rafmacalaba/datause-encoder-data huggingface dataset:
22

**Table 12. Measures of Fiscal Decentralization, %**

|**Summary Statistics / Year**|**1994**|**1995**|**1996**|
|---|---|---|---|
|1. Ratio between local governments total<br>revenues and the consolidated regional||||
|budget revenues||||
|1.1. Mean|62.37|67.83|70.25|
||(1.18)|(1.27)|(1.43)|
|1.2. Standard deviation|10.73|11.7|13.02|
|1.3. Coefficient of variation|0.17|0.17|0.19|
|1.4. Minimum value|26.81|22.76|11.96|
|1.5. Maximum value|85.35|97.76|96.69|
|1.6. Valid number of observations|83|85|83|
|2. Ratio between local governments total<br>expenditures before transfers and the||||
|consolidated regional budget expenditures||||
|2.1. Mean|62.8|65.94|67.67|
||(1.08)|_(1.05)_|_(1.15)_|
|2.2. Standard deviation|9.83|9.71|10.63|
|2.3. Coefficient of variation|0.16|0.15|0.16|
|2.4. Minimum value|26.81|33.34|36.59|
|2.5. Maximum value|84.27|83.8|86.58|
|2.6. Valid number of observations|83|85|85|

Standard error of the mean in parentheses.

Throughout the sample period, there was a clear tendency toward greater fiscal decentralization on both sides of consolidated regional budgets. Between 1994 and 1996, the average share of local governments in consolidated regional budget revenues has increased by 7.9 percentage points, while at the same time the relative size of their outlays in consolidated regional public expenditures has risen by 4.9 percentage points1<sup>7</sup> . Before 1995, the average expenditure share of local governments was broadly in line with their share in budget revenues. This trend was reversed in 1995-96. In 1996 local governments received 70.3% of all revenues but financed only 67.7% of consolidated regional budget expenditures. This was related to noticeable expansion of regional budget deficits.

The breakdown of Russian regions by administrative type into republics, oblasts and krais4<sup>8</sup> , and autonomous okrugs'<sup>9</sup> unravels interesting patterns in the degrees of fiscal decentralization across groups. Tables 13 and 14 present data on the average ratio between local governments total revenues and the consolidated regional budget revenues and local governments share in the consolidated regional final expenditures by administrative type of region.

- **17** These changes in the means for both variables are statistically significant at the 99% level in paired-samples t- tests of the equality of the respective means.

> 18 Oblasts and krais were pooled together because of the numerous similarities between them. 19 Including one autonomous oblast -Yevreyskaya Autonomous Oblast.
status.<sup>13</sup> However, only women who were also interested to join RMGs entered the lucky draw and are captured in our sample as RMG-eligible. For simplicity and clarity of exposition, we may refer to this sample as village head selection, though formally speaking this implies village head selection conditional on self-selection.

Overall, Table 1 suggests that the RMG program effectively targeted poorer households within villages. We next compare actual selection to a hypothetical selection based on PMT used by the Government of Lao PDR to identify the poor for participation in social programs. This PMT index was developed by the World Bank and Government of Lao PDR, following administration of LECS V in 2012-2013 (Pimhidzai et al. 2014). It relied on regression analysis of household consumption on surveyed household characteristics. The model aimed to accurately distinguish between poor and non-poor households. Nonsignificant characteristics were removed from the model; characteristics that minimized misidentification of poor and non-poor households were kept. Index variables and weights were determined based on the regression outcomes. Households scoring below 64 were classified as poor. Table A2 in the Appendix provides the complete list of variables used in the PMT index (World Bank forthcoming).

When we compare village head selection with those selected through PMT, we find that, though both methodologies selected approximately the same number of beneficiary households, they disagree on which households should benefit in 44% of cases. The magnitude of this disagreement holds true whether one considers the full sample of 1,888 study households (Table 2) or only 1,245 households ranked in the village head targeting survey (Table A3 in the Appendix). Although we expect differences in how PMT and village heads identify poor households, the consensus on population poverty rates and need for welfare support is striking.

