rafmacalaba/data-use-mentions-extended huggingface dataset:
# **B.  Robustness of Results**

Overall, the results are robust to the inclusion of additional variables known to explain cross-sectional differences in capital structure and the use of FC debt such as family group ownership structure, a foreign equity listing, foreign currency cash flows, capital intensity, and the level of business risk. We do not include these because the variables are not available for all firms and they do not appreciably change the results. We also examine other measures of legal effectiveness such as creditor rights (from LSSV, 1998) and a categorical variable for legal origin (i.e., English=1, German/Socialist=2, and French=3) and obtain results similar to those reported.

Another concern is that the results are specific to East Asia or the time period studied. Robustness checks using other years and countries are hampered by a lack of detailed data for FC debt. However, we examined an additional sample that provides some additional insights. We take all non-financial firms in the WorldScope database in 2001 and split the sample by the level of foreign sales (as a percent of total sales). We assume that firms with low foreign sales (less than 10%) will on average face greater constraints in accessing foreign debt markets (as discussed in the next section).<sup>8</sup> We repeat the regressions reported in Table 3 and find that for all countries, all developing countries, and only East Asian countries in 2001 the coefficient on legality is on average three times greater in magnitude for firms with low foreign sales. However, the difference is only statistically significant for East Asian countries. In sum, this analysis provides some additional evidence consistent with the main hypothesis, but the results are weak probably because of less precise data on foreign currency debt.

> 8 Allayannis et al. (2003) find that firms with FC debt also have higher levels of foreign sales, income, and assets though many firms without FC debt also
intra-household dynamics or a series of other competing hypotheses, which are also examined.

The paper proceeds by reviewing the different income sources and consumption patterns across the rural settings studied here (section 2). The argument of mental accounting by effort is then presented more formally in section 3. The empirical strategy is described in section 4. Section 5 discusses the empirical findings, including those from the different robustness tests. Section 6 concludes.

> 12 While the latter help greatly in establishing causality, they also require great care in addressing potential issues of randomization bias and framing (List, 2009).  The use of the more widely available, standard, panel household budget surveys to explore the hypotheses advanced here further facilitates re-examination of these hypotheses in other cultural settings. It also allows for considering a broader set of income sources and consumption sources than those examined in (laboratory and natural) experiments which are typically confined to particular consumption goods and/or  more precisely defined transfer (e.g. child allowance, coupon). 13 Duflo and Udry (2004) and Villa, Barrett and Just (2011) are noteworthy exceptions.
We follow Besley and Mueller (2015a) and analyze international investment flows in a fixed effects Pseudo Poisson framework. The investment flow at the country level is given by where _xit_ is the inflow of investment in country _i_ in year _t_ . We use the exposure model which controls for global investment flows through _Xt_ . The regression with total inflows as an exposure variable can be thought of as modeling the annual _rate_ of investment inflows into a country in each year.<sup>40</sup> The variable _peaceit_ is a dummy that takes a value of 1 in all years with peace. We lag this variable by one year to allow for the fact that investment needs some planning and will not react immediately to changes in the host country. We expect _γ_ 0 _>_ 0 if inflows increase after the end of conflict.

It is likely that effects of violence will be most visible if the conflict has been intense in terms of battle related deaths per capita. Yet, the right cut-off for the peace dummy is a priori not clear. India, for example, is coded as in conflict throughout the period if we choose a very low threshold. Choosing a higher cut-off means we treat low intensities as experiencing no conflict. In order to understand the role of threshold we use the fact that we have seven different data sets on investment flows from four different sources.<sup>41</sup> We can therefore take a pragmatic approach and run regressions at different cutoffs with all the different data sets we have and discuss the results jointly. Results are presented in Figure 17. Each point in the graph is a point estimate of _γ_ 0.<sup>42</sup> The Y-axis displays the magnitude of this coefficient.

