rafmacalaba/datause-encoder-data huggingface dataset:
dichotomy.<sup>10</sup> Despite more rigorous definitions in the 2008 SMPD compared to the 2006 survey, donors still use some subjective judgment in determining whether or not project aid is program-based, so _PBA_ may contain more measurement error than _DBS_ or _PFM_ .

All three dependent variables measure (to some degree) donors’ investment in the “public good” of improving recipient country systems, as well as donors’ trust in countries’ existing systems to use aid funds productively. Budget support and program-based aid usually are premised on policy dialogues with government, and are often explicitly or implicitly conditioned on certain policy reforms. Thus, _DBS_ and _PBA_ also measure (in part) donors’ trust in a common approach to development that may be country led, but influenced by the World Bank, IMF or other donors with leading roles in policy dialogues.

In the SMPD sample, all three dependent variables range from 0% to 100%. Means for _PFM_ , _DBS_ and _PBA_ respectively are 34%, 11.9% and 31%. Table 1 provides summary statistics for these and other variables in the analysis.

# _Independent variables_

Independent variables mostly fall into one of three groups:

- Trustworthiness of country systems ( γ in the model)

- Trust in aid’s effectiveness (in general) on the part of the donor’s constituents ( α )

- Donor’s ability to benefit from investments in country systems (<sup>_A_</sup> _i_<sup>)</sup>

We define _donor aid share_ as the percentage of total aid (inclusive of aid not to the government sector) to recipient _j_ accounted for by donor _i_ . This variable corresponds to<sup>_A_</sup> _i_<sup>in</sup> the model in Section 2, and is predicted to increase use of country systems. A donor providing a larger share of aid to a recipient has a larger reputational stake in the country’s development (Knack and Rahman, 2007) and internalizes more of the benefits of investments in strengthening country systems. The sample mean for _donor aid share_ is 6.4%, with a minimum value of 0.01%<sup>11</sup> (three observations) and a maximum of 70.4% (Australia in Papua New Guinea).<sup>12</sup> Donors’ reputational stake in a recipient’s development may also be greater, other things equal, in countries it once colonized. For example, media accounts of genocide, civil war and

> 10 “Even budget support...may not be fully aligned to the country budgeting process” if it is not committed or disbursed in time to be incorporated fully into policy and planning frameworks (OECD 2008: 13).  Conditions often attached to budget support also may be inconsistent with the principle of country ownership.

> 11 Values of 0 are not present in the sample because the dependent variables are all undefined for donor-recipient pairs with no aid transfers.

> 12 Larger donors at the global level do not necessarily have higher average aid shares at the recipient level, as some donors concentrate their aid in fewer countries.  For example, Portugal’s average aid share (14%) exceeds the average for the U.S. (12.9%), although the U.S. provided more than 100 times as much total aid to countries in the sample as Portugal.

13
9 number of microgrid cells, placing each customer location into the correct microgrid cell. Loop plant can therefore be designed to reach only populated microgrid cells.

_Loop design module._ Distribution plant consists of the set of analog cables, structures and other facilities, such as network interface devises, that are required to connect every customer location to the nearest serving area interface (SAI). Feeder plant consists of the set of fiber, digital copper (Ti) or analog copper cables and structure that connect every SAI to the central office. When there is more than one SAI, the model identifies a primary SAI, the one located closest to the central office.

The feeder network, which connects every primary SAI to the central office, is designed using a 'minimum cost spanning tree' algorithm, modified to take into account the cost of cable and structures rather than simple distance. Beginning at the central office, the algorithm builds a network sequentially by examining both the cable and structure costs involved in attaching new nodes to the network. Lowest cost nodes are attached first. When each new node is attached, the connection is chosen so as to minimize the cost of cable and structures that are required to connect that node to the central office using the currently existing network. Distance computation can be done using rectilinear distance or airline distance. In addition, 'junction nodes' are placed at points due north, south, east and west of the central office along what would be the main feeder routes in a traditional 'pine tree' feeder design.

