rafmacalaba/datause-encoder-data huggingface dataset:
# Table A3: Impact <u>of hosting refugees on household consumption expenditure per adult equivalent</u>

||||House<br>|holds<br>|||
|---|---|---|---|---|---|---|
||All|All|All|All|Rural|Urban|
||OLS|FE|FE|FE|FE|FE|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Refugee Intensity|-0.083|-0.177**|-0.166**|-0.189***|-0.227***|0.034|
||(0.072)|(0.069)|(0.070)|(0.069)|(0.075)|(0.134)|
|Constant|9.137***|9.728***|9.654***|9.886***|9.994***|9.002***|
||(0.449)|(0.432)|(0.397)|(0.403)|(0.442)|(0.766)|
|Round FEs|YES|YES|YES|YES|YES|YES|
|Fixed Effects (FEs)|WOREDA|EA|HH|HH|HH|HH|
|Controls|NO|NO|NO|YES|YES|YES|
|R-squared|0.293|0.319|0.024|0.059|0.065|0.062|
|# of EAs|433|433|433|433|290|143|
|N|13391|13391|13062|12937|9337|3600|

_Notes:_ This table provides estimates of the impact of logarithm of refugee intensity on logarithm of total household consumption expenditure per adult equivalent. Household sample weights from ESS2 applied. The first four columns report results for all households, while columns (5) and (6) report results for rural and urban households, respectively. All columns control for round FEs. Further, column (1) controls for woreda fixed effects, column (2) controls for EA FEs, and all other columns control for household (HH) FEs. Columns (4) to (6) additionally control for 12 time-varying characteristics. The coefficient estimates for these 12 control variables are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. I report the overall and the within R-squared in the first two and other columns, respectively. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

53
this, a list of villages that supplied to the selected market was developed. The enumerators interviewed 10 farmers in each of the two villages selected randomly from this list.<sup>8</sup> Market and community questionnaires collected detailed information on characteristics of the market and villages, and on the availability of di ff erent facilities at those locations. This linking of the villages to the market allows us to map farmers to markets and to examine how market facilities in uence farmer’s decisions.

Trading in agricultural produce in India is governed by the State Agricultural Produce Marketing Committee (APMC) Acts. In Tamil Nadu, the Act was adopted in 1987. It prescribes the setting up of a network of state controlled “regulated markets”. These markets are operated by a “Market Committee”, which are federated into a parastatal entity called the Tamil Nadu Agricultural Produce Marketing Board. All State government noti fi ed agricultural commodities grown in the surrounding area of the market are required to be taken to these markets, thus restricting the channels through which farmers can dispose their produce (World Bank, 2007). Among the four surveyed states which had legislated these Acts, Tamil Nadu was the only state which had not completely enforced the Act with respect to the establishment of wholesale markets. Other entities, such as individual traders, traders associations, local governments, were permitted to operate wholesale markets. As a result, only 10 percent of the sampled wholesale markets in Tamil Nadu were regulated.<sup>9</sup> In areas where the non-regulated markets operate, farmers have free choice in their place of sale. Our empirical analysis is thus based on the place of sales decisions of farmers located near these

> 8 For detailed sampling strategy and weight constructions, see Fafchamps, Minten and Vargas-Hill (2006).

> 9 The APMC acts were more strictly enforced in Maharashtra, Uttar Pradesh and Orissa with regards to the establishment of the wholesale markets. Regulated markets in the survey sample comprised 95%, 90%, and 85% of the markets in Maharashtra, Orissa and Uttar Pradesh respectively. The rules are poorly enforced in Orissa, and to some extent in Uttar Pradesh, resulting in some observed farm-gate sales. Since these sales are made under di ff erent constraints, we focus only on Tamil Nadu where no such restrictions on farm-gate sales are present.

