rafmacalaba/datause-encoder-data huggingface dataset:
resources from the exogenous inflows will bid up prices of domestic goods so that the real exchange rate appreciates immediately (see figure 5). The higher prices and the forward exchange rate favor postponing consumption, and, hence, consumption falls in the initial years (see figure 6). Investment rises throughout from additional savings, and over time, income and consumption also rise as a result of the increased supply (see figure 7). The increased supply will slowly reduce the initial appreciation of the exchange rate. This is the kind of story that is behind much of the standard thinking about the effect of aid. Because the inflows are grants and borrowing is fixed (zero), no debt story is relevant in this simulation.

# **Figure 5 Real Exchange Rate: Permanent Increase in Foreign Aid, with Borrowing Constraints**

<!-- Start of picture text -->
1.01<br>1<br>0.99<br>0.98<br>0.97<br>0.96<br>0.95<br>0.94<br>0.93 AID-P & I adj AID-P & I adj & trade AID-P & G adj<br>0.92<br>1 4 7 10 13 16 19 22 25 28 31 34 37 40<br>time periods (years)<br>real exchange rate<br><!-- End of picture text -->

_Source: Authors’ calculations._

# **Figure 6 Consumption Ratio to Base: Permanent Increase in Foreign Aid, with Borrowing Constraints**

<!-- Start of picture text -->
1.15<br>1.1<br>1.05<br>1<br>0.95<br>AID-P & I adj AID-P & I adj & trade AID-P & G adj<br>0.9<br>1 4 7 10 13 16 19 22 25 28 31 34 37 40<br>time periods (years)<br>consumption ratio<br><!-- End of picture text -->

_Source: Authors’ calculations._ 22
Policy Research Working Paper 10695

# Recording the Time Divide

A Comparative Study of Smartphone- and Recall-Based Approaches to Time Use Measurement _Talip Kilic Gayatri Koolwal Wilbert Drazi Vundru Thomas Daum Hannes Buchwald Greg Seymour Peter Mvula Alister Munthali Monice Kachinjika_ Development Economics Development Data Group February 2024
years on average vs 20.5 years for married girls at baseline) where information can more easily influence life decisions, and they faced fewer constraints from existing marriages. For married girls, gains emerged primarily when interventions directly engaged their environment, particularly through husbands’ clubs, or provided tangible economic resources. However, given reduced statistical power and multiple testing, these findings should be interpreted as suggestive rather than definitive.

Table 6: Impact on primary outcomes by marital status at baseline

||(1)|(2)|
|---|---|---|
||SRH||
||behavior<br>(15 yo+)|Economic<br>outcomes|
|T1 (safe spaces)|0.310***<br>|0.208**<br>|
||(0.114)|(0.101)|
|T1 x Married|-0.170|-0.251**|
||(0.145)|(0.118)|
|T1 + Leaders|0.267**|0.138|
||(0.123)|(0.133)|
|T1 + Leaders x Married|-0.210|-0.173|
||(0.167)|(0.152)|
|T2 (safe spaces + IGA)|-0.038|0.182|
||(0.115)|(0.112)|
|T2 x Married|0.140<br>|0.008<br>|
||(0.126)|(0.124)|
|T2 + Leaders|-0.121|0.326***|
||(0.216)|(0.111)|
|T2 + Leaders x Married|0.171|-0.128|
||(0.241)|(0.097)|
|T3 (safe spaces + husbands’ clubs)|0.242*|0.367***|
||(0.132)|(0.097)|
|T3 x Married|-0.067|-0.109|
||(0.128)|(0.123)|
|T3 + Leaders|0.261***|0.341**|
||(0.096)|(0.160)|
|T3 + Leaders x Married|-0.197|-0.101|
||(0.150)|(0.183)|
|T4 (control spillover)|0.156|0.159*|
||(0.104)|(0.092)|
|T4 (control spillover) x Married|-0.121|-0.129|
||(0.121)|(0.100)|
|T1 + T1 x Mar = 0|.147|.707|
|(T1 + L) + (T1 + L) x Mar = 0|.643|.796|
|T2 + T2 x Mar = 0|.276|.0711|
|(T2 + L) + (T2 + L) x Mar = 0|.683|.0355|
|T3 + T3 x Mar = 0|.0884|.0139|
|(T3 + L) + (T3 + L) x Mar = 0|.55|.0845|
|N|2,852|2,999|
|Control mean|0|0|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. IGA = income-generating activity; Mar = in union at baseline; SRH = sexual and reproductive health; yo = years old. The table presents estimated impacts of our 6 interventions’ combinations relative to the pure control group, for the unmarried then the married sample. All regressions control for the baseline value of the outcome variable, an indicator for being married at baseline, stratification fixed effects, and the following baseline variables: adolescent girl’s age, education level, an indicator for being engaged in an IGA, knowledge of modern contraception, household is rural, household head is female, and at least one household member has an agricultural activity. When the baseline value of the outcome variable is missing, it is imputed and a dummy indicating whether the variable is imputed or not is added as control.

