rafmacalaba/datause-encoder-data huggingface dataset:
school level, teachers complete the questionnaires delivered to them. These questionnaires are collected by district government officials and returned to the provincial education department, where the information is digitized.

Using the annual censuses, we construct a school-level panel.<sup>13</sup> On the basis of this panel, we construct the outcome measure—the absolute change in school-level girls’ enrollment in secondary grades for six subperiods of different lengths originating from baseline (2003): 2003–05, 2003–07, 2003–09, 2003–11, 2003–13, and 2003–15.

# _Empirical strategy_

We estimate program effects on the basis of a sharp RD design. One of two outcomes is possible for school s, conditional on the district literacy rate X�. Schools in districts with a literacy rate below 40 percent ( _X D_  40) are covered by the program. These schools have a potential girls’ secondary school enrollment outcome of<sup>_Y_</sup> _s_  1 . Schools in districts with a literacy rate equal to or above 40 percent ( _X D_  40) are not covered by the program. These schools have a potential outcome of _YS_ (0) . In theory, the average observed outcomes, given _X D_ , are two functions:  _YS_ (1)| _XD_  if _X D_  40 and  _YS_ (0)| _X D_  if _X D_  40 . The sharp

RD program effect at the cutoff,  _RD_ , reflects the difference between  _YS_ (1)| _XD_  and

(1)

> 13 While government schools are assigned unique identification (ID) numbers, school ID protocols were adjusted by the provincial and district education departments in certain years and assigned school IDs were not strictly adhered to during data collection, entry, and processing. Consequently, to construct an accurate school-level panel, we worked closely with provincial education department officials with intimate working knowledge of the Annual School Census data and ground-level knowledge of schools.

13
bindings are modest due to special and differential treatment. Similar detail is necessary for prospective reductions in domestic support (Jensen and Zobbe, 2006).

Given a set of plausible liberalizations, we need to translate these into a set of changes in prices, outputs, inputs, etc. around the world. Since reforms are widespread sectorally and geographically, this requires a global, multi-sectoral, general equilibrium approach as epitomized in global computable general equilibrium (CGE) models. Many such models have been used to analyze trade reforms, each emphasizing different features according to the authors’ purpose. Box 1 offers a brief introduction to the essential features of CGE analysis.

_Distributional Impacts for US Farm Households:_ US farm household population data are taken from the Agricultural Resource Management Survey (ARMS) (USDAERS, 2005). This comprehensive survey of US farm households is conducted over a sample of around 15,000 households using economic and geographic sampling frames. Sampled farms include calibrated weights for aggregating the individual data into groupwise representatives.

> 2 Studies dated prior to 2004 typically miss these features.

4
Similarly, for CIF/FOB ratio, using (1.2), we can express the Discrepancy Index in terms of r as: In comparison to the CIF/FOB ratio, the Discrepancy Index is a **nonlinear function** of measure r . Notice that slope of DI is steeper when r is in interval [-1,0] compared with when r is positive. This signifies that DI is more sensitive to changes in r when r is in [-1,0] than when r is positive. In the context of empirical trade data, this implies DI more heavily penalizes situations when imports are underreported as opposed to when imports are overreported. We find these functional properties important because structural reasons that result in import overreporting are guaranteed as opposed to structural reasons for import underreporting (which are not).

# 3.2 Aggregate indicators based on the discrepancy index

The Discrepancy Index is a bilateral asymmetry measure that quantifies the level of divergence between reported data and its mirror on a specified product and year. The product itself may be on any level of product aggregation if the level of product aggregation is consistent in both records. That is, we can quantify the discrepancy of reporting between a pair of countries on a disaggregated and sectoral level. However, countries trade in various products (or sectors) with a large set of trading partners. Therefore, the value of the Discrepancy Index on a single transaction may not be representative of the reporting patterns of the involved countries or products. This necessitates creation of aggregate level indicators that can summarize important properties of the distribution of Discrepancy Indexes. In this section, we discuss the aggregate level indicators used throughout this report.

The definition of the Discrepancy Index in (1) outlines a variable whose value is determined by selecting an ordered pair of countries ( A , B ) and product C ( DIABC = DI((A, B), C) ). Thus, calculating the DIABC for all country pairs and product combinations (for any given year) will allow us to obtain a distribution of Discrepancy Indexes that summarizes general trends in reporting across countries and products.