12
**Table 6:** Impact of Mobility Reducing Policies on Crashes: Robustness Tests

|||Double Difference||Triple Difference|
|---|---|---|---|---|
||Curfew Hours<br>(20:00-5:00)<br>(1)|Pre-Curfew Hours<br>(17:00-20:00)<br>(2)|Daytime<br>(5:00-17:00)<br>(3)|All<br>Intervals<br>(4)|
|**Panel A:**_Crashes Measured Using Data from Flare_|||||
|Curfew Period (Post) x 2020|-0.783***<br>(0.158)|0.424**<br>(0.203)|-0.423<br>(0.293)||
|Curfew Period (Post) x Curfew Hours x 2020||||-0.226<br>(0.332)|
|Curfew Period (Post) x Pre-Curfew Hours x 2020||||0.910***<br>(0.352)|
|Observations<br>|295|295|295|885|
|R<sup>2</sup>|0.301|0.281|0.253|0.254|
|**Panel B:**_Crashes from 2017-2019 Used as Controls_|||||
|Curfew Period (Post) x 2020|-0.897***<br>(0.181)|0.603***<br>(0.195)|-0.298<br>(0.268)||
|Curfew Period (Post) x Curfew Hours x 2020||||-0.595*<br>(0.321)|
|Curfew Period (Post) x Pre-Curfew Hours x 2020||||0.818**<br>(0.332)|
|Observations|589|589|589|1767|
|R<sup>2</sup>|0.157|0.119|0.080|0.135|
|**Panel C:**_Analysis at Hourly Level_|||||
|Curfew Period (Post) x 2020|-0.127***<br>(0.026)|0.191***<br>(0.071)|-0.030<br>(0.025)||
|Curfew Period (Post) x Curfew Hours x 2020||||-0.091**<br>(0.036)|
|Curfew Period (Post) x Pre-Curfew Hours x 2020||||0.191**<br>(0.075)|
|Observations|2655|885|3540|7080|
|R<sup>2</sup>|0.046|0.079|0.014|0.036|
|**Panel D:**_Analysis at Weekly Level_|||||
|Curfew Period (Post) x 2020|-7.814***<br>(1.469)|3.251**<br>(1.555)|-3.221<br>(2.717)||
|Curfew Period (Post) x Curfew Hours x 2020||||-4.190<br>(3.053)|
|Curfew Period (Post) x Pre-Curfew Hours x 2020||||6.508**<br>(3.020)|
|Observations|46|46|46|138|
|R<sup>2</sup>|0.520|0.442|0.305|0.500|
|**Panel E:**_Outlier Observations (5 or more crashes) Re_|_moved_||||
|Curfew Period (Post) x 2020|-1.217***<br>(0.210)|0.449**<br>(0.215)|-0.521*<br>(0.290)||
|Curfew Period (Post) x Curfew Hours x 2020||||-0.532<br>(0.348)|
|Curfew Period (Post) x Pre-Curfew Hours x 2020||||0.966***<br>(0.352)|
|Observations<br>R<sup>2</sup>|293<br>0.216|294<br>0.173|285<br>0.113|872<br>0.166|

_Notes_ . Robust standard errors are reported in parentheses. *** (**) (*) denotes significance at 1% (5%) (10%) level. Regressions include fixed effects for week and day of the week, and controls for weather (temperature, precipitation and two measures of wind) Columns 1 to 3 are double-difference estimates following Equation 1. Column 4 is a triple-difference specification following Equation 2.

18
Over the past decade, BRI-recipient LIDCs, particularly those with high risk of debt distress, have increased their exposure to non-concessional debt and debt from non-traditional creditors, including China (Figure 10). Debt from multilateral and traditional bilateral creditors represented more than 70 percent of all external public debt of BRI-recipient LIDCs in 2016 (panel a). Consistent with the general trend of increased debt vulnerabilities in LIDCs (IMF, 2018 and World Bank, 2018a and 2018b),<sup>22</sup> the group of BRIrecipient LIDCs has over time increased reliance on private financing, including bonds and bank financing, which has doubled in the past 10 years (panel b). Non-concessional financing has increased especially in those BRI-LIDCs with moderate risk of debt distress. Exposure to non-Paris Club official creditors, including China, has also increased for the group of BRI LIDCs, reaching 22 percent of total external public debt. The largest increase of the exposure to non-traditional creditors took place in LIDCs with moderate and high risk of debt distress, with most of the increase in the latter group due to large borrowing from Laos (panel c and d).

<!-- Start of picture text --> Figure 6. General Government Gross Debt<br>(% of GDP)<br>55%<br>50%<br>45%<br>40%<br>35%<br>30%<br>25%<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>LIDCs LIDCs - BRI<br>EMEs EMEs - BRI<br><!-- End of picture text --> Source: International Debt Statistics.

