rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1: Survey experiment consumption modules**

|Module|Consumption measurement|Recall/survey period|N households|
|---|---|---|---|
|1|Long list of 58 food items; 14 day recall|14 day|504|
|2|Long list of 58 food items; 7 day recall|7 day|504|
|3|Short list of 17 food items; 7 day recall|7 day|504|
|4|Long list of 58 food items; usual 12 month recall|Usual 12 months|504|
|5|14 day household diaries with frequent visits<sup>a</sup>|14 days|503|
|6|14 day household diaries with infrequent visits<sup>b</sup>|14 days|503|
|7|14 day individual diaries with frequent visits<sup>a</sup>|14 days|503|
||||3,525|

Notes: An 8<sup>~~th~~</sup> module was included in the experiment but not used in the analysis as it collected expenditure but no quantities. a. Frequent visits entailed daily visits by the local assistant and visits every other day by the survey enumerator for the duration of the 2-week diary.

b. Infrequent visits entail 3 visits: to deliver the diary (day 1), to pick up week 1 diary and drop off week 2 diary (day 8), and to pick up week 2 diary (day 15). Households assigned to the infrequent diary but who had no literate members (about 18 percent of the 503 households) were visited every other day by the local assistant and the enumerator.

19
closer contacts with the outside world, Shenzhen has opportunities for developing the cultural industry. Early in November of 2004, the successful First Shenzhen International Cultural Industry Fair took place. It was hosted by the Cultural Industry Department of the Ministry of Culture of the PRC, the Department of Culture, Guangdong Province, the Administration of Radio, Film and TV of Guangdong Province, the Press and Publication Bureau of Guangdong Province, and the Shenzhen Municipal Government.

China’s IT market, pharmaceutical market, cultural market and others are becoming more and more attractive for foreign investors following reform measures. Foreign investment is allowed into these sectors. China’s entry into the World Trade Organization will also help accelerate the restructuring.

**Figure 1:** The trade structure of export of high-tech industrial products (1993-2002)

<!-- Start of picture text --> 100%<br>Ot her s<br>90%<br>80%<br>Pr ocessi ng of<br>70% pur shased<br>mat er i al f r om<br>60% abr oad<br>Pr ocessi ng or<br>50% assembl i ng of<br>cust omer own<br>40% mat er i al s<br>Gener al t r ade<br>30%<br>20%<br>10%<br>0%<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> _Source_ : National Economic and trade commission (ed.) (2003): _Zhongguo Gongye Jingzhengli_ (China’s Industrial Competitiveness), China Machine Press, page 77.

There are some indications that the past high-tech development policy of China was effective in some ways. However, these efforts did not foster creativity. According to the National Statistics Bureau, the total sales of high tech industries in China increased by 7.7% in 2001 compared with 1996, but the value-added rate decreased 0.7% during the same period. Close to 90 percent of hi-tech exports come from export processing activities based on imported materials, and this rate has been rising (see Figure 1). The lack of domestically developed 7
enrollment sites.

Recruitment for the PRO-Jeunes program was carried out in two stages: (i) interested youth first registered, expressing their interest in the program; (ii) after screening all the applications, the project team invited eligible applicants to come to an “enrollment meeting.” According to data from the first two cohorts from the program, a significant share of eligible applicants who had expressed their interest in stage 1 did not attend the subsequent enrollment meeting in stage 2. The intervention evaluated in this study targeted the next cohort, and took place between the two stages, aiming to increase take-up from registration to enrollment.

Eligible applicants were randomly selected to receive an SMS reminding them that the program was free.<sup>1</sup> Youth were already informed that the program was free at pre-registration and the message served as a reminder. Randomization was stratified by gender and geographic location. The SMS message read as follows: “Come participate in an information session about PRO-Jeunes, a program offering free training, support for self-employment and employment opportunities.” In the first treatment, the SMS was randomly sent to the youth only. In the second, treatment, the SMS was sent to the youth and their listed contact person. At pre-registration, youth were asked to list a person to contact in case they could not be reached. It was specified that youth who did not live by themselves should provide the name of a person they lived with.

