rafmacalaba/datause-encoder-data huggingface dataset:
Bangladesh has welcomed the Rohingya and made considerable efforts to host them since 1978. The conflicts in Rakhine State have triggered five major waves of Rohingya migration to Bangladesh. The first wave happened in 1978, with over 200,000 people fleeing to Cox’s Bazar after reported evictions by the Myanmar military. Negotiations led to about 107,300 Rohingya returning by March 1979, totaling 180,000 returnees between 1978 and 1979 (HRW 2003). The second wave occurred in 1991–92, when an estimated 250,000 Rohingya fled Myanmar amid political turmoil and increased military presence (ACAPS 2017). Repatriation efforts started in April 1992, resulting in over 230,000 Rohingya returning to Myanmar by 1997. The third wave in 1997 resulted from high food prices and forced labor in Myanmar, with many Rohingya settling in Bangladeshi villages, making counting difficult. Repatriation briefly resumed in 1998, with around 800 Rohingya returning to Myanmar by October 1999 (HRW 2003). The fourth wave took place in October 2016 due to insurgent activity in Myanmar, with over 87,000 Rohingya settling primarily in Ukhia subdistrict and forming the Balukhali makeshift settlement (ACAPS 2017). The fifth and most significant wave occurred in August 2017, as described above.

In September 2017, Bangladesh’s Disaster Management and Relief Ministry announced there would be no restrictions on Rohingya entry, ensuring shelter for as long as needed. Subsequently, the government of Bangladesh began to consolidate all Rohingya into a specific location, constructing a formal refugee camp complex in the Ukhia and Teknaf subdistricts. The complex encompasses 33 camps intended to accommodate up to 800,000 forcibly displaced individuals (ICG 2023).

As a developing nation with limited resources, Bangladesh faces the challenge of providing humanitarian aid to the Rohingya while ensuring stability and security within its borders (The Diplomat 2018). Although the Bangladesh government offered physical space to host the displaced population, it confined the majority of them to camps. The rationale behind this restriction was rooted in concerns about the security of the host population.

Only around 50,000 displaced Rohingya from earlier caseloads (pre-August 2017) have received formal recognition as refugees. This status grants them certain privileges, including the freedom to seek employment outside the camps and to engage in commercial 9
Hendry, D.F. (2000). “On Detectable and Non-detectable Structural Change,” _Structural Change and Economic Dynamics_ , Volume 11, Issues 1-2, 45-65.

Hiebert, P. and I. Vansteenkiste (2010). “International trade, technological shocks and spillovers in the labour market : a GVAR analysis of the US manufacturing sector,” _Applied economics_ , Vol. 42, 22/24, 3045-3066.

Hooker, M. (1996). “What Happened to the Oil Price-Macroeconomy Relationship?,” _Journal of Monetary Economics_ , Volume 38, Issue 2, 195-213.

IMF Country Report (2011). “People’s Republic of China: 2011 Spillover Report - Selected Issues,” June 28, 2011. http://www.imf.org/external/pubs/ft/scr/2011/cr11193.pdf.

Inoue, T. and T. Okimoto (2008). “Were There Structural Breaks in the Effect of Japanese Monetary Policy?: Re-evaluating the Policy Effects of the Lost Decade,” _Journal of the Japanese and International Economies_ 22(3), 320-342.

Jockmann, M. and G. Koop (2011). “Regime-Switching Cointegration,” _mimeo_ , May 2011. http://personal.strath.ac.uk/gary.koop/jochmann ~~k~~ oop ~~2~~ 011.pdf.

Matsubayashi, Y. (2013). “Deepening of International Interdependence and the East Asian Economy: Empirical Evidence based on Global VAR,” in _Impacts that the Structural Transformation of the World Economy Has on the East Asian Region_ (Report of Joint International Research Project by Japan, China and Korea), Economic and Social Research Institute, Cabinet Office, Government of Japan. http://www.esri.go.jp/jp/prj/int ~~p~~ rj/2013/prj2013 ~~0~~ 1 ~~0~~ 5e.pdf. Pesaran, M.H., T. Schuermann, and S.M. Weiner (2004). “Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model,” _Journal of Business and Economic Statitics_ , 22, 129-162.

Pesaran, M. H., and Y. Shin (1998). “Generalized Impulse Response Analysis in Linear Multivariate Model,” _Economics Letters_ , 58(1), 17-29.

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http://www-cfap.jbs.cam.ac.uk/research/gvartoolbox/index.html.

