rafmacalaba/datause-encoder-data huggingface dataset:
from our CGE model to try to sort these (and other) different effects out and to determine the net poverty impacts.

# **3. Doha Results**

## **3.1  Macro and Sectoral Effects**

The macro effects of the Doha simulation are reported in the first column of Table 3. On average, export prices (0.41 percent) increase more than import prices (0.21 percent). Domestic producers increase their export volumes in response, and reduce their local sales. The combination of reduced local sales and increased import and export prices raises domestic consumer and output prices. As local prices increase relative to imports prices, Philippine consumers substitute toward imports.

At a sectoral level the Doha results suggest a reallocation of exports and production from the inward-oriented agricultural and service sectors toward the export-oriented industrial sectors. Table 2 presents the world import price, export price and export demand effects of the Doha scenario according to the 35 sectors of our CGE model. While world export prices and demand increase overall, they decline in the agricultural sector, as driven by declines in export demands for fruits and vegetables, fats and oils and rice. In the wake of the Doha reforms, Philippine exports of these products become relatively less competitive in several key markets, including: Japan, EU-15, Korea and Taiwan. For example, in the case of processed rice, Philippines has tariff-free access into the EU already, so that when tariffs on competitors are cut, the price of Philippine rice in the EU relative to the average import price for rice rises by 28 percent. Consequently there is little surprise that the Philippines loses export share in these markets.

16
Yang, Yongzheng and Sanjeev Gupta. (2008) “Regional Trade Arrangements in Africa: Past Performance and the Way Forward”, African Development Review, 19(3): 399-431.

Yeats, Alexander. 1990. “On the Accuracy of Economic Observations: Do Sub-Saharan Trade Statistics Mean Anything?” _The World Bank Economic Review_ 4 (2): 135-156.

# _Appendix 1: Gravity model estimates_

Table 9 presents various estimates based on the gravity model of Behar & Manners (2010). To evaluate the change in intra- and extra-regional trade flows of SADC countries over time, the gravity model is embedded in a panel setting where the time periods are specified as 1991-95, 1996-2000 and 2001-05. Each period reflects the average of (strictly positive) annual values and reflects a value of zero if no annual flows are recorded that period. With this data, we can include dummies for the time period to depict evolution over time. We can also include dummies to indicate whether the exporter, importer or both are SADC countries. The results here are based on IMF DOTS export data. However, the specification is also estimated using data from UNComtrade (using a combination of measures reported by the exporter and importer). While there are major discrepancies in trade flows between the databases, the econometric results are generally consistent for both sets of data.

The equation is estimated using OLS and zero values are ignored. Alternative gravity model estimates based on a cross-country database for 2007 & 08 and estimated using a Poisson Pseudo Maximum Likelihood (PPML) estimator proposed by Santos Silva and Tenreyro (2006) corroborate our findings regarding the regionalization of SADC trade.

# **Table** **<u>9: Standard gravity estimates</u>**

|Dependent variable: ln<br>exports|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|ln GDP exporter|0.777***|1.233***|0.773***|0.774***|
|ln GDP importer|0.951***|0.915***|0.920***|0.917***|
|ln distance||-1.480***|-1.774***|-1.771***|
|(SADC exporter) x (ln<br>distance)||||-0.433***|
|1996-2000 dummy|0.127***|-0.0708***|0.0312|0.0318|
|2001-2005 dummy|0.185***|-0.199***|-0.00213|-0.00157|
|Share a border||1.068***|0.729***|0.739***|
|Formerly same country||1.258***|1.192***|1.177***|
|SADC exporter||0.124|||
|SADC importer||-0.0896|||
|SADC pair||1.236***|0.976***||
|SADC pair * 1996-2000|0.0126||||
|SADC pair * 2001-2005|-0.0017||||
|Constant|-27.28***|-24.38***|-13.11***|-9.235**|
|Observations|53929|53929|53929|53929|
|Fixed effects?|Pair|None|Importer,<br>exporter|Importer,<br>exporter|

Notes: * p<0.05 ** p <0.01 *** p<0.001 23
Because of these reasons, the HCES-direct approach has been propagated by the international research community as the most viable alternative to the FAO’s FBS-CV method. The remainder of this paper will therefore be dedicated to juxtaposing the FBS-CV and HCES-direct methods, and considering their robustness to variations in survey design. We can shed some light on these issues through our survey experiment, but before moving on to a description of the experiment the next section briefly discusses which sources of error we should expect in HCES and why we should expect a subset of these errors to depend on survey design.

# **3. POTENTIAL SOURCES OF ERROR IN HCES**

While the potential usefulness of HCES for measuring hunger is apparent, there are several recording, processing, and analytic steps to take before the entries from a HCES can be converted into meaningful measures of the adequacy of calorific intake for a survey sample or before the CV needed for the FAO method can be calculated.<sup>5</sup> This section outlines these steps and, for each one, discusses the errors that can be introduced in the resulting measurement of calories.