25
- 39 -

**<u>Table 10. Two-stage quantile regression estimation</u>**

||||Quantile|||
|---|---|---|---|---|---|
||.10|.25|.50|.75|.90|
|_I_|–1.279 ***|–1.267 ***|–1.287 ***|–1.037 ***|–0.437 ***|
||(0.227)|(0.147)|(0.104)|(0.152)|(0.169)|
|ln_MP_|0.080|0.059|0.237 *|0.426 **|–0.093|
||(0.283)|(0.178)|(0.123)|(0.179)|(0.207)|
|ln_D_|0.028 ***|0.031 ***|0.025 ***|0.004|0.010|
||(0.010)|(0.006)|(0.004)|(0.006)|(0.007)|
|ln_TEMP_|0.084|0.150 **|–0.001|<br>0.076|0.059|
||(0.098)|(0.062)|(0.043)|(0.062)|(0.069)|
|ln_PREC_|–0.024|–0.016 *|–0.038 ***|–0.029 ***|–0.020 *|
||(0.016)|(0.010)|(0.007)|(0.011)|(0.013)|
|lnY|0.070 ***|0.050 ***|0.042 ***|<br>0.039 ***|0.034 ***|
||(0.014)|(0.009)|(0.006)|(0.010)|(0.011)|
|ln_HH_|0.088 **|0.072 ***|0.005|0.011|0.023|
||(0.036)|(0.021)|(0.014)|(0.021)|(0.024)|
|D(Power for lighting)|0.069|0.071|0.134 *|<br>0.157|0.151 *|
||(0.132)|(0.101)|(0.071)|(0.101)|(0.094)|
|D(Power for heating)|0.034|0.020|0.010|0.007|0.038 *|
||(0.029)|(0.018)|(0.013)|(0.018)|(0.021)|
|D(Power for cooking)|0.055 *|0.032 *|–0.005|–0.027|0.010|
||(0.030)|(0.019)|(0.013)|(0.019)|(0.021)|
|D(Power for water heating)|0.050 *|0.065 ***|0.035 ***|<br>0.045 **|0.047 **|
||(0.028)|(0.019)|(0.013)|(0.019)|(0.022)|
|D(Power for others)|0.055|0.026|0.024|<br>0.034|0.007|
||(0.038)|(0.024)|(0.017)|(0.024)|(0.027)|
|D(Gas for lighting)|–0.065|–0.027|0.013|0.026|–0.041|
||(0.094)|(0.060)|(0.042)|(0.062)|(0.062)|
|D(Gas for heating)|0.068 ***|0.022|0.019 *|0.001|–0.033|
||(0.025)|(0.016)|(0.011)|(0.016)|(0.018)|
|D(Gas for cooking)|–0.011|–0.015|–0.019|–0.032 *|0.001 *|
||(0.031)|(0.018)|(0.013)|(0.019)|(0.021)|
|D(Gas for others)|0.052|–0.065|0.007|0.073|0.148|
||(0.131)|(0.080)|(0.057)|(0.081)|(0.096)|
|ln_TV_|0.0087 **|0.0074 ***|0.0051 ***|0.0031|0.0026|
||(0.0037)|(0.0024)|(0.0017)|(0.0025)|(0.0026)|
|ln_Video_|0.0049 ***|0.0040 ***|0.0021 ***|<br>0.0030 ***|0.0028 ***|
||(0.0016)|(0.0010)|(0.0007)|(0.0010)|(0.0011)|
|ln_Refrigerator_|0.0023|0.0038 *|0.0019|–0.0004|0.0004|
||(0.0032)|(0.0020)|(0.0014)|(0.0020)|(0.0023)|
|ln_Freezer_|–0.0004|–0.0082 **|0.0004|0.0043|0.0028|
||(0.0059)|(0.0037)|(0.0026)|(0.0037)|(0.0038)|
|ln_Washer_|0.0133 ***|0.0094 ***|0.0044 ***|0.0051 ***|0.0047|
||(0.0021)|(0.0014)|(0.0010)|(0.0014)|(0.0015)|
|ln_Dishwasher_|0.0093 *|0.0089 ***|0.0118 ***|<br>0.0128 ***|0.0135 ***|
||(0.0048)|(0.0032)|(0.0022)|(0.0031)|(0.0033)|
|ln_Stove_|0.0067 ***|0.0044 ***|0.0047 ***|<br>0.0055 ***|0.0049 ***|
ing degraded forest lands, on food security. We do so using a Randomized Controlled Trial (RCT), implemented in arid Burkina Faso, which allows for the causal estimate of PES payments on food security outcomes. As part of this intervention, the communities living in the proximity of the project forests were invited to participate in reforestation campaigns.