**Table 2: Village heads vs PMT – Full Sample of 1,888 Households**

|RMG-eligible|PMT b<br>No|elow 64<br>Yes|Total|
|---|---|---|---|
|No|300 (16%)|442 (23%)|742 (39%)|
|Yes|401 (21%)|745 (39%)|1,146 (61%)|
|Total|701 (37%)|1,187 (63%)|1,888 (100%)|

The discrepancy in identification of the poor by the two methods leads us to further assess their targeting accuracy. We compare household characteristics typically indicative of wealth status between the eligible and ineligible/ poor and non-poor groups under village heads selection and PMT. We expect the more accurate method to produce a larger difference in observable welfare characteristics.

To reduce the complexity of analyzing overall wealth disparities through numerous household characteristics, we again construct composite z-scores similar to those we used for comparisons of RMG-

> 13 Thus, the selection may also reflect PMT and community validation carried out in 2016. However, based on the field supervisors’ reports, the changes in poverty ranks were substantial.

10
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57 Table A1: List of Fertilizer Products Subject to Import Bans

|HS8|Description|
|---|---|
|31024000|Mixtures of ammonium nitrate with calcium carbonate or other inorganic non-fertilizing<br>substances|
|31026000|Double salts and mixtures of calcium nitrate and ammonium nitrate|
|31028000|Mixtures of urea and ammonium nitrate in aqueous or ammoniacal solution|
|31029000|Other, including mixtures not specifed in the foregoing subheadings|
|31031100<br>31031900|Superphosphates containing by weight 35% or more of diphosphorus pentaoxide (P2O5)<br>Other|
|31039000|Other|
|31049000|Other|
|31051000|Goods of this Chapter in tablets or similar forms or in packages of a gross weight not<br>exceeding 10 kg|
|31052000|Mineral or chemical fertilizers containing the three fertilizing elements nitrogen,<br>phosphorus and potassium|
|31053000|Diammonium hydrogenorthophosphate (diammonium phosphate)|
|31054000|Ammonium dihydrogenorthophosphate (monoammonium phosphate) and mixtures<br>thereof with diammonium hydrogenorthophosphate (diammonium phosphate)|
|31055100|Containing nitrates and phosphates|
|31055900|Other|
|31056000|Mineral or chemical fertilizers containing the two fertilizing elements phosphorus<br>and potassium|
|31059000|Other|

_Notes_ : The table displays the list of HS8 fertilizer products that were subject to import bans at some point in 2021. An HS8 product is classified as fertilizer if it is listed in Chapter 31 of the HS system. The data is from Sri Lanka’s Extraordinary Gazettes.

# **A.B Trade Flows**

We use novel high-frequency granular trade data obtained from S&P Global Market Intelligence’s Panjiva data platform covering the universe of Sri Lanka’s export and import transactions from January 2017 to October 2022 based on bills of lading (BoL). The data includes the name and address of the shipper (exporter) and the consignee (importer) of each shipment as well as HS8 product codes. Exporting and importing firms are identified by their names, as BoL data does not require inclusion of tax identification numbers.<sup>96</sup> We use machine learning and text analysis techniques to clean firm names and assign to each name a unique firm identifier, thus constructing a panel of Sri Lankan firms engaged in exports or imports. Appendix A.B.1 below provides details on our fuzzy matching algorithms.

The data is subject to a series of cleaning procedures as in Fernandes et al. (2016). Specifically, we drop observations for which trade value is zero or missing, product codes are missing or not in the HS 2017 revision list, partner country is missing or is Sri Lanka (which may indicate trans-

> 96While Panjiva adds a firm identifier to the BoL data that Flaaen et al. (2023) use for the United States, we found such identifier to be extremely unreliable for Sri Lankan firms. The same firm appearing with a different name spelling has a different identifier, and a given firm making an export transaction has a different identifier than the same firm making an import transaction. On any given day, the same firm transacting more than once could also have different Panjiva identifiers.
**Table 4 Applied Tariff Rates on Industrial Goods for Selected Latin American Countries** Most-favored nation rates, percentage ad valorem)

|||Simple|Minimum|Maximum|
|---|---|---|---|---|
|Country|Year|Average|Rate|Rate|
|Argentina|2003|15|0|35|
|Brazil|2003|14|0|35|
|Chile|2002|7|0|7|
|Colombia|2002|13|0|35|
|Costa Rica|2002|6|0|15|
|Mexico|2003|18|0|35|
|Peru|2000|13|12|20|

_Source:_ UNCTAD (United Nations Conference on Trade and Development) TRAINS Data base.