<!-- Start of picture text --> Figure 7. Public and Publicly Guaranteed<br>External Debt<br>(Median levels, % of GDP)<br>30%<br>25%<br>20%<br>15%<br>10%<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>LIDC LIDC - BRI<br>EMs EMs - BRI<br><!-- End of picture text --> Source: World Bank and IMF debt sustainability analyses.

Figure 8. Debt vulnerabilities in Low Income Developing Countries Receiving BRI Investment <!-- Start of picture text --> 6<br>7<br>4<br>Low Moderate High<br><!-- End of picture text --> Source: World Bank and IMF debt sustainability analyses.

Figure 9. Debt vulnerabilities in Emerging Market Countries Receiving BRI Investment <!-- Start of picture text --> 13<br>18<br>Low* High scrutiny*<br><!-- End of picture text --> Source: World Bank and IMF debt sustainability analyses.

22 See IMF, Macroeconomic Developments and Prospects in Low-Income Developing Countries - 2018, March 2018, which discusses the public debt situation of LIDCs in detail; IMF and World Bank. 2018a. “Rising Debt Vulnerabilities in Low-Income Developing Countries,” Note to the Development Committee, April 2018; and World Bank, “Africa’s Pulse”, No. 17, April 2018.

11
Results from our analyses of the 54-country SMPD sample may not fully generalize to all aid recipients, despite the fact they account for $45 billion in ODA (not including debt relief and humanitarian aid), more than half of the total ODA delivered to all aid recipients.

# **4. Results**

Donors choose whether or not to use country systems. Their choices, however, reflect some combination of donor and recipient country characteristics. Both sources of variation turn out to be important, but as shown in Table 4, recipient characteristics matter somewhat more than donor characteristics. Each of the three dependent variables (arranged across columns) is regressed on, alternatively, (1) a full set of donor dummy variables, (2) a full set of recipient dummies, and (3) both sets together. Recipient dummies alone explain 28% of the variation in _PFM_ , compared to 21% for donor dummies. Donor and recipient dummies explain an equal share (21%) of the variation in _DBS_ . Recipient dummies explain 23% of the variation in _PBA_ , compared to only 14% for donor dummies. The importance of recipient characteristics has implications for the possible inclusion of SMPD-derived indicators in rankings of donor performance. Namely, if donor aggregates on use of country systems are not adjusted for recipient characteristics, donors can climb in the rankings merely by avoiding riskier countries.

The remainder of this section tests more substantive hypotheses regarding donor and recipient characteristics affecting use of country systems. We estimate regressions of the form: _yij_ = β _Zij_ + δ _X i_ +η _M j_ + _uij_ (6) where _Z_ is a vector of<sup>_y_</sup> _ij_<sup>is the share of donor</sup><sup>_i_’s aid to recipient</sup><sup>_j_that is recipient-managed,</sup> _ij_ regressors that vary by donor and recipient, while _X i_ and _M j_ respectively vary only by donor and by recipient.

The dataset can be treated as an unbalanced panel, with anywhere between 1 and 54 observations per donor. We can exploit this structure of the data to conduct stronger tests of _Zij_ that control for donor and recipient fixed effects, with regressions of the form: _yij_ = β _Zij_ + _vi_ + _w j_ + ε _ij_ (7) Similarly, tests of _X i_ and _M j_ respectively can control for recipient effects or donor effects: 20
Arnell, Nigel. 2002. _Hydrology and Global Environmental Change_ . Pearson Education Limited: Essex.

- Asquith, W. H. 2007. lmomco: L-moments, Trimmed L-moments, L-comoments, and Many Distributions. R package version 0.84.

- Bakker, Marloes. 2009. ―Transboundary River Floods and Institutional Capacity,‖ _Journal of the American Water Resources Association_ , 45(3): 553-566.

Barnett, Jon. 2003. ―Security and Climate Change,‖ _Global Environmental Change_ 13: 7-17.

Barnett, Jon, and Neil Adger. 2007. ―Climate Change, Human Security and Violent Conflict,‖ _Political Geography_ , 26: 639-655.

- Basist, A., Grody, N. C., Peterson, T. C., and Williams, C. N. 1998. ―Using the Special Sensor Microwave / Imager to Monitor Land Surface Temperatures, Wetness, and Snow Cover,‖ _Journal of Applied Meteorology_ , 37(September): 888-911.