The campaigns started with about 33,500 new trees being planted in August 2017 across 62 reforestation sites, in 11 gazetted forests. Subsequently, half of 630 community members, who were both interested in and eligible to participate in the project, were randomly assigned to the PES scheme and the other half were assigned to the control group. Those who ended up participating in the project were randomly matched into teams of five, who were subsequently made responsible for the maintenance of the trees planted on degraded forest land. Contracts were signed between each maintenance group and the implementing government agency stipulating that each group would receive about $0.70 for every tree still alive at verification almost a year later (in June 2018), and we made sure the payment was divided equally among all five members of each group. While such transfers could increase household consumption, the time and effort needed to take care of the trees, and ensure their survival, may displace labor from other productive (farm or non-farm activities), which could then negate the potentially positive effects of the financial compensation.

Using primary data collected 13 months after the start of the PES program among all 630 individuals in the treatment and control group, we find 2
# **2. The Pacific Access Category and PINZMS Data**

The natural experiment we use is based on the Pacific Access Category (PAC) under New Zealand’s immigration policy. The PAC was established in 2001 and allows an annual quota of 250 Tongans to migrate to New Zealand without going through the usual migration categories used for groups such as skilled migrants and business investors.<sup>3</sup> Specifically, any Tongan citizens aged between 18 and 45, who meet certain English, health and character requirements,<sup>4</sup> can register to migrate to New Zealand.<sup>5</sup> Many more applications are received than the quota allows, so a ballot is used by the New Zealand Department of Labour (DoL) to randomly select from amongst the registrations. The probability of success in the ballot is approximately 10%. Thus, we have a group of migrants and a comparison group who are similar to the migrants, but remain in Tonga only because they were not successful in the lottery. Once their ballot is selected in the lottery, applicants must provide a valid job offer in New Zealand within six months in order to have their application to migrate approved and be allowed to migrate.

The other options available for Tongans to migrate are fairly limited, unless they have close family members abroad. Ninety-four percent of all Tongan migrants are located in New Zealand, the United States and Australia.<sup>6</sup> In the 2004/05 financial year New

> 3 The Pacific Access Category also provides quotas for 75 citizens from Kiribati, 75 citizens from Tuvalu, and 250 citizens from Fiji to migrate to New Zealand.

> 4 Data supplied by the DoL for residence decisions made between November 2002 and October 2004 reveals that out of 98 applications, only 1 was rejected for failure to meet the English requirement, and only 3 others were rejected for failing other requirements of the policy.

