rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1. (Cont’d)**

|**Fiscal a**|**nd Economic Variables in Province, Prefecture and City levels**|
|---|---|
|Province GDP<br>per capita|Annual per capita GDP in the firm’s province, constant price in the year<br>2000. Source: China Statistical yearbooks.|
|Province state<br>sector share|Percentage of employment in SOEs in total urban employment in the firm’s<br>province. Source: China Statistical yearbooks.|
|Province<br>unemployment<br>rate|Annual urban employment rate in the firm’s province. Source: China<br>Statistical yearbooks.|
|Province fiscal<br>revenue p.c.|Annual per capita fiscal revenue in the firm’s province. Source: China<br>Statistical Yearbooks.|
|Province fiscal<br>autonomy|<br>Ratio of fiscal revenue to fiscal expenditure in the firm’s province. Source:<br>China Statistical Yearbooks.|
|Prefecture GDP<br>per capita|Annual per capita GDP in the firm’s prefecture. Source: Public Finance<br>Statistical Materials of Prefectures, Cities, and Counties (1998-2005).|
|Prefecture fiscal<br>revenue per<br>capita|Annual per capita GDP fiscal revenue in the firm’s prefecture. Source: Public<br>Finance Statistical Materials of Prefectures, Cities, and Counties (1998-<br>2005).|
|Prefecture fiscal<br>autonomy|Ratio of fiscal revenue to fiscal expenditure in the firm’s prefecture.<br>Source: Public Finance Statistical Materials of Prefectures, Cities, and<br>Counties (1998-2005).|
|County GDP p.c.|Annual per capita GDP in the firm’s county. Source: Public Finance<br>Statistical Materials of Prefectures, Cities, and Counties (1998-2005).|
|County fiscal<br>revenue per<br>capita|Annual per capita GDP fiscal revenue in the firm’s county. Source: Public<br>Finance Statistical Materials of Prefectures, Cities, and Counties (1998-<br>2005).|
|County fiscal<br>autonomy|Ratio of fiscal revenue to fiscal expenditure in the firm’s county. Source:<br>Public Finance Statistical Materials of Prefectures, Cities, and Counties<br>(1998-2005).|
|TFP dispersion|Standard deviation of firm TFP in the same 3-digit industry|
|ROS dispersion|Standard deviation of firm ROS in the same 3-digit industry|
|Intangibility|Average ratio of intangible assets to firm total assets in the same 3-digit<br>industry|
|Province telecom<br>infrastructure|Per capita number of mobile phone and fixed telephone users|
|Province internet<br>infrastructure|Per capita number of internet users|
|Province road<br>mileage|Per capita road mileage. Different levels of roads and railway are translated<br>into the equivalent of second-level road according to transport capacity.|

32
Lakshmi, P.V.M., N.K. Virdi, A. Sharma, J.P. Tripathy, K.R. Smith, M.N. Bates and R. Kumar, 2013. Household air pollution and stillbirths in India: Analysis of the DLHS-II National Survey. Environmental Research, Vol. 121, pp.17-22.

- Lashof DA, Delano D, Devine J et al. (2007). Coal in a changing climate. Natural Resources Defense Council. http://www.nrdc.org/globalwarming/coal/coalclimate.pdf.

- Lockwood, A.H., Kristen Welker-Hood, Molly Rauch and Barbara Gottlieb (2009). Coal’s Assault on Human Health: A Report from Physicians for Social Responsibility. Physicians for Social Responsibility, Washington, DC.

- Markandya, A. and P. Wilkinson (2007). Electricity generation and health. Lancet, Vol. 370, pp. 979–90.

- Mason, KL, KD Retzer, R Hill, JM Lincoln (2015). Occupational Fatalities During the Oil and Gas Boom — United States, 2003–2013, CDC Morbidity and Mortality Weekly Report. Vol. 64, No. 20 (May 29 Issue), pp. 551-554.

- Modi, V., S. McDade, D. Lallement and J. Saghir, 2005. Energy Services for the Millennium Development Goals. The World Bank and the United Nations Development Program (UNDP).

- Myers T. (2012). Potential contaminant pathways from hydraulically fractured shale to aquifers. Groundwater 50:872–82 83.

- Narain, Urvashi, and Chris Sall (2016). “Methodology for Valuing the Health Impacts of Air Pollution: Discussion of Challenges and Proposed Solutions.” World Bank, Washington DC.

