rafmacalaba/datause-encoder-data huggingface dataset:
claimed to be the main motivation for almost 70% of investors in the sample. The second main motivation for investing abroad was lowering production costs, chosen by 20% of investors surveyed, followed by acquiring natural resources and inputs (5%) (Gomez-Mera et al. 2014).

Investors were also asked to identify the three countries where they hold their largest investment. Of all investors in the sample, only a subset identified the three main destinations. We aggregate this firm-level data to generate a count of the number of investments from each country in each sector. The implicit assumption made here, and purely driven by data availability, is that these data give a good representation of the set of destinations in which firms from these four countries invest.

Although companies in our sample have investments in all regions of the world, there is a clear regional and cultural bias, particularly in services. Brazilian firms tend to invest in Latin America, South African firms invest in Africa, and Korean firms tend to invest in East Asia (Table 2). The exception is India, with a more widespread reach. This is why in the remainder of this paper we look at revealed investment decisions and test the trade-off between market size and informational costs associated with geographical and cultural distance, as well as the role that BITs may have in reducing these informational costs.

**Table 2** . Investment destinations by country and region (%)

||**Brazil**|**India**|**South Africa**|**Korea, Rep. **|
|---|---|---|---|---|
|Africa|6.98|8.33|74.7|2.58|
|East Asia & Pacific|2.33|23.5|8.0|69|
|Europe & Central Asia|16.3|22.7|10.7|5.81|
|Latin America & Caribbean|48.8|1.52|4.0|10.3|
|Middle East & North Africa|2.33|20.4|0.0|0.65|
|South Asia|0.0|6.82|1.33|7.1|
|US|23.3|16.7|1.33|4.52|
|**Total**|**100**|**100**|**100**|**100**|

Source: Gomez-Mera et al (2014) Consistent with the literature on the determinants of FDI, firms in this sample face a trade-off between investing in a larger, more developed market and investing in more familiar, closer destinations where they face lower transaction costs. Figure 1 shows this trade-off graphically. The scatter plots all possible bilateral investments for firms in each of the countries of origin in our sample. The red dots show the countries in which there is at least one investment (or one firm from that country that holds its largest investment there). Except for firms from Korea, which appear to have a wider, more global reach, we can see most of actual investments falling along or above a positively sloped line. The graph identifies a locus of combinations of market attractiveness and distance that makes firms willing to invest: firms accept greater transaction costs in exchange for a more attractive market. Thus, for example, although Brazilian firms would probably like to invest in large attractive markets such as Indonesia, they end up investing in smaller, but closer countries like Uruguay, Argentina, and Paraguay. It appears as if it took very large markets such as China, Japan or India, to offset the costs of investing in distant locations.

**Figure 1** . Market Size- Distance Trade-Off

10
ability of the regulator to get companies to cut connection costs from 2.35 cts/minute to 1.1 cts/minute.

_Table 8. Basic interconnection principles in a sample of countries_

||Natureof<br>interconnection<br>charges|Pricingmodel|Regulatory<br>approvalof<br>charges|Unbundlingof<br>facilitiesorof<br>accounts|
|---|---|---|---|---|
|Argentina|Commercial<br>Agreement(CA)|LRIC|Y|Y|
|Bolivia|Imposedby regulator<br>(IR)+CA|LRIC<br>_<br>_<br>_<br>_<br>_<br>_|N|N|
|Brazil|CA||N||
|Chile|IR|LRIC|Y|N|
|Colombia|IR|LRIC+ FDC|Y|Y|
|CostaRica|IR|OPPORTUNITY<br>COST|Y|N|
|ElSalvador|CA|LRIC|N|Y|
|Jamaica|CA|NONE|N|N|
|Mexico|CA|LRIC|N|Y|
|Panama|CA|LRIC|N|Y|
|Peru|IRworkingasacap<br>+CA_|LRIC|Y<br>_<br>_<br>_<br>_|Y<br>_<br>_<br>_<br>_<br>_<br>_|

Source: ITU Since so many in the region are working on the implementation of a similar model, it may be worth reviewing what theory has to suggest on its benefits and its potential limitations. LRIC is a measure of the true economic cost of an asset and sends the right makeor-buy signal to alternative suppliers of infrastructure. It is a long run measure of costs and does not overestimate the value of assets as it would be most likely done if one adopted historic costs. This is quite important for countries with a long tradition of accounting systems distorted by inflation as in Latin America. Capital is included in its measure (depreciation is rightly considered as an economic cost), allowing potentially for full recovery. In fact, if a new technology were more efficient than an existing one in a competitive market with free entry, then entry would occur and prices would have to equal LRIC. LRIC should represent the long run equilibrium level of charges, hence it guarantees to achieve allocative efficiency. Although the details are quite complicated, the underlying principles for its computation are the following:

- Assets are valued and depreciated on a current cost account basis, giving the current replacement cost of a modem efficient asset;

- Operating capital costs are grouped together according to the cost type and cost driver;

21
O.A.M.D.G.

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47
8 and the spouse along five categories (illiterate, some primary school, primary school completed, some secondary school, and secondary school completed or higher); and (e) occupation variables: the household head's main occupation or field of employment (twelve occupational classifications are used: five agricultural, six non agricultural, and a classification for non working heads). In addition, for the survey years 1988-89, 1991-92, and 1995-96, we also know (f) the amount of land owned by the household (five categories are considered, from the landless to those with more than 2.50 acres of land); and (g) the religion of the household.