14
|Earth/mud|0.4%|2.4%|2.0%|**|
|---|---|---|---|---|
|Cement/concrete|93.6%|93.3%|-0.2%||
|Vinyl tiles|1.2%|1.8%|0.6%||
|Other|4.9%|2.4%|-2.5%|*|
|Source of drinking water|||||
|Pipe-borne inside dwelling|16.7%|6.4%|-10.3%|***|
|Pipe-borne outside dwelling|25.7%|17.6%|-8.1%|***|
|Public tap|10.7%|11.3%|0.6%||
|Sachet water|45.8%|64.1%|18.4%|***|
|Other|1.1%|0.5%|-0.5%||
|Toilet type|||||
|W.C.|22.5%|7.5%|-15.0%|***|
|Pit latrine|3.2%|3.1%|-0.1%||
|KVIP|16.7%|9.6%|-7.1%|***|
|Public toilet|57.0%|78.8%|21.8%|***|
|Other|0.7%|1.0%|0.3%||
|Main income source of household|||||
|Monthly salary|23.2%|20.3%|-2.9%||
|Casual labor|8.3%|12.7%|4.4%|**|
|Hawking|6.5%|3.8%|-2.6%|*|
|Remittances|5.9%|5.3%|-0.6%||
|Safety nets|0.2%|0.3%|0.1%||
|Stable business|51.8%|54.0%|2.2%||
|Cash transfers|1.9%|2.0%|0.1%||
|Other|2.2%|1.5%|-0.7%||
|Duration of stay in household|||||
|Less than five years|22.0%|16.7%|-5.3%||
|Five to ten years|12.5%|18.6%|6.0%|*|
|Ten to twenty years|26.2%|22.8%|-3.5%||
|More than twenty years|24.3%|27.5%|3.2%||
|Received remittances|30.6%|27.7%|-2.9%||
|Able to save in the past month|73.2%|77.5%|4.3%||
|Owns a bank account|71.6%|66.8%|-4.7%||
|Can borrow moneyfrom someone if needed|76.2%|73.3%|-2.8%||

# **Appendix 5 – Regression tables related to rents and housing costs**

_Table 19: Annual rent amount explained by household and location characteristics_

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Unaffected|ref.|ref.|ref.|ref.|ref.|
|Affected|-176.2|-255.9*|-209.2|-208.0|-175.3|
||(110.9)|(149.0)|(137.8)|(125.6)|(112.4)|
|High-elevation|||ref.|||
|Low-elevation|||156.6<br>(173.5)|||

42
65

**Figure 23  Investments in Bonds**

<!-- Start of picture text --> 100% 0.1% 0.1% 0.0% 0.0%<br>90% 19.4% 16.4% 13.7% 12.5%<br>80%<br>70% 20.5% 24.7% 26.3% 27.3%<br>60%<br>8.4% 9.9% 11.6% 11.8%<br>50%<br>40%<br>30%<br>50.3% 47.7% 47.2% 47.3%<br>20%<br>10%<br>0%<br>1996 1998 2000 2002<br>Bonds CH Foreign bonds in CHF<br>Foreign bonds in currency Morgages in CH<br>Foreign mortgages<br><!-- End of picture text --> Source: Bundesamt für Statistik (2004a) Figure 24 shows the relative investments of pension funds with different degrees of risk coverage in 2002. Table 23 presents the total investment and the average investment per member for different forms of pension funds. It is not surprising that the _autonomous pension funds_ have the largest balances. First, they insure many individuals, and second, they bear all the risks themselves. As a direct consequence, they have a huge stock of capital, both in total and per member. Apparently, the amount of capital per member decreases with the degree a pension fund hands over its risks to an insurance company. Hence, _non–autonomous pension funds_ have just a tiny stock of capital left. This can be traced back to the fact that their risks are exclusively managed by an insurance company. De facto, they hand over directly most of their premiums, i.e., they merely act as intermediary between the members and the insurance company.

**Figure 24  Relative Investments and Degree of Risk Coverage in 2002**

<!-- Start of picture text --> 50%<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Liquid Investment Bonds Morgages Shares Real estate Others<br>assets, in empolyer<br>short-term<br>investments<br>autonomous autonomous (with reinsurance) semi-autonomous non-autonomous<br><!-- End of picture text --> Source: Bundesamt für Statistik (2004b), p.53 sqq., Tab. A.05
constant that does not vary by income. The fuel consumed in liters per kilometer is given by the polynomial function of the traffic speed _s_ in km/hour (see Davis and Diegel, 2004): _f_ ( _s_ ˆ)  (3.78541178/1.6093)  [0.122619 − 0.0117211  ( _s_ ˆ) +0.0006413  ˆ( _s_ )2 − 0.000018732  ( _s_ ˆ)3 +0.0000003  ˆ( _s_ )4 − 0.0000000024718  ( _s_ ˆ)5 +0.000000000008233  ˆ( _s_ )6 ].