Our study uses administrative data collected in 2019 on a sample of 2,926 eligible applicants to the PRO-Jeunes program who had completed the pre-registration stage, during which they provided identification and contact information for their contact person, as described in Table 2. The data include variables such as date of birth, phone number, gender and geographic location for youth, and the relationship to the youth and phone number for contacts. Contact gender was inferred from the relationship variable. Listed re1A second type of SMS emphasizing the long-term career benefits of the program was also tested. Results were inconclusive and are described in the appendix. The analysis presented in this paper focuses on the message emphasizing the free nature of the program.

3
**Table 5.2 Reduction in Morbidity Health Impacts Due to Ozone Pollution Reduction Scenarios for the ZMVM in 2010**

|||_S_<br>|_cenario_<br>||
|---|---|---|---|---|
|_Endpoints_|_10%_|_20%_|_AQSI_|_AQS2_|
|_Hospital admission_|||||
|Respiratory|3,300|6,600|20,404|22,597|
|Cardiocerebrovascular|842|1,684|5,207|5,767|
|_Emergencyroomvisits_|||||
|Respiratory|21,429|42,857|132,501|146,746|
|_Minorrestrictedactivitydays_|||||
|Total (adults)|2,495,805|4,991,610|15,432,494|17,091,616|
|_Effectsinasthmatics_|||||
|Asthmaattacksb|3,330|6,660|20,591|22,805|
|Somerespiratorysymptoms<br>(children)|404|809|2,501|2,770|

**Table 5.3 Reduction in Morbidity Health Effects Due to PM10 Pollution Reduction Scenarios for the ZMVM In 2010**

|||_Sce_<br>|_nario_<br>||
|---|---|---|---|---|
|_Endpoints_|_10%_|_20%_|_AQS1_|_AQS2_|
|_Hospitaladmission_|||||
|Respiratory|688|1,376|1,510|3,221|
|Cardiocerebrovascular|291|582|638|1,361|
|Congestiveheartfailure(elderly)|0.36|0.71|0.78|1.66|
|_Emergencyroomvisits_|||||
|Respiratory|11,858|23,717|26,029|55,507|
|_Restrictedactivity days_|||||
|Total (adults)|4,102,282|8,204,565|9,004,464|19,202,173|
|Work-lossdays(adults)|998,116|1,996,233|2,190,854|4,672,035|
|Total(children)|1,630,710|3,261,421|3,579,391|7,633,112<br>|
|Work-lossdaysforwomendue<br>toRADinchildren|428,269|856,537|940,045|2,004,662|
|_Minorrestrictedactivitydays_|||||
|Total(adults)|3,148,315|6,296,630|6,910,516|14,736,794|
|_Effectsinasthmatics_|||||
|Coughwithout phlegm(children)|1,569|3,139|3,445|7,346|
|Cough withphlegm(children)|115|230|252|537|
|_Chronicmorbidity_|||||
|Chronic bronchitis,newcases|3,063|6,126|6,723|14,337|
|Chroniccough,prevalence<br>(children)|574|1,148|1,260|2,686|

_27_
consumption and dietary diversity score. At the bottom 10<sup>th</sup> quantile of the z-score distribution, one standard deviation decrease in food share is associated with 0.233 standard deviation improvement in height-for-age z-score, and one standard deviation increase in food consumption score is associated with 0.218 standard deviation increase in height-for-age z-score. The ranking of these food security measures changes as we move up along the distribution. At the 25<sup>th</sup> quantile, food consumption score, with a coefficient of 0.243, has the highest impact on height-for-age z-score.

