007) shows that the positive association between the presence of foreign affiliates and new export connections by private Chinese exporters may be driven by information spillovers.

22
regions, implying that imports are not able to effectively discipline Argentine gasoline prices.

To further analyze the intensity of competition in the Argentine gasoline market we must consider inter-brand price competition. The distribution of prices among brands is wider for premium gasoline (figures 4 and 5). The uniformity of diesel prices is a consequence of a more price-sensitive consumer, a higher volume of imports and a more developed wholesale market. Until 2002, when the peso suffered a significant devaluation that led to increases in gasoline prices, the ranking of prices had been hardly altered, another symptom of a market not characterized by intense price competition.

<!-- Start of picture text --> Figure 4<br>Premium Gasoline Prices. Average Country. Net of Taxes<br>S1.20<br>$1.00<br>$0.80<br>5 $0.60<br>$0.40<br>$0.20<br>$0.00 ..............<br>1995 1996 1997 1998 1999 2000 2001 2002<br>-RepsolYPF -Esso - Shell --EG3<br><!-- End of picture text --> Source: Secretariat of Energy, Argentina
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