In calculating the cost of the distribution network, HCPM employs two separate algorithms. One algorithm deploys vertical backbone and horizontal branching distribution cables from the serving area interface to reach every populated microgrid. Branching cables run along every other microgrid boundary. Each microgrid is subdivided into equal-sized lots. Drop terminals are located to serve one to four lots and cables are placed on every other lot boundary to connect with the backbone and branching cable leading to the SAI. The second distribution algorithm uses the same minimum cost spanning tree network as was used to design the feeder network. In this approach, microgrids are divided into lots and drop terminal locations are determined as before. A spanning tree network is then constructed which connects every drop terminal
Policy Research Working Paper 9897

# **Abstract**

This paper studies the effect of a change in the amount of cash assistance provided to Afghan refugees returning from Pakistan on household outcomes post-return. Using a regression discontinuity design, it measures the impact of a large exogenous change in cash assistance amounts on post-return outcomes in a quasi-experimental setting. Administrative data and post-return monitoring data suggest that more than 16 months after their return, returnees who received a larger cash allowance of $350 per returnee— equivalent to 2.5 times the average annual pre-return annual income—were better off than those who received a smaller cash allowance of $150. Recipients of the $350 cash assistance were more likely to invest in durable assets, such as a house (17 percentage point difference); recipients of the $150 cash allowance were more likely to use the assistance for immediate food consumption needs (40 percentage point difference). Households that received $350 per returnee were significantly more likely to have been issued legal documentation for their household members. In line with the literature on cash assistance, the change in cash assistance had no effect on post-return employment outcomes. The findings provide new evidence on the effects of unconditional cash transfers on refugee reintegration and show that larger cash transfer programs can have a large and long-term impact following refugees’ return.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at nkrishnan@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
<!-- Start of picture text --> Fig la: Empirical Surw-ir Curw based on<br>Highest Grade Completed: Males<br>0.8<br>0 0.6-<br>C Q*4.<br>0<br>0.2<br>0 -<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18<br>years of education<br>- Albanian Males 16-26 -5Albanian Males 27-35 6 Serbs all (M&F)<br><!-- End of picture text --> <!-- Start of picture text --> Fig Ib: Empirical Surnier Curw based on<br>Highest Grade Completed.: Females<br>0.8<br>,20.6<br>i0 6<br>04-<br>0.2 -<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18<br>years of education<br>+4-- Albanian Females 16-26 --- Albanian Females 27-35 e Serbs all (M&F)<br><!-- End of picture text --> 23
|Energy2 Project|Sandu Ghidirim|
|---|---|
|Energy2 Project|Sandu Ghidirim|
|Energy2 Project|Sandu Ghidirim|
|Energy2 Project|Sandu Ghidirim|
|Energy2 Project|Sandu Ghidirim|
|Energy2 Project|Sandu Ghidirim|
|Energy2 Project|Sandu Ghidirim|
|Energy2 Project|Sandu Ghidirim|
|Energy2 Project|Sandu Ghidirim|
|Energy2 Project|Sandu Ghidirim|
|Financial Sector Structural Adjustment Loan|Alexander E. Fleming|
|Financial Sector Structural Adjustment Loan|Alexander E. Fleming|
|Financial Sector Structural Adjustment Loan|Alexander E. Fleming|
|Coal Sector Adjustment Loan Project|Heinz Hendriks|
|Coal Sector Adjustment Loan Project|Heinz Hendriks|
|Coal Sector Adjustment Loan Project|Heinz Hendriks|
|Coal Sector Adjustment Loan Project|Heinz Hendriks|
|Coal Sector Adjustment Loan Project|Heinz Hendriks|
|RURAL WATER SUPPLY AND SANITATION|Marie-Laure Lajaunie|
|RURAL WATER SUPPLY AND SANITATION|Marie-Laure Lajaunie|
|RURAL WATER SUPPLY AND SANITATION|Marie-Laure Lajaunie|
|RURAL WATER SUPPLY AND SANITATION|Marie-Laure Lajaunie|
|RURAL WATER SUPPLY AND SANITATION|Marie-Laure Lajaunie|
|RURAL WATER SUPPLY AND SANITATION|Marie-Laure Lajaunie|
|Eastern Indonesia Region Transport Project|SallyL. Burningham|
|Eastern Indonesia Region Transport Project|SallyL. Burningham|
|Eastern Indonesia Region Transport Project|SallyL. Burningham|
|Eastern Indonesia Region Transport Project|SallyL. Burningham|
|Eastern Indonesia Region Transport Project|SallyL. Burningham|
|Eastern Indonesia Region Transport Project|SallyL. Burningham|
|MAG: ENVIRONMENT II|Bienvenu Rajaonson|
|MAG: ENVIRONMENT II|Bienvenu Rajaonson|
|MAG: ENVIRONMENT II|Bienvenu Rajaonson|
|MAG: ENVIRONMENT II|Bienvenu Rajaonson|
|Second Tianjin Urban Development and Environment Project|Suhail J. S. Jme'An|
|Second Tianjin Urban Development and Environment Project|Suhail J. S. Jme'An|
|Second Tianjin Urban Development and Environment Project|Suhail J. S. Jme'An|
|Second Tianjin Urban Development and Environment Project|Suhail J. S. Jme'An|
|Second Tianjin Urban Development and Environment Project|Suhail J. S. Jme'An|
|Second Tianjin Urban Development and Environment Project|Suhail J. S. Jme'An|
|Second Tianjin Urban Development and Environment Project|Suhail J. S. Jme'An|
|Second Tianjin Urban Development and Environment Project|Suhail J. S. Jme'An|
|Second Tianjin Urban Development and Environment Project|Suhail J. S. Jme'An|
|Second Tianjin Urban Development and Environment Project|Suhail J. S. Jme'An|
|Second Tianjin Urban Development and Environment Project|Suhail J. S. Jme'An|
|Rajasthan Water Sector RestructuringProject|RadheyShyam Pathak|
|Rajasthan Water Sector RestructuringProject|RadheyShyam Pathak|
# **Table 8 What Characteristics are Correlated with Participation in the Urban Residents’ Medical Insurance Program?**