- National Research Council (2012). Potential Human Health Effects of Uranium Mining, Processing, and Reclamation (in Uranium Mining in Virginia: Scientific, Technical, Environmental, Human Health and Safety, and Regulatory Aspects of Uranium Mining and Processing in Virginia). National Academies Press. Washington, DC.

- Normile, D. (2012). "Is Nuclear Power Good for You?". Science. 337 (6093): 395–396 (27 July 2012)). doi:10.1126/science.337.6093.395-b.

30
enterprise’s performance (table 4.2). Having a bank account is not a common phenomenon in rural Yemen; however, money transfer is more often used in rural business. Therefore, a moneytransfer variable is used as a proxy for a financial service. Facing payment disputes is highly correlated with performance because disputes for payments happened more frequently with more business transactions. Our pooled data also show that transport and electricity are key factors that affect enterprises’ performance.

**Table 4.2 Household Farm and Nonfarm Business Revenue Model**

|Model<br>Enterprise Type<br>Independent Variables|(1)<br>Nonfarm<br>Pooled data|(2)<br>Nonfarm<br>Pooled data|(3)<br>Nonfarm<br>Male|(4)<br>Nonfarm<br>Female|(5)<br>Farm<br>Pooled data|(6)<br>Farm<br>Pooled data|
|---|---|---|---|---|---|---|
|**Enterprise Characteristics**<br>|||||||
|ln (enterprise age)|-0.044|-0.045|-0.085|0.442|-0.378|**-1.153****|
||[0.107]|[0.105]|[0.107]|[0.433]|[0.390]|**[0.472]**|
|dummy: female manager|**1.124*****<br>**[0.281]**|**0.822*****<br>**[0.292]**|||0.894<br>[0.736]|0.935<br>[0.648]|
|dummy: own founded|**0.435***|0.135|0.227|**0.662***|0.859|**0.975***|
||**[0.256]**|[0.240]|[0.269]|**[0.385]**|[0.658]|**[0.492]**|
|ln (manger’s education years)||0.043|0.085|-0.076||-0.093|
|||[0.072]|[0.073]|[0.289]||[0.215]|
|**Enterprise RIC**<br>Moneytransfer||**0.727*****|**0.725*****|0.851||0.390|
|||**[0.266]**|**[0.273]**|[1.582]||[0.652]|
|Facepayment dispute||**0.488****|**0.562*****|0.077||**-0.816***|
|||**[0.205]**|**[0.213]**|[0.420]||**[0.462]**|
|Corruption||0.187|0.191|0.632||-1.207|
|||[0.247]|[0.251]|[1.344]||[0.758]|
|Usetransport||**0.873*****|**0.871*****|-0.627||-1.554|
|||**[0.271]**|**[0.315]**|[0.413]||[1.014]|
|Useelectricity||**0.585*****|**0.474****|**0.504***||**-1.920***|
|||**[0.201]**|**[0.215]**|**[0.277]**||**[1.040]**|
|Hascell phone||0.013|-0.067|0.117||-0.102|
|||[0.210]|[0.222]|[0.675]||[0.373]|
|Management innovation||-0.309|**-0.370***|0.259||**1.002***|
|||[0.193]|**[0.202]**|[0.660]||**[0.507]**|
|Business service||0.047|0.131|-0.263||0.000|
|||[0.248]|[0.252]|[0.658]||[0.000]|
|ln (wait days for permit)||0.031|0.061|-0.208||**0.432****|
|||[0.061]|[0.065]|[0.249]||**[0.216]**|
|ln (distance to market)||0.089|**0.119****|-0.072||-0.004|
|||[0.056]|**[0.060]**|[0.139]||[0.153]|
|**Dummy**<br>Rural|-0.186|-0.253|**-0.333***|-0.424|0.496|0.155|
||[0.169]|[0.177]|**[0.195]**|[0.550]|[0.624]|[0.494]|
|Observations|325|324|291|33|69|69|
|R-squared|0.091|0.218|0.180|0.623|0.150|0.410|

_Source:_ 2010 Yemen RIC Survey; _Notes:_ Sampling weights are used; standard errors in parentheses. *, **, and *** denote coefficient significant levels at the 10, 5, and 1% respectively; the dependent variable for all models are revenue per full time employee equivalent in log form; according to Breusch-Pagan/Cook-Weisberg test for heteroskedasticity, robust results show that there is no heteroskedasticity problem in the data set.