47
WPS4220

# **Evaluating the Trade Effect of Developing Regional Trade Agreements: A Semi-parametric Approach**

### Souleymane Coulibaly

World Bank<sup>*</sup> 1818 H Street NW, 20433 Washington DC, USA Tel: +1 202 473 9845 Email: scoulibaly2@worldbank.org

## **Abstract**

Many recent papers have pointed to ambiguous trade effects of developing regional trade agreements (RTAs), calling for a reassessment of their economic merits. We focus on seven such agreements currently in force in Sub-Saharan Africa (ECOWAS and SADC), Asia (AFTA and SAPTA) and Latin America (CACM, CAN and MERCOSUR), estimating their impacts on their members' trade flows. Instead of the usual dummy variables for RTAs, we propose a variable taking into account the number of years of membership. We then combine a gravity model with kernel estimation techniques so as to capture the non-monotonic trade effects while imposing minimal structure on the model.

The results indicate that except for SAPTA, all these RTAs have had a positive impact on their members' intra-trade over the estimation period (1960-1999). AFTA seems to be the most successful among them with an estimated positive impact on its members’ imports from the rest of the world (ROW), but its impact on their exports to the ROW is rather limited. During its first ten years of existence, ECOWAS appears to have had a positive impact on its members’ imports from the ROW, but this positive impact vanished over time. SAPTA's negative impact on its members' intra-trade is probably an implicit effect of the India-Pakistan tensions over the estimation period.

### **J.E.L Classification:** F11, F15, O50

**Keywords:** regional trade agreement, kernel regression, trade impact

World Bank Policy Research Working Paper 4220, May 2007 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

**Acknowledgments:** I thank Jeffrey Bergstrand, Paul Brenton, Marius Brülhart, Celine Carrère, Renato Flores, Lionel Fontagné, Guillaume Gaulier, Thierry Mayer, Daniel Mirza, Eric Toulemonde and an anonymous referee for helpful comments and suggestions.
including for the effects of JOHAR irrigation on Area under Irrigation, High-Value Crop Cultivation, and Gross Value of Output in Rabi 2021 (Tables A.8 through A.10 in the appendix). This enables us to conclude with some confidence that (i) beneficiary farmers were not better off before the installation of the JCLIS system, and that (ii) their increase in GVO and area under high value crop cultivation can be attributed to the increase in irrigation delivered by the JOHAR systems.

JCLIS operators and managers do not keep written records of system use. Therefore, our assessment of system performance is also based on self-reported recall data from these respondents which may contain recall errors or systematic biases, including the possibility of deliberate misreporting. Data on system performance collected from beneficiary farmers were also used to validate operators’ responses. However, data on important aspects like the total operating hours of the system and the total command area served in each season could only be collected from the operators.

Disruptions from the COVID-19 pandemic may have led to lower price realizations for high-value crops grown by farmers and may even have affected their crop choices. It is therefore possible that the impacts of JCLIS may become measurably greater once the pandemic subsides. These limitations of the study should be kept in mind when interpreting the results of this analysis.

# **4. Data**

# _4.1. System characteristics_

50 system operators were interviewed. (Table A.1 in the Appendix reports the summary statistics for systems and their operators.) 48 of them were men. 36 of the 50 operators had received training from the JSLPS team. Most of them had been trained in the operation and management of the irrigation system including basic troubleshooting and care. Only a few of the operators had any training in record keeping or financial management, and none of them maintained written records of hours of irrigation services provided to farmers, fees collected, or area irrigated. The rest of the discussion in this section, therefore, relies on the operators’ recall.