Controlling for potential confounding factors in Model 2, only four out of seven food security measures remain statistically significant in OLS estimation. On average, food consumption score has the highest association with height-for-age, followed by per capita caloric availability, dietary diversity score, and per capita consumption. Across the distribution, the UQR results suggest that food security measures have stronger effects at the lower end of the z-score distribution, i.e., malnourished children benefit more from improvements in food security measures. Net of the confounding factors, none of the food security measures are statistically significant at the higher end of the distribution (at the 75<sup>th</sup> quantile). Even at the lower end of the distribution, only three out of seven food security measures remain statistically significant. In terms of their strength of association with the height-for-age, food consumption score has the strongest correlation across the distribution.

Results for weight-for-age are presented in Table 4. Results show that per capita consumption has the highest correlation with weight-for-age at the conditional means and as well as across the distribution in both the parsimonious and expanded models. In Model 1, in terms of the correlation with weight-forage, per capita consumption is followed by food share, expenditure share of non-starchy staple food (1SSEXR), share of calories derived from non-starchy staple food (1- SSR), and food consumption score. Controlling for the potential confounding factors in Model 2, for the OLS estimates, per capita consumption is followed by food share, food consumption score, and 1-SSEXR. On the lower end of the distribution (at the 10<sup>th</sup> quantile), except for per capita consumption, none of the food security measures are significant. At the 25<sup>th</sup> quantile, per capita consumption is followed by food consumption score and 1- SSEXR.

21
272 to 299 percent higher.

These household-level monetary indicators are only available from the long survey, while the next set of indicators on durables and earned income are available for both surveys. We find annual household labor earnings to be NZ$29,220 higher for the migrants, with this gain almost five times as large as labor earnings in Tonga. This is greater than the gain in total income, reflecting that households in New Zealand are not earning the same value from own production as households in Tonga. Migrant households also have substantially more durable goods: they have 0.9 more vehicles, are 63 percentage points more likely to own a computer, 28 percentage points more likely to own a DVD player, almost all have microwaves, and are 63 percentage points more likely to own a washing machine. The only durable asset that there is no change in is cell phone ownership. We do not see any significant impact on financial access as measured by having a bank account or ATM card, with there being little room for improvement here due to 99 percent of ballot loser households in Tonga having these. We also see migrant households having 18 times the level of savings as ballot loser households, and to be consuming a more diverse diet of food. All these significant impacts are significant at the 1 percent level, and survive corrections for multiple hypothesis testing. Households of migrants are therefore better off.

A key reason given for migration is often to improve the welfare of one’s children through gaining them access to better schooling opportunities. Schooling in Tonga is near universal through secondary education, and 100 percent of the 14 to 17 year olds in ballot loser households in Tonga are currently attending school. The margin of schooling action is therefore likely to occur in terms of tertiary education, where opportunities are much more limited in Tonga. However, most children are too young to be at this age range yet,<sup>13</sup> and so more time needs to pass before we are likely to see impacts on child’s education.

> 13 Only 28 of the 282 households in the long survey have a child in the 18 to 22 age range.

21
# **Table 14: Reported rates of formal account ownership from household surveys and the logarithm of deposit penetration.**

This table shows the results of the OLS regressions of reported rates of formal account ownership from household surveys and the logarithm of deposit accounts per thousand adults. Column (1) is surveys taken after 2003, (2) are surveys after 2003 where the reported fraction is of all adults (rather than households) (3) is all available household surveys (going back to 2000).

||(1)|(2)<br>(3)|
|---|---|---|
|ln(Deposits./ 1000 Adults)|25.2***|24.5***<br>24.8***|
||(7.51)|(5.73)<br>(6.64)|
|Constant|-112.2***|-105.4***<br>-115.1***|
||(-4.95)|(-3.51)<br>(-4.65)|
|Observations|25|17<br>31|
|R-squared|0.7101|0.6861<br>0.6034|
|Note: Standard errors inparenthe|ses;***p<0.01,|**p<0.05,*p<0.1|

49
this year is defined can be crucial for evaluating the macroeconomic trends around the onset of conflicts. To assess the robustness of the econometric estimates across various definitions of the onset of conflicts, which can affect both the starting dates as well as the number of conflicts, we utilize the following four alternative definitions of conflicts:

**Definition 1:** All conflicts with their corresponding starting dates. This definition includes all the conflicts involving developing economies reported by the UCDP/PRIO Armed Conflict Dataset. The starting years were retrieved from the data set and correspond to the date of the first recorded battle‐related death.