In analyzing a household nonfarm enterprise’s performance, a gender issue is also taken into consideration. There is total of 291 male managers and 33 female managers in nonfarm 15
# **Figure 14. Fiscal performance and fiscal frameworks**

_Government revenue and expenditure in small states over the past decade were characterized by volatile growth, leaving limited fiscal buffers in many cases, as indicated by levels of public debt and primary balances. Shortcomings in fiscal management may reflect, in part, the relatively low proportion of small states with fiscal rules, fiscal councils, and sovereign wealth funds, and the relatively low quality of governance._

## **A. Revenue and expenditure growth in small states**

## **B. Public debt and primary fiscal balances in small states, 2020-23**

<!-- Start of picture text --> Percent Revenue growth Percent of GDP<br>15 Expenditure growth 150<br>120<br>10<br>90<br>5<br>60<br>0<br>30<br>-5<br>0<br>-10<br>-15 -10 -5 0 5 10<br>Primary balance, percent of GDP<br>C. Share of economies with fiscal rules, 2021 D. Share of economies with fiscal councils, 2021<br>Percent EMDEs excl. small states Percent EMDEs excl. small states<br>100 Advanced economies 100<br>Advanced economies<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>Small states Small states: Small states: Small states Small states: Small states:<br>Commodity Commodity Commodity Commodity<br>exporters importers exporters importers<br>Debt<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023<br><!-- End of picture text --> 46
||||TArLE*1|.KAYA<br>B|ASITCFISCA|L INDITCAT|ORS|||
|---|---|---|---|---|---|---|---|---|---|
|||||(Fisca|l<br>Years)|||||
||||||||||1980-1987|
||||1111 1|112IRfi|iRMA|1984|11|1212|_-Averau_|
|Centrnl Cov-rnwnt||||(in perc|ent of CO|P)||||
|Cauh Deficit|-6.0|-9.6|-6.|-3.6|-3.9|-6.1|-6.4|-7.6|-6.8|
|OperationalDeficit|0.6|-5.6|-3.2|2.2|1.9|4.0|_-o.8_|-8.3|-0.9|
|Primary Deficit|-3.1|-7.1|-2.9|0.3|5.2|-5.9|-O.5|-2.9|-2.1|
|Uonetarv Authority<br>1/||||||||||
|CashDeficit|-4.7|-9.4|-0.6|-3.7|-3.8|-6.1|-6.4|-7.8|-5.8|
|Operational<br>Deficit|-o.3|-6.7|_-a.o_|2.4|1.8|3.2|-O.6|-8.1|-1.0|
|PrimaryDeficit|-3.1|-7.1|-2.9|_o.3_|0.2|-0.9|-4.6|-2.9|-2.1|
|M,moranduu<br>Itmi||||(in|percent)|||||
|UWPGrowthRate|6.7|4.6|3.0|1.6|1.6|3.1|S.O|6.4|3.7|
|Average Nominal Domestic<br>Interest<br>Rate|3.4|4.7|11.9|14.9|14.4|14.0|16.1|14.2|11.7|
|Domestic<br>Inflation<br>Rate|10.6|12.7|17.7|18.0|11.6|9.9|8.1|8.4|<br>11.9|
|Change<br>InRealEffoctive<br>ExchangeRate2/|-3.0|-1.9|-1.6|1.3|0.0|8.8|-12.6|-9.3|-2.6|
|Debt/COP<br>3/|36.4|39.0|42.8|44.2|44.4|46.3|48.1|48.9|43.6|

1/ Consolidated accounts of Central Govornment **and** Control Bank in respect of holding of lnternational roserveS and positions vis-a-vis the IMF. 2/ A negative sign signifies depreciation. A positivo sign signifies appreclation. 3/ Not domestic and external debt of the Monetary Authority.
Table 4: First-Stage Probit for the Probability of Participating in an Export Finance Scheme

||E|FS|LT|FF|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Log export value|0.320***|0.298***|0.589***|0.559***|
||(0.009)|(0.010)|(0.044)|(0.044)|
|Shr. exports in negative list||-0.329***<br>(0.062)|||
|Shr. exports in eligible list||||0.601**<br>(0.256)|
|Importer||0.090**||0.707***|
|||(0.041)||(0.182)|
|Observations|14,765|13,468|14,765|14,765|
|Pseudo R-squared|0.224|0.238|0.488|0.505|
|Joint signifcance test (p-value)|0.00|0.00|0.00|0.00|