# Dependent Variable: Participation (Yes=1, No=0)

Probit Marginal Effects

|Regressors|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Age|0.006***|0.006***|0.006***|0.007***|0.007***|0.005***|
||(0.002)|(0.002)|(0.001)|(0.001)|(0.001)|(0.001)|
|Age-Squared|-0.000***|-0.000***|-0.000***|-0.000***|-0.000***|-0.000***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Years of Schooling|-0.005***|-0.005***|0.000|-0.003***|-0.003***|0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Post-Secondary Education|-0.018***|-0.018***|-0.009**|-0.006|-0.006|-0.007|
||(0.006)|(0.006)|(0.004)|(0.006)|(0.006)|(0.004)|
|Married|-0.002|-0.002|-0.000|-0.002|-0.002|-0.000|
||(0.006)|(0.006)|(0.004)|(0.005)|(0.005)|(0.004)|
|Rural Hukou Migrant|-0.086***|-0.082***|-0.079***|-0.092***|-0.092***|-0.079***|
||(0.004)|(0.006)|(0.004)|(0.004)|(0.006)|(0.004)|
|Urban Migrant w/ Other Urban|-0.006|-0.003|-0.023***|-0.017***|-0.016***|-0.023***|
|Hukou|(0.006)|(0.008)|(0.002)|(0.004)|(0.006)|(0.002)|
|Female|-0.000|0.002|-0.002|-0.005|-0.005|-0.002|
||(0.004)|(0.005)|(0.003)|(0.004)|(0.004)|(0.003)|
|Female x Rural Migrant||-0.009|||-0.000||
|||(0.009)|||(0.009)||
|Female x Urban Migrant||-0.006|||-0.001||
|||(0.011)|||(0.011)||
|Participate in Medical Insurance|||-0.112***|||-0.111***|
|<br>Through Employer|||(0.005)|||(0.006)|
|Self-Employed||||0.068***|0.068***|0.001|
|||||(0.022)|(0.022)|(0.011)|
|_Alternate Controls_|||||||
|Industry|no|no|no|yes|yes|yes|
|Work Unit Ownership|no|no|no|yes|yes|yes|
|Observations|11,769|11,769|11,769|11,749|11,749|11,749|
|Average Participation|0.0794|0.0794|0.0794|0.0796|0.0796|0.0796|

