rafmacalaba/datause-encoder-data huggingface dataset:
<mark>Khandker, S. R., Bakht, Z., & Koolwal, G. B. (2009). The poverty impact of rural roads: Evidence from Bangladesh.</mark> _<mark>Economic Development and Cultural Change</mark>_ <mark>,</mark> _<mark>57</mark>_ <mark>(4), 685722.</mark>

- Kraay, A., & McKenzie, D. (2014). Do poverty traps exist? Assessing the evidence. _Journal of Economic Perspectives_ , 28(3), 127-148.

<mark>Ksoll, C., Lilleør, H. B., Lønborg, J. H., & Rasmussen, O. D. (2016). Impact of village savings and loan associations: Evidence from a cluster randomized trial.</mark> _<mark>Journal of Development Economics</mark>_ <mark>,</mark> _<mark>120</mark>_ <mark>, 70-85.</mark>

- <mark>Lanjouw, P., Marra, M., & Nguyen, C. (2017). Vietnam’s evolving poverty index map: Patterns and implications for policy.</mark> _<mark>Social Indicators Research</mark>_ <mark>,</mark> _<mark>133</mark>_ <mark>(1), 93-118.</mark>

- Lensink, R. and Pham, T. T. T. 2012. The impact of microcredit on self-employment profits in Vietnam. _Economics of Transition_ , 20 (1), 73–111.

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- <mark>Medeiros, V., Ribeiro, R. S. M., & do Amaral, P. V. M. (2021). Infrastructure and household poverty in Brazil: A regional approach using multilevel models.</mark> _<mark>World Development</mark>_ <mark>,</mark> _<mark>137</mark>_ <mark>, 105118.</mark>

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31
10 Table 3

# Regressions Explaining Economic Growth

## I) McMahon. (1987, p. 189)

Regeion equatbn for 30 Afdcn Countris

||j_NIN_|IVY<br>||<br>,ff|J.|IJY|IJS,_j__|R||
|---|---|---|---|---|---|---|
|AY/Y_NIN_|_-035_<br>(0.8)|0.65<br>(3.1)|162<br>(2.2)|-5.02<br>(1.5)||Not given|
|AY/Y -hNN|-0O4<br>(2.4)|031<br>(13)|0.92<br>(05)|408<br>(11)|715<br>(20)|Not given|

AY/Y - _NN_ GOrovtb in labor productivity for 30 Ark countries am _the_ five-year periodsl 197075, 1975-80 &W1980-85S U/Y ibntent ratio for each country in the fhnt year of each five-year peiod 4HY Invwstmet in education u fradion of G}DP in the firt year of each S-yer period IJY Invatment in higher education as fraction of ODP in the fint year of each S-year period. Te subscript -1 indIates a 4g of 5 yea.

Ihe rrsin incuded as other expltoy vibles initial productivity, changes in utilization of capital rmtes, dummies for oil eaporters, oil and drought sbocks, banae (english or french) an a logged dependent variabl.

## 4i) BDumoL lacknan a Wolff (1989)

Regreuion equations eplanln the ratio of 1981 to 1960 real GDP _per capta_ measured at 1975 international prices

|ConDant|InitielRODP|PrimarY|Secondary|Tertiary|R<br>_|SampleSize|
|---|---|---|---|---|---|---|
|-00t<br>(0.9)|4.599<br>(1.6)|0.791<br>(5.8)<br>_|||0.273|103|
|0318<br>(5.7)|-1678<br>(2.)|_<br>_<br>_<br>_|1386<br>(4.7)||0.227<br>_<br>_<br>_<br>_<br>_|103<br> <br>_<br>_<br>_<br>_|
|0375<br>(63)|40.627<br>(1.1)|||2.703<br>(2.7)|0.094|103|
|ntli RODP<br>primay<br>seconda<br>trtiaty|ODP_par capita_in 1<br>primay shool erolm<br>or<br>shoo en<br>hier<br>education enr|960<br>ent_nte_in 1965<br>olment rate in 1965<br>olment rte in 196S|||||