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Vansteenkiste, I. (2007). “Regional Housing Market Spillovers in the US: Lessons from Regional Divergences in a Common Monetary Policy Setting,” _ECB Working Paper_ No.708.

38
# **_Financial Services_**

In CEE and Baltic countries the banking sector is presently characterized by small shares of credit allocated through state-owned banks and high foreign participation. Although weaknesses remain in the legal framework (e.g., creditor rights, the bankruptcy code), central bank independence has been strengthened in most of these countries. Cukierman, Miller, and Neyapti (2001) develop a measure of independence that has 16 (weighted) components. They conclude that in CEE and Baltic countries the degree of independence has converged to that of the German Bundesbank in the 1980s. Most other transition countries fall substantially short of this. Their measure reflects only legal, not actual independence. If the latter is taken into account, the divergence across countries increases further. The Central Bank of Belarus, for instance, has a high degree of legal, but a low degree of actual independence.

Banking markets in many FSU countries tend to be relatively closed in either a formal or informal (de facto) sense. However, there is significant variation across countries. Thus, Armenia’s financial sector is relatively open and scores higher in terms of regulation. Formal limits on foreign participation (globally or on an individual bank basis) play a role in some countries, but bureaucratic impediments are more prominent in inhibiting foreign participation. Examples include limitations on foreign staff, lengthy licensing procedures, financial repression, public ownership of major banks, and lax regulatory practices. In general the banking sector in these countries suffers from a weak capital base.

# **_Infrastructure Services_**

Policy reforms in the area of utility and infrastructure services include better regulation of the provision of these services, removal of cross-subsidization, more efficient pricing, improved revenue collection, and separation/unbundling of activities. Three types of reforms are particularly important in increasing the efficiency of provision of regulated infrastructure services: (i) allowing entry of new domestic and foreign providers; (ii), where feasible, opening the domestic market to imports of such services; and (iii) the establishment of an independent regulator. The latter is likely to be a key determinant of regulatory effectiveness. Reforms may and often do include privatization, but this variable is captured in the overall investment climate variable, not in the infrastructure services policy reform index. Even if incumbent providers remain state-owned, if regulators permit entry of new providers in the market, such competition can be expected to yield efficiency gains in the industry overall. The EBRD indices suggest that 8
63 UN High Commissioner for Refugees (UNHCR), _Global Trends: Forced Displacement in 2015_ , 20 June 2016, available at: http://www.refworld.org/docid/57678f3d4.html [accessed 6 July 2017] UNHCR, _The Refugee Convention, 1951_ , available at: http://www.unhcr.org/4ca34be29.pdf _UNHCR Population Statistics ‐ Data ‐ Time Series_ . N.p., n.d. Web. 03 July 2017.

<mark>UNHCR, Protection, R. (2007). A 10‐Point Plan of action.</mark> _<mark>T</mark> he 10‐Point Plan in action_ <mark>, 15.</mark> UNHCR Tough choices for Afghan refugees returning home after years in exile. Retrieved August 29, 2017, from http://www.unhcr.org/en‐ us/news/briefing/2017/2/589453557/tough‐choices‐afghan‐refugees‐returning‐home‐ years‐exile.html _UN‐ Summary Overview Document. (2016, September 20). Retrieved July 6, 2017, from_ _<u>https://refugeesmigrants.un.org/sites/default/files/public_summary_document_refugee_sum mit_final_11‐11‐2016.pdf</u>_ Van Houte, M. And M. De Koning. 2008. _Towards a Better Embeddedness? Monitoring Assistance to Involuntary Returning Migrants from Western countries_ . Nijmegen / Amsterdam: CIDIN / AMIDSt.

Verme, P., Gigliarano, C., Wieser, C., Hedlund, K., Petzoldt, M., & Santacroce, M. (2015). _The welfare of Syrian refugees: evidence from Jordan and Lebanon_ . World Bank Publications.