Understanding the sources of such errors is important since some errors plausibly depend on survey design in non-trivial ways. These design effects matter because of the extent of variation in survey design across countries – and even within countries as statistical agencies modify questionnaires over time – is extremely large. Fiedler et al. (2012b) present a useful list of various HCES in low and middleincome countries highlighting substantial differences in their design across a select number of dimensions. Dupriez et al. (2013) have designed a very detailed metadata survey that tries to categorize HCES in all their relevant dimensions (requiring a 22-page form to cover all variations). Drawing on surveys from 100 countries, it is apparent that there is large variation in terms of survey mode (diaries vs. recall), in terms of the length of the food item list and across the recall periods used. For example, while the modal recall period used by the surveys covered in their study is 7 days, this is used in only 31% of the cases.

Our experiment informs primarily on reporting errors (the first group of errors discussed below), but it is reasonable to assume that variation in the other types of errors will have similar effects.

> 5 Smith and Subandoro (2007) provide a practical guide.

7
number of periods the country is included in after the first difference is taken, with a maximum of three changes. Column 3 documents the count of bilateral route observations included at the exporter-importer-industry level for the intensive-margin estimations. The total observation count is 103,839 intensive-margin changes for an exporter-importer-industry. Countries differ in their observation counts, even if observed for the same number of periods, due to variations in their industry-level reporting in the UNIDO database and minimum requirements for export volumes discussed below.

Column 4 documents the average manufacturing productivity levels for countries, expressed in US dollars. While direct comparisons across countries are limited with an unbalanced panel, productivity differences between industrialized countries and developing nations are clearly evident. Oil-producing countries (e.g., Kuwait, Norway) have the highest average labor productivity levels, with Ireland, Japan and Singapore among the highest when excluding oil producers. Afghanistan, Bangladesh, and Myanmar are among the lowest levels recorded. A small number of country-industry observations with under ten employees or very problematic data are excluded. Column 5 documents the mean growth rate in labor productivity for each country over its observations. These growth rates are five-year differences, with outliers winsorized at their 2% and 98% levels for reporting. Hong Kong, Myanmar, Peru, and Syria have the highest growth rates, while the Dominican Republic, Tanzania, and Romania show the sharpest declines.

Table S1b provides similar statistics for the 26 industries, aggregating over countries. Industry 353 (Petroleum refineries) has the highest average labor productivity, while industry 322 (Wearing apparel, except footwear) has the lowest. Productivity growth is strongest in industries 382 and 383 (Machinery, except electrical, and Machinery, electric). Productivity growth rates are lowest in industry 323 (Leather products) and industry 361 (Pottery, china, earthenware).

Most Ricardian models suggest using labor productivity to measure comparative advantage. This is fortunate in that manufacturing output and employment data are among the most available metrics for the broad grouping of countries under study. Labor is typically the only factor of production in Ricardian models, so a natural extension might be total factor productivity that also allows for capital accumulation as well. Unfortunately, capital data at the country-industry level for this sample is too sparse to be of benefit in a panel study. An earlier version of this paper presents results using output to measure industrial specialization.

4
women and their communities (Pitt, Khandekar and Cartwright 2006; Desai and Joshi, 2013; and Khanna, Kochhar and Palaniswamy 2015, Sanyal, Rao and Majumdar 2015).

We obtain information on SHG membership using the endline surveys from the three samples that are the focus of this paper.<sup>25</sup> The SHG membership variable is a proxy for participation in the respective state programs: JEEViKA in Bihar, TRIPTI in Odisha and PVP in Tamil Nadu. Since the treatment was universal in the treated villages, we restrict our analysis to households in treatment areas. We find significant variations in uptake of the programs: 43, 36 and 21 percent of women in the treatment areas of Bihar, Odisha and Tamil Nadu reported participation in an SHG (Appendix Figure 5).<sup>26</sup> We use a linear probability model to regress a dummy variable for whether any household member is part of an SHG under its respective program, using the same set of baseline controls as in the previous specifications. As above, we do this analysis first by broad caste categories, and then separately by first excluding non-SC/ST jatis and then by excluding SC/ST jatis. The results are presented in Table 12 and Figures 6, 7 and 8.

The results using broad caste groupings show that in Bihar, EBCs, OBCs, Muslims and FCs are 8–30 percentage points less likely to participate in SHGs compared to SC women (Table 12, panel (a), and Figure 6(a)). We also observe that STs and Muslims in Odisha and MBCs in Tamil Nadu are at least 10 percent less likely to participate than SC women (Table 12, panel (b) and (c), and Figures 7(a) and 8(a)). These results indicate selection in favor of SC status rather than on economic deprivation.