Notes: Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. The sample includes all working age adults in 2010, regardless of work status. The sample is drawn from the five cities common to the 2005 and 2010 CULS survey rounds (Fuzhou, Shanghai, Shenyang, Wuhan and Xian). All models include city dummy variables.

35
**Table 5: Industry Labor Demand for Blue Collar Workers in Mexico (1987-90) (standard errors in parenthesis)**

||Ln L**t-1**|Ln WBt|Ln WBt-**1**|Ln WWt|Ln WWt- I|Ln Industry<br>Value<br>Added|Constant|Wald<br>Test: p-<br>value(b)|L.R.<br>Elast.(a)|Sargan<br>Test|Autoco.<br>Test<br>(2nd o.)|No. Obs<br>(Plants)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Base<br>Specification(c)|0.239<br>(0.184)|-0.194(*)<br>(0.028)|0.054<br>(0.041)|0.047(*)<br>(0.016)|0.012<br>(0.015)|0.065(*)<br>(0.019)|-0.019(**)<br>(0.009)|--|-0.183|0.614|0.173|9532<br>(2383)|
|Time Variant<br>Specification|0.105<br>(0.127)|-0.1 19(*)<br>(0.038)|0.024<br>(0.025)|0.060(*)<br>(0.020)|0.034(*)<br>(0.012)|0.039<br>(0.049)|-0.017(*)<br>(0.007)|--|-0.106|0.813|0.179|9532<br>(2383)|
|Variable* T88|-0.189<br>(0.407)|-0.1I9(***)<br>(0.067)|-0.024<br>(0.056)|0.002<br>(0.031)|-0.024<br>(0.030)|0.011<br>(0.118)|0.011<br>(0.013)|0.407|-0.220||||
|Variable* T89|0.112<br>(1.134)|-0.202(**)<br>(0.086)|0.127<br>(0.221)|-0.037<br>(0.047)|-0.030<br>(0.089)|0.065<br>(0.121)|0.067(***)<br>(0.036)|0.195|-0.217||||
|Variable* T90|0.311<br>(0.671)|-0.016<br>(0.060)|0.144<br>(0.188)|-0.008<br>(0.040)|-0.024<br>(0.054)|0.311<br>(0.671)|-0.026<br>(0.026)|0.875|0.055||||
|Wald Test: p-<br>value(d)|0.907|0.118|0.683|0.851|0.854|0.835|0.018||||||

Notes: GMM estimates with first-differenced data. All variables are assumed exogenous except for lagged employment. Instruments are based on the second and further lags of employment and capital stocks. (*) Significant at the 1% level. (**) Significant at the 5% level. (***) Significant at the 10% level. (a) Best point estimates calculated as [(Ln WBt + Ln WBt-l )/ Ln Lt-1 ]. (b) Wald Test of Joint Significance of the variables interacted with a given year dummy. (c) Time dummies were included but are here omitted (d) Wald Test of Joint Significance of year dummies interacted with a given variable.
Table 3: Anticipatory Slumps & Bumps