The regressions (5.1) and (5.2) can be used for assessing the impact of household characteristics on consumption since the parameters in jU and **O3R** give the percentage increase in consumption associated with these characteristics. The regressions can also be used to asses the impact of changes in household characteristics on the probability of being poor. Denoting the cumulative normal density by F, its first derivative (the density function) by f, and the standard error of, say, the urban regressions by au, the impact on the probability of being poor of a marginal change in a continuous characteristic XA is -PA f (-IU'Xi /aU)/crU since the urban probability of being poor for a household with characteristics **Xi** is obtained through: Prob [Log yUi < 0] = Prob **[Pu'Xi <** &Ui] = F[-U'Xi /aU] (6) Due to properties of the linear regressions, the expected consumption levels in the urban and rural sectors obtained by conditioning on each sector's sample means must equal the actual mean values observed in the two sectors. This in turn provides for a way to assess the impact of household characteristics and the returns to these characteristics on growth. Consider a change in mean per capita consumption from time t to t+1. Denoting by XU the mean characteristics of all urban households, the growth in urban (or rural) per capita consumption between t and t+1 can be decomposed as follows: - Growth Et+l [Log yU]-Et [Log yU]= **(I3t+lU'- PtU')XtU** + OtU'(X t+lU XtU) + R (7)
scribed above. _φt_ are the year fixed effects. In some specifications we also include the log of one plus the U.S. MFN tariff, and a vector of dummy variables indicating that a country has a preferential trade agreements with the U.S. The coefficient on the MFN tariff provides an internal estimate of _σ_<sup>_k_</sup> , under a restrictive assumption that _σ_<sup>_k_</sup> has a common value across products.

The reduced form model is useful for illustrating the cross-product variation in home bias. For measuring welfare we need to infer structural trade costs. As discussed, the regression coefficients in (13) conflate the effects of trade costs and trade responses. Our solution to this problem is to include in the estimation external estimates of _σ_<sup>_k_</sup> from Fontagné et al. (2022), treating them as data for the purpose of identification. _σ_<sup>_k_</sup> enters into the regression alone. We also interact it with distance and _HOMEj_ , and with the interactions of these variables with<sup>_−→_</sup> _Z_<sup>_k_</sup> _t_<sup>.According to</sup> the structural model, the coefficients on the interaction terms can be used to infer the trade costs that each friction imposes on each product _k_ . Our new specification is as follows: where _σ_<sup>_k_</sup> is taken from Fontagné et al. (2022), and _ε_ is an estimate of the conditional correlation between _σ_<sup>_k_</sup> and _M_<sup>_k_The key difference between (14) and (13) is that</sup> _j,t_<sup>.</sup> we have interacted _σ_<sup>_k_</sup> with all of the geographic frictions, so that we can give a structural interpretation to the coefficient estimates. The coefficients of interest, _−→ γ_ and<sup>_−→_</sup> _γ_ are structural equivalents to _β_ and<sup>_−→_</sup> _β_ (with _γ_ = _− σ_<sup>_<u>βk</u>_and</sup><sup>_−→γ_=</sup><sup>_−_</sup> _σβ_<sup>_k_).</sup> The _γ_ term becomes _lnb_ in equation (10), and<sup>_−→_</sup> _γ ×_<sup>_−→_</sup> _Z_<sup>_k_</sup> _t_<sup>_× HOMEj_producesa</sup> predicted distribution of _τJA,t_<sup>_k_.Theseestimatesarenotquitecomplete,because</sup> they are relative, rather than absolute measures of JA trade costs. We describe our process for turning relative into absolute values once our<sup>_−→_</sup> _γ_ estimates are in hand.

23
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21
Ministry of Energy and Water Development (2011). “Press Statement on Government Programme for Blending of Biofuels with Petroleum Products”. Ministry of Energy and Water Development (MEWD), Government of the Republic of Zambia.

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16
# Inflation, liquidity and innovation

Michael Evers<sup>_∗_</sup> Stefan Niemann<sup>_†_</sup> Marc Schiffbauer<sup>_‡_</sup> JEL _classification:_ E22, E31, O16, O31, O42 _Keywords:_ inflation, liquidity, technology choice, innovation, investment

> _∗_ Department of Economics, University of Hohenheim, Germany. E-mail: michael.evers@uni-hohenheim.de

> _†_ Department of Economics, University of Essex, United Kingdom. E-mail: sniem@essex.ac.uk

> _‡_ The World Bank. E-mail: mschiffbauer@worldbank.org
**Table 5 Assumptions about the International Economic Environment in the 1990s: Projections and  Actual Values**