## i) Buro (1991. p. 429)

Regreion equatiom e.planIn growth _in_ rea _GD per capita_ 1960.85 at an nnual rate and fertility rate net of child deaths up to the age of 4.

|cn|ODP<br>60|Prim.<br>smy|Second-<br>a*|9W|REV|ASS|PPI<br>DEV|_Ly_|FERT-<br>NET|R s|
|---|---|---|---|---|---|---|---|---|---|---|
|0<br>9<br>(3.2)|-. 0072<br>(8.0)<br>............-|040181<br>(340)<br>|0.0225<br>(2.5)|4.119<br>(4.4)|4O19<br>(2.)|-0.0315<br>(1.7)|-0.0119<br>(2.1)|0.068<br>(2.1)||0.59|
|0.049<br>4<br>uhee<br>arns|44)077<br>(86)<br>in eat|00t18<br>(2.0)|040100<br>(1.1)|4.114<br>_(4A)_|0.0167<br>(2.6)<br>*|4O54<br>(15)|-0.0103<br>(1.7)|04064<br>(2.0)|44)043<br>(3.1)|0.62|

GROWTH grwth in _per cita_ GDP between 1960 and 1985 at 1985 pries at an annual nte ODP6O ODPper _capit_ in 1960 a International pries Primay prly _shoo_ enrolment rate In 1960 Secday seoday chool enrobment rte in 1960 _ely avep_ ratio of _gwvenmt_ cotmmption (emduding defence and eduation) to GDP. 197045 REV _Aveap_ nmber of rolatbon ad _cup_ per year 196085. ASS N_mber of asunatior per milon population per year, 19608 PPIDiV Deastion of prike of investment pods from sample m In 1960 Uty Aera ratio of domaetk Invetment to GDP, 1960-85 FERD7KF No of chir per wOmansu vivin beyond the ap of 4. Avenge of 1960-85.
|**Table 9: Co**|**st of Ba**|**nk Rest**<br>**(p**|**ructuri**<br>**ercent**|**ng for t**<br>**of GDP)**|**he Gove**<br>|**rnment**|**(1991-9**|**8)**||
|---|---|---|---|---|---|---|---|---|---|
||**1991**|**1992**|**1993**|**1994**|**1995**|**1996**|**1997**|**1998**|**Total/**|
|**Bularia**||||||||||
|Bond issued|0.0|2.1|10.9|23.1|0.3|0.0|_0.5_|0.0|26.5|
|Interest payments on bonds|0.0|1.3|1.3|2.9|2.9|10.7|0.8|0.4||
|**Total**|||||||||**2**|
|**Czech Republic**2'||||||||||
|<br>Bond issued|4.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|3.4|
|Cash transfers|0.0|0.0|0.9|0.7|0.3|0.0|N/A|N/A|1.5|
|Purchase of bad loans|10.9|1.8|2.7|0.3|0.0|0.1|0.7|1.0|**15.7**|
|**Total**|||||||||**2O6**|
|**Hun2ary**||||||||||
|Bond issued<br>|0.0|2.7|3.6|2.1|0.1|0.1|0.0|1.6|12.4|
|Interest payments on bonds|0.0|0.0|0.0|1.2|1.7|_1.5_|1.0|0.5||
|Guarantees called less recovered|0.0|0.1|0.0|0.0|0.1|0.0|0.1|0.0|**0.5**|
|**Total**|||||||||**12.9**|
|**Macedonia**<br>||||||||||
|Bond issued|0.0|0.0|0.0|3.3|0.0|0.0|0.0|0.0|5.1|
|Interest payments on bonds|0.0|0.0|0.0|0.0|0.4|0.4|0.2|N/A||
|**Total**|||||||||**S.1**|
|**Poland**||||||||||
|Bonds issued|7.1|0.0|1.3|0.8|0.0|0.0|0.0|0.0|8.2|
|o/w placed in Central Bank|1.5|0.0|0.0|0.0|0.0|0.0|0.0|0.0|1.3|
|Interest payments on bonds|0.3|0.4|0.5|0.7|0.6|0.4|0.3|0.2||
|o/w to Central Bank|0.1|0.1|0.1|0.1|0.1|0.04|0.03|0.02||
|**Total**|||||||||**8.2**|
|**Estonia**||||||||||
|Bonds issued|0.0|0.0|2.0|0.0|0.0|0.0|0.0|0.0|1.0|
|Interest payments on bonds|0.0|0.0|0.1|0.1|0.1|0.1|0.0|0.0||
|<br>Purchase of bad loans<br>|0.0|0.0|0.0|0.0|0.05|0.0|0.0|0.0|0.0|
|Recapitalization of Central Bank<sup>3</sup>'|0.0|0.0|0.0|0.0|0.0|0.4|0.0|0.0|**0.3**|
|**Total**|||||||||**I**|
|**Latvia**||||||||||
|Bonds issued|0.0|0.0|1.6|1.9|0.0|0.0|0.0|0.0|2.5|
|Interest payments on bonds|0.0|0.0|0.3|0.4|0.04|0.03|0.03|0.03||
|<br>**Total**|||||||||2|
|**Lithuania**||||||||||
|Bonds issued|0.0|0.0|0.0|0.0|0.0|0.7|0.4|0.5|**1.6**|
|Interest payments on bonds|0.0|0.0|0.0|0.0|0.0|0.0|0.1|0.1||
|<br>Cash|0.0|0.0|0.0|0.0|0.0|0.03|0.0|0.0|0.0|
|Property transfer|0.0|0.0|0.0|0.0|0.0|0.02|0.0|0.0|0.0|
|Called guarantee on interbank loan|0.0|0.0|0.0|0.0|0.0|0.0|0.1|0.0|0.1|
|<br>**Total**|||.|||||||