**Definition 2:** High intensity conflicts with their corresponding starting dates. This definition considers only conflicts that in at least one year since their onset experienced a minimum of 1,000 battle‐related deaths, which is the highest intensity level reported in the data set. If a country experiences more than one conflict in the same year, we considered in the analysis the episode with the highest level of intensity. The starting year, retrieved from the data set, corresponds to the date of the first battle‐related death recorded in the conflict, and thus the dates of the onsets of conflicts are the same as in definition 1 but the number of conflicts is lower than under definition 1.

**Definition 3:** High intensity conflicts with adjusted starting dates. This definition considers only conflicts that in at least one year since their onset experienced a minimum of 1,000 battle‐related deaths. But, in contrast with Definition 2, the starting date corresponds to the year when the threshold of 1,000 battle‐ related casualties in a year was reached.

**Definition 4:** All conflicts with starting years based on cumulative intensity. Considering the temporal dimension of the conflicts, this definition includes all the confrontations reported by the UCDP/PRIO Armed Conflict Dataset that since their onset exceed 1,000 battle‐related deaths. The starting date corresponds to the year that the cumulative number of battle‐related casualties exceeded 1,000.

To estimate the effect of conflict on the evolution of the three macroeconomic variables of interest before and after the onset of armed conflict, the episodes to be studied must be preceded by a period of peace. In the ongoing, an episode of conflict was defined as being preceded by at least 10 years of peace.<sup>5</sup> Considering the four definitions of conflict, these periods of peace have been adapted in the following way. For definitions 1 and 2, the conflicts studied are the ones that have been preceded by at least 10 years free of any type of conflict; for Definition 3, conflicts are preceded by 10 years free of high intensity

> 5 For the UCDP/PRIO Armed Conflict Dataset, this means less than 25 battle‐related deaths in a calendar year.

6
level analysis yields new and interesting policy patterns about the dynamics of welfare over time.

Incidentally, we present further validation of the synthetic panel method, which is predicated on certain underlying assumptions. While we are unable to test these assumptions directly with the NSS data, we probe them by validating our methodology with the IHDS data—the one nationally representative panel data set that has been collected in India in recent years. Seeing that the method works well in replicating true panel estimates from the IHDS further adds to the existing validation studies that analyze data from other countries.<sup>3</sup> These offer us scope to comment on patterns of per-capita consumption mobility based on the synthetic panels constructed from the NSS data.

To preview our findings, our analysis indicates that as economic growth in India accelerated in the late 1990s and 2000s, consumption mobility also increased: a larger share of the poor in the later survey years was able to transition out of poverty than in the first half of the study period. Chronic poverty declined. Less encouraging, however, is that relatively few of the poor were able to escape poverty to the extent that they were able to join the secure— i.e. the group that is neither poor nor vulnerable to falling back into poverty. Even in the second half of the study period, transitioning out of poverty and directly into the ‘middle class’ was a very rare occurrence. While fewer people dropped back into poverty over time than managed to escape poverty, it remains important to note that downward mobility was not entirely absent. Indeed, focusing on the characteristics of the population that moved up or down in relative terms reveals that forward castes (neither Schedule Castes and Schedule Tribes, nor Other Backward Castes) exhibited a particularly high propensity to move up in relative rankings. In contrast, Scheduled Castes and Scheduled Tribes were much more likely to see their relative

> 3  See Dang, Jolliffe, and Carletto (2019) for a recent review of studies that validate and employ the synthetic panel methods.

