rafmacalaba/datause-encoder-data huggingface dataset:
external debt to output ratio, _φ_ , is set at 50 percent in annual termsReinhart, Rogo, and Savastano (2003) report an average ratio of external debt to output of 44 percent for a group of emerging countries with some history of external default, 27 percent for a group of emerging countries with no history of default, and 54 percent for a group of industrial countries.

The remaining parameters are calibrated to match the average moments of a representative small open economy. In this economy, the population and productivity growth rates are 2 percent and 1 percent in annual terms; the world interest rate, _r_<sup>_⋆_</sup> , and average risk premium, log _Z_<sup> ̄</sup> , are 4 percent and 3 percent per year; the average eciency wedge is _A_<sup> ̄</sup> = 1 ; the labor and investment wedges are _τ_ ̄ _l_ = _τ_ ̄ _x_ = 0 ; the government wedge, _g_ ̄ , is chosen to match a ratio of government consumption to output of 15 percentthe average government consumption to output ratio is 10 percent in Mexico and 20 percent in Canada; the parameter _ρ_ is set to induce a steady state labor supply of 1 _/_ 3 ; the parameter _δ_ is chosen to induce a steady state investment to output ratio of 20 percentthe average investment to output ratio is 20.5 percent in Mexico and 21 percent in Canada; and the capital adjustment cost parameter _φ_ is set to induce an elasticity of the price of capital with respect to the investment to capital ratio of 25 percent (Chari, Kehoe, and McGrattan, 2007).<sup>6</sup> Table 2 summarizes these numbers.

# Estimation and Measurement of the Wedges

The parameters _s_ ̄ , _P_ , and _V_ of the process (11) are estimated using a maximum likelihood approach and data on output, investment, net exports, hours worked, and government consumption.

The data are quarterly observations on gross domestic product, aggregate investment, hours worked, net exports of goods and services, and government consumption expenditures. Data from Mexico covers the period 1987:12007:4; data from Canada, the period 1976:1

> 6Most papers in the literature calibrate the parameter _φ_ to match the volatility of aggregate investment generated by the model with the data. In the Business Cycle Accounting methodology, however, the model matches the data exactly for any value the parameters. Because it is not possible to follow the traditional approach, I set this number to induce an elasticity of the price of capital with respect to the investment to capital ratio similar to what is estimated for the U.S., 25 percent.

12
non-listed) subsidiaries to make issuance data consistent with the level of aggregation of the income and balance sheet data. Listed subsidiaries with income and balance sheet data are considered as separate firms with their own matching issuance activity. Our final sample includes separately both listed parent firms and listed subsidiaries, in addition to the many other firms that are not part of conglomerates. To make sure that our results are not affected by the inclusion of parent firms, we repeated the analysis by excluding parent firms that have subsidiaries with issuance activity in capital markets over the 1991-2016 period. Our main findings are robust to this change, implying that firm performance around issuance is not driven by the capital raised through subsidiaries. This is not surprising because the number of issuances through subsidiaries is 14,725 (less than 10 percent of the total) and the number of firms with issuing subsidiaries is 4,232 (less than 7 percent of the total).

We exclude all firms with a primary industry classification in the financial sector (SIC codes 6000-6999) or the public sector (SIC codes 9100-9999). Moreover, we exclude both countries with less than 10 firms issuing in capital markets between 1991 and 2016 as well as offshore financial centers.<sup>7</sup> Firm-level attributes (total assets, sales, tangible fixed assets, intangible assets, and R&D expenditures) are all measured in logs in constant 2011 U.S. dollars (except for the number of employees, which is just measured in logs). Their growth rates are thus calculated as the difference in logs. All data on firm-level attributes are winsorized by country at the 1 percent level.