Webber, F. (2011). How voluntary are voluntary returns? _Race & Class_ , _52_ (4), 98‐107. Aiyar Aiyar Zetter, R., & Ruaudel, H. (2016). Refugees’ right to work and access to labor markets–An assessment.
# **Table 6: Factors affecting** **<u>perceived tenure insecurity</u>**

||**Pe**<br>**Allparcels**|**rception of in**<br>**State land**|**security**<br>**Customary land**|**Perce**<br>**Allparcels**|**ption of high**<br>**State land**|**insecurity**<br>**Customary land**|
|---|---|---|---|---|---|---|
|**Panel A. Baseline regression**|||||||
|Has title|-0.243***<br>(0.041)|-0.192***<br>(0.046)||-0.087***<br>(0.034)|-0.085**<br>(0.036)||
|Title in process|-0.141***<br>(0.042)|-0.080<br>(0.060)|-0.176***<br>(0.057)|-0.113***<br>(0.031)|-0.089**<br>(0.042)|-0.149***<br>(0.038)|
|Has informal certificate|-0.032|-0.154*|-0.035|-0.037|0.057|-0.041|
||(0.036)|(0.086)|(0.039)|(0.030)|(0.082)|(0.029)|
|Residential parcel|-0.011|-0.073*|0.003|0.014|-0.042|0.021**|
||(0.011)|(0.040)|(0.011)|(0.010)|(0.031)|(0.010)|
|Customary tenure|-0.064**|||0.042*|||
||(0.027)|||(0.022)|||
|R-squared<br>**Panel B. Control function approach**|0.181|0.187|0.189|0.118|0.061|0.135|
|Has title|-0.243***<br>(0.041)|-0.190***<br>(0.046)||-0.087***<br>(0.034)|-0.082**<br>(0.036)||
|Title in process|-0.141***|-0.078|-0.176***|-0.113***|-0.087**|-0.149***|
||(0.042)|(0.060)|(0.057)|(0.031)|(0.042)|(0.038)|
|Has informal certificate|-0.032|-0.151*|-0.035|-0.037|0.061|-0.041|
||(0.036)|(0.085)|(0.039)|(0.030)|(0.081)|(0.029)|
|Residential parcel|-0.011|-0.071*|0.003|0.014|-0.040|0.021**|
||(0.011)|(0.040)|(0.011)|(0.010)|(0.031)|(0.010)|
|Customary tenure|-0.065**|||0.041*|||
||(0.027)|||(0.022)|||
|Est. Probability of non-response|-0.123|-0.314|-0.020|-0.116|-0.324|-0.018|
||(0.202)|(0.259)|(0.275)|(0.159)|(0.203)|(0.220)|
|R-squared|0.181|0.188|0.189|0.119|0.063|0.135|
|Observations|7,457|1,341|6,116|7,457|1,341|6,116|
|Dep. var. mean|0.428|0.395|0.436|0.252|0.198|0.264|
|Test (p-value):|||||||
|Title = Inf. certificate|0.0001|0.673||0.272|0.103||
|Title in process=Inf. Cert.|0.043|0.439|0.039|0.077|0.103|0.025|

_Notes_ : Each column reports results from a parcel-level regression. Perceived tenure insecurity is the likelihood of involuntarily losing ownership or use rights to land in the next 5 years on a 1-5 scale (with 1 is not at all likely and 5 extremely likely) and tenure perceived to be (very) insecure if the value given is 2 (3) or higher. Household characteristics, region and survey quarter fixed effects are included. Constant is not reported, and standard errors are clustered at village level. Significance is denoted by *** for p<0.01, ** for p<0.05, and * for p<0.1.

26
_Gender Equality and Economic Development: The Role for ICTs_ 8 In light of the above, lower gender inequality in terms of higher female education would indirectly lead to a transitory increase in economic growth via demographic effects (Figure 8).

# **4. Information and Communication Technologies and Gender Inequality**

Information and communications technologies in recent years have been recognized as an effective tool for promoting economic growth and sustainable development. With relatively low usage costs and its ability to overcome distance, ICTs have revolutionized the transfer of information and knowledge around the world.

The World Bank Group defines ICT to consist of hardware, software, networks, and media for collection, storage, processing transmission, and presentation of information in the form of voice, data, text, and images. They range from the telephone, radio and television to the Internet (World Bank, 2003b and c). On the other hand, the OECD defines ICT sectors as a combination of manufacturing and service industries that capture, transmit and display data and information electronically. Table 1 lists the ICT producing sectors under the OECD definition.

## **4.1 ICTs and Economic Development**

Over the decade, there been a series of studies that show that both ICT production and ICT usage have contributed to economic growth<sup>5</sup> . ICT producing sectors have experienced major technological advancements, which have showed up as large gains in total factor productivity at level of the economy. As for the non-ICT producing sectors, investment in ICT has resulted in capital deepening, and hence increases in labor productivity. More importantly, various studies have produced empirical evidence suggesting that substantial productivity gains have been experienced from ICT usage<sup>6</sup> .

