rafmacalaba/datause-encoder-data huggingface dataset:
For example, evidence shows it is more likely for women to report intimate partner violence when the instrument guarantees full anonymity, relative to another with a lower level of anonymity (Aguero and Frisancho, 2021). Therefore, in light of the inherent problems, how can researchers and policy makers assess the convenience of each method?

In this paper, we evaluate the relative effectiveness of collecting data on sensitive topics through two remote survey methods: phone-based surveys and self-completion surveys over WhatsApp. We conduct a randomized-controlled experiment, embedded on a survey-mode evaluation of a positive-parenting and stress-management intervention for caregivers in El Salvador, to assess the relative convenience of each method. In particular, we randomly allocate respondents to either phone-based - Computer Assisted Telephone Interview (CATI), which was conducted by a live person – or self-completion surveys – surveys done over WhatsApp – and evaluate their relative effect. In order to provide specific guidance for researchers and policy makers, we compare different dimensions of analysis such as completion rate, changes in composition of respondents, quality of responses,<sup>3</sup> and implementation costs. We use mental health and attitudes toward the effectiveness of physical punishment of children as measures of sensitive information, due to the large incidence of these issues over time and their greater relevance during the pandemic crisis (WHO, 2020; Cullen et al., 2020; Kumar and Nayar, 2021).<sup>4</sup> To credibly identify the relative convenience of the two modes, we ensured that aside from the survey mode itself, both groups were treated equally.<sup>5</sup> We document five main results. First, we find that phone surveys increase the response rate by 42 percentage points. This represents an increase of 140 percent relative to the response rate among those who were contacted via WhatsApp messages. To put the magnitude of this estimate in perspective, our back-of-

> 3As we explain in more detail later, we measure the quality of responses by checking the consistency of responses from the caregivers in a survey conducted at midline against those reported in the baseline (before the intervention) and endline (at the end of the intervention) surveys.

4For example, as of 2017, 792 million people (more than one in 10 people) worldwide lived with a mental health disorder (Saloni Dattani and Roser, 2021), an issue that is more relevant for individuals from the lowest income strata (Ridley et al., 2020). Moreover, close to 300 million (three in four) children aged two to four years worldwide experience violent discipline by their caregivers on a regular basis; 250 million (around six in 10) are punished by physical means (UNICEF, 2017).

5Specifically, we used the same instrument, provided the same monetary incentive and time to complete the survey, there were no costs of completing the survey by either method (such as data plan usage), and the maximum number of reminders was similar across groups.

3
and analysis. But we will dive into distribution patterns and shipment-level analysis of dwell time in later sections because defining a standardized objective is by no mean the right way of tackling the long dwell time issue.

|**Designation**|**Stakeholder**|**Dwell time objective**|
|---|---|---|
|Global benchmark|All|3 to 4 days|
|“7 jours à l’import” objective|All – Facilitation Committee|7 days|
|Free time|DIT / PAD|11 days|
|Desired storage time|Shippers|5 to 35 days|
|Maximum clearance delay|Customs administration|90 days|
|**Proposed benchmark**|**All**|**11 days **|

**Table 1 –Alternative long dwell time definitions for the Douala Port**

# **Importance of the long cargo dwell time issue in Douala**

Container traffic represents about 45% of the total tonnage that transits through the Port of Douala annually<sup>9</sup> . Containers are also the primary mode for Cameroonian exports representing about 75% of total traffic in tons while they account for about 45% of Cameroonian imports.

<!-- Start of picture text --> Dwell time in DIT<br>Import traffic<br>(TEU '000) (days)<br>Import volume dwell time<br>160 143.882 25<br>138.475<br>140<br>112.671 20<br>120<br>101.384<br>94.631<br>100 15<br>80<br>60 10<br>40<br>5<br>20<br>0 0<br><!-- End of picture text -->

**Graph 1 – Container imports traffic and dwell time in the Port of Douala (2005-2009)** Source: Port Autonome de Douala

Most recent DIT statistics indicate an average dwell time of 19.3 days for the first semester of 2010 while means of 18 days and 20 days respectively have been observed in July and August<sup>10</sup> . This value has been quite stable in the last few years despite strong and consistent growth in traffic.

