rafmacalaba/data-use-mentions-extended huggingface dataset:
measures, by export origin, implemented in the form listed.<sup>62</sup> The EU imposed almost two thirds of antidumping measures as ad valorem duties and roughly 10 percent as specific duties; as noted earlier, these specific duties can also discriminate between trading partners and in particular against those producing lower-priced varieties.

Next consider the breakdown of the form of antidumping measures by export origin. Broadly, the EU tends to favor ad valorem import tariffs to restrict imports from high income and emerging economies. In contrast, the EU negotiates a price undertaking in roughly 40 percent of the instances in which it imposes an antidumping measure against a developing country. This result could be another means to discriminate between trading partners (against new entrants). On the other hand, price undertakings may actually be preferred by the exporters if the alternative is an EU

> 60Bown (2002a) presents a discussion. As we further describe below in Section 4, one of the political motivations for the attempts to ban VERs was that they had become a common outcome to US safeguard and antidumping investigations in the 1970s through early 1990s, especially with respect to bilateral frictions that the United States had at the time with Japan (Bown and McCulloch, 2009).

> 61However, these are non-transparent in that official EU publications do not report the negotiated prices or market shares. Rather, official _Decisions_ and _Regulations_ report the names of the lead foreign negotiating authority (for example, a foreign Chamber of Commerce or industry association) and all firms that are participating in the undertaking. This set-up leaves the Commission with flexibility to adjust minimum import prices and market shares as the situation warrants.

> 62During this period, according to the Temporary Trade Barriers Database, the EU implemented a total of 492 antidumping measures. In roughly 5 percent of cases, the form of the final antidumping measure is unknown.

35
Table A3 Permanent Household Surveys Availability and Sample Sizes

|Year|May|October|
|---|---|---|
|1974<br>|−<br>|1299<br>|
|1975|1039|923|
|1976|−|886|
|1977|−|834|
|1978|−|811|
|1979|−|−|
|1980|721|−|
|1981|−|757|
|1982|162|−|
|1983|−|−|
|1984|−|−|
|1985|−|891|
|1986|−|978|
|1987|1015|947|
|1988|1006|1008|
|1989|988|932|
|1990|528|589|
|1991|1009|599|
|1992|−|640|
|1993|704|680|
|1994|681|617|
|1995|585|613|
|1996|576|522|
|1997|644|599|
|1998|614|634|
|1999|534|533|
|2000|519|522|
|2001|461|453|
|Total|11786|17267|

Note: Description of Permanent Household Surveys used in the analysis and effective sample sizes in each data set.

23
# **Migration of Dirty Sectors Within China?**

In the previous section, we established that trade reforms have not had a 'dirty-sector' bias during the past decade. However, it is at least possible that internal differences in regulatory strictness could have induced relocation of dirty industry within China. Despite the evident trend toward equalization of the effective levy in the eastern provinces, Figure 8 includes differences as great as 3:1 in levy rates. Lucas (1996) has recently investigated whether such differences have induced faster growth of pollutionintensive sectors in provinces with laxer regulation. His results suggest that differential levies are not significant as location factors, so there has apparently been no regulationinduced migration of dirty sectors within China. 12

# **Economic Reform and The Cost of Abatement**

<!-- Start of picture text --> (20a)<br>Pariculate Marginal Abatement Costs. Large Plants<br>70 .<br>s0o/<br>I i<br>20 -<br>10<br>o , O : :<br>R e of AbY ...<br><!-- End of picture text --> In recent econometric research, we have used plant-level data from NEPA to develop detailed estimates of abatement costs for air and water emissions by Chinese industry (Appendix; Dasgupta, Huq, Wheeler and Zhang, 1996). Our results show that marginal abatement costs (MAC) vary greatly by sector, scale, abatement rate, ownership, and pollutant. The impact of these variables on marginal cost is dramatically illustrated by a few comparisons. Holding other factors constant, MAC ratios can vary as much as 20:1 between large and small facilities; 45:1 across sectors; 13:1 between 10% and 90% abatement; 5:1 between state-owned plants (SOE's) and non-SOE's; and 4:1 across pollutants. The range of potential variation is revealed by the comparative MAC
firm owner did not think it would be approved. Because a sizable share of firm owners that did not apply for loans chose not to answer these questions, our sample is too small to present results for specific reasons. We therefore lump all non-loan applicants that did not respond that they had no need for external finance into a single category, ―Discouraged.‖ In the 2005 survey, small firms were divided relatively evenly between the three categories: had loan, discouraged from applying, and had no need for a loan. By contrast, nearly sixty percent of large firms had loans, while a third of them reported no need. Less than ten percent of large firms were discouraged from applying for any of the reasons listed above. The pattern confirms much better access to credit for the large firms in our sample as was found in 29
- Only in the last quarter of the project’s life (measured by time or by disbursement amount) does the DO rating reach the prediction power of the model using only the CPIA and task team leader quality variables. Until then, it is lower than or equal to 20 percent.