5
27 Table 2: Summary statistics for urban areas and rural areas, 2012/2019

|Economies<br>V|olume km<sup>3</sup>|Per Cap. m<sup>3</sup>|% Outward|% Upward|% Low-Rise|% High-Rise|
|---|---|---|---|---|---|---|
|_Panel A:_||Urban Areas<br>(De|fned as Cities|_≥_50,000 Inha|bitants in 2015|)|
|World|677|193|44|55|52|3|
|Developed|232|399|34|65|58|7|
|Developing|445|152|46|54|51|3|
|High-Income|232|399|34|65|58|7|
|Upper-Middle-Inc.|318|243|37|63|59|4|
|Lower-Middle-Inc.|109|81|51|49|47|2|
|Low-Income|17|65|63|37|37|0|
|Africa|54|99|58|41|41|0|
|Asia|352|165|42|58|55|3|
|Europe|127|439|30|70|64|6|
|Latin America & Carib.|56|160|47|53|47|6|
|North America|82|473|42|59|54|5|
|Pacifc|6|374|45|55|51|4|
|_Panel B:_|Rura|l Areas<br>(Defned a|s Villages and T|owns_≥_50,00|0 Inhabitants in|2015)|
|World|916|238|60|40|40|0|
|Developed|343|552|50|51|50|1|
|Developing|572|177|62|38|38|0|
|High-Income|343|552|50|51|50|1|
|Upper-Middle-Inc.|388|262|54|45|45|0|
|Lower-Middle-Inc.|145|104|67|32|32|0|
|Low-Income|38|112|73|27|27|0|
|Africa|94|147|72|28|28|0|
|Asia|398|174|60|40|40|0|
|Europe|245|540|50|50|49|1|
|Latin America & Carib.|58|215|61|39|39|0|
|North America|111|604|50|49|49|0|
|Pacifc|9|417|59|41|41|0|

_Notes:_ This table shows summary statistics for four groupings of countries. Developed Status is based on the World Bank (WB)’s classification of countries in 2019 (= high-income economies). Outward volume = built area x 2.5m. Upward volume = volume - outward volume. High-rise volume = from high-rises ( _≥_ 50 m). Low-rise volume = from low-rises (between 2.5m and 50m).
Table 3: Transitions from studying or out of labor force (OLF) to employment sector, conditional on moving, by gender and income, in percent