The message from Figure 17 is quite clear. The end of civil war is associated with large positive changes in foreign investment inflows - regardless which dataset we use. Estimates vary somewhat but lie around 0.5 which implies that investment inflows increase by 50 percent on
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 14. Efficient market gap, benchmarked against Central African countries**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Existing access Efficient market gap Coverage gap<br>Percent of population<br><!-- End of picture text --> _Source:_ Mayer and others (2009) using GSM coverage figures as of 2005. _Note:_ Existing access represents the percentage of the population currently covered by voice infrastructure as of the third quarter of 2006. Efficient market gap represents the percentage of the population for whom voice telecommunications services are commercially viable given efficient and competitive markets.

Coverage gap represents the percentage of the population for whom services are not viable without subsidy.

The presence of multiple operators in the mobile market (table 12) has contributed to the rapid expansion of connections and the impressive drop in prices. Between 2004 and 2009 the number of connections (prepaid and postpaid) increased eightfold, from 60,000 connections to more than 500,000. The price of an international call dropped 50 percent, from $1.20 in 2004 to $0.60 in 2009. The connection charge to a prepaid mobile dropped 76 percent, from $25.60 to $6.30.

**Table 12. Players in the ICT sector**

|**Market**|**Mobile**|**Internet**|**Fixed line**|
|---|---|---|---|
|**Operators**|Telecel Centrafrique (1995)<br>Nationlink Telecom (2004)<br>Moov (
# **Appendix B: Key Country Characteristics**

# **Table B1: Key Country Characteristics**

||Indonesia|Bangla|desh|Sri La|nka|Ethiopia|
|---|---|---|---|---|---|---|
|Population (million)|227.3|143.|9|19.|4|75.9|
|Rural population (% of total population)|51.9%|73.3|%|84.7|%|83.6%|
|Human Development Index|0.572|0.47|8|0.66|2|0.327|
|GDP per capita (current US$)|1,258|475||97|5|200|
|Poverty headcount ratio at rural poverty line|||||||
|(% of rural population)|20%|43.8|%|24.7|%|39.3%|
|Poverty headcount ratio at urban poverty line<br>(% of urban population)|11.7%|28.4|%|7.9|%|35.1%|
|Agriculture, value added (% of GDP)|13.1%|19.2|%|13.2|%|47.9%|
|Social Institutions and Gender Index /<br>OECD<sup>a</sup>|0.128|0.24|5|0.05|9|0.233|
|Gender Inequality Index / UNDP<sup>b</sup>|0.549|0.60|2|0.44|7|-|
||Muslim 86.1%,|Muslim|89.5%,|Buddhist|69.1%,|
followed by the vulnerable classes and lastly by the middle classes. The considerable and statistically different differences between the vulnerable class and the middle class emphasize the importance of making the distinction of vulnerable households from the middle class.

**Table 5: Select statistically significant mean comparisons**<sup>**/a**</sup>

_Percentage of households_

||**Poor**<br>|**Vulnerable**|**Middle**|
|---|---|---|---|
|||**Chile**||
|Dirt floor|10.7|6.4|3.2|
|Head is farmer|40.3|23.4|12.9|
|Rural|33.8|22.7|13.5|
|||**Mexico**||
|Dirt floor|24.8|10|3.5|
|Head is farmer|48.4|19.5|10.3|
|Rural|76.8|48.5|31.5|
|||**Peru**||
|Dirt floor|69.4|34.2|17.4|
|Head is farmer|71.6|31.5|16.6|
|Rural|77.5|44.8|26.8|

_Source_ : Author‘s calculations based on data from CASEN, MxFLS and ENAHO panel databases. /a Full table with t-tests in annex; table A5.

# **6. The size of the middle class over the last two decades**

Applying our absolute definition to cross-sectional surveys, the results show that the percentage of middle class households has increased significantly from 28.3 to 52.8 percent in Chile, from 24.9 to 42 percent in Mexico, and from 23 to 39.4 percent in Peru over the
, when germane. Because the Annual Survey of Industries is structured as a rotating panel for the majority of the plant size distribution, we limit the sample to the set of plants observed in the data in the five years prior to their nearest GQ segment being completed, thereby halving the number of observations. For changes at the intensive margin, our sample contains two observations per plant, corresponding to records observed just before and just after the opening of the nearest GQ segment. While the resulting subsamples do not permit a direct mapping to the district-level findings, the results are remarkably consistent.