||Dep.|Var.:||∆_n_<br>:<br>_n_<sup>_mDD_</sup><br>_z,t−_|1||∆_n_<br>:<br>_n_<sup>_mDD_</sup><br>_z,t_||
|---|---|---|---|---|---|---|---|---|
||||Slump|Slump|Slump|Bump|Bump|Bump|
|||_n_|11:12|11:12|10:12|1:4|1:3|1:4|
|||_n_|4:8|4:10|4:9|11:12|11:12|10:12|
||||(1)|(2)|(3)|(4)|(5)|(6)|
|_Panel A_: Phaseout Categories|||||||||
|**1**_{Phased}_∆_τ _<sup>_DD_</sup><br>_z,t_|||6.1<sup>_∗∗∗_</sup>|4.6<sup>_∗∗∗_</sup>|4.1<sup>_∗∗∗_</sup>|-12.2<sup>_∗∗_</sup>|-9.2<sup>_∗∗_</sup>|-7.6<sup>_∗∗_</sup>|
||||(2.1)|(1.3)|(1.5)|(4.5)|(4.0)|(3.1)|
|**1**_{Other}_∆_τ _<sup>_DD_</sup><br>_z,t_|||-1.6|-1.0|-0.2|-5.3<sup>_∗_</sup>|-4.5|-5.8|
||||(2.8)|(2.3)|(2.4)|(2.8)|(2.7)|(2.6)|
|_Panel B_: All Goods|||||||||
|∆_τ _<sup>_DD_</sup><br>_z,t_|||0.7|0.7|1.1|-7.4<sup>_∗∗∗_</sup>|-5.9<sup>_∗∗_</sup>|-6.3<sup>_∗∗∗_</sup>|
||||(1.5)|(1.4)|(1.7)|(2.5)|(2.4)|(2.2)|
|HS6 FE|||✓|✓|✓|✓|✓|✓|
|_N_|||6023|6285|7076|7014|7014|8323|
|Adj R2|||0.075|0.103|0.087|0.26|0.237|0.257|

_Note:_ Estimates are obtained using (5) (columns 1 to 3) and (6) (columns 4 to 6). Estimates in Panel A separate between the effect of ∆ _τzt_<sup>_DD_</sup> for good’s whose tariffs were phased-out (class B,C,C+) and the rest. Standard errors, in parentheses, are clustered at HS-2 product * ** *** level, _p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01.

# **2.5 Anticipation and Storability**

Measuring storability for a wide range of products is not a straightforward task and no off-the-shelf measure is readily available. To make progress we follow Alessandria et al. (2010a) and proxy it by considering the lumpiness of a goods’ trade flows.<sup>20</sup> In the model we present in section 4, more storable goods with lower depreciation rates are characterized higher inventory-sales ratios and more infrequent orders. We build on this insight and relate the goods inventory intensity with its observed trade lumpiness.<sup>21</sup> We measure trade lumpiness using the Herfindahl–Hirschman index of monthly concentration of the annual imports, calculated as follows:

> 20Alessandria et al. (2010a) also use a direct measure of a product’s physical depreciation and obsolescence rate but only for a limited number of goods and at more aggregate levels than HS-6.

> 21Lumpiness in trade flows can be explained by lumpiness of demand or the usage of inventories. We take the second view. The importance of inventories in international trade has been widely documented as we emphasized in the introduction.

12
Table 2: **Total value added in exports relative to gross value of exports**

||1992|1995|1997|2001|2004|2007|
|---|---|---|---|---|---|---|
|Total|0.830|0.783|0.748|0.735|0.723|0.711|
|Public Services|1.569|1.139|1.463|1.433|1.390|1.390|
|Commercial Services|2.125|2.666|2.168|1.985|2.072|1.873|
|Trade and Transport|1.174|1.161|1.221|1.234|1.068|1.028|
|Construction|2.957|1.976|1.482|1.833|1.863|1.472|
|Other Manufactures|0.662|0.625|0.607|0.595|0.620|0.623|
|Machinery|0.494|0.476|0.430|0.416|0.392|0.395|
|Transport Equipment|0.405|0.374|0.347|0.339|0.323|0.323|
|Metals|0.828|0.702|0.660|0.645|0.642|0.553|
|Chemicals|0.591|0.604|0.543|0.515|0.470|0.449|
|Textiles, clothing, footwear|0.427|0.417|0.412|0.426|0.399|0.407|
|Energy and mining|1.108|1.077|0.925|1.017|0.949|0.873|
|Agriculture and processed foods|0.781|0.741|0.710|0.753|0.744|0.756|