- For EAP, the predictive performance of the ISR DO rating was even lower.

- Combining the first model with the ISR DO rating during each quarter of the life of the project leads to a predictive power of slightly above 60 percent in the last quarter.

- Using the IP rating instead of the DO rating improves the prediction model, and using the two together further enhanced the results.

- Applying the model to the EAP portfolio as of July 1, 2014, yielded a predicted rate of unsatisfactory outcome of 17 percent.

The two studies clarify the relative importance of country and project variables, including flags and other indicators for predicting project outcome, but they have several limitations. First, since the model significantly underestimates current IEG rates of unsatisfactory outcomes for EAP, now close to 30 percent, it will need to be re-run and calibrated using more recent data. Second, the quality of the task team leader is a difficult, sensitive, and controversial variable to estimate fairly and regularly. Third, the model still only predicts about 40 percent of the unsatisfactory outcomes.

# III. Foundation for a New Outcome Prediction Tool for Moderately Satisfactory (or Higher) Rated Projects

Given the limitations of existing monitoring tools and predictive models, it would be advantageous to find a simpler approach that could yield similar or better results, by using real-time data, such as the IP and DO ratings, even if it meant moving away from a pure prediction model, i.e., a model that would mostly include variables already known at the project start. Hence, EAPDE conducted a thorough analysis of the projects rated less than moderately satisfactory and particularly disconnects in search of a tool that would predict whether a project rated moderately satisfactory would
)|-13583|-13296|-13099|-12681|-10082|-10624|-11097|

Notes: Values in Malawi Kwacha. Demand estimates are based on coefficients from unconditional regression of the total phase 2 bus rides on a set of dummies for each price level.

Our findings suggest that subsidizing a daily bus to the market cluster included in this study would be costly. We see that a daily subsidy of MK10,082 (US$67) would be at least needed to achieve a breakeven point for the bus operator (for a roundtrip). This finding is crucial since there is a usual plea to call for competition in transport services in rural areas in order to curb transport prices. This experiment demonstrates that competition, in this case, is impossible to get and, even worse, one operator can never break-even.

The requisite subsidy is due to both supply and demand factors. On the cost side, high fixed costs of bus rental and discontinuities in costs caused by the discrete jump in costs once a bus becomes full drive up the cost of bus operation. On the revenue side, we find that population density and significant price sensitivity drive down aggregate demand for the bus service to the point where the bus route would not be profitable for a private firm<sup>18</sup> .

> 17 Calibration over the full sample.

> 18 This is similar to the findings of Rutter et al. (2000) and Hine and Rutter (2000) with, for instance, an inverse relationship between loading times and population density: in low population density districts, loading times are longer, which is detrimental to the quality of services.

15
# **ANNEX MEASURING THE VALUE ADDED IN EXPORTS**

Exports (of both goods and services) can be measured as:

**Gross exports:** The transaction value of a sector’s exports. This captures both the value added embodied in the production of the export and all domestic and imported intermediate inputs. Gross measures of trade statistics are registered in customs or balance of payments, usually at the transaction value, that is, the price actually paid or payable for the goods and services.

**Direct value added of exports:** A sector’s domestic value added embodied in its own exports, measured as gross exports less domestic and foreign inputs. This measure captures the true sector‐specific, or the direct, value‐added contribution of exports.

**Total value added of exports:** This measure adds to the direct value added of exports the value added of inputs produced domestically. It captures the indirect contribution through value chain linkages with other export activities, expressed in terms of forward or backward linkages. This is increasingly important in an environment where global production is fragmented across production sharing networks.