Our final matched dataset comprises 62,653 firms from 65 countries, of which 45,306 issued equity and/or bonds at least once during the sample period (Appendix Table 1). We examine 150,165 issuance events: 102,161 equity issuances, 38,487 corporate bond issuances, and 9,517 mixed issuances (when a firm issues equity and bonds in the same year). We keep the 17,347 publicly listed firms that did not issue as part of the control group in the analyses. By excluding unlisted firms, the sample leaves

> 7 ” The list of offshore centers is based on the IMF report “Offshore Financial Centers (OFCs): IMF Staff Assessments.

9
**Road quality** : proxied by primary, secondary or tertiary status assigned by Delorme (Table 1).

**Legal protection status** :  1 if the cell is in a protected area identified by the World Database on Protected Areas (WDPA); 0 otherwise.  The WDPA shapefile has been downloaded from http://www.protectedplanet.net/.

**Travel time** :  time from the cell centroid to the nearest urban center with a population of 50,000 or greater, as estimated by Uchida and Nelson (2009).  Raster resolution: .0083 decimal degrees.

**Agricultural opportunity value** :  mean value for a cell, calculated from the high-resolution global grid developed by Deveny et al. (2009).  Raster resolution: .0025 decimal degrees.

**Elevation** :  Lao PDR data extracted from the CGIAR-SRTM data set (3 arc-seconds (approximately 90 m) resolution), downloaded from http://srtm.csi.cgiar.org.

# **5.3  Estimation**

For this exercise, we utilize an 11% random sample to minimize potential estimation bias and inconsistency produced by spatial autocorrelation among neighboring cells.<sup>4</sup> The dependent variable is continuously distributed in the range [0,1], so the appropriate estimator is fractional logit.<sup>5</sup> Table 2 presents our results, which are uniformly robust: All parameter signs match our prior expectations, and all have high statistical significance. For 500 m Hansen cells, forest clearing declines with distance from the nearest road segment, transport time to the nearest urban center and elevation. We have excluded Delorme primary status, so the results for road quality dummy variables should be interpreted as deviations from the impact of primary road segments: Ceteris paribus, secondary roads have lower impact on forest clearing than primary roads, and the impact of tertiary roads is lower still. As expected, clearing is greater on land with higher agricultural opportunity value. Our result for protected status is striking and holds independent

> 4 The full data set contains 795,072 observations for Lao PDR, georeferenced by centroids of Hansen-resampled .005degree cells.  We randomly select 100,000 observations from the data set.  After elimination of rows with missing values for at least one regression variable, the regression data set contains 90,330 observations -- 11.4% of the full data set.

> 5 We employ the Stata estimator glm [link(logit); family(binomial); vce(robust)].

16
improve banks’ liquidity-risk-management systems and to strengthen their liquidity position, which was achieved through the adoption of the decision on liquidity risk management at the end of 2008.<sup>28</sup>

**Chart 16. Liquidity position of banks before and after the introduction of the liquidity ratios**

<!-- Start of picture text --> 60.0%<br>53.3% 92,283<br>50.0%<br>40.0%<br>30.0%<br>19.8%<br>30.2%<br>20.0%<br>48,900<br>10.0%<br>0.0%<br>-10.0%<br>-20.0%<br>-30.0%<br>-27.7%<br>-40.0%<br><!-- End of picture text --> <!-- Start of picture text --> Liquid assets (lhs)<br><!-- End of picture text --> Source: NBRM This decision quantifies the adequate level of liquidity of banks in the banking system. Banks are required to adhere to two liquidity ratios of assets and liabilities maturing in the following 30 and 180 days (LR30 and LR180). Both ratios are calculated and maintained separately for assets and liabilities in domestic and in foreign currency, and should be at least equal to one. Assets and liabilities are slotted into maturity buckets up to 30 days and up to 180 days, according to their residual maturity, that is, according to the remaining period to their contractual maturity.