22
time periods, in this paper, we primarily focus on the weekly sub-sample in order to identify severe illness shocks that lead to an entire week of missed work. Note also that the daily data we collected allow us to confirm that week-long illness shocks are more empirically relevant for labor supply than shorter illness shocks. Indeed, table A1 shows that there is no evidence of labor supply responses to another worker’s absence from work in a given day. When we examine the time path of response to a daily illness shock using an event study framework in figure A1, we find some evidence of lagged responses among men, but these responses (a greater probability of working the day after illness, but a lower probability two days later) still lead to a net zero response during the period surrounding a daily illness shock.

Participants interviewed by phone were given a phone at the beginning of the survey to avoid the possibility of selection into the survey based on phone ownership. Respondents were paid 3 cedis ($1.36 US) for each completed phone survey. Together with the fact that surveys were quite short (4 minutes on average), 89 percent of scheduled weekly phone surveys and 94 percent of weekly in-person interviews were successfully conducted.

The illness shocks we can detect in this high-frequency data occur when a working-age adult did not work in a particular week, but had planned to work. These respondents were then asked to give the reason for missing work. In coding these free responses, we consider as an illness shock any response that mentioned the illness or injury of the respondent or another person. (See the online appendix for a list of the exact responses included as illness shocks.<sup>5</sup> ) That is, we do not separately examine the effects of absence due to own illness and caretaking both for reasons of power and because we suspect that the modal response of “I was sick” – given in 74 percent of the instances of illness shocks that we categorize as illness – may reflect

> 5 See http://faculty.washington.edu/rmheath/onlineappendix_HMR.pdf

9
Somali population (Table A.13; Figure 7).<sup>31</sup> Overall, 77 percent of the Somali population lived below the poverty line in December 2017. This poverty incidence was 26 percentage points higher than the unweighted average of low‐income countries in Sub‐Saharan Africa (51 percent) in 2017. The country has the third‐highest poverty rate in the region, after Burundi and South Sudan (Figure 5).<sup>32</sup> The high poverty incidence of Somalia is in line with its low levels of Gross Domestic Production (GDP) per capita, which was estimated at US$450 in 2017 (Figure 5).<sup>33</sup>