Based on these definitions, the following terminology is used when speaking about contribution to exports:

**Forward linkages:** The value added when considering the contribution of that particular sector as an input to other sectors’ exports. This treats the particular sector as an upstream activity. In other words, forward linkages show how important particular service is as inputs to other export activities.

**Backward linkages:** The value added when considering the contribution of all other sectors to that particular sector’s exports. This treats the particular sector as a downstream activity. In other words, backward linkages show how important a service sector is for the demand of other sector’s value added.

Source: Saez et al. (2014), Francois, Manchin, and Tomberger (2013).
Table 3: The effect of migration on private education inputs: controlling for household assets

|Secondstage IV regressions|D(Paym<br>teachers|ents to<br>) > 0|Caregi|ver time|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Migration|-0.964**|—-0.636**|=3.887*|266s"*TM|
||<br>(0.434)|<br>(0.259)|<br>(1.996)|(1.003)|
|Household asset index (log)|0212"*"<br>(0.080)||-0.417<br>(0.364)||
|Household asset index 1999 (log)||0.031<br>(0.037)||0.151<br>(0.098)|
|Child characteristics|Yes|Yes|Yes|Yes|
|Household characteristics|Yes|Yes|Yes|Yes|
|Main migration destinations|Yes|Yes|Yes|Yes|
|N|2186|1708|2354|1824|
|K-Pweakid|6.601|16.857|6.935|22.776|
firms and employment.<sup>11</sup>

# **3. Data**

## 3.1. The Turkish Enterprise Information System

The primary data source used in this paper is the Turkish Enterprise Information System (EIS), an exceptionally rich, linked, employer-employee dataset covering the universe of registered nonfinancial firms in the economy. The records of registered employees of a given registered firm are linked to the records of that firm. The firms and their employees are observed every quarter from

> 10 The Turkish minimum wage was of 950 TL in the first half of 2015, and 1000 TL in the second half.

> 11 We provide further evidence on the validity of the identification strategy in the remainder of the paper.

8
be important to consider the moral dimension of complying with societies‘ rules, underlying legal structure and countries‘ security of property rights. A failure of a country‘s legal system undermines tax morale and tax compliance. Also, regulatory restraints and bureaucratic procedures limit the operation of markets, enhancing the

> 13 Djankov et al. (2002) show with data from 75 countries that in general heavier regulation of entry goes in line with a higher corruption and a higher shadow economy. On the other hand, countries with more democratic and limited governments have less entry regulations.

53
<!-- Start of picture text --> daily average, SSB (all day)<br>44<br>i,<br>Maarjary(<br>o 80 80 120 00 20 240 = ©) 400 440 400 520 0<br><!-- End of picture text --> <!-- Start of picture text --> daily average, SSB (b/w 6pm and 10pm)<br>ws |<br>i-<br>tt<br>oo 8 WH tH 0 200 260 = m0 400 440 aso 20 "0<br><!-- End of picture text -->
natural agricultural endowments may be partly responsible for the differences in sectoral employment shares observed across routes.

The empirical analysis produces three key findings. First, we find that a substantial part of the economy in both the Abidjan and non-Abidjan routes remained essentially in isolated autarkic equilibrium both before and after the CIV trade shock. The trade shock shifted the spatial margin of integration (SMI) outward in the non-Abidjan route, thus expanding the zone of market integration, and had the opposite effects in Abidjan route. Second, the CIV trade cost shock substantially increased the employment share in manufacturing, and trade and services in the integrated areas of non-Abidjan route, suggesting accelerated structural change in the non-Abidjan route when compared to the Abidjan route that bore the direct impact of the civil war in CIV. Third, the effects of the CIV war on regional development (Abidjan vs. non-Abidjan) may be long-lasting; the evidence suggests positive impacts on sunk investment in built-up density, higher growth in night-lights density (capturing greater expansion of electricity grids), and geographic concentration of skilled labor (capturing agglomeration) in the integrated areas in the nonAbidjan route. The persistence in the impacts of CIV-crisis is supported by the trade flow data, which confirm that the share of maritime trade flow through the Abidjan route never recovered to its pre-crisis level even 10 years after the end of the crisis.