> 9 Source : Annual statistics, _Port Autonome de Douala_

> 10 Source: DIT

8
states increases protection; protection decreases in farm income and as prices improve; imported commodities receive greater price supports; the greater the share of output exported, the greater the amount of price support. All these coefficients support some political economy model, though the precise connection is not made formally clear and the connection of the variables to any underlying theory is tenuous. Regardless, the collective evidence in favor of the influence of special interest is impressive.<sup>9</sup> The next two sections contain the empirical contribution of this paper. Data on the structure of lobbying by agricultural political action committees (PACs) is merged with data on agricultural protection in order investigate evidence of direct association between the two. The models discussed above all indicate that lobbying matters, whether lobbying involves money in exchange for favorable policy or whether it involves provision of information that is mutually beneficial to the lobbyist and policymaker. The detailed graphical analysis of the structure of lobbying spending undertaken in the next section does inform the debate about whether lobbying in the US agricultural sector is informational or of a quid-pro-quo nature. The formal econometric analysis that follows, however, does not distinguish between the two types of lobbying. Rather, it is motivated by the need to make the association between lobbying and protection explicit.

# **3. Graphical Analysis of PAC Contributions 1991-2000**

# _A. Data_

Raw lobbying data for the five congressional election cycles (for the 103<sup>rd</sup> through 106<sup>th</sup> Congress) between 1991-2000 were downloaded from the Federal Election Commission (FEC) website (www.fec.org). The data are in three relational data files: candidate information files (CN), PAC committee information files (CM), and files containing transactions between PACs and candidates (PAS). For each election cycle, aggregate contributions by each PAC to every candidate were computed from the PAS files, and then

> 9   While the three sets of models described up to this point emphasized varying degrees of pressure by farm interest groups, Paarlberg (1989) believes that they do not provide a complete understanding of U.S. agricultural policy to data.  What these theories miss, according to Paarlberg, is the government’s concern for poor economic conditions in the sector.  Such concerns motivated Roosevelt’s introduction of price support programs via the Agricultural Adjustment Act of 1933.  Those price supports are integral to U.S. agricultural policy to this day.

16
In a related article, Fitzgerald and Hallak[13] estimate the effect of factor endowments on the pattern of manufacturing specialization in a cross-section of OECD countries, taking into account that factor accumulation responds to productivity. The authors show that the failure to control for productivity differences across countries produces biased estimates of the Rybczynski coefficients. Their model explains 2 _/_ 3 of the observed differences in the pattern of specialization between the poorest and richest OECD countries.

Hakura[15] explores the role of differences in production techniques to explain the empirical failure of the Heckscher-Ohlin-Vaneck (H-O-V) model. The paper develops a 2x2 modified H-O-V model that relaxes the assumption of identical production techniques across countries. Using input-output data for six member countries of the European Community for the years 1970 and 1980, the paper shows that allowing for international technique differences significantly improves the predictive power of the H-O-V model.

In Redding[29], a country’s pattern of specialization at any point in time is characterized by the distribution of shares of GDP across industries. Its dynamics are represented by the evolution of the entire cross-sectional distribution of output shares over time. Redding[29] utilizes data on 20 industries in 7 OECD countries from 1970-1990. A comparison of GDP shares between 1970 and 1990 reveals substantial variation across sectors and countries.

Perhaps more importantly, Redding[29] concludes that in the short run, common cross-country effects such as technological progress are more important in explaining observed changes in specialization than factor endowments for the majority of the countries. Over longer periods, factor endowments become relatively more important, and in the infinite horizon, factor endowments account for most of the observed variation in specialization. This evidence is consistent with the idea that changes in relative factor abundance occur gradually and take time to affect the structure of production.