|||**A**|**rgentina**|||**Brazil**|||**Mexico**||
|---|---|---|---|---|---|---|---|---|---|---|
|**Studyi**|**ng**|**Self**<br>**Employed **|<sup>**Formal**</sup>|<sup>**Informal**</sup>|**Self**<br>**Employed **|<sup>**Formal**</sup>|<sup>**Informal**</sup>|**Self**<br>**Employed **|<sup>**Formal**</sup>|<sup>**Informal**</sup>|
||15-18|5.6|4.8|28.5|10.9|21.4|53.9|3.4|17.1|36.0|
|**All**|19-24|8.3|15.1|34.2|13.8|31.4|38.7|7.3|29.8|33.3|
||25-44|12.0|15.1|24.9|19.7|23.2|21.2|12.3|26.4|18.6|
||15-18|7.8|5.0|31.0|11.9|22.0|56.0|4.8|18.4|45.6|
|**Males**|19-24|10.9|15.7|35.6|16.8|32.2|40.4|9.6|31.4|38.4|
||25-44|19.1|20.7|28.4|21.7|33.9|24.7|19.2|34.3|26.4|
||15-18|3.0|4.5|25.5|9.6|20.8|51.2|1.7|15.7|24.3|
|**Females**|19-24|5.9|14.5|32.8|10.6|30.5|37.0|4.7|28.0|27.5|
||25-44|8.3|12.1|23.1|18.7|18.0|19.4|7.1|20.3|12.6|
||15-18|5.6|2.7|26.2|15.2|15.9|54.8|4.1|16.2|39.5|
|**Poor**|19-24|10.3|10.3|31.9|18.4|26.6|38.3|7.8|28.6|34.5|
||25-44|13.9|14.8|27.5|23.9|22.4|22.1|10.7|26.5|25.7|
||15-18|5.4|9.0|34.9|11.5|21.6|50.2|3.5|18.0|32.9|
|**Non poor**|19-24|6.9|18.8|45.0|15.1|33.1|33.2|8.4|30.6|32.8|
||25-44|12.8|16.7|20.7|17.6|29.4|19.0|12.7|23.8|16.0|
|**OLF**||**Self**<br>**Employed **|<sup>**Formal**</sup>|<sup>**Informal**</sup>|**Self**<br>**Employed **|<sup>**Formal**</sup>|<sup>**Informal**</sup>|**Self**<br>**Employed **|<sup>**Formal**</sup>|<sup>**Informal**</sup>|
||15-18|4.9|1.4|20.8|11.8|7.4|33.8|3.1|17.5|24.9|
|**All**|19-24|8.1|6.4|27.2|17.4|13.0|24.3|9.5|28.0|26.0|
||25-44|21.4|8.4|27.9|40.2|12.7|18.6|30.0|21.9|25.7|
||15-18|5.5|1.5|22.0|14.4|6.3|36.7|4.2|13.4|29.6|
|**Males**|19-24|9.5|8.0|24.9|17.1|12.9|26.2|8.5|23.4|27.7|
||25-44|26.7|10.4|19.1|32.3|16.3|17.5|30.0|21.3|27.4|
||15-18|4.3|1.4|19.6|9.1|8.5|30.8|2.7|19.3|22.8|
|**Females**|19-24|7.4|5.6|28.3|17.5|13.1|23.3|9.6|28.9|25.7|
||25-44|20.6|8.1|29.1|42.5|11.6|18.9|30.0|21.9|25.5|
||15-18|5.6|1.4|20.8|14.2|7.0|39.1|3.7|16.9|28.4|
|**Poor**|19-24|8.8|5.3|27.9|18.3|11.2|27.9|9.2|26.1|29.4|
||25-44|20.7|5.4|34.6|42.1|13.0|19.7|32.9|17.1|28.9|
||15-18|3.8|3.8|9.6|10.4|7.3|29.7|2.8|12.7|12.7|
|**Non poor**|19-24|8.0|11.5|15.0|15.6|14.6|18.8|10.2|29.7|18.1|
||25-44|23.1|13.1|15.1|39.8|13.3|16.5|27.5|28.2|20.5|

21
Figure 14: Counterfactual output in the West Bank in the absence of restrictions <!-- Start of picture text --> 2004 2006 2008 2010 2012<br>year<br>θ=30 θ=40<br>θ=60 θ=80<br>.3<br>.25<br>.2<br>.15<br>log difference in NTL per cap<br>.1<br><!-- End of picture text --> Counterfactual output in the West Bank in the absence of restrictions (using different distance decay parameters _θ_ ) Despite these contextual differences, our findings resonate with this broader literature, confirming the importance of spatial interdependence in the productivity of market economies. We find that the localized, temporary, and reversible shocks to road network connectivity significantly impact economic output (proxied for by nighttime lights). The association between market access and nighttime lights is robust to controlling for conflict. Moreover, it strengthens when we instrument market access using the count of obstacles located in a radius between 10 and 25km from the locality. The relationship is furthermore robust to using alternative market access and nighttime lights measures, and also obtains when estimating a difference-in-differences specification using 4-year changes in nighttime lights and market access. A back-of-the-envelope calculation suggests that over the period 2005-2012 market access constraints resulting from the deployment of road closure obstacles reduced GDP per capita in the West Bank between 4.1% and 6.1% on average each year.

# **References**

Abrahams, Alexei (2018a). Hard traveling: Commuting costs and urban unemployment with deficient labor demand. _ESOC Working Paper No._ 8.

Abrahams, Alexei (2018b). Not dark yet: the Israel-PA relationship 1993-2017. In 37