rafmacalaba/data-use-mentions-extended huggingface dataset:
and mode choice. Looking at the reverse sense of causation (i.e., from traffic to economic outcomes), several papers point to externalities from cars (mainly congestion) as a significant bottleneck for economic activities. There is empirical evidence for the US that congestion hinders employment growth (Boarnet, 1997), lowers productivity (Fernald, 1999), shrinks output growth (Hymel, 2009) and reduces income (Jin and Rafferty, 2017).<sup>3</sup> The economic effects of congestion are found to be stronger in vehicle-intensive industries (e.g., manufacturing) relative to other industries, as documented for the US (Fernald, 1999) and the UK (Graham, 2007). These findings, however, have been challenged by a recent study, which reexamines US metropolitan area data over 30 years, and concludes that the impacts have been overestimated (Marshall and Dumbaugh, 2018).

By generating information on people, places, and activities at high spatial and temporal scales, big data has the potential to shed light on the causes and consequences of both mobility and congestion. Regarding the causes of congestion, a recent paper exploits vehicle counts from aerial imagery to document the impact of ride-hail services on congestion in New York City (Mangrum and Molnar, 2018). To our knowledge, the and of have not been assessed productivity employment consequences congestion yet using big data. As for mobility, several studies use big data to relate it to productivity and employment outcomes. For example, Toole et al. (2015) use CDR data to study the reciprocal links between employment status and individual mobility in two undisclosed European countries. In the first country, the authors focus on the closure of a large manufacturing firm and measure the reduction in the intra-urban mobility of laid-off workers inferred from changes in geo-referenced mobile phone activity. Conversely, the authors apply IA techniques in the second country to estimate aggregate unemployment rates from individual users’ mobile phone behavior. Another study uses a
of differing intensity, ranging from protests and riots to lethal attacks.

# **Food price inflation**

Food price inflation can directly cause food insecurity by raising the cost of living for vulnerable households (Baffes et al., 2008; Concei ̧c ̃ao and Mendoza, 2009). Rising prices can also signal whether fiscal space is adequate or tightened, for example due to episodes of conflict (Adam et al., 2008). Over longer time horizons, price uncertainty may lead businesses to postpone investments, see (Koomen et al., 2015; Andr ́ee et al., 2017) for the specific case of agricultural investments. We use monthly sub-national food price inflation estimates developed by Andr ́ee (2020a) to capture these important dynamics.<sup>6</sup>

> 5The impact on the forecasting performance is insignificant but the choice results in larger models. Note that, under suitable conditions, a finite-order moving average approximation of a lower-order autoregressive process remains reasonably accurate. Since innovations can be substituted by exogenous variables by defining a process for lower-level constituents, stable autoregressive processes may also be approximated using a sufficient number of lags of relevant exogenous variables.

> 6The paper assembles sporadically available food price data collected by the World Food Programme, and uses a stochastic imputation that leverages unobserved cross-correlations between intermittent market-level price time series of individual food items to impute price data for up to six staple foods for all markets and months in a given country. These are aggregated into a simple equally-weighted geometric price index and interpolated geographically using inverse distance weighting.

6
directly linked to the extent to which CfW income is used to enhance the productive potential and overall resilience of targeted households. It is on this dimension that some of the most interesting program impacts emerge, despite the fact that the CfW program’s emphasis was on temporary employment rather than on boosting the productive capacity of poor households.

First, there were positive impacts on savings. Among treated households, participation in informal savings groups ( _osusu_ ) increased by 16 percent (4 percentage points; Table 10, columns 2). However, there were no discernable impacts on the likelihood of having a formal savings account. Furthermore, the result was driven primarily by the impact in rural areas, as there was no statistically significant change observed in urban areas. The total monetary value of reported savings also rose substantially among rural households (by about 25 percent).

Various types of investments were examined, and a pattern emerges of increased investment in homes and businesses among treated households (Table 10, columns 6‐9). Investments by treated households in home improvements rose by 33 percent.<sup>29</sup> There was also a 39 percent expansion in investments in existing businesses in rural areas. These findings validate the argument that PW programs may promote resilience against future shocks.

> 28 Higher absenteeism may signal higher drop‐out rates in the medium term given that the endline survey was conducted in July– August and the new school year starts in September. 29 This is consistent with findings from an unconditional cash transfer program in Kenya where treatment households increased their ownership of metal roofs appreciably (Haushofer and Shapiro 2013).

14
exogenous trade shock” may have influenced Vietnam’s labor market, including its impact on migration patterns.