Most JCLISs have started irrigating farmers’ fields only recently. Our sample of 50 systems was selected from those which had been installed for more than 12 months when we went to the field. 9 of the 50 systems had started irrigation after September 2020. The newest system in our sample 8
gap, as it could be the case that use of oDesk by ethnic Indians across countries is uncorrelated with the general Indian-ethnicity populations of countries. Rauch and Trindade (2002), for example, relate trade flows to the distribution of the ethnic Chinese population across countries, rather than the greater likelihood that two observed traders are Chinese.

Table 10 closes this gap using empirical models similar to the gravity framework from the trade literature. The dependent variable in Columns 1-7 is the share of contracts originating from a country on oDesk that are outsourced to India. We focus on shares of contracts, rather than contract volumes, as the adoption of oDesk across countries as a platform for e-commerce is still underway and somewhat idiosyncratic to date. Shares allow us to consider the choice of India for outsourcing independent of this overall penetration of oDesk.

The core regressor is taken from the World Bank’s Bilateral Migration and Remittances 2010 database. This database builds upon the initial work of Ratha and Shaw (2007) to provide estimates of migrant stocks by country. We form the Indian diaspora share of each country’s population by dividing these stocks by the population levels of the country. We complement this diaspora measure with distances to India calculated using the great circle method, population and GDP per capita levels taken from the United Nations, and telephone lines per capita in 2007 taken from World Development Indicators. We also calculate a control variable of the overall fit of the country’s outsourcing needs with the typical worker in India.<sup>20</sup> Column 1 presents our base estimation. We have 92 observations, and we weight by the log number of worldwide contracts formed on oDesk. The first row shows the connection of oDesk outsourcing to the diaspora population share, which is quite strong. A 1% increase in the Indian diaspora share of a country is associated with a 1% increase in the share of oDesk contracts outsourced to India. The country-level placement of oDesk contracts in India systematically followed the pre-existing levels of Indian diaspora communities. Looking at the other covariates, distance does not matter in the oDesk context like it does in many estimates of

> 20 We calculate this control by first measuring the share of contracts outsourced from the country in nine job categories indicated in the appendix. We likewise measure the distribution of oDesk work performed in India across the nine job categories, independent of where the company contact is located. We then calculate the sum of the squared deviations of these two distributions to measure how closely the work typically filled in India matches the needs of a given country. We subtract this sum of deviations from one, so that positive values represent a better fit, and we transform the measure to have unit standard deviation to aid interpretation.

21
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 50

**Table A3:** Comparison between sea-dike unit cost derived from recent Dutch rural estimates. multiplied by DIVA country unit cost factors (left) and taken from available case studies (right). Unit costs are in M Euro km<sup>-1</sup> m<sup>-1</sup> , reference year 2014

||**DIVA**|||**Case study**|**results**|
|---|---|---|---|---|---|
|**Country**|**Current**<br>**estimate**|**New**<br>**estimate**|**Case**<br>**study**<br>**estimate**|**Location**<br>**or area**<br>**type **|**Meta source**|
|Netherlands|4.2|[4.9-13.5]|[4.9-13.5]|Rural|Jonkman et al.<br>(2013)|
||||[16.9-24.4]|Urban|Jonkman et al.<br>(2013)|
|USA|3.8|[4.3-11.9]|[4.0-12.8]|New<br>Orleans|Jonkman et al.<br>(2013)|
|Canada|3.6|[4.2-11.4]|[2-12]|Any|Lenk et al. (2016)|
|UK|4.7|[5.5-15.1]|[16-20]|Thames|Environment<br>Agency (2008)|
|Vietnam|4.6|[5.3-14.6]|[0.8-1.3]|Rural|Jonkman et al.<br>(2013)|

Overall, the replacement of DIVA point estimates by ranges based on recent Dutch estimates has some appealing features. It clarifies that unit cost estimates of countries are highly uncertain, and it may improve unit cost estimates if country cost factors are correctly specified. However, the external validity of the method remains limited due to inaccurate country cost factors. Replacing the Dutch unit costs with recent estimates without updating country cost factors does not resolve systematic biases.