The table reports the coefficients of a probit model estimated among the set of firms observed in 2015. The dependent variable in columns (1) and (2) takes the value 1 if a firm participated in the Export Finance Scheme (EFS) at any point between 2015 and 2017 and 0 otherwise. The dependent variable in columns (3) and (4) takes the value 1 if a firm participated in the Long-Term Financing Facility for Machinery & Equipment (LTFF) at any point between 2015 and 2017 and 0 otherwise. All covariates are measured in 2015. The propensity score used to weight the regressions presented below corresponds to the specification in column (2) for EFS and in column (4) for LTFF. Standard errors are in parenthesis. *, **, *** indicate significance at the 10 percent, 5 percent and 1 percent levels, respectively.

Table 4 presents the estimates of the probit model used to calculate the propensity score for the Export Finance Scheme (EFS) and the Long-Term Financing Facility (LTFF). Columns (1) and (3) shows that exporter size (in terms of export sales) on its own is a strong predictor of treatment for firms’ participation in both export finance support schemes. Recall from Section 2 that there is a negative list of sectors for which EFS is not available and that LTFF is in turn only available to firms in 20 sectors. Thus, in columns (2) and (4) we augment the specifications predicting firms’ participating status with the share of value that firms export in sectors included in EFS’s negative list and on the sectors for which LTFF is available respectively. We also include an importer status dummy in both estimations to account for the fact that firms that import goods from abroad tend to be more productive than those that only source their inputs domestically and are therefore more likely to export and therefore require export finance (Kasahara and Lapham, 2013; Halpern et al., 2015).

17
In contrast to traditional indemnity-based crop insurance, the contracts are index based, which means that the insurer will pay the contractual claim if rainfall falls below a specified level regardless of crop damage. In other words, index-based insurance is against _events_ that cause loss, not against the loss itself (Turvey, 2001). Because payouts are independent of the farmers’ practices, index-based insurance greatly reduce transaction costs and eliminate moral hazard. By enabling farmers to engage in more productive agriculture, the insurance program can operate independent of subsidies, and appears thus to be a win-win proposition for all the stakeholders: the farmers expect a substantial net gain, and the market actors involved in the scheme foresee a lucrative new market.

A household survey of 160 farmers that participated in the first pilot was implemented in Lilongwe and Kasungu. Survey data shows that 86% of subjects wanted to join the scheme again the following season, and 67% said they had encouraged other farmers to join. In response to farmers’ demand, hybrid maize and maize-related fertilizer were added to groundnuts in the second season as a choice for farmers. A total of 2536 farmers joined the scheme in October and November 2006, and plans for covering more farmers and more regions are under way. Stakeholders interviewed during this research indicated that they expect demand to systematically exceed supply for the foreseeable future.

There is a need to develop a strategy for addressing the interactions between index-based weather insurance and seasonal climate predictions. In addition to promoting an actuarially fair approach to insurance, it may be possible to formulate risk-sharing mechanisms that help farmers make better decisions with regards to crop production. This possibility is discussed in the following sections.