(15) ˆ Figure 2 plots the relationship. _s_  _s_ /1.6093 , used in (15) is the traffic speed in miles per hour used in the original equation.<sup>7</sup> Note that at low speeds fuel consumption per mile is very high. As speed increases fuel per km falls rapidly making a broad bottom, then rising again at high speeds. The rising portion of the curve is not relevant to our highly aggregative model, since the average speed in Beijing is very low (18.3 km/hr or 11.4 miles/hr), and falls into the rapidly falling part of the curve displayed in Figure 2.

1 Calculation of the speed of traffic by auto in km/hour is crucial. It is calculated as _s_  _d_ 4 _G_ 4<sup>,</sup> where the congested travel time, _G_ 4<sup>,</sup> is endogenous to the model and is determined by the road congestion function, for which we use the well-known Bureau of Public Roads form: In this equation, _Z_ is the aggregate road capacity and _T_ is the aggregate car-equivalent volume of traffic (hereafter, “traffic”) composed of cars, buses and trucks as we shall see below. The key parameters controlling congestion are _b_ and _c_ . We will refer to _b_ as the “slope of congestion” and to _c_ as the “exponential of congestion”. The units of _a_ are in hour/km and measure the reciprocal of the free-flow or uncongested speed of travel that would occur by setting _b_ = 0. Given _a_ , and the same volume to capacity ratio, _TZ_<sup></sup> 1, the same congested travel time can be obtained by alternative pairs of ( _b_ , _c_ ) where one parameter is increased while the other decreased. Such alternative parameter pairs would be consistent with the same travel time _G_ 4<sup>,</sup> but give different sensitivities to congestion as reflected in the volume to capacity ratio. We will revisit this issue when we discuss how the model was calibrated.

> 7 The equation presented by Davis and Diegel (2004) calculates fuel use in gallons/mile from speed in miles/hour. We converted the equation to the liters/km version by making the three adjustments shown in (15). First, the speed in kilometers/ hour is divided by 1.6903 km/mile in order to get the speed in miles/hour. This is then used in the original equation to predict gas consumption in gallons/mile. Secondly, the result is multiplied by 3.785 liters/gallon to get fuel use in liters/mile and, lastly, that result is divided by 1.6903 to get the fuel use in liters/km.

14
**Table 6: Counterfactual change in market output and labor earnings: Using barriers and preferences from 1995, 2005**

||Labor ma<br>|rket only<br>|Human c<br>|apital only<br>|Preferen<br>|ces only<br>|A<br>|ll<br>|Da<br>|ta<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||1995|2005|1995|2005|1995|2005|1995|2005|1995|2005|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|**Panel A: Market o**|**utput, relative t**|**o 2015**|||||||||
|Costa Rica|1.468|1.008|0.587|0.798|0.981|1.064|0.689|0.855|0.584|0.700|
|El Salvador|1.013|1.001|0.971|1.017|1.074|1.034|1.038|1.067|0.522|0.762|
|Panama|2.999|1.754|0.146|0.298|0.718|0.819|0.149|0.385|0.354|0.575|
|**Panel B: Labor ear**|**nings, relative t**|**o 2015**|||||||||
|Costa Rica|1.445|1.007|0.599|0.804|0.988|1.065|0.703|0.863|0.139|0.424|
|El Salvador|1.014|1.001|0.973|1.016|1.050|1.017|1.019|1.047|0.659|0.886|
|Panama|2.987|1.750|0.148|0.300|0.719|0.819|0.151|0.386|0.494|0.576|

27
# Table Cl.

<!-- Start of picture text --> Distribution of Manufacturing Establishments<br>by Region and Source of Electricity, Indonesia<br><!-- End of picture text --> <!-- Start of picture text --> REION ELECTRIC CIAECE OF ELECTRICITY)<br><!-- End of picture text --> <!-- Start of picture text --> Frequw |<br>Percent<br>Raw Pet<br>CoL Pet IPuilc IPbIUc lOwn Gen.lown Gen.l<br>lnty imin Immn lonty I Total<br>................ +........ +............ +++--+--- ..... ....<br>Jakarts 60 65 14 31 142<br>20.69 22.41 4 83 1.03 48.97'<br>42.25 45.77 9.J6 2.11<br>60.00 44.22 45.16 25.00<br>Botabak 29 611 9 7 106<br>10.00 21.03 3.10 2.41 36.55<br>27.36 5.S 8.49 6.60<br>29.00 t 41.50 I 29.03 I 58.33 1<br>................................... .. -.......<br>Sacreng 11 21 ' 2 42<br>3.79 7.24 1 2.76 0.69 14.48<br>26.19 50.00 1 19.05 4.76<br>11.00 14.29 I 25.81 16.67<br>..... ........ ........<br>Total 100 147 31 12 290<br>34.48 50.69 10.69 4.14 100.00<br><!-- End of picture text -->
1 ° TABLE 2: REAL CONSOLDATED PUBLIC SECTOR DEFICIf (P g od GNP)

