rafmacalaba/datause-encoder-data huggingface dataset:
where Ωi is simply the second half of equation (2): ∑Tj=1 (τij1−σ PjE1−σj ). In words, Ωi is a market openness index, which represents country _i_ ’s export orientation towards world markets. By substitution of equation (3) into (1), the micro-founded gravity equation can thus be expressed as: In gravity estimations, _Ej_ is typically proxied by _j_ ’s GDP, as in equation (2) we are adding up over constraints on _j_ and per Dixit-Stiglitz the number of varieties is proportional to GDP.

In this short recap of the micro-founded gravity model, it is important to note that, per equation (4), the term for trade costs, τ _ij_ , pops out quite nicely. In the international trade literature, it is typically asserted that τ _ij_ is a linear function of distance alone, or a linear function of distance and other _ij-_ specific barriers to trade (such as contiguity, common language, etc.) whereby each trade cost variable enters into the gravity equation independently.

Yet there is no theoretical reason to believe that distance impacts the marginal cost of trade linearly or in isolation. On the contrary, if the distance between two countries doubles it is unlikely that it will be twice as expensive to ship goods between them as many of the fixed costs associated with exporting over any distance occur early on anyways. Also, in terms of trade agreements, it is not unreasonable to think that EIAs might affect the marginal cost of distance for trade for many reasons, leading to a reinforcing effect of these two variables.

Thus, in our analysis, we depart from the standard literature by asserting that τ _ij_ is a function of standard trade cost variables (including trade agreements) _in addition to_ the interaction of trade agreements and distance. Mathematically, this assertion boils down to the following functional relationship: To unpack the channel through which distance and EIAs might interact, we note that closeness alone leads to a larger share of two-way trade (in intermediates/parts and components) than oneway trade (in final goods). We also note that EIAs alone impact the share of two-way trade more than one-way trade in final goods.<sup>8</sup> Thus, we explore whether the interaction between bilateral distance and EIAs has a multiplicative effect on intermediates in particular.

# _<u>2.2 Data sources and variable definitions</u>_

Data for our analysis are drawn upon from various sources. Annual 4-digit SITC Rev. 2 data on bilateral trade flows—from NBER-UN and described in Feenstra _et al._ (2005)—were used in the aggregation of total trade statistics. In order to examine trade flows by type of good, we translated

> 8 Regression results supporting these statements are in Table 1. We do not explicitly explore why EIAs might impact two-way more than one-way trade in this analysis, however note that this would be a useful extension to our paper.

5
10 CNPR - a.CEPR + (l-a).NPRi

- where. - a is the. Value Added to Output ratio (in international prices);

- CEPR is the Compensatory Effective Protection Rate defined earlier; and

- NPRi is the average Nominal Protection Rate of<sup>inputs.</sup>

The first term a.CFPR measures the impact of<sup>investment costs on the output</sup> production cost, and the second term (l-a).N?Ri measures<sup>the impact of input</sup> costs.

23. This anal-sis applied to the sample projects indicates that most of the additional production costs, and thus most of the Compensatory Nominal Protection (CNPR) required for output, stems from the second<sup>term capturing the impact of</sup> input costs, as indicated below.

Table **5: Ipct** of Investment **md Irpjt** Costs **on** Nominal Protection **(in** X of Output Value at International Price)

|Export<br>Catecory|Input Cost<br>Incect|VA/CEPR**/1**<br>lct|**P-R**/a|Actual<br>"Pt|**U**Projects (%) in<br>ExcessShortfat|
|---|---|---|---|---|---|
|**None**|39.5|9.6-13.3|49.1-52.8|55.6<br>|65/35<br>|
|Potential|14.9|27.5-28.5<br>|42.4-43.4<br>|0<br>|0/100<br>|
|Marginal|32.7|6.2-10.5|38.9-43.2|**43.3**|33/67|
|Significant|22.9|10.8-17.3|33.7-40.2|21.9|23/77|
|Overall|31.7|11.7-16.1|43.4-47.8|.1|42/58|

**/a** - The **Lower** (resp. hijfer) **vaLue** of the indicated rwge corresponds to yl (resp. y2). Source: **Arnex** VI.