> 5 See Pilat and Lee (2001), Jorgenson and Stiroh (2000), Oliner and Sichel (2000), Whelan (2000), and Schreyer (2000).

> 6 Some national studies point to the use of ICT as an important factor in improved TFP growth.  For the United States, the Economic Report of the President (Council of Economic Advisors, 2000, 2001), Whelan (2000), Oliner and Sichel (2000) and Jorgenson and Stiroh (2000) attribute a considerable part of the increase in TFP growth to ICT-using sectors of the economy that do not produce ICT.  For Australia, there is evidence that increased productivity has been accompanied by greater technology use, which includes use of ICT (Productivity Commission, 1999).  There are also sectoral and firm studies that suggest that ICT investment has had positive impact on TFP.  For examples in the distribution sector, see Readon et al. (1996), and Broersma and McGuckin (1999), while Brynjolfsson
# **Table 5: Panel Regressions – Main result: Volume of transactions of all sizes for all crypto-assets**

- - <u>Dependent Variable: Log monthly on chain crypto assets volume (all transaction sizes), US dollars</u>

|Variable|(1)|(2)|(3)<br>**All Countries**|(4)|(5)|
|---|---|---|---|---|---|
|Inflation (yoy)|1.480***|0.724|-0.024|0.444|-0.003|
|Change exchange rate (mom)|-3.220***|-0.754|1.765***|2.808***|0.763*|
|Change Bitcoin price (mom)||0.468***|0.273***|0.367***|0.271***|
|Change Ether price (mom)||0.611***|0.058|0.347***|0.094**|
|US 10-year Yield (inflation adjusted)|||-0.386***||-0.490***|
|US Inflation Expectation Rate|||2.745***||2.869***|
|VIX||||-0.062***|0.011***|
|Change gold price (mom)||||-2.609***|-1.380***|
|Pandemic dummy||1.448***|1.318***|2.344***|1.057***|
|N of Observations|3095|3095|3095|3095|3095|
|N of clusters (countries)|136|136|136|136|136|
|Adjusted R squared|0.008|0.450|0.751|0.575|0.755|
|Country-Fixed Effects?|YES|YES|YES|YES|YES|
|Variable|(6)|(7)|(8)<br>**EMDEs only**|(9)|(10)|
|Inflation (yoy)|1.525***|0.420|-0.144|0.169|-0.132|
|Change exchange rate (mom)|-2.025**|-1.132*|1.576***|1.962***|0.976*|
|Change Bitcoin price (mom)||0.527***|0.170*|0.376***|0.188*|
|Change Ether price (mom)||0.515***|0.064|0.303***|0.100*|
|US 10-year Yield (inflation adjusted)|||-0.397***||-0.492***|
|US Inflation Expectation Rate|||2.680***||2.722***|
|VIX||||-0.058***|0.007***|
|Change gold price (mom)||||-3.100***|-1.636***|
|Pandemic dummy||1.459***|1.343***|2.311***|1.132***|
|N of Observations|1837|1837|1837|1837|1837|
|N of clusters (countries)|85|85|85|85|85|
|Adjusted R squared|0.008|0.433|0.698|0.545|0.702|
|Country-FixedEffects?|YES|YES|YES|YES|YES|

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the country level. All independent variables are lagged by one month, except the pandemic dummy. Bitcoin, ether, and gold prices expressed in US dollars. The gold price is expressed per Troy ounce. A constant is estimated, but not reported.
# **Table B2: OLS and Unconditional Quantile Regression Coefficients on Log Expenditure, 2007**