There are identifiable differences across sectors. On the low end, trade in machinery, transport equipment, and textiles and clothing all have value added contents less than 41% of the gross trade values as of 2007. These drops are between 2 and 10 percent points since 1992. To put this into perspective this also means that for every $100 in gross exports flows globally in machinery in 1992, there was $49.40 in value added. By 2007, this had dropped to $39.50, a drop of 20 percent in the value added content of trade in this sector. For transport equipment, the drop is from $40.50 to $32.30, or a 20 percent drop in the value added content of trade.

The pattern is quite different when we focus on services sectors. A first point to note in Table 2 is that the ratio of value added to gross exports is consistently greater than 1. What this means is that most exports in services, on a value added basis, are embodied in exports of goods. Direct exports in services are small, globally, relative to indirect of embodied exports. Another point to note is that this ratio has not really changed since the early 1990s. In other words, though trade in services has grown rapidly, so has trade in goods. With the exception of construction, where there has been substantial growth in direct exports, the basic patten is one of embodied exports dominating direct exports, and relative stability in this pattern in the 1992-2007 period.

We turn next to the basic composition of trade for the world as a whole, both in gross value terms and adjusted to measure instead the value added content of trade. We report these in Tables 3, 4, 5, and 6. The basic pattern is again one of stability 9
from a flatter world. This focus is also motivated by our earlier work (Brun et al., 2005) where we found that an increasing ‘burden’ of distance was restricted to poor countries and conjectured that they may have been marginalized by the current wave of globalization.

Section 2 reviews the explanations for this finding: composition effects, sample selection, econometric methods and omitted variable bias. This preliminary exploration, in part based on a meta-analysis on 103 published papers (based on the Disdier and Head’s (2008) database), leads us to suggest that the puzzle mainly holds for developing countries. The rest of the paper seeks to check if this is still the case when determinants of distance-sensitive trade costs are brought into the determination of bilateral trade. Section 3 analyzes the evolution of the average distance of trade, confirming and sharpening the results from the meta-analysis. Over time, the third poorest countries in the sample of 124 countries (i.e. the low-income countries according the World Bank classification of 2006) shift, among existing trade partners, towards physically closer partners. Also, their new trading partners are closer than existing partners. No such pattern is apparent in the data for the remaining countries. These findings confirm a changing role of distance in bilateral trade. In section 4, we revisit the gravity-predicted θ elasticities. To control for as many factors as possible, and to maximize robustness, we rely on cross-section and panel formulations and use several methods to deal with zero trade flows. In all cases, a distance puzzle is revealed for the bottom third (39 countries) in the sample, leading us to conclude that trade has become regionalized for low income countries. Section 5 concludes.

# **2. THE DISTANCE PUZZLE IN THE GRAVITY LITERATURE**

# **2.1. The Rising Distance Effect**

While there are several approaches to estimate the impact of transport costs on the volume of trade, the great majority of estimates rely on the popular gravity model which states that the volume of bilateral trade between two countries ( _i_ and _j_ ) should be proportional to their economic size, proxied by GDP ( _Yi_ ( _j_ ) ) and inversely proportional to transport costs, proxied by the distance between partners ( _Dij_ ). The numerous studies in the literature deliver an estimate of the elasticity of bilateral trade to distance, θ<sup></sup> , which is then used to predict bilateral trade volumes as a function of distance. For example, using the range of estimates in the  literature, with θ = − 1.4 [ − 0.7] doubling the distance reduces trade by 63% 3