> 5 The person who registers is a Principal Applicant. If they are successful, their immediate family (spouse and children under age 18) can also apply to migrate as Secondary Applicants. The quota of 250 applies to the total of Primary and Secondary Applicants, and corresponds to about 70 migrant households.

> 6 Source: GTAP database of Parsons et al. (2005).

- 7 -
_<_ **Figure 1 here.** _>_

# **4 Results: Average effects**

## **4.1 Impact on firms**

In this section, we analyze the impact of the job fairs on our sample of firms; this follows our pre-analysis plan.<sup>18</sup> We divide outcomes into families. For each outcome of interest we use an ITT approach with an ANCOVA specification; we also include the set of covariates used for the randomization. We use robust standard errors.<sup>19</sup> Specifically, we estimate: In this specification, the ‘balance’ variables included in **_x_** _i_ 0 are all the variables listed in Table 2. Variable _yic,pre_ is the dependent variable measured at baseline. Throughout this analysis we distinguish between professional workers and non-professional workers. ‘Professional workers’ refers to traditional notions of ‘white-collar employees’: typically those with some degree or diploma, working in relatively highly-trained positions. For manufacturing firms, ‘nonprofessional workers’ refers mostly to labourers or ‘production’ workers; for service-based firms, these include mostly ‘client services’ (tellers, waiters, receptionists, _etc_ .) The main results on firm outcomes are presented in Tables 3, 4, 10, and 5; in each table, we show each regression as a row, in which we report the estimated ITT ( _β_<sup>ˆ</sup> 1), the mean of the control group, and the number of observations. In each case, we report both _p_ -values and False Discovery Rate _q_ -values, taken across the family of outcomes (Benjamini et al., 2006).

First, in Table 3, we test whether the fairs had an impact on firms’ recruitment processes, as measured by firms’ ability to fill vacancies. We find no impact on these outcomes, nor on how long it took fill positions that were made available, nor on firms’ reported costs of recruitment. We do find a small but significant positive impact of the fairs on unfilled vacancies. That is, firms reported having more vacancies that they were unable to fill during the year. (However, this effect becomes marginally insignificant after we apply multiple hypothesis testing corrections.) In Table 4, we then look at the impact of the job fairs on firm hiring outcomes at the main endline survey, which took place about six months after the second job fair. We find that

> 18 This is available at `https://www.socialscienceregistry.org/trials/1495` .

> 19 We do not cluster our errors since randomisation was conducted at the firm level.

12
knowledge of the interviewees. After the visit, team members combined their notes from the interviews, revisited and revised the basin background paper, reviewed other materials, and composed this paper summarizing and analyzing the river basin management situation in the Fraser basin.<sup>3</sup> The following analysis of the Fraser basin case is therefore based on a combination of sources—documentary materials on the basin and the various governmental and non-governmental organizations at work there, the background paper prepared for the visit, and the interviews conducted during the site visit. The findings and conclusions therefore do not represent the point of view of a single individual or organization, but emerge from a composite of data collected and reviewed by the research project team.

# **4. The Fraser River Basin**<sup>**4**</sup>

The Fraser River drains 238,000 km<sup>2</sup> of British Columbia, an area about the size of the United Kingdom. The Fraser River basin supports a population of more than 2.7 million residents, and an economy that includes forestry and pulp and paper production, ranching and agriculture, fishing, mining, recreation and tourism and other industries. Seventyeight percent of the basin’s population lives in the lower Fraser River valley and estuary region where the Vancouver metropolitan area is located.

The basin has been home to aboriginal peoples, or First Nations, for thousands of years. The current population of indigenous residents is estimated to be 50,000. The number of distinct First Nations is subject to varying estimates, but Fraser Basin Council estimates place it around 100, which may be categorized into eight major language groups.

The river itself (named for explorer/settler Simon Fraser) is 1,399 km long, originating in the Rocky Mountains and emptying into the Strait of Georgia and the Pacific Ocean after flowing through the Vancouver metropolitan area. There are 13 principal watersheds or sub-basins of the Fraser basin, identified in Figure 1, but on a broader scale one could identify three main hydrologic regions—the coastal mountains, the interior plateau, and the eastern (Columbia and Rocky) mountains. The interior plateau is the driest of these regions, the coastal mountains the wettest.