The contribution of this paper to the literature is threefold. First, the paper particularly focuses on the role of foreign firms in creating “formal’ job opportunities (hereafter “foreign job opportunities”) as a determinant of migration. As a proxy of the probability of obtaining a job in

> 5 These eleven destinations were selected based on the high levels of _in_ -migrants in the 1999 census (GSO, 2005).
|119.4|
|Plastic Goods|139,324|116,606|119.4|
|Leather and Leather Products|48,243|42,537|113.4|
|Stone, Clay and Glass Products|546,969<br>|427,728<br>|127.9<br>|
|Metal Products|945,936|523,212|180.8|
|Machinery<br>(except<br>electric)|335,683|530,119|63.3|
|Electric Machinery and Equipment|<br>302,590|238,972|126.6|
|Transport<br>Equipment|466,064|276,508|168.6|
|Miscellaneous<br>Manufactures|241,545|106,406|227.0|
|Total Manufacturing|6,939,421|4,839,253|143.4|

Source: Population census figures from IBGE (1983). Industrial census figures **from** IBGE (1984).
<!-- Start of picture text --> Figure 5.7: Comparisons between LAYS and Schoellman (2012)’s returns‐based measures<br>Returns to schooling in the US labor market Human capital derived using Schoellman’s<br>for immigrants from different countries approach applied to Barro‐Lee data versus<br>(Schoellman 2012) versus learning‐per‐year LAYS<br>relative to Singapore<br>0.5 0.6 0.7 0.8 0.9 1.0<br>TIMSS: Learning per year rel. to Singapore 0 5 10 15<br>LAYS using TIMSS<br>Source: Authors’ calculations, using US labor‐market returns data from Schoellman (2012) and LAYS analysis<br>using TIMSS.<br>Notes: For ln(h), see text for details. For LAYS, numeraire country is Singapore. Illustration for case where<br>learning starts at Grade 0 for TIMSS. For relative learning per year: Correlation coefficient is 0.23 and Spearman<br>rank correlation is 0.29. Excluding just three countries (Kuwait, South Africa, Rep. of Korea) increases these<br>correlations to 0.65 and 0.64, respectively. For LAYS: Correlation coefficient is 0.61 and Spearman rank<br>correlation is 0.68. Excluding the same three countries increases these to 0.84 and 0.85, respectively.<br>0.15 3.5<br>3.0<br>0.10 2.5<br>2.0<br>0.05 1.5<br>ln(h) using Schoellman (2012) approach<br>1.0<br>Returns to schooling in US labor market (Schoellman 2012)<br>0.00<br>0.5<br><!-- End of
|No|
|Observations|574|574|574|574|
|R-squared|0.466|0.481|0.489|0.473|

Notes: Table shows results of a regression of the annualized log change in poverty headcount on the annualized log change in survey means. Sample consists of all consecutive spells between 1981 and 2021, dropping spells with baseline and endline poverty rates lower than 2 percent. Robust standard errors in brackets. ***: Statistically significant at 1%; **: Statistically significant at 5%; *: Statistically significant at 10%.

21
the sample period, with or without export bans in place. On the other hand, the median price dispersion between markets across the border appears to be responsive to export bans. This lends validity to the key assumption that underpins our empirical strategy: export bans do not affect within-country price dispersions. We formally test this assumption later in our regression analysis.

An essential dimension of the export ban impact that we examine is the role of production conditions in mediating the impact on prices. To do so, we use national maize harvest data from the Food and Agriculture Organization’s (FAO) FAOSTAT. We convert annual maize production into per capita maize output estimated from annual maize production and population data. Figure 4 presents the price dispersion along with the deviation of annual maize production per capita from the long-term average (1960–2017) for each country pair (MW-ZM, MW-TZ, and MW-MOZ). The overall patterns indicate that the relative price of maize is lower in Malawi during periods when the domestic harvest is relatively good.

On the other hand, poor maize harvest periods in Malawi are associated with higher relative prices. For instance, we observe a consistently high, positive relative price dispersion (that is, higher relative prices in Malawi) during 2015–2016 when Malawi’s maize production dropped significantly below the long-term average maize harvest per capita. On the other hand, when Malawi’s maize harvest is more favorable, for example, in 2010–2012, we see that the relative price dispersion tends to fall, that is, relative prices in Malawi fall. These patterns of relationships between harvest realizations and the relative price dispersion appear to hold across all the three country pairs shown in Figure 4. In the regression analysis, we examine in detail the role of harvest in mediating the effect of export bans on price dispersions as well as domestic price levels.