Overall, the factor proportions model provides a story about static and dynamic specialization around the world. Some evidence shows that technological differences across countries can produce similar patterns of specialization in spite of large differences in factor endowments (Schott[33]). Our model extends the standard factor-proportions theory to allow for technology differences across countries, thus introducing elements of the literature 4
savings range between 1,404 and 2,106 Rs., representing between 3.0 and 4.5 percent of household consumption.

These percentage values have roughly the same order of magnitude as those based on the per capita approach, contrary to expectations. The reason is that household consumption figure of 46,400 Rs. is only 64 percent larger than the per capita income value of 28,300 Rs. even though the average Indian household contains approximately 5 people.<sup>11</sup> This discrepancy partly reflects a growing divergence between measures of Indian household purchasing power based, alternatively, on survey data and the national income accounts.<sup>12</sup> However, since the survey data may provide a more realistic picture of household circumstances, the above savings percentages, which lie in the 3-6 percent range of household consumption, may be credible.

As noted above, this percentage range is derived using an estimate of _t_ based on late-1990's data, not on data for the 1990 city. However, if commuting costs and incomes grew by the roughly the same proportion over the period since 1990, then use of 1990 data would have yielded similar percentage values. As a result, the 3-6 percent range for welfare cost as a share of housing consumption expenditure is relevant for 1990. Finally, note that since these percentages represent the gains from removal of the FAR restriction, they capture the welfare cost of its presence.

A welfare cost of 3-6 of household consumption is substantial in size, being larger in magnitude than the measured welfare cost of some key distortions in Western economies.<sup>13</sup> Moreover, in a country like India, where vast numbers of people live on the edge of impoverishment, this welfare loss may represent the difference between poverty and non-poverty status for many households.

22
**Appendix Table 2: Determinants** **<u>of land sale and land purchase (simple probit)</u>**

||**Sale**|**Purchase**|
|---|---|---|
|Agricultural ability (technical efficiency)|-0.028**<br>(1.96)|0.051**<br>(2.00)|
|Household Size in 1982|-0.001|-0.001|
||(1.17)|(0.88)|
|No. of unmarried sons below 25 in 1981|-0.027<br>(0.76)|0.253***<br>(4.47)|
|Dynasty land endowment|0.032*<br>(1.96)|-0.179***<br>(3.39)|
|Landless dynasty dummy||0.142***<br>(3.94)|
|Value of total assets (log)|0.003|0.016**|
||(0.81)|(2.30)|
|Years since households’ independence|0.003***|0.009***|
||(4.08)|(7.43)|
|Village income growth rate from 1982-1999|-0.182*|0.020|
||(1.75)|(0.11)|
|Per capita land Gini in village|0.100***|0.048|
||(3.77)|(1.07)|
|Dummy for ST and SC castes|-0.025**|-0.026|
||(2.50)|(1.49)|
|No.of climatic shocks|0.026***|0.004|
||(3.01)|(0.31)|
|Employment guarantee scheme (EGS) 1982|0.027|-0.018|
||(1.47)|(0.55)|
|EGS in 1982×No. of climatic shocks|-0.010|0.001|
||(1.47)|(0.10)|
|Bank access 1982|0.048**|0.055**|
||(2.49)|(2.14)|
|Bank access in 1982×No. of climatic shocks|-0.019***|-0.003|
||(2.70)|(0.30)|
|Observations|4583|4583|

Robust z statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Regional dummies included throughout but not reported.

25
satisfied and obtain the private sector account as the residual that closes the historical flow of funds. Finally, we present the sector accounts in a concise matrix format.

# A1.1 Bxternal Debt and the Balance of Payments

Data on the balance of payments and external debt are commonly expressed in a single foreign currency. The current account of the balance of payments should be consistent with the national accounts. If the data are constructed jointly, as is the case in Turkey, no inconsistencies arise. The links between the capital account of the balance of payments, representing financing flows for the domestic agents, and the stocks of external debt as well as the net foreign asset position of the various sectors are somewhat more complicated.