The decision allows for a special treatment of the monetary policy instruments of the NBRM and the sight deposits and transaction accounts with banks:

1. The instruments of the NBRM (excluding the foreign currency reserve requirement) are included in the calculation of the liquidity ratios, regardless of their maturity and currency. Banks have an option to choose whether they will use the NBRM’s instruments for achieving the required liquidity ratio in domestic currency or in foreign currency (but not for both ratios).  This option was introduced in May 2009 and was effective until March 2011.  In February 2011, the NBRM introduced a new instrument for the banks,<sup>29</sup> a six-

> 28 A similar regulation was adopted in previous years, but it was mainly of a qualitative nature—it had defined the qualitative requirements for adequate liquidity risk management.

> 29 A denar deposit with the NBRM, with a six months maturity and an interest rate equal to the six-month EURIBOR plus 0.5 percentage points.  The early withdrawal of the deposit is allowed.  The option given to the banks to use this instrument for achieving the denar or FX liquidity ratios should increase its attractiveness, thus stimulating banks to transform part of their CB bills investments into this instrument. In this way, the remaining portion of the CB bills will have the cleaner function of a fine tuning instrument.

21
Table S.6: Semiparametric specification: Conflict

||ACD|WGI|WGImed|
|---|---|---|---|
||(1)|(2)|(3)|
|ACD Confict|0.244<sup>_∗∗∗_</sup><br>(0.058)|||
|ACD Post-Confict|0.023<br>(0.065)|||
|WGI Instability||0.103<sup>_∗∗∗_</sup><br>(0.038)||
|WGI Instability>Median|||0.123<sup>_∗∗_</sup><br>(0.057)|
|Log Ruggedness|0.095<sup>_∗∗∗_</sup><br>(0.036)|0.114<sup>_∗∗∗_</sup><br>(0.04)|0.113<sup>_∗∗∗_</sup><br>(0.041)|
|Log of Rainfall|-.097|-.100|-.088|
||(0.071)|(0.071)|(0.072)|
|Log dist to coast|-.0006|-.019|-.018|
||(0.04)|(0.04)|(0.041)|
|Log of Surface Area|0.041<sup>_∗∗_</sup>|0.047<sup>_∗∗_</sup>|0.06<sup>_∗∗∗_</sup>|
||(0.017)|(0.019)|(0.016)|
|Length>than 50km|-.117<sup>_∗∗∗_</sup>|-.098<sup>_∗∗_</sup>|-.103<sup>_∗∗_</sup>|
||(0.042)|(0.045)|(0.044)|
|Estimate|-.006|-.011|-.013|
||(0.057)|(0.059)|(0.059)|
|Contract|-.055|-.042|-.050|
||(0.075)|(0.073)|(0.073)|
|Log of GDP pc (1985)|-.015|-.020|-.034|
||(0.045)|(0.045)|(0.043)|
|Obs.|3322|3322|3322|
|_R_<sup>2</sup>|0.887|0.886|0.886|

_Notes_ : Dependent variable is the log of cost per km; all models control for work activity fixed effects, year fixed effects, an interaction between work type and five-year period fixed effects and region fixed effects; base categories are actual costs; robust standard errors in parentheses, clustered at the country;<sup>_∗_</sup> ,<sup>_∗∗_</sup> ,<sup>_∗∗∗_</sup> denote significance at 10%, 5% and 1% levels. _Source_ : Authors’ analysis based on data described in the text.

9
Table 3: Baseline results

|Dep. var. ln(postings+1)|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Output tariff exposure|-0.048***|||-0.020||-0.039***|-0.016|
||(0.014)|||(0.013)||(0.013)|(0.014)|
|Imported input tariff||-0.019***||-0.017***|-0.016**||-0.015**|
|exposure||(0.006)||(0.006)|(0.006)||(0.006)|
|Export tariff exposure|||-0.112***||-0.096***|-0.105***|-0.094***|
||||(0.029)||(0.032)|(0.028)|(0.032)|
|Observations|15,000|15,000|15,000|15,000|15,000|15,000|15,000|
|Adjusted R-squared|0.976|0.976|0.976|0.976|0.976|0.976|0.976|
|<br>FE|CZ YM|CZ YM|CZ YM|CZ YM|CZ YM|CZ YM|CZ YM|
|Cluster|CZ YM|CZ YM|CZ YM|CZ YM|CZ YM|CZ YM|CZ YM|