The rest of the paper is organized as follows. We discuss the related literature in section (2) and put the contributions of the paper in context. The next section provides details on the country contexts and the civil war in Cˆote d’Ivoire. Section (4) develops a simple general equilibrium model and derives testable predictions about reallocation and employment structure across the Abidjan and non-Abidjan routes. Data sources and variables are described in section (5). Section (6) develops the empirical strategy, and section (7) contains the main empirical results. The paper concludes with a summary of the main findings.

4
were also unable to find actual port costs for the 1970s and earlier. To project the observed port charges forward to 1995 and back to 1971 we used the public sector emolument index, on the reasoning that this would approximate labor costs in the ports, which are managed by public sector port authorities. This index is not available before 1971, and for these years we used the consumer price index for industrial workers. The two together give a consistent series to project port charges, and are shown in Annex 2.

# _Marketing costs_

Moving commodities to be exported from port cities or to compete with imports there or elsewhere also involves marketing costs which include interest charges, handling expenses, storage charges, overhead expenses, miscellaneous expenses arising out of transit and storage losses, and wholesale distribution margins. These costs vary considerably by year, by season and by crop. In the case of each crop, the general treatment follows that adopted in the original commodity studies. However some general rules of thumb have also been used. In particular, based on the study by Sharma and a review of a number of studies by Gulati et al (1990)8, in the case of foodgrains and oilseeds in the 1 980s, we have taken marketing costs to consist of an interest charge for two months at an 18 percent rate applied to the procurement price, plus Re 1 per quintal to represent other marketing expenses. To project these estimates forwards and backwards, we calculated the average of these marketing costs expressed as a percentages of the prices of each commodity during five years in the 1980s. These percentages were applied to

> 7 The significance of this assumption is apparent from reports of port bottlenecks which were creating long waiting times for ships when substantial exports of wheat and rice started in the mid 1 990s. 8Gulati, Ashok with James Hanson and Garry Pursell. 1990. "Effective Incentives in Indian Agriculture: Cotton, Groundnuts, Wheat, and Rice".

xviii
## **6.2 Implications for the Revised Network**

Figure 8 and Table C1 summarize how alternative assumptions about informal firms affect the distribution of outlinks across counties. If we assume informal firms source half of their inputs from the formal sector but have no formal buyers, inequality in county-level outlinks declines by 16% compared to our model-predicted network. This represents an 8.5 percentage point larger reduction than our default scenario where small formal and informal firms share similar linking patterns. The share of total outlinks accounted for by Nairobi- and Mombasa-based firms now drops down to 69%, down from 80% in the baseline network and 75% in the first scenario with informal firms (see Table 5).

Allowing informal firms to sell 25% of their output to formal firms, instead of none, has little implication for both the overall decline in inequality and Nairobi’s share of outlinks. Allowing for a larger share of informal firms’ inputs to be sourced from the formal sector results in a smaller reduction in inequality (11%) and a higher share of Nairobi- and Mombasa-based links (73%), more closely matching the initial network with informal firms. This pattern is driven by the greater reliance of informal firms on formal suppliers in this scenario, which in turn tend to locate in counties with larger formal economies.

Our simulations of domestic and international trade shocks under these alternative assumptions reinforce our earlier findings, showing even stronger relative effects in sectors and counties with high informality (Figure C6). This more pronounced impact stems from increased intra-county linkages among informal firms. While more prominent local linkages reduce spatial inequality and urban concentration, they amplify the impact of domestic shocks through stronger withincounty multiplier effects.

# **7 Conclusion**

Firm-to-firm transaction-level data sourced from tax records have become a valuable tool for mapping domestic firm networks and trade flows. Aside from studying the micro dynamics of firm-to-firm relationships, they allow researchers to compute often hard-to-observe flows of goods and services across regions within national economies
average twice as restrictive as actual policies (Figure 3) (if countries made no Doha offer, existing commitments are used).