## _i. Extensive margin_

Exit, defined as the failure to observe the same plant five years after the completion of the nearest GQ segment, falls significantly with proximity, consistent with the rising district plant count in figure 1. This is true across firm age, but is particularly so among younger firms where exit falls 14% (table 1, column 1) compared to roughly 10% for mature and older firms. There is no significant effect in the larger firm sample (Annex Table A.6) for any age cohort, and the point estimates are a third to a half smaller, suggesting that

> 16 All variables with nominal values are deflated to 2005 prices, and districts are concorded to be consistently defined across the timespan analyzed (2000 to 2009). We further inspect the plant panel data for outliers and make a small number of values missing that imply implausibly large within-plant year-to-year changes. We calculate TFP-R under the assumption of constant-returns-toscale Cobb-Douglas production with structural parameters set as the median input cost share by three-digit industry.

18
countries; that is, foreign R&D is more important for small countries. Coe, Helpman, and Hoffmaister (1997) extend these results based on a sample of 77 developing countries. They find developing countries that do little R&D on their own, have benefited substantially from industrialized country R&D through trade in intermediate products and capital equipment with industrialized countries. Regarding the impact of FDI in services, we have cited numerous papers above that establish that FDI in services increases TFP in the economy.

In summary, this literature shows that the purchase of intermediate inputs from industrialized countries and FDI in services is an important mechanism for the transmission of R&D and productivity growth in developing countries. For small developing countries, trading with large technologically advanced countries is crucial for TFP growth. In our model, the parameter that reflects the ability of a region to increase total factor productivity through the transmission of modern technologies is the elasticity of firm supply with respect to the price. The greater the elasticity of firm supply in a sector the more varieties will be received in response to a price increase with respect to that country. Based on these considerations, in the increasing returns to scale sectors of our model, we assume that the elasticity of firms’ supply with respect to price is 3 for all IRTS sectors in The Philippines, India and the Rest of ASEAN; 4 for China and 5 for all other regions. These values are in a footnote to table 4. We conduct sensitivity analysis on these parameters, to determine the impact of these parameter values on the results.

# **4.2 Ad Valorem Equivalents (AVEs) of the Barriers against Business Services. 4.2.1 Estimates of the AVEs of Barriers against FDI in Business Services.** Our

estimates of the ad valorem equivalents of discriminatory barriers against foreign providers of services in the services sectors are taken from Jafari and Tarr (2015) and shown in table 3. The Jafari and Tarr estimates are based on the World
# **6 Applications**

## **6.1 Consumption Poverty and Inequality as Correlates of Malnutrition**

The first application we illustrate in this paper is the analysis of the relationship between consumption poverty, inequality and malnutrition. We utilize the estimates of consumption poverty and inequality derived by Fujii (2004) using the standard ELL approach. One way to look at the relationship is to overlay maps. Figure 5(a) and Figure 5(b) are the stunting and underweight maps overlaid with the poverty map. Table 6 gives the number of communes by poverty rate and prevalence of underweight. While there is not an obvious pattern between poverty and malnutrition, there are at least two notable features on these maps. First, the northern part of Siem Reap and southern part of Prey Veng have high levels of stunting, underweight and poverty. These places have food security problems because many people lack means of production there. Southern Prey Veng has been repeatedly hit by drought and flood. Second, the northeastern provinces of Kratie, Stueng Treng and Ratanakiri have low levels of poverty and high levels of malnutrition. This may be partly because of diseases and poor child care in this region (See Hardy and Health Unlimited Ratanakiri Team (2001) for the case of Ratanakiri), both of which are not direct results of poverty. By overlaying nutrition map with other maps, we can investigate the plausible causes of malnutrition in different parts of the country.