Notes: This table displays the results from the regressions of the log of monthly job postings in each commuting zone on the associated trade barrier exposure measures. All specifications include CZ and month-year (YM) fixed effects, and standard errors (in parentheses) are two-way clustered at the CZ and month-year level. Tariff exposure measures should be interpreted in units of $1,000 dollars per worker. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

term effects and it remains possible that, over time, the effects could be stronger or weaker. For example, if individuals in the US believe they will receive longer term protection from foreign competition they may begin to establish new firms to produce products which would otherwise have been produced almost entirely abroad, and hence increase hiring. Similarly, if it becomes clear that the long term tariff rates are actually lower than those implemented during the trade war then there could be some reversal of the short term negative effects. It is also unclear to what extent the observed effects are due to uncertainty around future costs, as opposed to the direct costs themselves. As discussed in Section 2, there was a significant lack of clarity regarding the likely duration, scope and intensity of the trade war throughout 2018.

# **5.2 Agricultural subsidies**

Table 4 presents the baseline results with additional controls for both the value of agricultural subsidies in a CZ, as defined in Section 3.6, and the interaction of these subsidies with the export tariff exposure. Both of these variables are interacted with a dummy taking the value 1 from August 2018 onwards, as the subsidies were announced in July 2018.

The main results are robust to the inclusion of both the subsidies term and its interaction 26
(16)

> where w i is the average across all households of the share of item i in total

consumption, and Δpi is the average across all households (effectively, across all pi regions since I don't have within-region price variation) of depreciation-induced price changes. The first term in this decomposition is the compensating variation for a hypothetical household facing average price changes and having average expenditure shares. The value of this is 7.5 percent which is (almost) the mean effect, and is the same across households. The remaining terms vary across households and isolate the different sources of cross-household variation in the estimated compensating variation. The first of these captures cross-household variation due to cross-household differences in price changes (since it holds the expenditure shares fixed for all households). The second captures differences due to cross-household differences in expenditure shares, keeping price changes constant, and the third term captures purely household- and price-specific variation. The standard deviation across households of these three components are 0.6 percent, 1.5 percent, and 0.3 percent. This suggests that crosshousehold differences in expenditure shares are the most important source of crosshousehold differences in the welfare effects of the depreciation, while price differences (across regions, in our case) are less important, but still non-trivial.

I next examine the poverty impacts of these price changes. To do this, I begin with the 2000 distribution of expenditure across households in Egypt. I then subtract from each household the direct estimate of the compensating variation to arrive at a counterfactual distribution of expenditure which reflects the losses due to the subsequent depreciation. I then calculate the change in the headcount measure of poverty between these two distributions, for Egypt as a whole, and by regions. The results are summarized in Table 5. The first column provides the benchmark estimates 17
# _Appendix Table A.8. OLS Regression Results: Explaining Wage Growth with the U.S. Tariff Cut Measure_