We encounter four problems during the reconciliation of dollar denominated stocks of external debt, the net foreign asset position, dollar flows of the balance of payments, TL flows and TL stocks. First, the stock of external debt expressed in dollars does not only change because of new foreign borrowing but also because of changes in the cross-currency rates among the foreign currencies that constitute the stock. Hence, the stocks of debt need to be adjusted for the "cross-currency effects" in order to reconcile the capital flows from the balance of payments with the changes in the stocks. Second, the net foreign asset position of each of the domestic sectors does not correspond with its external debt position. This is the result of transactions among domestic sectors in foreign currency. For example, the private sector could either sell proceeds of exports or deposit them in the domestic 4
individuals who are 25-50 years old; similarly, the unemployment rate of the individuals who are 15-25 years old is three times that of the age group 25-50 (table 2). Youth employment rate in Croatia is about twice of the EU average.<sup>12</sup> Croatian youth who manage to find a job also suffer from low earnings – in average, the youth who are employed earn less than 2500 kuna per month, which is roughly 1000 kuna (or 30% in relative terms) less than those who are 25 years or older. With a high unemployment rate and a low earnings level, the youth in Croatia face a particularly difficult situation.

**Table 2: Employment rate, unemployment rate and monthly earnings by age groups in Croatia (2002-2004)**

|Agerange|Employment rate|ILO unemployment rate|Monthly earnings (kuna)|
|---|---|---|---|
|(15, 25)|24.87%|35.93%|2449|
|[25, 50)|72.41%|12.86%|3316|
|[50, 65)|42.05%|8.47%|3463|

As in many other countries, the individuals in Croatia with better education have higher probabilities to get employed, and the earnings level monotonically increases with their levels of schooling (table 3). The individuals with tertiary education not only have lower unemployment rate than those with less education, but also have a much higher earnings level if they get employed. For example, the unemployment rate of the individuals with university education and above is about 7.5%, and that of the individuals with basic education is 16%. The earnings level of the individuals with university education and above is in average three times that of the individuals with no school or uncompleted basic education, and about 70% higher than the individuals with lower secondary vocational education.<sup>13</sup>

**Table 3: Employment rate, unemployment rate and monthly earnings by education groups in Croatia (2002-2004)**

||Employment<br>rate|Unemployment<br>rate|Monthly<br>earnings<br>(kuna)|
|---|---|---|---|
|No school or uncompleted basic education|30.79%|12.47%|1675|
|Basic education|37.02%|16.05%|2256|
|Lower secondary vocational education|61.56%|16.03%|3115|
|Higher secondary education|31.68%|17.52%|3247|
|Two-year post secondary education|73.50%|8.69%|4313|
|University education andabove|82.80%|7.46%|5252|

12 According to the Eurostat statistics of “total unemployment rate by age group”, in 2002-2004, the average youth unemployment rate of the EU 25 countries is 18.7% and the EU 15 countries is 16.6%. Website http://epp.eurostat.cec.eu.int/portal/page?_pageid=1996,39140985&_dad=portal&_schema=PORTAL&scr een=detailref&language=en&product=sdi_ed&root=sdi_ed/sdi_ed/sdi_ed_emp/sdi_ed1432 13 The low employment rate/high unemployment rate of the individuals with “high secondary education” (grammar school) may be linked to their low participation rate, which is less than 40% compared with 7090% of the other individuals with secondary education and above. They represent less than 4% of the working-age population.

6
rural areas. Consistent with other studies, the results presented here suggest that increasing women’s access to education and skills, which is important in its own right, will not necessarily lead to a rise in FLFP. Gains will not be realized unless social norms around women’s (and men’s) work also change, and/or unless rural labor markets offer forms of employment that are acceptable and attractive for women and their families. Thus, programs such as the MGNREGS and the NLRM, which may be effective in reaching poorer women, may also have limited impact in terms of increasing FLFP. Policies should center both on promoting the acceptability of female employment and investing in economic sectors in rural areas that are more attractive in terms of female employment. Moreover, strategies to communicate the importance of women’s work should take into account the roles of women, husbands and in-laws in decisions related to FLFP.