Basist, A., C. Williams Jr.,N. Grody, T.F. Ross, S. Shen, A. T.C. Chang, R. Ferraro, and M.J. Menne. 2001. ―Using the Special Sensor Microwave imager to Monitor Surface Wetness,‖ _Journal of Hydrometeorology_ , 2: 297-308.

Barrett, Scott. 2003. _Environment and Statecraft: The Strategy of Environmental Treaty-Making_ . Oxford: Oxford University Press.

- Bates, B.C., Z.W. Kundzewicz, S. Wu and J.P. Palutikof, Eds. 2008: Climate Change and Water. Technical Paper of the Intergovernmental Panel on Climate Change, IPCC Secretariat, Geneva, 210 pp.

Bennett, Lynne, Shannon Ragland, and Peter Yolles. 1998. ̳Facilitating International

- Agreements through an Interconnected Game Approach: The Case of River Basins‘ in Richard Just and Sinaia Netanyahu, eds., _Conflict and Cooperation on Trans-boundary Water Resources_ , 61-85. Boston, MA: Kluwer Academic Publishers.

- Bernauer, Thomas. 1996. ―Protecting the River Rhine against Chloride Pollution,‖ in Robert Keohane and Marc Levy, eds., _Institutions for the Earth: Pitfalls and Promise_ , 201-232. Cambridge, MA: The MIT Press.

24
Table A.7: Productivity Decompositions and Policy Changes: Removing Outlying Tariff Changes

||Aggregate|Average|Reallocation|
|---|---|---|---|
||(1)|(2)|(3)|
|Final Goods Tariff|-.057|-.046|-.011|
||(.026)<sup>_∗∗_</sup>|(.030)|(.015)|
|Input Tariff|-.552|-.549|-.003|
||(.105)<sup>_∗∗∗_</sup>|(.115)<sup>_∗∗∗_</sup>|(.061)|
|FDI Reform|.022|.047|-.026|
||(.014)|(.014)<sup>_∗∗∗_</sup>|(.010)<sup>_∗∗∗_</sup>|
|Delicensed|-.008|.003|-.011|
||(.017)|(.017)|(.011)|
|Obs.|16898|16898|16898|
|_R_<sup>2</sup>|.087|.083|.014|

Each observation is a state-industry. Dependent variable names are given at the top of each column. “Aggregate” indicates market-share-weighted mean productivity, “Average” indicates unweighted mean productivity, and “Reallocation” indicates the covariance between market share and productivity. All specifications are fixed-effects analyses at the state-industry level, and include year dummies. Each observation is weighted by the total number of firms in the state-industry across all years, and standard errors are clustered at the state-industry level. The two industries that do not fit the overall pattern shown in Figure 1 are excluded.

59
# **Table 12: Poverty profiles**

||**BRAZIL**|**COLOMBIA**|**ECUADOR**|**GUATEMALA**|**PANAMA**|**PERU**|
|---|---|---|---|---|---|---|
|Member of ethnic minority|68.5|14.9|31.8|70.2|53.7|56.4|
|Father's agricultural<br>occupation|56.3||80.0|75.7|80.3||
|Other father's occupation|43.7||20.0|24.3|19.7||
|Father without education|77.2|57.8|55.0|90.1|66.8|59.9|
|Father's primary education<br>Fh' d di|21.8|40.3|42.2|9.0|29.0|31.8|
|aters seconary eucaton<br>(or complete primary)|1.0|1.9|2.8|0.9|4.2|8.3|
|Mother without education|79.4|53.5|59.7|96.3|75.2|82.5|
|Mother's primary education|19.4|44.6|38.5|3.0|23.7|15.4|
|Mother's secondary education<br>(or complete primary)|1.1|1.9|1.8|0.7|1.1|2.2|
|Birth regions|Northeast<br>and North<br>(70%),<br>Southeast,<br>Center-<br>west, South<br>(28%)|Periphery<br>(65%),<br>Center<br>(34%)|Sierra and<br>Amazonia<br>(48%),<br>Coast and<br>insular<br>(45%)|North and<br>Northwest<br>(49%), South<br>and Center<br>(46%)|Rural<br>areas<br>(91%),<br>small<br>towns<br>(5%)|Inland<br>(59%),<br>South and<br>Coast<br>(40%)|
|Share of total outcome|0.7|1.5|1.9|1.8|1.5|1.8|

45
In addition to analyses that examine evidence of diaspora connections within the oDesk data, we demonstrate how the broader Indian diaspora in a country—measured as migrants from India as a share of country population—systematically connects to a greater ethnic Indian use of oDesk and a larger share of contracts being sent to India from the country. This background connects studies that consider diasporas from a macro perspective (e.g., linking trade flows to diaspora shares by country) with studies that consider micro evidence (e.g., that international patent citations are more likely among inventors of the same ethnicity).