|||Model A<br>||Mo<br>|del B and Mode<br>|l C<br>|
|---|---|---|---|---|---|---|
||Total|Unskilled|Skilled|Total|Unskilled|Skilled|
|U.S. tariff cut measure|.029*<br>|.066***<br>|.0068<br>|.010<br>|.045*<br>|-.027*<br>|
||(.015)|(.017)|(.014)|(.018)|(.024)|(.014)|
|∆Skilled/unskilled status|-.057<br>(.062)|.096<br>(.093)|.028<br>(.063)|-.082<br>(.065)|.058<br>(.091)|-.067<br>(.078)|
|∆Marital status|-.12*<br>(.072)|-.14*<br>(.077)|-.015<br>(.094)|-.10<br>(.063)|-.14*<br>(.069)|-.0022<br>(.093)|
|Initial wage in log|-.26***|-.38***|-.27***|-.40***|-.49***|-.33***|
||(.025)|(.031)|(.037)|(.031)|(.034)|(.042)|
|Female||||-.096***|-.24***|.021|
|||||(.024)|(.034)|(.036)|
|Minority||||.00016<br>(.089)|-.13<br>(.12)|.10<br>(.071)|
|Education||||.041***<br>(.0035)|.023***<br>(.0046)|.027***<br>(.0067)|
|Potential Experience||||.0016<br>(.0015)|-.000014<br>(.0016)|.00046<br>(.0018)|
|Urban||||.043|.059|.0076|
|||||(.031)|(.042)|(.042)|
|Agriculture share 2002<sup>a</sup>||||-.34<br>(.23)|-.13<br>(.28)|-.77***<br>(.25)|
|Industry share 2002<sup>a</sup>||||-.0041<br>(.48)|.29<br>(.43)|-.52<br>(.38)|
|Construction share 2002<sup>a</sup>||||-.067<br>(.73)|1.44<br>(1.06)|-1.31<br>(1.14)|
|Government share 2002<sup>a</sup>||||-1.40<br>(1.24)|-.52<br>(1.42)|-2.77**<br>(1.20)|
|Constant|3.60***|5.07***|3.90***|5.37***|6.43***|5.10***|
||(.32)|(.39)|(.40)|(.44)|(.46)|(.65)|
|Observations<sup>b</sup>|1718|1051|647|1718|1051|647|
|R<sup>2</sup>|.118|.187|.146|.209|.272|.186|

_Notes_ : The dependent variable is the log difference of monthly wage ( _∆Ln W_ ). The classification between skilled and unskilled workers is based on the status in 2002.

*, **, *** indicate that the coefficients are significant at the 10, 5, and 1 percent level respectively. The standard errors in parentheses are based on heteroskedasticity-consistent estimates of the variancecovariance matrix and corrected for within province correlation (clustering).

a The share of workers employed in the service sector is the omitted category.

b The total sample includes those who worked for “Army force”. As the skill category is not assigned for the latter workers, the number of skilled and unskilled workers do not add up to total number.

52
The value added component of the shrimp chain provides ample room to cover both the primary input costs as well as the logistics costs. The profit rate is 18% of value added. This “success” is a turnaround for a previously declining industry and promises to be a major source of job creation and future export earnings.<sup>29</sup> Two major factors in this positive result include the nature of the product – high value shrimp – as well as the **production of quality goods** – shrimp that meets international quality standards as determined by HACCP. The latter has enabled Nigerian shrimp producers to access high end European markets which are willing to pay premium prices for high quality goods, allowing them to meet the high costs of doing business yet make a decent profit.

In essence, a shift to higher value products and higher quality goods has provided the Nigerian fish industry a chance to expand and provide positive returns. However, the industry is concerned with the long-term sustainability of the stock of wild shrimp, as the natural growth of wild stock is not anticipated to keep pace with the rate of harvesting. Regulatory controls will protect the stock but not likely help to increase the stock. Wild shrimp supply will act as the constraint to industry growth, unless alternative sources of harvesting are found. To this end, the industry may want to diversify. A promising alternative option may be farmed shrimp as a means of sustaining industry growth, a feasibility that is being contemplated by some of the trawling companies such as Atlantic Shrimpers.

The Nigerian government has been influential in fostering growth in its national shrimp industry. The key factor for Nigeria has been to acquire the HAACP certificate that has opened up the European market. Thanks to the right policy implementation, Nigeria was included in the E.U. “Decision 2001/635/EC - Part 1” list of countries in 2001; under this arrangement, the Federal Department of Fisheries of Nigeria was designated the Competent Authority or central point of responsibility. It is hard to imagine that the Nigerian shrimp industry would have been able to reverse its fate without E.U. market access. Furthermore, since demand for shrimp shows a clear growing trend and has a relatively straightforward value chain, it allowed for relatively easy integration of Nigerian shrimp production into the global value chain.