# **References**

- Abraham, Vinoj (2009): _Employment Growth in Rural India: Distress-Driven?_ Economic and Political Weekly, Vol. XLIV, No. 16, April 18.

- Abraham, Vinoj (2013): _Missing Labor or Consistent ‘Defeminisation’?_ Economic and Political Weekly, Vol. XLVIII, No. 31, August 03.

- Bhalla, Surjit S. and R. Kaur (2011): “Labor Force Participation of Women in India: Some facts, some queries.” Asia Research Centre Working Paper 40, London School of Economics.

- Boserup, E. (2008): _Women’s Role in Economic Development, South Asian Edition_ . London: Earthscan.

- Chatterjee, U., R. Murgai, and M. Rama (2015) “Job Opportunities along the Rural-Urban Gradation and Female Labor Force Participation in India.” Policy Research Working Paper 7412, September 2015.

- Eswaran, M., B. Ramaswami and W. Wadhwa (2011): “Status, Caste, and the Time Allocation of Women in Rural India.” _Indian Statistical Institute Discussion Paper_ 11-12.

- GOI (2011): “Employment and Unemployment Situation in India 2009-10.” NSS Report No. 537(66/10/1), NSS 66th Round, National Sample Survey Office, MOSPI, New Delhi.

- Himanshu (2011): _Employment Trends in India: A Re-examination_ . Economic and Political Weekly, Vol. 46, No. 37, September 10.

36
with the findings for the United States (Acharya and Steffen, 2020; Chodorow-Reich et al., 2021; Greenwald et al., 2021). But three months after the pandemic starts, the loan growth rate to mega firms starts decreasing and credit instead flows to SMEs and large firms. In sum, year-to-year overall credit growth during 2020 is explained exclusively by guaranteed loans to smaller firms. This contrasts strikingly with the collapse in credit during the 1998 Asian crisis and the 2009 subprime crisis, when the public credit guarantee program in Chile was negligible (Didier et al., 2021).

A sizable share of firms (almost 25% of eligible firms) obtain guaranteed loans by December 2020.<sup>13</sup> The adoption happens right in the program’s first few months (Figure 3). This constitutes a large take-up compared to other countries that implement similar public credit guarantee programs during the pandemic. For example, in Peru, Colombia, and Mexico, 14%, 16%, and 23% of eligible firms use the credit program, respectively.<sup>14</sup> The low take-up is not restricted to developing countries; only 16% of firms use the 2020 public credit guarantee program in Italy (Core and De Marco, 2021). The employment program in Chile is smaller in value than the credit guarantee program, but it is also used by a significant fraction of firms, more than 16% of active firms by December 2020 (Figure 3). Around 33% of all active firms in the economy participate in either the credit or the employment program, and almost 7% of all firms participate in both programs.

In terms of applications, approvals, and usage within the active firms sample, 156,847 firms apply for credit guaranteed loans by December 2020, corresponding to 36% of all the eligible firms. Of all the loan applications, banks approve loans for 111,205 firms, indicating a high approval rate of 88%.<sup>15</sup> But not all firms that receive an approval end up using the program. In the estimations, we distinguish between approvals and usage to construct separate dummy indicators.

The credit guarantee program is used by banked and unbanked firms: 61% of the firms within the active firms sample that receive a guaranteed loan are banked and 39% are unbanked. This indicates that the program has an important effect on financial inclusion, by providing bank credit to a significant number of firms with no previous bank debt. Banked and unbanked firms receive 92% and 8% of the total value of guaranteed credit, respectively. Regarding size, the program is allocated primarily to smaller firms: 96% of the firms using

> 13The 25% take-up is based on the active firms sample. The take-up is higher (35%) when using the selection and leverage model sample.

> 14This information is obtained from reports by the Central Reserve Bank and the National Institute of Statistics and Informatics of Peru, the National Guarantee Fund of Colombia, and the Government of Mexico.

> 15The approval rate is calculated over 126,524 firms that are not only eligible but also provide the correct paperwork to process the guaranteed loan application.

12