24. A broad conclusion emerges clearly from the above table. Input extra costs increase production costs on average<sup>by about 32% of the output</sup> international price. This impact cost impact is<sup>only 15% in the category of</sup> Potential exporters, due to their access to relatively<sup>cheap resource-based</sup> inputs (cf. para. 10) and to their high VA content<sup>(cf. para. 9). Surcharges</sup> on investment costs increase production costs<sup>on average by 12 to 16%. This</sup> investment cost impact is significantly lower<sup>(6-10%) for Marginal exporters,</sup> due to their low VA content (25%, as against 37%<sup>for other categories) and the</sup> lower tariffs paid for their imported capital goods (57%, compared to 72% for - other categories cf. Table 3). The investment cost impact is markedly higher for Potential exporters, for symmetrical reasons (high VA content, and highest tariffs of 76% on imported capital goods). The overall impact is that all these industrial projects would normally require on average a Compensatory Nominal Protection (CNPR) of 43 to 48%, say about 45%, two-thirds of which to offset the impact of high input costs. It should be noted that, due to the partial tariff exemptions enjoyed by Significant exporters for their imported inputs and equipment (cf. Tables 2 and 3) as per the<sup>trade</sup> regime for exporters, such projects would require a slightly lower CNPR (ranging<sup>around 37%) for their</sup> output. It further confirms the findings<sup>and recommendations of the Export</sup> Strategy Report (6663-IN) regarding the critical importance of providing accesr to production factors at competitive prices for a successful export policy.
first randomly drop low-skilled and high-skilled migrant stock data for five countries from the sample for which we have actual data. We then estimate the gravity model as explained in the previous section, as if these five destination countries also had missing data. We impute the missing low-skilled and high-skilled migrant stocks for these five countries and calculate the log-ratios of these imputed data to the actual migrant data. We repeat these four steps 100 times and subsequently evaluate their performance.

Figure 2 presents the density functions of these log-ratios for female migrants for 2000. The left hand side graphs refer to low-skilled migrants, while the right hand side figures refer to high-skilled migrants. We use different cutoffs to assess the predictions of corridors of different sizes. More specifically, the top graphs capture all corridors, the middle set refers to corridors comprising greater than 1,000 migrants, while the bottom graphs refer to corridors of greater than 10,000 migrants. In each case, the densities are bell-shaped and the median is around zero.<sup>15</sup> Inherent idiosyncratic factors exist in the estimation of small migration corridors. As clearly seen in the comparison of Figures 2a, 2b and 2c or 2d, 2e and 2f, the distribution of smaller corridors (2a and 2d) span a wider range, indicating a larger standard deviation. As corridors get larger however, the distribution of the log ratio becomes increasingly centred around zero, indicating higher precision. Since these larger corridors comprise the vast majority of the migrant stocks, their higher precision increases our confidence in overall interpretation of our results.

# [INSERT FIGURE 2 AROUND HERE]

> 15The results are similar for other types and the year 1990.

25
various types of structural change, Garbaccio et al. (1999) employ input-output tables. They conclude that between 1987 and 1992, technical change within sectors accounted for most of the fall in the energy output ratio and structural change actually increased the use of energy. An increase in the import of some energy-intensive products also contributed to the decline in energy intensity.

Greening et al. (1998) apply the Adaptive Weighted Divisia rolling base year index to total carbon emissions from the manufacturing sector of 10 OECD countries for the period 1971– 1991. They find that declines in aggregate carbon intensity for these countries may be primarily attributed to decreases in energy intensity, i.e. increases in the average factor productivity of energy.

Using data of 2,582 large and medium-size Chinese enterprises over the period 1997-1999 and employing the multiplicative Divisia index, Fisher-Vanden et al. (2004) decompose the intensity of three types of energy – coal, refined oil, and electricity into the contributions of sectoral shift (i.e., changes in the sectoral composition of total industrial output) and subsector productivity change (i.e. changes in energy intensity within individual industrial sectors). They document that while subsector productivity change emerges as the dominant factor driving declines in oil and electricity intensities, sectoral shift plays a near equal role in declining coal and refined oil intensities.

Applying logarithmic mean Divisia index (LMDI) techniques, Ma et al. (2008) investigate the role of inter-fuel substitution in the decline in energy intensity or the causes of the rise in energy intensity in the period 1980-2003 for Chinese manufacturing industry. They find that technological change is the dominant contributor to the decline in energy intensity and structural change at the industry and sector (sub-industry) levels actually increased energy intensity over the period. On the other hand, structural change involving shifts of production between sub-sectors decreased overall energy intensity with inter-fuel substitution contributing little to the changes in energy intensity<sup>3</sup> .