|ALGEIUA|19641|106244<br>1|9549|MOROCCO|196041|196244|196t54|
|---|---|---|---|---|---|---|---|
|Toa ReaI PS DelI<br>(Exc. K locs of For. Deb<br>|6.5<br>|10.4|6.9|Total Real PS Deficit<br>(Excl. K**koins**of For. Debt)|0.0|10.0|4.5|
|A Adjusted MoneyFinance<br>|1.4|3.3|4.0|A. Adjusted Money Finance|1.2|1.0|0.5|
|Inlbation Tax<br>Senorag|2.3<br>-09|2.2<br>11|2.8<br>12|Inflation Tax<br>|1.5<br>|1.2<br>|0.7<br>|
|<br> <br>|.|.|.|Seignorage|-0.4|-0.1|-0.2|
|S.N Dometic Dobt FIanc<br>_(1)_<br>|1.2|2.2|1.6|B. Net DomeL;c Debt Finance|0.9|2.6|3.3|
|**Banks**<br>Privat Sctor|-0.1<br>02|0.5<br>|1.0<br>|Net Bank Credit<br>|0.5<br>|0.9|2.1|
|<br>BAD&CNEP|.<br>12|0.6<br>12|0.0<br>|Speclized Fin. Org.<br>|0.2<br>|0.2<br>|0.0|
||.|.|0.0|NonFinancial Privte Sector<br>|-0.1<br>|0.1<br>|1.5<br>|
|C. Net Foreign Debt Financ|39|49|13|Arrers|0.4|1.4|-0.3|
|<br>(Excl<br>l<br>bI<br>of<br>aFor Debt)|.|.|.|||||
|.<br> <br>.<br> <br>.<br>Incr"<br>in Foreign LbiWnlitI|70|28|06|C. Net Foreign Debt**Fince**<br>(Excl. K lame of For. Deb"|3.9|6.3|0 6|
|<br> <br>_ier_<br>in Foreign Aset|.<br>31|.<br>-20|.<br>06|I||||
|<br>|.|.|-.|ncrease in ForeignUbilitues<br>I|2.7<br>|6.0<br>|0.9<br>|
|_D._Net Foreign Debt Finance|-38|-08|40|ncrease In Forein Asste|-1.2|-0.3|0.3|
|<br>(hug.<br>lo<br>dof For. Deb|.|.||D N||||
|<br> <br> <br>MEMO:||||et Foreign Debt Finance<br>_(ncW. K._<br>lo<br>of For. Deb|11.5|15.7|0.4|
|F~on<br>Ehf. Looms (D-C)|-77|56|20|||||
|<br>|.|-.|.|MEMO:<br>||||
|_TOal_eA Ps_Deficit_I|129|112||Foreign Exch. Lome D-C|7.6|10.3|-0.2|
|.<br>BAD**Loans**to Enb ils|.|.|3.9|||||
|p||||Net Foeign Dbt Fnance<br>(Excl. K$oeseofFor Debt<br>andDebt RseeQ|3.9|3.3|1.7|
Policy Research Working Paper 7311

# **Abstract**

Freight forwarders play a key role in moving goods across international borders. They arrange transport, oversee customs clearance on behalf of their clients, and more generally troubleshoot issues that arise while goods are in transit. This paper reports the results from a survey of 153 freight forwarding firms in Serbia. Respondents report on firm characteristics, operational choices, and conditions at the border posts and terminals where imported goods are cleared for release. One key purpose of the study is to investigate operational trade-offs between time and cost that arise when import shipments are in transit. In three of four hypotheticals, respondents suggest that money savings dominate time savings. Responses regarding real operational decisions such as route choices reinforce this finding. Respondents also reported penalty rates for late delivery of import shipments as well as the value of a typical import shipment. From these responses, it is estimated that the contracted value of one additional (unexpected) day of delivery time in Serbia appears to be approximately 1 percent of the value of the underlying shipment.

This paper is a product of the Trade and International Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at amendoza1@worldbank.org, afernandes@worldbank.org, and rhillberry@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Table 1. Shares of the five employment sectors: 1983, 1989 and 2002**

||**Jan-83**|**Dec-89**|**Nov-02**|
|---|---|---|---|
|Out of the Labor Force|38.74|39.40|43.53|
|Unemployed|3.86|1.41|4.02|
|Informal Self Employed|11.62|12.95|13.80|
|Informal Salaried|9.15|7.69|10.54|
|Formal Sector|35.74|37.98|27.67|
|Unassigned|0.89|0.55|0.44|

Author’s calculations using the PME.