These results are quite striking. oDesk’s business model minimizes many frictions and barriers to outsourcing—for example, providing companies with knowledge of workers for hire overseas and their qualifications, providing infrastructure for monitoring and payments between companies and workers, and creating a labor market where workers build reputations that enable future work and higher wages. These frictions that oDesk minimizes, of course, are frictions that diaspora networks have historically been used to overcome. Our work suggests that diasporas continue to be important in an online world—if for no other reason than the taste-based preferences that shape contract placement. We view our results as a lower bound on the importance of diasporas in settings where frictions to interactions are larger.

At a higher level, the Indian diaspora likely played an important, but modest, role in India’s rapid development on oDesk. At several points, we provide descriptive evidence of the magnitudes of these interactions that place upper bounds on how large this role could have been. For example, ethnic Indians account for 3.9% of oDesk company users in the United States by contract volume, while 29% of outsourced contracts from the United States go to India. We likewise find that only 5.7% of workers in India who complete three or more jobs on oDesk had their initial contract with an overseas ethnic Indian employer. These magnitudes suggest that diasporas continue to use online platforms in an effective manner, but that they played a modest role in the overall development of online work, at least for a country of India’s properties.

With these results in mind, it is important to place our study of the Indian diaspora in perspective. We focus on a single ethnicity in this analysis, rather than undertaking a multiethnicity comparison study, to facilitate greater depth around one example. India was the natural choice given its worldwide importance for outsourcing. India also has operational advantages in that its common names are fairly distinct from other ethnic groups. Yet it is also important to 4
which are less subject to this possible confounding e¤ect, and are thus arguably more reliable. We also extract the common signal in these four variables by forming a principal component, which we call “lack of self-control”.<sup>14</sup> High scores indicate a lower likelihood of using a susu and of saving regularly, and a higher likelihood of being hyperbolic and of having a high discount rate. The lack of self-control index is thus the common component of using a susu and saving regularly that is correlated with hyperbolic time preferences.

The ...rst ...ve columns of Table 7 estimate model (8) to examine the heterogeneity in the treatment e¤ect with respect to these self-control variables for the pooled sample in panel A, and for females only in panel B. Point estimates show a larger impact of the cash treatment when the ...rm owner uses a susu, saves regularly, is patient, and is not hyperbolic. The signs of the coe¢ cients on the interaction with cash treatment for each of the four variables are consistent with the idea that lack of self-control is associated with a smaller increase in business pro...ts from the cash treatment. Column 5 shows that when we combine all these variables into an index of self-control using principal components, the interaction between cash treatment and lack of self-control is signi...cant at the 5 percent level. This implies that the e¤ect of the cash treatment for someone with the most self-control is 29.2, which is similar to the average impact of the in-kind treatment of 30.9 in column 4 of Table 3. The female only sample in panel B shows a similar pattern in terms of signs of the coe¢ cients, but interaction terms are not signi...cant, possibly because the sample is smaller.

We also investigate whether cash and in-kind grants have a di¤erentiated e¤ect on capital retention depending on entrepreneur self-control. Figure 5 presents the evolution of capital stock after treatment, contrasting between cash and in-kind grants for entrepreneurs with and without self-control. The Figure shows that in-kind grants are retained in the ...rm and lead to further accumulation of capital whether the entrepreneur lacks self-control or not –although the e¤ect on capital accumulation is strongest for those with low self-control. In contrast, cash grants have, if anything a negative e¤ect on capital accumulation among low self-control entrepreneurs while the e¤ect is positive and growing for those with high self-control. This con...rms that the reason why in-kind grants have a strong positive e¤ect on pro...t is related to self-control issues and their e¤ect on the accumulation of capital. If we further interact the self-control index with high baseline pro...ts, we ...nd that self-control a¤ects the impact of treatment only for female entrepreneurs with high baseline pro...ts. For those with low initial pro...ts, treatment

> 14 The principal component has mean zero, and ranges from -1.67 to 2.26.

33