**Figure 5: Cross‐country comparison of poverty and GDP**

**Figure 6: Poverty incidence**

<!-- Start of picture text --> 95<br>100<br>85 Overall average (survey estimates)<br>SSD<br>75 SOM 80<br>MWI<br>65<br>60<br>55 RWA<br>45 TZA 40<br>UGA<br>35<br>20<br>25 ETH<br>15 0<br>0 500 1,000 1,500 2,000 2,500 3,000 Mogadishu Other urban Rural IDPs in Nomads<br>settlements<br>GDP per capita (US$ PPP)<br>Source: Authors’ calculation and World Bank Source: Authors' calculations.<br>% of population<br>Poverty incidence (% of population)<br><!-- End of picture text --> Source: Authors’ calculation and World Bank Open Data.

Poverty is somewhat heterogeneous between different population types and regions. Urban areas have a lower poverty headcount rate (60 percent), than the rest of the Somali population (Figure 6; p<0.01 vs. Mogadishu, p<0.05 vs. IDPs in settlements and nomads, and p<0.10 vs. rural areas).<sup>34</sup> This comparison excludes the capital, Mogadishu, whose residents are poorer than in other urban areas (between 72 and 76 percent). This higher poverty rate in Mogadishu compared to other urban areas is likely the result of a

> 31 To derive a nation‐wide poverty rate, survey and satellite estimates were combined in the following way. For each pre‐war region and population type, the satellite prediction was considered if the accessibility rate in wave 2 was 90 percent or less, and the survey estimate was used if accessibility exceeded this threshold.

> 32 The countries used for regional comparison are all the African low‐income countries as defined by the World Bank: Benin, Burkina Faso, Burundi, Central African Republic, Chad, Comoros, Democratic Republic of Congo, Eritrea, Ethiopia, Guinea, Guinea‐Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, South Sudan, Tanzania, Togo, Uganda, and Zimbabwe. For each country, we include the most recent available year for each indicator.

33 For international comparisons, the poverty rate for Somalia was derived from satellite estimates. In the rest of the section, the figures refer to survey estimates unless explicitly noted.

34 Urban areas usually benefit from agglomeration effects that result in more economic opportunities and access to services, relative to rural areas (Lall et al., 2017).

32
highest among poorer households. This suggests that efforts to improve the accuracy in reported consumption of FAFH even among those households who do not consume much in absolute terms is critically important.

# **6. Discussion**

Three conclusions can be drawn from the results presented thus far: (1) One-line is not enough to collect information on FAFH; (2) The bounding/salience arm performed the best at collection of FAFH consumption; and (3) despite the fact that the individual recall arm was less accurate than the bounding/salience arm, it did perform significantly better than the one-line option. The different methods implemented present some significant trade-offs in terms of accuracy and cost of implementation. We explore further both of these aspects.

## 6.1 Accuracy

From our analysis, we have a good understanding of the relative accuracy of each treatment arm. The next important question to answer is why certain variations outperformed others. Was it due to the respondent (individual vs informant), partial collection of FAFH information (particularly for the individual-level recall arm), bounding, or salience? While we cannot empirically determine which of the components made the largest contribution to accuracy, we explore the trade-offs of each arm as well as examine some qualitative information collected from enumerators to give some indications of successful components. An evaluation of the trade-offs (in terms of accuracy and cost) and qualitative information combined with our empirical findings will allow us to provide guidance to survey practitioners on the most effective design of an FAFH consumption module.

In terms of accuracy, the individual-level arm had the advantage of eliciting information from the best informant possible: the individual. However, it came at the cost of requiring interviewers to reach all adults in the household, which was only partially addressed by allowing phone interviews. Additionally, the arm was subject to the sources of measurement error affecting all recall surveys: informants were asked to make the cognitive effort of recalling the consumption over the last seven days, without any help in identifying the starting of the recall period or the tracking of consumption throughout the reference period.