Finally, being the only firm level study in the literature, Petrick (2015) decomposes changes in total CO2 emissions in German manufacturing industry to changes in activity level, structural change between sectors, shifting market shares within sectors, energy intensity at the firm level, fuel mix, and emission factors, applying an LMDI index decomposition technique. He reveals that shifts in production across firms in manufacturing sub-sectors and shift of production across sub-sectors play larger roles in determining the path of energy use

> 3 Please see Liu and Ang (2007) and Ang (2015) for a detailed review of the related literature.

5
# References

- [1] Ahuja, A., and M. Nabar. 2012. “Investment-Led Growth in China: Global Spillovers.”Working Paper 12/267, International Monetary Fund, Washington, DC.

- [2] Alturki, F., J. Espinosa-Bowen, and N. Ilahi. 2009. “How Russia Affects the Neighborhood: Trade, Finance, and Remittance Channels.” Working Paper 09/277, International Monetary Fund, Washington, DC.

- [3] Ambler, S., E. Cardia, C. Zimmermann. 2002. “International Transmission of the Business Cycle in a Multi-sector Model.”European Economic Review 46 (2): 273—300.

- [4] Andrle, M., R. Garcia-Saltos, and G. Ho. 2013. “The Role of Domestic and External Shocks in Poland: Results from an Agnostic Estimation Procedure.”Working Paper 13/220, International Monetary Fund, Washington, DC.

- [5] Canova. F. 2005. “The Transmission of US Shocks to Latin America.” Journal of Applied Econometrics 20 (2): 229-251

- [6] Canova, F., and J. Marrinan. 1998. “Sources and Propagation of International Output Cycles: Common Shocks or Transmission?”Journal of International Economics 46 (1): 133—166.

- [7] Claessens, S., M. A. Kose, M. E. Terrones. 2012. “How do Business and Financial Cycles Interact?”Journal of International Economics 87(1): 178-190.

- [8] Comin, D., N. Loayza, F. Pasha, and L. Serven. 2014. “Medium Term Business Cycles in Developing Countries.”American Economic Journal: Macroeconomics, 6(4): 209-45.

- [9] De Walque, G., F. Smets, and R. Wouters. 2005. “An Estimated two-country DSGE Model for the Euro Area and the US Economy.”National Bank of Belgium, Mimeo, European Central Bank.

- [10] Didier, T., M.A. Kose, F. Ohnsorge, and L. Ye. 2015. “Slowdown in Emerging Markets: Rough Patch or Prolonged Weakness?”Policy Research Note 4, World Bank, Washington, DC.

- [11] Diebold, F. X. and K. Yilmaz. 2015. “Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring.”Oxford: Oxford University Press.

- [12] Doyle, B., and J. Faust. 2002. “An Investigation of Comovements among the Growth Rates of the G-7 Countries.”Federal Reserve Bulletin 427- 437, Board of Governors of the Federal Reserve System, Washington, DC.

- [13] Eickmeier, S. 2007. “Business Cycle Transmission from the US to Germany– A Structural Factor Approach.”European Economic Review 51 (3): 521-551.

- [14] Frankel, J., and A. Rose. 1998. “The Endogeneity of the Optimum Currency Area Criteria.” The Economic Journal 108: 1009—1025.

- [15] Galí, J. and T. Monacelli. 2005. “Monetary Policy and Exchange Rate Volatility in a Small Open Economy.”Review of Economic Studies 72: 707—734.

- [16] Gregory, A.W., A. Head, J. Raynauld. 1997. “Measuring World Business Cycles.”International Economic Review 38: 667—702.

13
**Figure 7 Share of Households in Largest Family Lineage Group (1995) and Responsibilities of the Village Representative Assembly**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Share of Households in the Largest Patrilineal Clan (1995)<br>Authorized to Examine Financial Record<br>Ever Changed the Decisions of Village Leaders<br>1.1<br>1<br>.3<br>.9<br>.2<br>.8<br>.1<br>to Examine Village Financial Record .7<br>Villagers' Representative Assembly Authorized<br>Villagers' Representative Assembly Ever Changed<br>0<br>the Decisions of the Party Committee or Villagers Comittee<br><!-- End of picture text --> Source: 2004 Rural Household Social Network, Labor Allocation and Land Use Survey, RCRE, Ministry of Agriculture.