# **Table 2. Effective Protection and Import Penetration before and after the Trade Reforms**

|**Industry**|**Effective**|**Protection**|**Rate**|**Import P**|**enetration**|**Ratio**|
|---|---|---|---|---|---|---|
||1983|1990|1998|1983|1990|2002|
|Nonmetallic Mineral Goods|-19.6|38.8|15.4|0.8|1.3|3.8|
|Metallic Mineral Goods|34.2|15.8|14.2|1.5|2.4|4.9|
|Machinery and Equipment|93.3|41.5|18.6|8.8|7.3|20.1|
|Electrical and Electronic Equipment and Components|129.3|62.5|24.5|14.5|13.7|24.5|
|Vehicle and Vehicle Parts|-6.5|351.1|129.2|8.2|9.1|13.5|
|Wood Sawing, Wood Products and Furniture|35.2|29.4|15.1|0.5|0.4|2.3|
|Paper Manufacturing, Publishing and Printing|6.7|22.6|14.7|1.7|2.5|4.3|
|Rubber|-21.4|70.2|16|2.3|4.9|16.0|
|Non petrochemical Chemicals|86.4|25.2|24.2|8.4|12.8|17.4|
|Petroleum Refining and Petrochemical|62.9|38.5|5.7|3.8|3.8|8.8|
|Pharmaceutical Products, Perfumes and Detergents|103.95|35.8|10|1.9|6.7|12.4|
|Plastics|28.3|50.7|21.9|1.8|2.2|11.5|
|Textiles|36.7|49.2|24.9|1.1|2.8|10.1|
|Apparel and apparel accessories|46.7|67|26.1|0.3|0.5|2.0|
|Footwear and Leather and Hide Products|30.3|28.8|19.4|1.7|4.4|7.0|
|Plant Product Processing (including tobacco)|5.7|30.6|15.4|0.1|3.3|1.6|
|Food|26.1|80.6|20.8|0.9|2.1|2.7|
|Beverages|-1.1|94.5|24.1|2.6|2.9|3.1|

Source: Effective Protection rates come from Pinheiro and Bacha de Almeida (1994) for 1983 and from Kume et al. (2003) for 1990 and 1998. Import Penetration Ratios come from Pinheiro and Bacha de Almeida (1994) for 1983, from Muendler (2002) for 1990 and from Nassif and Pimentel (2004) for 2002.

38
**Table 5. Average Cost of Irrigation by Source in Rabi 2021**

|**Source**<br>**of irrigation**|**(1)**<br>**Average Cost**<br>**(Rs/hour)**|**(2)**<br>**Average**<br>**Irrigation**<br>**intensity (Hours/acre)**|**use**|**(3 = 1 * 2)**<br>**The overall cost of irrigation**<br>**(Rs/acre)**|
|---|---|---|---|---|
|JCLIS|29|19||551|
|Own diesel pump|75|27||2025|
|Rented diesel pump|120|19||2280|
|Own electric pump|8|31||248|

_Source: Calculated by authors using primary data collected from farmers._ Note: This table has been generated using simple means or average values of hours irrigated/acre and average hourly cost of irrigation. These two values are multiplied to generate the average cost of irrigation/acre for each group of farmers. JCLIS refers to the cost for JOHAR farmers while the diesel/ electric/ rental pump values pertain to control farmers.

# _5.3. Cropped area_

Jharkhand has one of the lowest cropping intensities among all states of India. Limited access to irrigation is a major reason for the low cropping intensity in the state. Large areas in the state are under the rice-fallow system where no second crop is grown after the Kharif rice is harvested. The cropped area in Rabi and the summer season can increase if farmers have access to affordable irrigation.

Kharif is the main crop growing season, paddy in particular. However, the JCLIS beneficiaries are much more likely to grow a Rabi or a Summer crop (Figure 2a). The JCLIS affects Rabi and Summer cropping on both extensive and intensive margins. The beneficiaries are not only more likely to cultivate in Rabi and summer seasons, but they also bring larger portions of their land under cultivation in both seasons (Figure 2b).

We collected data from all respondents on the area left fallow in the Rabi and the summer season and asked them the main reasons for leaving land fallow. 64 percent of the non-beneficiary farmers cited lack of access to irrigation as the reason for not cultivating the land, compared to 45 percent of JCLIS beneficiaries.

18