12
significantly higher in language and math tests in the early years of primary school compared to their peers who enrolled only in playgroup or kindergarten. In addition to the sequence of early education service participation, we found that the timing of entry into playgroups and kindergartens was an important predictor of children’s subsequent learning outcomes. Children who enrolled in playgroup at age 3-4 followed by kindergarten at age 5-6 performed significantly higher in language and math tests in primary school than their peers who enrolled in other early education pathways. Finally, over and above the sequence and timing, we found that duration of early education was a significant predictor of children’s math test scores in primary school, as those who enrolled in at least two years of playgroup and kindergarten scored significantly better than their peers who enrolled for shorter periods of time.<sup>5</sup>

# **8. Costs of various early education pathways**

The Indonesian government is currently weighing alternatives for investing smartly in early childhood education to ensure that all young children enter primary school ready to learn.

In this section, we estimate the cost of various early education pathways to inform public resource allocation decisions for early childhood education in Indonesia. We do not have causal estimates of ECED pathways on primary school test scores. Thus we cannot calculate actual estimates of the different pathways. Despite this, we feel that our controls and robustness checks validate the presentation of these estimates as cost-effectiveness estimates.<sup>6</sup> We do so by calculating the marginal effect at representative values (MER) of the ECED timing variable at discrete values of ECED duration (shown in Tables 5c). We used the average of the language and math assessments for our effectiveness measure as these two tests were aligned with what children were expected to know in the early years of primary school.

The cost of each early education pathway was drawn from the Indonesia ECED Project evaluation data (World Bank 2014) and the 2012 _Nomor Unik Pendidik Dan Tenaga Kepandidikan_

> 5 As
,053|1,350,561|

Source: Own elaboration from data from Puertos del Estado (some years).

# **5.1.2 The factors**

In order to perform their function as infrastructure service providers, port authorities use three productive factors (see table 3 for a quantitative summary). First, they employ the **labor** factor, approximated by the mean number of employees of port authorities, where not only administrative staff is included but also more specialized technical employees. The nature of the activities performed by this productive factor (supervision of port facilities, control of port operations, port promotion, management of garbage, etc.) makes labor a very important resource.

Another important factor consists of the **berths** necessary for ships’ docking. Berths have been measured in linear meters and we have considered not only the berths owned by port authorities but also by private people (for example, shipyards). Only those berths not reaching a 4-meter depth have been excluded since they are places basically intended for water-sports activities.

Finally, the third factor is the **land area or surface** that has been measured in square meters and includes warehouses, roads and the rest (gardens, buildings, etc.). This area encompasses all port facilities, i.e. property owned by port authorities as well as property assigned under administrative concessions to port companies.

**<u>Table 3: Factor means per port authority: 1990-2002</u>**

|**Port Authority**|**Berths**<br>**(meters)**|**Surface**<br>**(squared meters)**|**Labor**<br>**(employees)**|
|---|---|---|---|
|B. Algeciras|10,239|2,486,245|
-12-

We cite below guidelines of the United Nations Statistical Office for defining value added according to "census" concepts and "national<sup>accounts"</sup> concepts. The "national accounts" concept is designed to<sup>avoid duplication</sup> for the economy as a whole, and the "census" concept is concerned<sup>mainly to</sup> avoid duplication of output within the industrial sector. This "census" concept uf value added has very little legitimacy as a construct for avoiding duplication because manufacturing has very big inputs from the rest of the economy. There are large purchases of agricultural materials<sup>for food</sup> processing and large and increasirg purchases of services such as advertising, accountancy, cleaning, transport, etc. In fact, one of the reasons why modern economies are apparently increasingly concentrated on services, is that manufacturers now purchase these services externally whereas they previously produced them within their enterprises.

For these reasons, the old "census" definitions of value added are becoming increasingly anachronistic. Furthermore, the definitions of census value added vary between countries. This is not so important if the census contains enough information to permit estimation of value added<sup>on national</sup> accounting definitions, but unfortunately the US census information is not adequate for this, as we shall see below. For our purposes, the most useful value added concept is that used in the national accounts and in particular what we have called the "former national accounts" concept, where deduction is made at the industry level for inputs of intermediate financial services (see also chapter IV). In most countries, these intermediate financial services are usually deducted at a global level for all industries combined.

In practice in the detailed analysis of chapter III we had to settle for comparisons using the US Census of Manufactures definition of value added and switch to our preferred concept only at the level of manufacturing branches and manufacturing as a whole. In
limited retail investors and few distribution channels (often just banks)?

This study is aimed primarily at government authorities in the Philippines (mainly the Ministry of Finance and its Bureau of Treasury, both as the issuer of sovereign debt and as a regulator and policymaker) and elsewhere, as well as members of the development finance community interested in learning how technology can improve management of government debt and increase public access to government securities.

As with any analysis of this type, some important provisions should be kept in mind. First, the Expanded Small Investors Program was just being launched when this study was conducted, so the analysis had to rely on data from the original program and tests of the new one. Second, more research is needed on the characteristics of the small investors participating in the program. Such research was beyond the scope of this study.