# **_B. Buyer-Driven Supply Chains: Textile/Apparel Chains in Kenya & Lesotho_**

# **a. Kenya: Apparel**

The performance of the Kenyan garment sector is highly dependent on exports to the United States under the AGOA agreement. The high levels of dependence on specific markets combined with an equally high dependence on imported material 29 USAID, Industry Action Plan for Nigerian Shrimp and Prawns, 2002. Online available at: <u>http://www.usaid.gov/ng/downloads/markets/shrimp_and_prawns_industry_action_plan.pdf</u> 17
- **Sicouri, Gemma, Lucy Tully, Daniel Collins, Matthew Burn, Kristina Sargeant, Paul Frick, Vicki Anderson, David Hawes, Eva Kimonis, Caroline Moul, Roshel Lenroot, and Mark Dadds** , “Toward Father-friendly Parenting Interventions: A Qualitative Study,” _Australian and New Zealand Journal of Family Therapy_ , 2018, _39_ (2), 218–231. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/anzf.1307.

- **Sulemana, A., A. Mumuni, and D. M. Badasu** , “Young people’s experiences in accessing sexual and reproductive health services in sub-Saharan Africa: A content analysis,” in “African Population Conference, Johannesburg, South Africa” Johannesburg, South Africa 2015.

- **Tiedje, Linda Beth and Cynthia Darling-Fisher** , “Promoting Father-Friendly Healthcare,” _MCN: The American Journal of Maternal/Child Nursing_ , December 2003, _28_ (6), 350–357.

- **Townsend, Robert M** , “Risk and insurance in village India,” _Econometrica: journal of the Econometric Society_ , 1994, pp. 539–591.

- **Tran, Nguyen Toan, Armando Seuc, Béatrice Tshikaya, Maurice Mutuale, Sihem Landoulsi, Brigitte Kini, Bernadette Mbu Nkolomonyi, Jean Nyandwe Kyloka, Félicité Langwana, Asa Cuzin-Kihl, James Kiarie, Mary Eluned Gaffield, Rachel Yodi, and Désiré Mashinda Kulimba** , “Effectiveness of post-partum family planning interventions on contraceptive use and method mix at 1 year after childbirth in Kinshasa, DR Congo (Yam Daabo): a single-blind, cluster-randomised controlled trial,” _The Lancet Global Health_ , January 2020.

- **United Nations** , “World Contraceptive Use 2018,” Technical Report, United Nations, Department of Economic and Social Affairs, Population Division, New York, NY 2018.

- **Upadhyay, Ushma D., Shari L. Dworkin, Tracy A. Weitz, and Diana Greene Foster** , “Development and Validation of a Reproductive Autonomy Scale,” _Studies in Family Planning_ , 2014, _45_ (1).

- **Varghese, Rekha and Manan Roy** , “Coresidence with mother-in-law and maternal anemia in rural India,” _Social Science & Medicine_ , April 2019, _226_ , 37–46.

- **Weiss, Mitchell G.** , “Stigma and the Social Burden of Neglected Tropical Diseases,” _PLOS Neglected Tropical Diseases_ , May 2008, _2_ (5), e237.

32
development economists who reflect on the future of the discipline and emphasize the need for a non-dogmatic approach to the study of development issues, and for the Bank itself, with its renewed attention to the importance of evaluation for effective development policies (see for example Ravallion 2008). The history of the practice of development economics, together with the use of archival material, can bring new perspectives to a better understanding of the discipline's evolution.<sup>86</sup>

> Bank’s Archives do not have a copy of Please’s letter to Hirschman), Albert O. Hirschman to Alexander Stevenson, January 28, 1968, World Bank Hirschman Folders Vol. 2.

> 86 Robert Tignor’s biography of W. A. Lewis, for example, vividly demonstrates this point (Tignor 2005).

33