Weather systems moving onshore from the Pacific deposit large volumes of precipitation during autumn and winter in the mountain ranges, increasing with elevation and occurring predominantly as snow that thaws through the spring and summer months. While snow also is the principal winter precipitation in the interior plateau, peak precipitation in this region occurs as spring and summer rainfall. In some areas of the basin, glacial melt is also an important source of surface stream flow.

> 3 Comments on an earlier draft were received from Allen Domaas, Daryl Fields, and Hans Schreier and are appreciated.

> 4 See map at the beginning of the paper.

6
Table 4. Sumary Fsatures of Econometric Studles of Japan's Trade Barriers

|Author(s)<br>of Study|Time<br>Period|Countries<br>In Samplo|Level of<br>Aggregation|Dependent<br>Variable|Independent<br>Variable|Model|Findings|
|---|---|---|---|---|---|---|---|
|Balassa(1986b)|19;3-1983|18<br>Industrial|Oneeconomywide<br>sector,<br>imports dis-<br>aggregated by total<br>manufacturing,<br>pri-<br>mry,<br>and by source;<br>developing,<br>Industrial<br>economies|Log leportGP,<br>lmport/GW|Log 6NP/per capita,<br>log population,<br>primary imports/<br>total<br>Imports,<br>transport<br>costs|Stylized<br>version of<br>Chenery (1960) model|SignIficant<br>du*y<br>variables<br>forJapan|
|Balassa-Noland<br>(1988)|1973-1983|16<br>Industrial|Oneeconomy wide<br>sector,<br>Importsdis-<br>aggregated by total<br>manufacturing,<br>pri-<br>nary,<br>andby so, rce;<br>developing,<br>Industrial<br>economies|Log_l_portW,<br>ImportG|Log_GW/_per capita.<br>log population,<br>primaryImports/<br>total<br>leports,<br>transport<br>costs|Stylized<br>version of<br>Chenery (1960|Significant<br>duy<br>varlawles<br>forJapan<br>°|
|Barbone (1988)|1974-1983|23<br>OECD<br>countries|One economy wide<br>sector|Ratloof<br>**Im-**<br>ports toGOP;<br>Ratlo ofmenu-<br>factured<br>**i-**<br>poets to GOP|Logpercapita<br>_GW;_Log of pop-<br>ulation;<br>Log squared<br>population;<br>Square<br>GNP|Stylized<br>version of<br>Chenery (1960)model|Dumiesnegative<br>and significant<br>for 6 countries<br>but not forJapan;<br>For manufactured<br>goodsratio,du_y<br>Is significant<br>forJapan|
|Dergsten-Cline<br>(1985, 1987)|1974-1984|11<br>Industrial<br>countries<br>plus EEC<br>as asingle<br>group|Oneeconomywide<br>sector|I port/GNP|Log6WP,logcrude<br>oil<br>production<br>per<br>capita<br>log**erable**<br>land per capita,<br>transport<br>costs,<br>di<br>for<br>Ironre-<br>serve|Stylized<br>version of<br>Chenery (1960)<br>model|Insignificant<br>dumyvariable<br>forJapan|
Maffioli, A., Ubfal, D., Baré, G. V., & Cerdán-Infantes, P. (2011). Extension services, product quality and yields: the case of grapes in Argentina. _Agricultural Economics_ , _42_ (6), 727–734.

Munshi, K. (2004). Social learning in a heterogeneous population: technology diffusion in the Indian Green Revolution. _Journal of Development Economics_ , _73_ (1), 185–213.

Owens, T., Hoddinott, J., & Kinsey, B. (2003). The Impact of Agricultural Extension on Farm Production in Resettlement Areas of Zimbabwe. _Economic Development and Cultural Change_ , _51_ (2), 337–357.

Palacios-Lopez, A., Christiaensen, L., & Kilic, T. (2017). How much of the labor in African agriculture is provided by women? _Food Policy_ , _67_ , 52–63..