In contrast, the bounding/salience arm had the advantage of following traditional practices of interviewing only one person, but at the cost of eliciting the information from a less well-informed individual. To minimize the measurement error associated with relying on the report of one informant, a second visit was introduced. This allowed: a) giving the informant the opportunity to know what information was going to be asked and to collect the necessary information from other household members, 28
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23 | P a g e
# **B Appendix: Robustness and Additional Results**

## **B.1 Parallel Trend and TWFE**

The identifying assumption of our DID analysis is that the patent activity in both the treatment and control groups follows the same trend before sanctioned firms are added to the Entity List. The assumption of parallel trends, central in the TWFE approach, is violated if firms enter the treatment group at different stages. We test the presence of parallel trends and estimate this dynamic event study equation: where _Preij,t_ + _k_ (or _Postij,t_ + _k_ )is a dummy variable that equals 1 if _k_ year before (or after) the year when firm _i_ of sector _j_ has been added into the Entity List. _Xij,t−_ 1 is a set of controls including log of firm _i_ of sector _j_ ’s patent stock at time _t −_ 1, a dummy variable that equals 1 if firm _i_ of sector _j_ has patented at year _t −_ 1; _ψi_ is the firm fixed effect to capture the unobserved firm characteristics. _δjt_ is the sector-year fixed effect to capture the unobserved sector-year changes that affect firms’ patenting activity. Figure A.3 confirms the sanctioned and unsanctioned firms had parallel trends over all patent categories before the sanctioned firms were added to the Entity List. The estimates show a negative effect of U.S. sanctions on sanctioned Chinese firms’ total, high-tech and international patent applications, compared to non-sanctioned firms. The decline is significant and magnified for those patent applications two years after inclusion of the firms in the Entity List. However, the treatment effect is insignificant for triadic patent applications.

Callaway and Sant’Anna (2021) warn about potential heterogenous treatment effects. Our two-way fixed effect (TWFE) estimates would be biased if we compared firms recently added to the Entity List to firms already subject to U.S. sanctions. We re-ran the event study analysis using Callaway and Sant’Anna (2021)’s estimator. Figure 4 in section 4.1 shows the 52
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above propositions but may pick up unobserved local culture and tradition independent of average income or schooling in local city/township.

In particular, we estimate the following specication: where _Yi_ denotes marital outcomes, wife characteristics, and husband _i_ 's belief in old age support; _αrural_ is a dummy for rural areas; _Zi_ denotes husband's observable characteristics such as age, religion, schooling, and party membership (we also include his parents' schooling); _Ztownship_ denotes average income and schooling at district/township level; and _ParentMatchedi_ is a dummy indicating whether _i_ 's parents were involved in the search for his wife.

We shall estimate this equation with both OLS and 2SLS. While _β_ 1<sup>_ols_</sup> suers from individual-level selection issues, _β_ 1<sup>2</sup><sup>_sls_</sup> is free of such issues. Because the variation of our instrument is mostly cross-sectional, we should interpret the 2SLS estimate in the following way: if _i_ lived in a dierent marriage market that is more oriented towards parental matchmaking and this relocation increases his own probability of using parental matchmaking by ∆ _p_ , then the outcome _Yi_ would change by _β_ 1<sup>2</sup><sup>_sls_</sup> _·_ ∆ _p_ . Markets that dier in parental matchmaking norms are likely to have dierent family value and dierent social networking patterns along with the matchmaking norm. These dierences, if independent of township-level average income and average schooling, are embodied in our instrument. In other words, the 2SLS estimate captures the eect of moving from a market with low parentmatchmaking norm (and less traditional value) to a market with high parent-matchmaking norm (and more traditional value). It does not capture the eect of moving from low to high parent-matchmaking norm, conditional on the same level of (unobserved) traditional value. In other words, this instrument does not allow us to separate the eect of parent matching norms and other highly correlated traditional values.

# 3 Data and Measurements

## 3.1 Data Source

We use the Study of the Status of Contemporary Chinese Women (SSCCW), a data set collected jointly by the Population Institute of Chinese Academy of Social Science and the Population Council of United Nations in 1991 (Institute of Population Studies, 1993). SSCCW collects information on personal traits, marriage characteristics, fertility, work, intrafamily arrangements, and gender norms. The survey used stratied random sampling to 14