8
48

# **Table 2**

## **Dependent Variable: Growth Rate of Real GDP Innovation Pillar Indicators**

|**Years : 1960 - 2000**|**Reg 2.1**|**Reg 2.2**|**Reg 2.3**|**Reg 2.4**|**Reg 2.5**|**Reg 2.6**|**Reg 2.7**|
|---|---|---|---|---|---|---|---|
|**Growth of Capital**|**0.2128*****<br>**(0.0442)**|**0.2157*****<br>**(0.0451)**|**0.2252*****<br>**(0.0394)**|**0.2128*****<br>**(0.0513)**|**0.2033*****<br>**(0.0494)**|**0.2127*****<br>**(0.0504)**|**0.2141*****<br>**(0.0504)**|
|**Growth of Labor**|**0.5694*****<br>**(0.1335)**|**0.5775*****<br>**(0.1392)**|**0.5948*****<br>**(0.1259)**|**0.5996*****<br>**(0.1485)**|**0.5703*****<br>**(0.1500)**|**0.5948*****<br>**(0.1519)**|**0.5933*****<br>**(0.1509)**|
|**Initial GDP (1960)**|**-3.8747*** **<br>**(0.8375)**|**-3.9019*** **<br>**(0.8201)**|**-3.6842*** **<br>**(0.7789)**|**-3.6173*** **<br>**(0.7132)**|**-3.8133*****<br>**(0.7348)**|**-3.6136*****<br>**(0.7204)**|**-3.6067*****<br>**(0.7254)**|
|**HC15**|**4.3613****<br>**(1.9441)**|**4.5361****<br>**(1.9274)**|**3.9374****<br>**(1.8746)**|**2.8637***<br>**(1.6399)**|**2.2266**<br>**(1.6457)**|**2.8002***<br>**(1.5394)**|**2.8968***<br>**(1.5483)**|
|**Sq of HC15**|**-1.0208****<br>**(0.4654)**|**-1.0544****<br>**(0.4616)**|**-0.9253****<br>**(0.4465)**|**-0.6367***<br>**(0.3682)**|**-0.4574**<br>**(0.3744)**|**-0.5983***<br>**(0.3656)**|**-0.6273***<br>**(0.3665)**|
|**Log (Educational**|**0.2933**|**0.3338**|**0.4639**|**0.2820**|**0.4720**|**0.3959**|**0.3980**|
|**Quality)**|**(0.5121)**|**(0.5253)**|**(0.4799)**|**(0.5296)**|**(0.5142)**|**(0.5563)**|**(0.5516)**|
|**Log (Patents)**|**0.1909*****<br>**(0.0728)**|||||||
|**Log (Utility Patents)**||**0.1759****<br>**(0.0741)**||||||
|**Log (Journals)**|||**0.2254*****<br>**(0.0500)**|||||
|**Log (Royalty Payments)**||||**-0.0040**<br>**(0.0188)**||||
|**Log (Royalty Receipts)**||||**0.0336****<br>**(0.0166)**||||
|**Log (Royalties)**|||||**0.0253***<br>**(0.0136)**|||
|**Royalty Payments**<br>**per capita**||||||**0.000004*****<br>**(0.000001)**||
|**Royalty Receipts**<br>**per capita**||||||**0.000001**<br>**(0.000006)**||
|**Royalties per capita**|||||||**0.000004*****<br>**(0.000001)**|
|**Phones**|**0.0033*****<br>**(0.0010)**|**0.0036*****<br>**(0.0010)**|**0.0032*****<br>**(0.0009)**|**0.0046*****<br>**(0.0012)**|**0.0046*****<br>**(0.0012)**|**0.0044*****<br>**(0.0012)**|**0.0044*****<br>**(0.0012)**|
|**Constant**|**-3.6660***|**-4.0111***|**-4.7967****|**-2.4010**|**-2.4736**|**-2.5903**|**-2.5903**|
||**(2.1096)**|**(2.1394)**|**(2.0922)**|**(1.8850)**|**(1.8782)**|**(2.0501)**|**(2.0318)**|
|**R squared**|**0.6137**|**0.6071**|**0.646**|**0.6410**|**0.6318**|**0.6320**|**0.6320**|
|<br>**Number of Countries**<br>*, **, *** denotes significance at th<br>Robust standard errors obtained|**85**<br>e 90, 95 and 9<br>usingWhite's c|**85**<br>9 percent conf<br>orrection for h|**85**<br>idence level re<br>eteroscedastic|**84**<br>spectively<br>ity.|**84**|**84**|**84**|
**Table 8: Count of enterprises in presumed comparative advantage industries by enterprise age group, UBR 2001 and UBR 2010**