|_Eq Name:_|OLS|Quantile 10|Quantile 25|Quantile 50|Quantile 75|Quantile 90|
|---|---|---|---|---|---|---|
|_Dep. Var:_|LPCEXP|RIFQT_10|RIFQT_25|RIFQT_50|RIFQT_75|RIFQT_90|
|Constant|13.304263|12.197708|13.120938|13.238181|13.718763|13.899547|
||(0.0629)**|(0.1408)**|(0.1113)**|(0.0882)**|(0.1018)**|(0.1419)**|
|Male|0.070325|-0.154760|-0.224476|0.071366|0.401929|0.244113|
||(0.0367)|(0.0822)|(0.0650)**|(0.0515)|(0.0595)**|(0.0829)**|
|Age of Head<br>|-0.013158<br>(0.0018)**|-0.008564<br>(0.0041)*|-0.016820<br>(0.0033)**|-0.010976<br>(0.0026)**|-0.014366<br>(0.0030)**|-0.005371<br>(0.0041)|
|Age Head|||||||
|<br>Squared|0.000114<br>(0.0000)**|0.000121<br>(0.0000)**|0.000139<br>(0.0000)**|0.000089<br>(0.0000)**|0.000095<br>(0.0000)**|0.000041<br>(0.0000)|
|Age<5 (% of|||||||
|<br>Household)|-0.005484|0.000316|-0.005747|-0.005578|-0.007783|-0.010410|
||(0.0004)**|(0.0009)|(0.0007)**|(0.0005)**|(0.0006)**|(0.0009)**|
|Age 5 to <10|||||||
|(%HH)|-0.007811<br>(0.0004)**|-0.003733<br>(0.0009)**|-0.007685<br>(0.0007)**|-0.007811<br>(0.0006)**|-0.011619<br>(0.0007)**|-0.011752<br>(0.0009)**|
|Age 10 to < 15|||||||
|(%HH)|-0.008318<br>(0.0004)**|-0.011155<br>(0.0009)**|-0.009006<br>(0.0007)**|-0.007767<br>(0.0006)**|-0.007190<br>(0.0007)**|-0.008996<br>(0.0009)**|
|Age 15 to <20|||||||
|<br>(%HH)|-0.007076<br>(0.0004)**|-0.005308<br>(0.0009)**|-0.006029<br>(0.0007)**|-0.006312<br>(0.0005)**|-0.009886<br>(0.0006)**|-0.010217<br>(0.0009)**|
|Age 20 to <25|||||||
|<br>(%HH)|-0.001457|0.000448|-0.000346|-0.000942|-0.002167|-0.002464|
||(0.0004)**|(0.0009)|(0.0007)|(0.0006)|(0.0007)**|(0.0010)**|
|Schooling|||||||
|(Years)|0.027913|0.022405|0.020552|0.027924|0.031056|0.038209|
||(0.0014)**|(0.0031)**|(0.0025)**|(0.0020)**|(0.0023)**|(0.0032)**|
|Land|0.000909|0.001197|0.001421|0.000104|0.000284|0.002935|
||(0.0003)**|(0.0006)|(0.0005)**|(0.0004)|(0.0005)|(0.0006)**|
|Access to Credit|0.121824|0.326021|-0.003454|0.153329|0.183045|0.187816|
||(0.0164)**|(0.0367)**|(0.0290)|(0.0230)**|(0.0265)**|(0.0370)**|
|Has Migrant (s)|0.007313<br>(0.0092)|0.059992<br>(0.0206)**|-0.047730<br>(0.0163)**|-0.027361<br>(0.0129)*|0.053349<br>(0.0149)**|0.025172<br>(0.0208)|
|Distance to|||||||
|Nearest Hospital|-0.001734<br>(0.0005)**|0.003170<br>(0.0011)**|-0.003044<br>(0.0009)**|-0.002540<br>(0.0007)**|0.000407<br>(0.0008)|-0.001568<br>(0.0011)|
|Distance to|||||||
|Nearest Tarred|||||||
|Road|0.000625|0.001278|0.001463|0.000091|0.000473|-0.000360|
||(0.0001)**|(0.0003)**|(0.0002)**|(0.0002)|(0.0002)*|(0.0003)|
|Public Sector|0.101134|0.145912|0.018378|-0.033898|0.294486|0.314242|
||<br>(0.0396)*|<br>(0.0886)|<br>(0.0700)|<br>(0.0555)|<br>(0.0641)**|<br>(0.0893)**|
|Private Sector|||||||
|Formal|0.011229|0.121331|0.017644|-0.156668|0.013729|0.243870|
||(0.0404)|(0.0905)|(0.0716)|(0.0567)**|(0.0655)|(0.0913)**|
|Agriculture|-0.253115|-0.142792|-0.362748|-0.348374|-0.149680|-0.090588|
||(0.0358)**|(0.0801)|(0.0633)**|(0.0502)**|(0.0579)**|(0.0808)|
|Non-Agriculture|<br>-0.144915|<br>0.126827|<br>-0.050514|<br>-0.181781|<br>-0.160270|<br>-0.221364|