**Figure 3: Existing commitments, Doha offers and actual policy by GDP per capita, 2010** (62 WTO Members that submitted Doha offers)

<!-- Start of picture text --> KEN NGAPAKNICNICINDIND PHL IDNIDN HND MAR BOL LKALKA PRY GTMGTM EGY TUNTUNTHATHACOLCOL CRIURY MUSPAN MYS CHLCHL TTOTTO BHRBHR<br>KENNNGAPAKPGANICIND AP IDN KH HLMARMARBOLLKANDPRYGTJORJORJOREGY CHN CHN M DOMDOMDOMALB ALB TUNPE RPE R PE R THA CO BRABBRAZAFZAF ZA L RA CF BGR URYBGRMUSMUSPANPANRI TURTURTUR ARG ARGMYSCHLMEXMEXMEX POLPOLPOLTULLTUL TU HUNHUNHUN PRTPRTPRTOMN OMN CZECZE C KORKORKORZE N NZL ZL GRCGG RC RCBHRESPESPESPITAITAITA FRAFRAFRAJPNJJP PN NAUS A ADEUDEUDEFINFINFINUS U BELBELBGBR GG SELSWESWESWE UB BRRDNKDNKDNKAUTAUT A UTCANCANCANNLDNLDNLD IRLIRLIRL USA USA<br>TTO<br>0 10000 20000 30000 40000<br>GDP per capita,PPP<br> Restrictive ness of GATS co mmitment Fitted values<br> Restrictive ness of DOHA Offers Fitted values<br> Restrictive ness of actual policy Fitted values<br>STRI for 61 countries, excluding Qatar and 31 countries that did not submit offers<br>Source: Borchert, Gootiiz and Mattoo (2010).<br>Figure 4: Existing commitments, Doha offers and actual policy by region<br>Binding gap, offer gap and applied policy for 93 countries<br>SAR GCC LAC AFR MENA EAP OECD ECA<br> Actual Policy<br>Offer gap (Doha Offer-Actual policy)<br>Offer Improvement (Uruguay Round commitment-Doha Offer)<br>100<br>80<br>60<br>40<br>20<br>Services trade restrictiveness index 0<br>80<br>60<br>40<br>20<br>Services trade restrictiveness index<br>0<br><!-- End of picture text --> Source: Borchert, Gootiiz and Mattoo (2010).

**Figure 4: Existing commitments, Doha offers
Figure 30: _p_ -values for Rainfall Metrics by Weather Product <!-- Start of picture text --> p>0.90 p>0.95 p>0.99 95% C.I.<br>Mean Daily Rainfall Total Seasonal Rainfall<br>1 1<br>.9 .9<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>Number of Rainy Days % Rainy Days<br>1 1<br>.9 .9<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>CHIRPS CPC MERRA-2 ARC2 ERA5 TAMSAT CHIRPS CPC MERRA-2 ARC2 ERA5 TAMSAT<br>CHIRPS CPC MERRA-2 ARC2 ERA5 TAMSAT CHIRPS CPC MERRA-2 ARC2 ERA5 TAMSAT<br>Share of Significant Point Estimates Share of Significant Point Estimates<br>Share of Significant Point Estimates Share of Significant Point Estimates<br><!-- End of picture text --> _Note_ : The figure displays the share of coefficients on the rainfall variables that are statistically significant for three rainfall metrics for each product, aggregated over country, outcome variable, and specification. Only one extraction method is included. Each panel includes 432 regressions, with each column representing 72 regressions.

91
Table 11: Leadership Competition

||Mean Control|Effect of VH<br>Training|Effect of VH<br>+ CL Training|CL Effect|N|
|---|---|---|---|---|---|
|Whether community leaders involved in|0.242|-0.008|-0.124|-0.116|195|
|distributing food aid (0/1) (CL survey)|(0.431)|(0.074)|(0.076)|(0.086)||
|Whether HHs would take dispute (land/livestock)|0.025|-0.002|0.003|0.005|2154|
|to CL for resolution (0/1) (HH survey)|(0.156)|(0.007)|(0.009)|(0.008)||
|Whether HHs ask CLs for assistance|0.255|0.006|-0.033|-0.039|2151|
|(0/1) (HH survey)|(0.436)|(0.026)|(0.029)|(0.030)||

Table displays standard errors in parentheses. *, ** and *** indicate significance at the 90, 95 and 99 percent confidence levels respectively.

regulations and should interact more with village heads as a result of the intervention, in the first instance in order to help them implement reforms and in the second instance to monitor their behavior. Because our data measures effects across civil society leaders as a whole in the study communities, not on the trained civil society leaders specifically, we are underpowered with respect to identifying these effects. Even so, table 12 shows that civil society leaders as a whole appear more informed of village regulations and more likely to