||**_A. All comparative advantage industries_**|||**_E. Commercialfarming_**|||
|---|---|---|---|---|---|---|
|||% share in|sector||% share in|sector|
|**Age Group**|Number of enterprises|total||Number of enterprises|total||
|**(years)**|2001<br>2010|2001|2010|2001<br>2010|2001|2010|
|<5|3,685<br>12,985|64.7|46.2|102<br>1,958|26.4|40.6|
|5‐10|957<br>5,077|16.8|18.1|77<br>883|19.9|18.3|
|More than 10|1,050<br>10,053|18.4|35.8|208<br>1,976|53.7|41.0|
|Total|5,692<br>28,115|100.0|100.0|387<br>4,817|100.0|100.0|
||**_B. Comparative advantage primary industries_**|||**_F. Food and beverages industry_**|||
|||% share|in||||
|**Age Group**|Number of enterprises|sector tota|l|Number of enterprises|% share in se|ctor total|
||2001<br>2010|2001|2010|2001<br>2010|2001|2010|
|<5|208<br>2,773|34.0|34.7|1,476<br>3,083|67.1|53.5|
|5‐10|138<br>1,751|22.6|21.9|355<br>871|16.1|15.1|
|More than 10|265<br>3,462|43.4|43.4|368<br>1,807|16.7|31.4|
|Total|611<br>7,986|100.0|100.0|2,199<br>5,761|100.0|100.0|
||**_C. Comparative advantage manufacturing indus_**|**_tries_**<br>% share|in|**G. Thegarments industry**|||
|**Age Group**|Number of enterprises|sector tota|l|Number of enterprises|% share in s|ector total|
||2001<br>2010|2001|2010|2001<br>2010|2001|2010|
|<5|3,229<br>9,979|68.9|51.1|1,677<br>6,721|70.7|50.6|
|5‐10|737<br>3,162|15.7|16.2|358<br>2,215|15.1|16.7|
|More than 10|720<br>6,374|15.4|32.7|336<br>4,348|14.2|32.7|
|Total|4,686<br>19,515|100.0|100.0|2,371<br>13,284|100.0|100.0|
||**_D. Comparative advantage services_**|||**H. Land transport services**|||
|||% share i|n sector||||
|**Age Group**|Number of enterprises|total||Number of enterprises|% share in se|ctor total|
||2001<br>2010|2001|2010|2001<br>2010|2001|2010|
|<5|248<br>233|62.8|37.9|224<br>162|44.8|45.5|
|5‐10|82<br>164|20.8|26.7|71<br>86|19.9|24.2|
|More than 10|65<br>217|16.5|35.3|61<br>114|17.1|32.0|
|Total|395<br>614|100.0|100.0|356<br>362|100.0|101.7|

42
WPS4144 Trade and Human Capital Accumulation - Evidence from U.S. Immigrants

# Dörte Dömeland*

# Abstract

This study provides empirical evidence that trade increases on-the-job human capital accumulation by estimating the effect of home country openness on estimated returns to home country experience of U.S. immigrants. The positive effect of trade on on-the-job human capital accumulation remains significant when controlling for GDP, educational attainment and institutional quality. It is not the result of self-selection, heterogeneity in returns to experience, English speaking origin or cultural background. The effect persists when restricting the sample to non-OECD countries, thereby resolving the theoretical ambiguity whether trade increases or decreases learning-by-doing. The role of trade in generating economic growth is therefore likely to be more important than generally considered.

JEL Classiffication: O15, F00

# World Bank Policy Research Working Paper 4144, March 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._

*I am very grateful to Antonio Ciccone for excellent advice and suggestions and thank Jaume Garcia, Christian Haefke, Adriana Kugler, Martin Menner, Joel Shapiro, Ernesto Villanueva, Lutz Weinke and Michael Wolf for very useful comments. Email: ddomelandnarvaez@worldbank.org. Tel: ++1-202-458-1238
with the freeze on deposits, resulted in a large contraction in national output. The almost immediate welfare impacts were severe. Unemployment rose sharply as did various indicators of poverty (Fiszbein et al., 2002; World Bank, 2003). The government’s statistics office estimates that the proportion of people living below the poverty line rose from 37% just prior to the crisis (October 2001) to 58% a year later (World Bank, 2003). Widespread political and social instability ensued from the economic crisis.

As the government’s main safety net response to this crisis, _Jefes_ provided a cash transfer of 150 pesos per month to each eligible individual, representing about one half of mean household income per capita per month in Argentina in 2002. Those formally deemed eligible to participate were unemployed household heads with dependents (children aged less than 18 or incapacitated). In order to enroll, the potential participants had to request participation through the local municipality or through local offices of the Ministry of Labor.

_Jefes_ replaced a previous program, _Trabajar_ . This was a workfare program, though smaller scale than _Jefes_ . _Trabajar_ entailed a tightly enforced work requirement of 30-40 hours, with targeting criteria to help assure that the work was of value to residents of poor communities. _Trabajar_ has been found to have been effective in reaching the poorest, both as workers and residents (Ravallion, 2000; Jalan and Ravallion, 2003). For example, 80% of _Trabajar_ workers came from the poorest 20% of the Argentine population (Jalan and Ravallion, 2003).

Given the magnitude of the crisis, the government’s explicit aim for the _Jefes_ program was to reach a broader segment of the population than _Trabajar_ . At its inception, _Jefes_ was advertised as a “universal” program, meaning that it was intended that anyone who wanted the transfer amongst those eligible could get it. And (contrary to its predecessor), _Jefes_ did not have an explicitly stated poverty focus. However, genuine universality amongst households with 6