42
<u>Table 3: GDP per capita, $2014</u>

||2014|2017|2020|2025|2030|
|---|---|---|---|---|---|
|World|10,792|11,683|12,655|14,217|15,695|
|BRI Area|5,014|5,883|6,859|8,512|10,105|
|Cambodia|1,095|1,314|1,570|2,013|2,498|
|China|7,587|9,593|11,906|15,851|19,601|
|Indonesia|3,500|4,140|4,911|6,417|8,056|
|Lao PDR|1,751|2,118|2,529|3,256|4,030|
|Malaysia|11,307|12,468|13,724|15,953|18,340|
|Philippines|2,873|3,149|3,464|4,038|4,700|
|Thailand|5,970|6,782|7,772|9,655|11,753|
|Vietnam|2,052|2,444|2,925|3,735|4,620|
|Rest of East Asia|5,290|5,918|6,609|7,711|8,784|
|Bangladesh|1,087|1,287|1,530|1,980|2,481|
|India|1,577|1,845|2,171|2,781|3,452|
|Nepal|702|758|835|1,008|1,232|
|Pakistan|1,315|1,391|1,502|1,754|2,084|
|Sri Lanka|3,853|4,573|5,369|6,830|8,393|
|Rest of South Asia|765|825|897|1,042|1,218|
|EU ECA|10,578|11,750|13,012|15,233|17,531|
|Russian Federation|14,122|15,863|17,675|20,939|24,436|
|Poland|14,337|15,694|17,246|19,630|21,989|
|Rest of Eastern Europe|3,739|4,247|4,784|5,810|6,953|
|Kazakhstan|13,155|15,169|17,559|22,561|26,714|
|Kyrgyz Republic|1,280|1,439|1,616|2,020|2,542|
|Rest of Central Asia|3,615|4,090|4,675|5,707|6,859|
|Iran,Islamic Rep.|5,443|5,563|5,940|6,750|7,628|
|Egypt,Arab Rep.|3,366|3,728|4,298|5,307|6,489|
|Turkey|10,304|11,431|12,536|14,363|16,179|
|Rest of MENA|11,099|12,228|13,243|14,981|16,837|
|Kenya|1,368|1,508|1,678|2,030|2,470|
|Tanzania|927|1,037|1,178|1,494|1,885|
|Non-BRI Area|22,321|23,171|24,029|25,192|26,190|
|Ethiopia|574|653|746|918|1,140|
|Rest of Sub-Saharan Africa|2,047|2,250|2,495|2,933|3,433|
|Latin America and<br>Caribbean|10,223|11,116|12,088|13,708|15,361|
|United States|54,399|58,074|61,696|66,572|70,627|
|Rest of High-income|37,792|40,086|42,366|46,153|49,852|
|Rest of Western Europe|42,661|44,436|46,328|49,267|52,340|

- 24 -
Table N15: Effect of SNAP-benefit changes on consumption of households, ARRA expiration

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Specifcation|OLS, log-log|IV, log-log|OLS, log-log|IV, log-log|
|VARIABLES||Consu|mption||
|Both ineligible|-0.0641|-0.452|0.0141|0.217|
||(0.121)|(0.489)|(0.0926)|(0.308)|
|Household eligible only|-0.0302|-0.142|-0.123|0.0915|
||(0.250)|(0.535)|(0.154)|(0.335)|
|Product eligible only|-0.144|-0.525|-0.0681|0.202|
||(0.106)|(0.526)|(0.0954)|(0.277)|
|Both eligible|0.207|0.220|-0.0182|0.304|
||(0.269)|(0.701)|(0.182)|(0.318)|
|Observations|518146|518146|1943066|1943066|
|R-squared|0.510|0.510|0.467|0.467|
|Prob _>_ F|0.000|0.000|0.000|0.000|
|Number of units|24748|24748|24748|24748|
|Number of clusters|49|49|49|49|
|First stage F-stat||0.936||6.693|
|Sample|2013|2013|2012-2015|2012-2015|

Notes: Robust standard errors are in parentheses, clustered by state. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Each coefficient comes from a regression of (log) consumption on (log) SNAP benefits per recipient, interacted with four group indicators. Both ineligible refers to expenditures by SNAP-ineligible households on SNAP-ineligible products, product eligible only refers to expenditures by SNAP-ineligible households on SNAP-eligible products, household eligible only refers to expenditures by SNAP-eligible households on SNAP-ineligible products, and both eligible refers to expenditures by SNAP-eligible households on SNAP-eligible products. Control variables, household-month and period fixed effects are also included. Observations are weighted by sampling weights.

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