> 6 By contrast, in industrial countries such as Canada and the United States the main motivations for such systems are to lower the costs of distributing small-value securities and to make it more convenient for investors to buy them, thereby increasing savings.

21
patterns of compliance in allocating scarce enforcement resources, leading to a correlation between past and present import refusals, even after controlling for other factors. We refer to this as the “own reputation” effect. In addition, the structure of the US import refusals system is suggestive of two other effects that might be in operation. One is a “sector reputation” effect, by which we mean the possibility that import refusals for a particular product are associated with past import refusals affecting closely related products. The second is a “neighbor reputation” effect, namely the possibility that import refusals affecting a given product from one country might be more likely if neighboring exporters of the same product have a history of non-compliance. In the remainder of this section, we outline the data and model we will use to test for the existence of all three effects.

# **3.1 US Import Refusals Data**

This paper uses a new dataset of US import refusals for the period 1998-2008. It extends the data used in Jouanjean (2011), and covers HS chapters 3 (fish and crustaceans), 7 (vegetables), 8 (fruits), 9 (coffee, tea, mate, and spices), 16 (preparations of meat, fish, and crustaceans), and 20 (preparations of vegetables, fruits, and nuts). The reason for focusing on these sectors is that they correspond to FDA Industry Codes 16 (fishery and seafood products), 20-22 (fruit and fruit products), and 24-25 (vegetables and vegetable products). Refusals in these sectors accounted for over 50% of all FDA import violations over the 1998-2004 period (Buzby et al., 2008). We are therefore confident that by focusing on these three sectors, we are capturing an important part of overall import refusal activity in the US. This subsection describes the US import refusals regime in more detail, focusing on the way in which the data used here were collected.

Since 1998, the FDA has implemented an
the role of a healthy banking sector in the development of the country (Jayaratne and Strahan 1996; Black and Strahan 2002; Levine 2005; Beck, Demirgüç-Kunt, and Levine 2007).

> 2 Many interpret Islamic law to prohibit dealing with _riba,_ often translated as usury. In practice this includes prohibiting receiving interest on savings, as well as paying interest on borrowing. Historically the Jewish and Christian faiths also prohibited usury ( _Bible, Exodus 22:25_ 2009; Moehlman 1934).

> 3 See Appendix Table A1. Data on religious populations around the world are taken from the World Religion Dataset (Maoz and Henderson 2013), while data about financial inclusion come from the World Bank’s global findex database (Demirgüç-Kunt et al. 2015). While both borrowing from and saving at banks is lower, borrowing from stores (which is more limited but also more likely to be Sharia-compliant) is higher in Muslim countries.

2
<!-- Start of picture text --> 120<br>100<br>80 37% 38% 34%<br>41%<br>60<br>30%<br>40<br>11%<br>20<br>0<br>Low income Lower middle income Upper middle income<br>Primary GER Secondary GER Bars: Host country averages Markers: Refugee averages<br><!-- End of picture text -->

# _Source: UNHCR (2023c); UIS (n.d.)_

Direct comparisons between refugee and host country GERs are possible for 58 refugee situations worldwide. These comparisons reveal that refugee GERs are higher than host country GERs in 15 countries, with 14 of these countries LICs or MICs. In fact, 33 percent of LICs for which comparable GER data is available (seven out of 21 countries) had lower levels of education access among the host population compared to the refugee population. For some LICs, lower national enrollment rates were due to national fragility/conflict affecting the host population’s access to education, as was the case in Syria, Burundi and the Central African Republic. For other LICs, high GERs among both refugee and host populations mean that differences between the population are slight and that both experience high levels of education access, as was the case in Nepal and The Gambia. While for the majority of LMICs, higher rates of refugee enrollment were often due to teacher and/or classroom shortages in host country education systems, which affected host community education in Angola and Tanzania. For those UMICs and HIC where refugee GERs surpassed national GERs, refugee numbers are typically very low, with school-age refugee populations often in the tens or low hundreds, making comparisons between host and refugee GERs unreliable. As such, while refugee GERs are higher in some countries, these differences are typically small or the result of extreme circumstances, such as state fragility or significant constraints to state education.
Figure 5. Correlations between Technology Readiness and Attitude Traits <!-- Start of picture text --> Technology Readiness Technology Readiness<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Power Distance Individualism<br>Technology Readiness<br>Technology Readiness<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Indulgence<br>Long-term Orientation<br>Technology Readiness Technology Readiness<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Masculinity Uncertainty Avoidance<br>0 0<br>50 50<br>100 100<br>150 150<br>0 0<br>50 50<br>100 100<br>150 150<br>0 0<br>50 50<br>100 100<br>150 150<br><!-- End of picture text --> Source: World Economic Forum, The Global Competitiveness Index dataset 2007-2017; and Hofstede Insights.

12