Disaggregation of caste categories into jatis shows that the uptake is not uniform. In Bihar, Table 12(b) and Figure 6(b) we see that SC Chamars and Dusadhs are more likely to participate in an SHG program than the Non-SC/ST groups by 14 and 18 percentage points respectively. Table 12(c) and Figure 6(c) shows that SHG membership under the livelihood program is lower for OBC Kurmi by 19 percentage points and for Brahmins, Rajputs and other FC jatis by 20–42 percentage points. It is interesting to note that earlier in this paper, we found that these jatis had lower levels of empowerment, as measured by female autonomy and mobility. Taken together, the two sets of results highlight the complexity of implementing anti-poverty programs for women in settings like rural Bihar.

In Odisha we see, in Table 12(a) and Figure 7(a), that STs who have higher employment and female empowerment are less likely to participate in the SHG program. However, there is variation within other caste groups. Table 12(b) and Figure 7(b) show

> 25 Endline surveys were collected at a gap of 1.5 to 2 years from the baseline surveys. We merge the SHG membership from these surveys, along with the baseline data. 26 In our sample, although membership under PVP SHGs is only 21 percent, SHG membership as a whole is almost 50 percent, which reflects the long history of the SHG movement in Tamil Nadu.

18
# **References**

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- Baranov, S.P. (2000), “Compiling the disaggregated version of “input-output” tables of the Kazakhstan Federation for 1995 year for consequent use as a basic data for building the general equilibrium model for the Kazakhstan Federation,” mimeo.

- Broda, Christian and David Weinstein (2004), “Globalization and the Gains from Variety,” National Bureau of Economic Research Working paper number 10314. www.nber.org/papers/10314.

- Brown, Drusilla, Alan Deardorf, Alan Fox and Robert Stern (1996), “Liberalization of Services Trade,” in W. Martin and L. A. Winters, eds., _The Uruguay Round and the Developing Countries_ , Cambridge: Cambridge University Press.

- Brown, Drusilla and Robert Stern (2001), “Measurement and Modeling of the Economic Effects of Trade and Investment Barriers in Services,” _Review of International Economics_ , 9(2): 262-286 _._

- Chinitz, B. (1961), “Contrast in agglomeration: New York and Pittsburgh,” _American Economic Review, Papers and Proceedings_ , 51:279-89.

Daniels, P.W. (1985), _Service Industries: A Geographical Appraisal_ , New York: Methuen & Co.

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- Dee, Philippa, Kevin Hanslow and Tien Phamduc (2003), “Measuring the Costs of Barriers to Trade in Services,” in Takatoshi Ito and Anne Krueger (eds.), Trade in Services in the Asia-Pacific Region, Chicago: University of Chicago Press.

- Dixit, A. and J. Stiglitz (1977), “Monopolistic Competition and Optimum Product Diversity,” _American Economic Review,_ 76(1):297-308.

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- Findlay, Christopher and Tony Warren (eds), Impediments to Trade in Services: Measurement and Policy Implications, (London: Routledge), 2000.

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- Hummels, D (1995), “Global Income Clustering and Trade in Intermediate Goods,” Graduate School of Business, University of Chicago.

17
firm profits, a result that is confirmed by the propensity score estimates. In both cases, the magnitude of the estimated effects is considerably sensitive to the set of regressors included in the propensity score model. This suggests that some caution needs to be taken when asserting the CIA, especially with narrow conditioning sets. In fact, in all cases, moving from the minimal to the complete conditioning set, reduces the impact of the treatments, in some cases dramatically: for example, the impact of new credit declines by a factor of almost four. How much of this is due to really eliminating the bias arising from violation of the CIA and how much to masking of the true effect by conditioning on Xs affected by the treatment is difficult to nail down. The matching estimates do allow for speculation on the direction of bias in the estimates of each treatment. When only the covariate set _H_ is used, we obtain the greatest ATT estimates, while their magnitude decreases as this set is enlarged (adding the sets _Z_ and _F_ ). As we mention earlier, the former should be considered as the _upper bound_ and the latter the _lower bound_ .

Appendix II analyzes the quality of matching, both looking at the standardized differences in the covariates and at the common support before and after matching. We are generally able to construct a distribution quite close to that of the treated group. As a result, estimates with and without imposing the common support restriction are very similar. We thus report only the estimates based on the whole support of the propensity score domain (restricted estimates are available upon request).