To analyze the correlational structure of aggregate indicators, we first regressed estimates of malnutrition indicators on the estimates of the logarithm of the commune-level mean consumption _Y_<sup> ̄</sup> and the generalized entropy inequality measure with parameter 0 (GE(0)).<sup>11</sup> Table 4 shows the results.<sup>12</sup> Not surprisingly, we found that the logarithmic mean consump-

> 11GE(0) is also known as the Theil’s L index. See Shorrocks (1980
on housing varies with total household expenditure. It is conceivable that one might find values of _β_ 1 that are below 1. We would however still expect it to be above<sup><u>1</u></sup> 2<sup>.</sup> (Note that the model in that case does not rule out expenditure shares above 1 for extreme household expenditure values.) Let _Fε_ ( _e_ ; _σ_ ) denote the distribution function of _εh_ with unknown parameter vector _σ_ . We will assume that _εh_ is identically distributed across households, although this assumption can easily be relaxed. Note that the unknown parameter vectors _β_ and _σ_ both have to be estimated. In our empirical application, where the value of housing is considered as a predictor of income, the two can be estimated using the household income survey, since it includes both data on household incomes and data on the value of housing.

It will be convenient to define _n_ ( _τ, y_ ) as the number of households with income between _τ_ and _y_ , _n_ ( _τ_ ) as the number of households with income exceeding _τ_ , and _n_ as the total number of households in the population. For ease of exposition we will ignore the fact that the data may constitute a sample with sampling weights. _F_ 2( _y_ ) (= _Pr_ [ _Y ≤ y|Y > τ_ ]) and _λ_ (= _Pr_ [ _Y > τ_ ]) are seen to solve:

When DB-2 does not contain data on household incomes but data on a predictor of household incomes instead, we have that _n_ ( _τ, y_ ) and _n_ ( _τ_ ) can no longer be observed with certainty and so have to be estimated. Consider first an estimator 8
10 STCs are more likely to indicate the presence of a discriminatory measure, and have the additional benefit of having been coded by the WTO Secretariat and made available with the World Trade Report (2012). However, it is not obvious how representative these measures are of the entire spectrum of potentially discriminatory TBT and SPS measures, as governments may have a number of reasons to raise, or avoid raising, a trade concern vis-à-vis other partners.

The third source of information on NTMs focuses on business concerns. The International Trade Center (ITC) regularly conducts business surveys to identify non-tariff measures that create major concerns for exporters. In addition, certain countries compile information on trade barriers faced by their exporting firms, in some cases making this information publicly available.<sup>8</sup> As for specific trade concerns, business surveys are more likely to identify discriminatory NTMs and can often be quite detailed. However, surveys from different sources tend to be of heterogeneous quality and may suffer from sampling design problems. Moreover, they uniquely represent the view of firms and may be subject to a self-selection bias, as firms that respond are not necessarily a representative sample of the universe of exporters or potential exporters.

In sum, data on NTMs are highly incomplete and subject to measurement problems, particularly with regard to the proper identification of discriminatory measures. However, there are currently a number of initiatives to improve upon the existing situation. One such initiative is a multi-agency consortium on Transparency in Trade (TNT) launched by the African Development Bank, ITC, UNCTAD and the World Bank that, among other things, aims at refining and expanding data collection on NTMs and making new data available by 2016. Another initiative is through the Integrated Trade Intelligence Portal (I-TIP) of the WTO, which provides improved information on members’ notifications of NTMs and other sources of WTO-specific information on non-tariff measures. Yet another initiative is called PRONTO, which brings together a number of academic and policy institutions with the goal of improving data availability and
la pesquer ́ıa del Stock Norte-Centro de la anchoveta peruana.” Instituto del Mar del Per ́u (IMARPE) IMPDGIRP/AFDPERP Edici ́on 4 Revisi ́on 1.