_Land Tenure Insecurity and Migration, Page 44_
# **ASSESSMENT OF THE ECONOMIC IMPACTS OF CLIMATE CHANGE ON AGRICULTURE IN ZIMBABWE: A RICARDIAN APPROACH**<sup>**1**</sup>

# **Reneth Mano and Charles Nhemachena**<sup>**2**</sup>

> 1 An earlier version of this Working Paper was published as CEEPA Discussion Paper number 11.

> 2 Respectively, Department of Agricultural Economics & Extension, University of Zimbabwe; and CEEPA, Department of Agricultural Economics & Rural Development, University of Pretoria; e-mails: <u>rtmano@mweb.co.zw nhemachenacharles@yahoo.co.uk</u>

This paper was funded by the GEF and the World Bank. It is part of a larger study on the effect of climate change on agriculture in Africa, managed by the World Bank and coordinated by the Centre for Environmental Economics and Policy in Africa (CEEPA), University of Pretoria, South Africa. The authors are grateful to CEEPA for excellence in coordinating Pan African projects of this magnitude; to Dr James Benhin for academic leadership and mentoring; to Prof. Rashid Hassan for useful comments on this report; and to Ms Glwadys Gbetibouo for much assistance. The model underlying this report was developed collaboratively by the authors and the Yale University team, to whom we are grateful for essential training and technical backstopping. We owe an intellectual debt to the Yale team, in particular to Pradeep Kurukulasuriya, whose dedication to our work has greatly inspired us. We are also grateful to the enumerators and drivers who did the field work and assisted with data entry (Ben and Livingstone Chilimanzi, Charity Mudombi, Phillipa Kanyoka, Gerald Chikazunga, Melinda Murandu, Moreblessing Mano and Lyona Madondo); to the AREX district offices and field extension workers whose cooperation and support helped make this project a reality; and particularly to Dr Manzungu and Philip Jindu for much help during data collection in Manicaland and Mashonaland East provinces and to Wellington Jogo for supervising data collection. Special appreciation goes to the University of Zimbabwe agricultural economics staff and graduate students for support and comments at various stages of the research, in particular to Dr Rugube and Dr Sukume and to Unity Chipfupa and Josphat Chisora.
infrastructure is still generally<sup>inplace,itis.beingsteadily erodedbypersistentfinancial</sup> problems. The low salaries of<sup>medical personnelin-mining townslead themto migrate in search</sup> of better prospects (in Anina,<sup>inRomania,tfireedoctorsremainatthehospital, whichisin</sup> danger of being shut down).<sup>Hospitalsstruggle tomaintain medicalequipment,which was</sup> reported to be heavily deprecia.ted<sup>in some locations.And peoplefind it increasinglydifficult to</sup> afford even basic medical services,<sup>to say nothing of specializedservices.</sup> 100. The research in Ukraine revealed<sup>twointeresting aspectsof theproblemof healthcare</sup> services in that country. First,<sup>thecomparativesurvey results showedthat theassessmentof the</sup> quality of health services available<sup>inthetwomining townswasessentiallyidenticalto the</sup> assessments given in like-sized<sup>towns intherestofthe country,underscoringthenational</sup> character of the problem.<sup>26</sup> Second, cooperative group<sup>insurancepools havebeen establishedin</sup> both Gorlovka and Stakhanov.<sup>These groupinsurance schemes,whichare basedonindividual</sup> memberships and cover hospitalization<sup>fees,havenotbeeninexistence forverylong(and</sup> accordingly, have not been<sup>not, assessedintany. meaningfulway),but areapparently popular;in</sup> Gorlovka, the cooperative has<sup>reportedlymorethan15,000membersinlittleoverayearof</sup> operation.

> 26 These services were assessed<sup>as"bad"by about 37-40%of respondentsinthetwosamples,as"good"by about</sup>

> 13-17% of respondents, and<sup>as "neither good nor bad" by about one-third of respondents.</sup>

29
adjusted Amihud illiquidity measure. This hazard ratio would suggest that the average investor is 20% less likely to sell a stock she or he holds when the stock’s illiquidity measure increases by one standard deviation, suggesting that households in Finland are cognizant of transaction costs and pay attention to liquidity when trading. The economic significance of the estimated hazard ratio of AdjIIliq for Finnish stocks is similar to that for the US stocks.