# **A.2.4 Suggestions for further research on dike costs**

The case study results suggest that some country cost factors may be inaccurate. These may be improved by more empirical research on unit costs.

## **A.2.4.1 Production factors of dike construction**

Case studies on dike unit costs indicate that the production factors labor (wages), land and physical capital (materials, machinery) are among the key determinants of the unit costs of sea dikes. Unit cost estimates may be improved by estimation of a production function of sea dikes: X� �f�Labour�, Land�, Capital� �
and doctor-driven demand at the margin between severe treatment and simple treatment. Within our model, doctor-driven demand at the margin of any versus no treatment is not possible because doctors cannot force patients to buy unwanted treatment (although doctor-driven demand inducement through influencing the patient’s beliefs, which is independent of the experimental interventions and therefore not part of our model, might still occur).

While our predictions work on different margins, finding evidence of demand driven by one side suggests that this side has a stronger preference for treatment than the other. For example, in the presence of patient-driven demand as in prediction (2), doctors have a weaker preference for simple treatment than patients, and so there would not be doctor-driven demand at this margin. Similarly, if there is doctor-driven demand for severe over simple treatment as in prediction (3), it implies that patients have a weaker preference for severe and so there would not be patient-driven demand for severe treatment.

All proofs are in Appendix A, where we also discuss additional results on prescribing simple treatment without using the voucher, and on doctors who are purely revenue motivated. We now turn to the experiment we designed to identify the role of patient and doctor preferences in driving demand for malaria treatment.

# **3 Experimental Design and Sample**

## **3.1 Experimental Design**

**Sampling Frame.** At the outset of our study, we obtained an administrative list of all CSComs in the city of Bamako and nearby Kati and Kalaban Coro in Koulikoro. After conducting a census of these clinics, we dropped clinics that had closed or were more than 15 kilometers away from Bamako, and removed 21 clinics that were working with a local NGO to offer subsidized, improved care to mothers and children. This yielded a final sample of 60 clinics. Four health workers at each clinic were invited to attend a refresher training that covered Mali’s official malaria guidelines and a hands-on training on how to administer an RDT. The training materials were prepared by the research team in cooperation with researchers at the University of Bamako, as well as the _Programme National de Lutte contre le Paludisme_

18
25 For each household group we report welfare impacts by household for the tax change involved, in terms of Hicksian equivalent variations expressed as a fraction of the base case gross income of the household group. A positive number indicates a gain from the tax change, and a negative number a loss from the change from the present tax regime to one containing an equal yield broadly based sales tax in place of the replaced tax.

Table 3

**Incidence Impacts of Ivorian** Taxes **from the Central Case Model Variant (Hicksian EVs expressed as percentage of benchmark gross income)**

|||Introduci|ng an equal yi|eld, non-d|istorting co|nsumption tax|to replace:|
|---|---|---|---|---|---|---|---|
|||Prnl|Production|Exort|Imort|Stabilization||
||Households|esoa<br>income tax|<br> <br>taxes|p<br>tax|p<br>duties|<br>Fund|Subsidies|
|||1|2|3|4|5|6|
|Poor|Food croppers<br>in Savannah|-0.65|-6.44|0.25|0.67|0.46|-3.58|
||Export croppers|-0.26|4.51|1.12|24.11|27.87|0.86|
||Small businesses|<br>-1.58|1.24|0.09|-0.18|-1.60|0.22|
||Other food<br>croppers|-1.52|-7.93|0.04|0.16|-3.03|-3.20|
||Inactive|2.66|-2.53|0.42|1.12|5.35|-2.79|
||Government<br>cmployees|3.44|2.59|-0.61|_-8.50_|-10.33|1.52|
|R|Households in<br>formal sector|-0.63|3.48|-0.38|-10.8|-8.73|1.73|
|R ich|I<br>_<br>_<br>_<br>_<br>_|__<br>__<br>__<br>__|I_|_I_I_I||||

economy are distributed across all of these groups. The ranldng in Table _5_ is only by the mean incomes of each group.