- **Instituto Humboldt, and SNP.** 2017. “Segundo conversatorio sobre el dise ̃no de las redes de cerco de anchoveta y el dise ̃no de una red experimental para su uso en Operaciones Eureka.” Instituto Humboldt de Investigaci ́on Marina y Acu ́ıcola (Instituto Humboldt) and Sociedad Nacional de Pesquer ́ıa (SNP).

- **Instituto Humboldt, SNP, and CeDePesca.** 2018. “Propuesta de modificaciones al r ́egimen sancionador pesquero en relaci ́on con las limitaciones de selectividad y el incentivo de pr ́acticas que apoyen la sostenibilidad de la pesquer ́ıa del recurso anchoveta desde la perspectiva de la pesca industrial.” Instituto Humboldt de Investigaci ́on Marina y Acu ́ıcola (Instituto Humboldt), Sociedad Nacional de Pesquer ́ıa (SNP), Centro Desarrollo y Pesca Sustentable (CeDePesca).

- **Isaksen, Elisabeth Thuestad, and Andries Richter.** 2019. “Tragedy, Property Rights, and the Commons: Investigating the Causal Relationship from Institutions to Ecosystem Collapse.” _Journal of the Association of Environmental and Resource Economists_ , 6(4): 741–781.

- **Joo, Rocio, Omar Salcedo, Mariano Gutierrez, Ronan Fablet, and Sophie Bertrand.** 2015. “Defining fishing spatial strategies from VMS data: Insights from the world’s largest monospecific fishery.” _Fisheries Research_ , 164: 223–230.

49
.113)|(0.095)|(0.074)|(0.084)|(0.220)|
|Baseline Food Security (FS)|0.287***|0.152**|0.103|0.148***|0.176|0.075|0.018|0.189*|0.072|0.108|0.311|
||(0.085)|(0.051)|(0.084)|(0.031)|(0.117)|(0.053)|(0.076)|(0.074)|(0.058)|(0.066)|(0.164)|
|Observations|14,506|14,506|14,506|14,506|14,370|14,370|14,370|5,335|5,335|5,335|5,335|

_Note_ : The table displays regression results from our ANCOVA specification with round and regioncontrols (see Equation 3.3). HHI stands for HerfindahlHirschman Index while FS stands for our standardized index of food insecurity. Note that in the Malawi baseline data no respondent reported being severely food insecure and so we cannot estimate the ANCOVA specification for severe food insecurity in Malawi. Standard errors, clustered at the household, are reported in parentheses (*** p _<_ 0.001, ** p _<_ 0.01, * p _<_ 0.05).
of indicator (zero/one) variables and average across the variables to create indices for each of these dimensions.<sup>16</sup> Teacher absence was measured separately from this observation and during an unannounced visit to the school. Enumerators recorded the attendance of teachers who, according to the teaching schedule, were supposed to be teaching at the time of the visit. They were then recorded as being absent from the school, present in the school but absent from the classroom, or present in the classroom. We use this to define two indicator variables: (1) absent from the school versus present in the school (which we call school absence), and (2) absent from the school or classroom versus present in the classroom (which we call classroom absence).

In addition to the SDI data, we also make use of data from the UNESCO Institute for Statistics (UIS) to describe trends in the number of primary-level teachers in each country (accessed via the World Bank World Development Indicators database).

# 3.2 Empirical approach

The general approach used here is based on a regression discontinuity design (RDD) approach, which assesses whether there is a break in trend for teachers that were hired before or after the implementation of FPE. The models are estimated on the pooled sample (including all countries) with a specification that includes country fixed effects, and on country-specific samples. In the analysis of student test scores, the empirical specification for the pooled model is: _T_ itc = α + β × ( _Year Hired_ tc – _FPE year_ c) + δ × ( _Hired after FPE year_ tc) + _Country FE_ + εitc (1) where _T_ itc is the test score for student i, of teacher t, in country c. _Year Hired_ tc is the year that teacher t in country c was hired, _FPE year_ c is the year that the FPE reform was implemented in

> 16 The specific variables used are as follows. Good teacher demeanor: Teacher was