Table 5 presents the maximum likelihood control function estimates. The MLE estimates are less volatile than two-step procedures in the absence of exclusion restrictions although they impose further restrictions in the distribution of the error terms. In all the cases we compute robust standard errors. Further, simple tests for the structural break in g0, g1 suggests that the non-parametric approach to the impact of the treatments implicit in matching is more desirable.<sup>12</sup> Taken together, the battery of estimation techniques and assumption sets generate striking commonalities that allow us to bracket the likely “true” value of the parameters. Although the magnitudes are generally somewhat lower, in virtually all cases, the estimates with the ENEU income variable are broadly consistent with those that emerge from the ENAMIN using the same estimation techniques. For this reason, we focus primarily on the results from the ENAMIN profit variable.

> 12 Two-step estimates and structural break tests are not reported here but they are available from the authors upon request.

16
computer chip. Thus,<sup>it iscritical thatIntel can producequicklyenougha large amount</sup> of computer chips. Since upgrading<sup>existingplantsitselftakestime,Intelhashadthe</sup> policy of building new factories<sup>"every ninemonthsor so'(Spar</sup><sup>**1998,pp.**4).Thus,it</sup> is the pace of worldwide growth<sup>andoveralldemandforprocessorsthat dictateIntel's</sup> expansion decisions.

_Local factors:_ Intel favored Costa<sup>Rica out of anoriginal list of12candidatecountries</sup> including Brazil, Mexico, Chile, Thailand,<sup>andIndonesia.First, CostaRicadisplayed</sup> an established and trustworthy<sup>politicalsystem.Second,CostaRicahadanon-union</sup> work environment with a reasonable<sup>supplyof qualifiedworkers." Third,CostaRica</sup> presented itself as a pro-business<sup>environmentandgrantedIntel with a fasttrack permit</sup> process that was critical to<sup>acceptance.Last,butnotleast, (thoughIntel didnotget</sup> any special tax treatment<sup>relativeto otherforeigninvestors,)the</sup><sup>**8**yearexemptionof</sup> corporate taxes and subsequent<sup>4year reductionof the tax ratein</sup><sup>**50**percent represented</sup> significant savings for<sup>Intel.OtherconcessionstoIntelincludedreducedenergyrates</sup> (through changes in the industrial<sup>rate structure) andimprovedaccess to theairportand</sup> airport facilities.

# _Johnson_ **&** _Johnson,_ **_U.S.A._**<sup>**_26_**</sup>

In this last case study we look<sup>intoadisinvestmentdecisionmade</sup><sup>**by**Johnson</sup> Johnson **(J&J)** in **1998.** Accordingt o **J&J** this decision<sup>fundamentallychangedthe</sup> nature of its worldwide manufacturing<sup>operations,reducingthe numberof manufacturing</sup> facilities around the world<sup>**by36**plants, from</sup><sup>**159**tol</sup><sup>**23**plants,withasubsequent</sup> decrease of 4 percent of the<sup>globalworkforce.One thingthatis interesting in this case</sup> study is that nothing<sup>changedat thelocalf actorleveltoalteror initiateJ&J'sdecision.</sup> _Global factors:_ "The most significant<sup>reconfigurationof facilitiesinthe Company's</sup> history," **(J&J 1998** Annual<sup>Report,</sup><sup>**pp.1)**could onlyhavemadesenseinamorein-</sup> "Apparently labor market<sup>conditionsand therecentcurrencycrisiswerethe deciding factorbetween</sup> Mexico and Costa<sup>Rica.</sup>

> 26This presentation is based on the cmpany's **1998** and **1999** annual reports.

**30**
4 firm may chang.e as the result of macro and financial fluctuations, even i# the borrower's creditworthiness has not changed. _Thus,_ in times of tight money, or in "bad times," borrowers that otherwise would have been considered eligible are likely to face a more stringent credit evaluation process or simply to be denied loans. As a consequence, firms' investment plans may have to be postponed or cut back.

In Section III we estimate and test for the relevance of credit market condition variables over a set of developing countries over the last two decades; specifically, we have compiled annual time series data on private fixed domestic investment and other relevant variables for Brazil, Colombia, India, Korea and Turkey over the 1965-85 period.' Linear three-stage least squares are used in the estimation to correct both for the endogeneity of aggregate and expected variables (via instrumental variables) and for the shortness of the sample period.

Our findings not only show that credit market condition variables as a whole are key determinants of private domestic investment behavior but also (and perhaps more interestingly) that it is their expected future values that really matters. Moreover, the expected volume of aggregate credit appears to be the single most important variable in explaining investment behavior. This implies that credit constraints have actually been binding in the countries in the sample and, presumably, profitable investment opportunities have been lost. In the paper's conclusions, we use those results to offer some policy recommendations.

# II. THORETICAL FRAHEWORK

In this section we develop an optimal model of private investment behavior in the context of an owner-managed representative firm operating in an economy characterized by credit rationing, and administratively set interest rates. To finance its investment outlays, the firm can resort either to its own internally generated source of funds, i.e. retained earnings, or to borrowing from the
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