<u>(annual percentage change)</u>

|||**_History_**||**_Projectio_**|**_n for 1990s_**|**_Actu_**|**_al_**|
|---|---|---|---|---|---|---|---|
|_Income level/indicator _|_1965–88_|_1980–87_|_1988_|_Baseline_|_Low case_|_1990–2000_|2000–10|
|_High-income countries_||||||||
|Real GDP|3.3|2.6|4.8|3.0|2.4|2.5 (2.9)<sup>a</sup>|1.4|
|Inflation|5.6|4.8|2.6|4.0|4.4|1.5|1.4|
|Manufactures unit<br>value|6.2|3.2|6.5|3.9|5.0|-0.3|1.9|
|Real rate of interest|3.1|5.7|4.6|2.7|3.8|3.2|0.4|
|Nominal rate of interest|8.7|10.5|8.1|7.8|9.6|5.4|2.7|
|_Low- and middle-_<br>_income countries_||||||||
|Export value in dollars|10.0|4.2|11.6|11.0|9.7|9.6|13.7|
|Export volume|5.0|5.6|6.2|5.5|4.2|7.0|8.9|
|Real price of oil|6.2|–5.0|–19.4|3.5|3.0|2.4|8.4|
|Real price of 33 nonoil<br>commodities|–3.5|–4.0|11.0|0.6|0.0|–0.8|8.2|

_Note_ : This is table 16 from _A Long-Term Outlook_ (Fardoust and Dhareshwar 1990), to which actual figures for the 1990s and the 2000s were added. Actual values for the 1990s for commodity prices are from _Global Economic Prospects_ (World Bank 2001). The current commodity price data of the World Bank give a value of –1.4 percent a year for the 1990s. Values for 2010 are estimates. aFigure in parentheses is the real average annual GDP growth of high-income OECD countries excluding Japan.

47
-2-

construction exr'nditure fell from 12.5 percent In 1979 to 4.6 percent In 1986. However, the reintroduction of compulsory labor services for maintenance of irrigation Infrastructure was equlvalent to more than half the state's budget for capital construction In agriculture during 1987. Information on farmers' farm Investment Is scant, but there are some indications of a response to the stimull provided by the reform: In 1986 about 90 percent of the purchases of walking tractors, and a rapidly expanding share of truck purchases were by Individual farmers. On-farm investment by farm households has likely been substantial, but It Is not clear to what extent It has been overtaken by households' expenditure on non-productive assets (consumer durables and housing) and non-farm enterprises.

Whilo the household responsibility system stimulated production Incentives, It can be argued that concerns regarding the stability of the land tenure system introduced by the reforms, extremely small farm sizes, and credit Inadequacies hinder farm Investment, and may have caused a preference for Investing In nonproductive assets (e.g., housing) and In non-agricultural activities. Such arguments have been raised quite frequently by observers of Chinese agriculture, but there has been a paucity of empirical research to assess their validity and importan^e The c.,_Jective of this paper Is to clarify, on the basis of detailed farm level data derived from recent surveys, the importance of factors relatid to tenure security, farm size and credit availability In constralning farmers' agricultural Investment. In particular, a direct measure of farmers' perceptions regarding tenure security will be utilized, as well as Information on transactions In the credit market. The next section provides a description of the study areas.
based on fuel use and production. These pollutants are the most important of all air pollutants in terms of their health impacts and are associated with significant additional mortality and morbidity to the population, including the labor force (see Box 2). In this study, morbidity was quantified by estimating the days lost due to reduced activities and increased hospital admissions due to respiratory illnesses. Each of these impacts was quantified based on epidemiological studies (more details are in Annex 1). Based on the CGE model estimation of emissions, the increase in PM10 and other small particle <u>concentrations</u> was estimated using the concept of uniform rollback<sup>14</sup> . Under this assumption, health impacts can be linked directly to levels of emissions; the analysis does not include a characterization of how emissions affect air quality (pollutant concentration), the physical measure one would typically see in the health literature to estimate changes in illness and risk of premature death.

11. The morbidity and premature mortality impacts of PM concentrations were measured in monetary terms as follows. For morbidity, an estimate was made of losses in productivity and costs of treatment for illness. For premature mortality the impacts were valued in terms of both loss of future productivity (where appropriate) and the welfare loss associated with early death (see Annex I, section VII for details).

12. It is often the case that if an environmental policy such as a tax induces technical change, for example by triggering emission or resource-saving technical change, it reduces the cost of achieving a given abatement or resource conservation target. For example, emission air pollutants can be reduced cost-effectively by fuel substitution (non-energy for energy or within-energy inputs), and by efficiency improvements in power generation and use. Most CGE models, however, assume no difference in the **pattern** of technical change between the base case and the policy case, which often leads to an upward bias in the cost estimate of policy. Other common approaches to technical change are the use of capital vintages involving different technologies or the modeling of autonomous energy efficiency improvements. An attempt is therefore made in the CGE model to capture these technological shifts over time by altering the elasticity of substitution between capital and energy and by altering levels and types of investments and corresponding emission coefficients (in line with the existing bottom up analyses for India). These are described in detail in the methodology section.

> 14 The concept of ―uniform rollback‖ states that the percentage change in pollutant emissions can be assumed to be equal to the percentage change in pollutant concentration.  This assumption invariably involves a simplification of how emissions affect air quality; how much of a simplification depends on specific circumstances.

6