Pamuk, H., Bulte, E., & Adekunle, A. A. (2014). Do decentralized innovation systems promote agricultural technology adoption? Experimental evidence from Africa. _Food Policy_ , _44_ , 227–236.

Quisumbing, A. R., & Pandolfelli, L. (2010). Promising Approaches to Address the Needs of Poor Female Farmers: Resources, Constraints, and Interventions. _World Development_ , _38_ (4), 581–592.

Ragasa, C. (2014). Improving Gender Responsiveness of Agricultural Extension. In A. R. Quisumbing, R. Meinzen-Dick, T. L. Raney, A. Croppenstedt, J. A. Behrman, & A. Peterman (Eds.), _Gender in Agriculture_ (pp. 411–430). Dordrecht: Springer Netherlands.

Rivera, W. M., Qamar, M. K., & Van Crowder, L. (2001). _Agricultural and Rural Extension Worldwide: Options for Institutional Reform in Developing Countries._ Rome: Food and Agriculture Organization.

Romani, M. (2003). _The impact of extension services in times of crisis: Côte d’Ivoire (1997-2000)_ . Retrieved from http://aida.econ.yale.edu/conference/neudc03/papers/4a-romani.pdf Owens, T., Hoddinott, J., & Kinsey, B. (2003). The Impact of Agricultural Extension on Farm Production in Resettlement Areas of Zimbabwe. _Economic Development and Cultural Change_ , _51_ (2), 337–357.

O’Sullivan, M., Rao, A., Banerjee, R., Gulati, K., & Vinez, M. (2014). _Levelling the field : improving opportunities for women farmers in Africa_ (No. 86039) (pp. 1–86). The World Bank.

Taffesse, A., Dorosh, P., & Asrat, S. (2011). _Crop Production in Ethiopia: Regional Patterns and Trends_ . Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.190.9513&rep=rep1&type=pdf Waddington, H., Snilstveit, B., Hombrados, J. G., Vojtkova, M., Anderson, J., & White, H. (2014). Farmer field schools for improving farming practices and farmer outcomes in low-and middle-income countries: a systematic review. _Campbell Systematic Reviews_ , _10_ (6). Retrieved from http://campbellcollaboration.org/lib/project/203/ World Bank, and IFPRI. (2010) _Gender and Governance in Rural Services: Insights from India, Ghana, and Ethiopia_ . The World Bank and International Food Policy Research Institute.

14
each province. For a given province, these alternatives are defined as the product-country pairs for which at least one export start is observed over the 1997-2007 period.

For these province-product-country triads, the dataset is originally a balanced panel from 1997 to 2007 covering 211 countries and 1213 HS4 products. The dataset includes 1,050,516 observations each year, resulting in a total of 11,551,716 (province/product/country/year) observations over the 1997-2007 period. Approximately 11% of the observations from the entire database correspond to domestic starts, that is, to provinces where domestic firms do not export product _k_ to country _j_ at time _t_ but do export _k_ to _j_ at time _t_ + 1.

These domestic starts are the trade flows to be explained. As in Koenig et al. (2010), ceasing and continuing export flows are not included in the study. Given the time span, for a given province-product-country triad, several starts might be observed. For example, the subsequent export statuses 00011001111 become .001..01... in our sample, with 1 denoting positive exports, 0 denoting no exports, and . denoting a missing value. By definition, all of the observations are missing for 1997, the first year in the sample, because the export statuses in 1996 are not observed. Continuing export flows (a 1 preceded by another 1) and ceasing export flows (a 0 preceded by a 1) are also coded as a dot because they are excluded from the analysis.

Because the estimations will include province-product-country fixed effects, taking into account a broader definition of the possible exported products or the destination countries would not change the final sample used for the estimations. The behavior of the province-productcountry triads for which we observe positive export flows or null export flows every year of the period would be explained by the fixed effect.

Unreported results, available upon request, show that the conclusions are very much the same when the sample is restricted to durable starts, defined as export starts leading to positive export values for at least two consecutive years. This finding suggests that the foreign export 8