We further investigate how household characteristics introduce heterogeneity in the relationship between transaction costs and holding periods. In particular, we examine if financially more sophisticated investors pay more attention to transaction costs. In doing so, we control for the effects of demographic characteristics available in the Finnish data set such as age and gender. As in the US analysis, we assume that financial sophistication is correlated with education and resources available to each investor. Investors who invest larger amounts of capital in the stock market – higher Log (Equity Holding)-, households with experience trading options – one for Option user Dummy- or that have higher portfolio concentration (Concentration) are assumed to be financially more sophisticated. Since a very small percentage of Finnish households have ever held short positions, we do not include this criteria in the construction of our Sophistication measure. Since the Finland transaction data doesn’t provide information regarding investors’ income and professions, or information about whether the investor has ever traded any foreign securities, we exclude these criteria in the construction of the Finnish sophistication measure.

Our sophistication measure for Finland ranges from a minimum of zero (0) for the least sophisticated investors to a maximum of three (3) for the most sophisticated investors. Column (1) of Panel B in Table 8 reports that the hazard ratio of the Amihud illiquidity measure interacted with the overall sophistication measure, AdjIlliq×Sophistication, is statistically significant and less than one (0.996). This result suggests that Finnish investors who are more financially sophisticated 34
<!-- Start of picture text --> es . ~<br>\ TM,ms LIBYA jpth interior<br>ALGERIA jexth interior ‘<br>/ &<br>eer eed<br>Nor :<br>I a<br>Gh<br>“nN (CY orrapert yo \,,<br>SY Neel A Ps CHAD<br>BURKI KSOBinAY,a2 aa SS tt<br>an +a eee, “ N . 4<br>< . tF BENIA”’ Tee. , cAMEROO!<br><!-- End of picture text --> <!-- Start of picture text --> AirportsBixrs0 (1000 Passsnges perAPU)<br>GB) rsa. so00 CB) = seo<br>P& vine [MB] owes tansivmet;<br>Railroad (Millon Traffic Unit ser Annum)<br>‘OpeningTe Tos geet fez ep<br><!-- End of picture text --> <!-- Start of picture text --> Rael Traffic (Avy Annual Daly trafic)<br>ee<br>3007000<br>UninewsRoo qyoe < 100& So«100-300-1000Condition<br>Ges Far Poor Urine<br>Se Se mesParos<br>C0000 Sonn memes comes Lispeved<br><!-- End of picture text --> <!-- Start of picture text --> ON,<br>oe\ UBYA _~<br>ALGERIA a a oN N\ ~ q-<br>Loz “> )pooN \<br>} _ AGADEZ<br>} J<br>a a SA<br>2 =a TM /<br>" aT BER : 2 0 .<br>—— oe + ay + . at { CHAD<br>BURKINAbe FASO. te: }<br>! NIGERIA<br>aP1080) \ “ shite<br><!-- End of picture text --> <!-- Start of picture text --> Persea Plas | Type end Copcty(My<br>~— “o “oO. ota<br>8 e 8 »ya. 199100<br>Power Lines (Vv)<br><!-- End of picture text -->

<!-- Start of picture text --> aN UBYA<br>ALGERIA a —S, _{a<br>“ mo ~<br>Ns aaaa f \ .\<br>1 a ‘ \<br>ss~ _ A . AOA 4 |Y<br>y% A<br>ws Y; J<br>» |<br>= ay,"Cy goeeZ a - on J/<br>Oph . _ G75 4 7 it be<br>aes pa te . ys Vy CHAD<br>‘ \<br>ah 4<br>a *<br>__BURKINAFASO Ge e + i<br>Yon_, ene 4 i<br><!-- End of picture text -->

<!-- Start of picture text -->
-Itomalicnal Gateways<br>mae Fined Transmission Network<br>iy \Ov - G80 Coverage<br><!-- End of picture text -->

<!-- Start of picture text --> ~\ UBYA<br>a SN Lcoa<br>ALGERIA a mx ~ \<br>~ ~ a wvP, ; \ ,\,<br>Pa ) \<br>_*D AGADEZ |’<br>ar<br>J<br>% .<br>wo |<br>-=" Lahhie ae Jva<br>3 oO” ail! ZIND) :<br>+ i q a ot = “cn CHAD<br>‘ J t be w<br>q<br>= Ses ~ et<br>BURKINA FASO ihe Y<br>> 4 NIGERIA §<br><!-- End of picture text -->

<!-- Start of picture text --> a<br>© weersse<br>S@> a= onceia00- z000<br><!-- End of picture text --> <!-- Start of picture text --> WATER<br>Eurront Irrigation (% Area}<br><8 @H-25 Boss<br>UY dane<br><!-- End of picture text -->