rafmacalaba/data-use-mentions-extended huggingface dataset:
Sicular and Zhao (2002) explore the question of rural-urban inequality in greater detail for nine different provinces using the China Health and Nutrition Survey (CHNS). The authors conclude that the apparent labor market distortion is about 42% of the rural-urban labor income differential and 48% of the hourly earnings differential. 4 When applied to the average wage differential, this amounts to an _ad valorem_ rate of apparent transactions “tax” on rural wages of 81%.5 We model these transactions costs as real costs that are assumed by the temporary migrants. Of course these migrants are heterogeneous and the extent of the burden varies widely. Those individuals who are single, and live close to the urban area in which they are working are likely to experience minor inconvenience as a result of this temporary migration. We expect them to be the first to migrate ( _ceteris paribus)_ in response to higher urban wages. On the other hand, some migrants have large families and come from a great distance. Their urban living conditions are often very poor and it is not uncommon for them to be robbed on the train when they are returning home after their work. For such individuals, the decision to migrate temporarily is likely to be a marginal one – and one which they may not choose to repeat. With this heterogeneous population in mind, we postulate a transactions cost function that is increasing in the proportion of the rural population engaged in temporary work. This transactions cost function has a simple, constant elasticity functional form, which begins at the origin and reaches the observed wage gap (adjusted for transport and living costs) at the current level of

> 3 In this model we assume that the elasticity of land with respect to on-farm labor is unitary

> 4 There are likely other, unobserved factors inducing this rural-urban wage differential, in which case estimation of the labor market distortion via subtraction of known factors is biased in the direction of overstating the _hukou_ -related distortion. Therefore, it is useful to also estimate the direct impact of
Chaudhuri (2002, 2003) and modeled by Gunther & Hartgen (2009) following Hox (2010) and used by Skoufias et al. (2023) for Ethiopia and Rud and Robayo-Abril (2023) for El Salvador.

However, our analysis differs and expands past studies in various ways. It explicitly considers climate disaster risk as an aggregate shock by exploiting natural disaster and extreme weather data. Doing so allows us to establish the proportion of households in different countries within the region that are likely to become poor due to structural characteristics (poverty-induced -PI) or due to the occurrence 3
, to construct domestic trade for services, we employ average data over the years 2010 to 2015 and we repeat the above steps with these data for the services sector.

We also employ several other data sets in addition to ITPD-E. Data on the standard gravity covariates for our gravity estimations come from the Dynamic Gravity Dataset (DGD) of the U.S. International Trade Commission, and we refer the reader to Gurevich and Herman (2018) for further bourg (LUX), Latvia (LVA), Morocco (MAR), Moldova (MDA), Mexico (MEX), Macedonia (MKD), Malta (MLT), Myanmar (MMR), Montenegro (MNE), Malaysia (MYS), Nigeria (NGA), Netherlands (NLD), Norway (NOR), New Zealand (NZL), Oman (OMN), Pakistan (PAK), Peru (PER), Philippines (PHL), Papua New Guinea (PNG), Poland (POL), Portugal (PRT), Qatar (QAT), Romania (ROU), the Russian Federation (RUS), Saudi Arabia (SAU), Singapore (SGP), El Salvador (SLV), Yugoslavia (SRB), the Slovak Republic (SVK), Slovenia (SVN), Sweden (SWE), Thailand (THA), Turkmenistan (TKM), Trinidad and Tobago (TTO), Tunisia (TUN), Turkey (TUR), Taiwan, China (TWN), Ukraine (UKR), the United States (USA), the RepÃoblica Bolivariana de Venezuela (VEN), Vietnam (VNM), the Republic of Yemen (YEM), South Africa (ZAF), and Zambia (ZMB).

> 16In the robustness analysis, we reproduce our main estimates after replacing all missing bilateral trade flows with zeros. These results are very similar to our main estimates.

14
it comes to the digitalization of the informal sector, mobile money is the predominant avenue (Islam and Jolevski, 2019). We see widespread adoption of mobile money among informal businesses as well. Between 40% and 50% of informal businesses in India, Mozambique, Zambia, and Zimbabwe, and over 80% in Somalia report using mobile money in their current operations. In contrast, only 7% of enterprises in Iraq and less than 3% of informal businesses in Lao PDR use mobile money, as the currently prevalent M Money offered by Lao Telecom was not operational at the time of the survey. Data on the use of mobile money by formal micro-enterprises is available only for India, Zambia, and Somalia.<sup>15</sup> Interestingly, micro-enterprises appear more likely than their informal counterparts to use mobile money in India and Zambia but less likely to do so in Somalia.<sup>16</sup>

# **Operations**

The ability of informal business owners to set their own hours and work in a largely unregulated setting has been provided as a potential reason for the sector's persistence in the developing world (Maloney, 2004; Loayza and Rigolini, 2011). To investigate these rationales, the Informal Sector Enterprise Surveys first ask whether the business was started by its current owner. Those that did are then asked a battery of questions on potential reasons for starting an informal business. <mark>The vast majority of informal businesses (over 90%) were started by their current owner, which, interestingly is slightly less than formally registered micro-enterprises (95%), which are likely more transferable across owners due to their legal status. Female informal business owners are more likely to have started the businesses: 94% compared to 88% for men. The shares of informal businesses started by their current owner vary somewhat across cities, ranging from 56.2% in Varanasi (India) to 99.1% in Pakse (Lao PDR).<
|**Division**|**District**|**Stratum**|**Quarter 1**|**Quarter 2**|**Quarter 3**|**Quarter 4**|**Total**|
|---|---|---|---|---|---|---|---|
|55|94|18|4|3|4|4|15|
|55|94|17|5|6|5|5|21|

Table 9 shows the final allocation of PSUs across the 20 sampling strata. The four main CCs each have less than 36 PSUs allocated as these areas have only one sub-stratum. This means that the final sample might not provide reliable poverty and welfare statistics at the division level for urban, rural, and CCs, but the sample will provide reliable poverty and welfare estimates at the district level. This illustrates the trade-offs that household survey sampling experts must assess when designing a sample for a survey with multiple objectives. For the HIES 2016/17, BBS decided to prioritize the production of reliable annual district-level poverty and welfare statistics over annual division-level poverty and welfare statistics for rural, urban, and CC. This decision stems from the fact that district-level statistics correspond to clear administrative policy making units, while division-level statistics do not.

Table 10 shows the final allocation of PSUs for urban and rural areas.<sup>21</sup> The share of urban PSUs in the HIES 2016/17 sample is slightly higher than 30 percent, while only 23 percent of Bangladesh’s population lives in cities, according to the 2011 Census of Population and Housing. This is explained by the manual adjustments conducted during the allocation of PSUs. As described above, we purposely designed the survey to oversample urban PSUs to help BBS study the relationship between living standards and rapid urbanization.

> 21 BBS has traditionally
capital requirements. Using bank-level data, we show bank solvency risk is more sensitive to regulatory capital ratios when greater stringency in the definition of Tier 1 capital is imposed by domestic supervisors. Second, the GFC led to unprecedented government interventions to rescue distressed banks. Deposit insurance systems around the world have become more generous, expanding in both scope and coverage. These expansions may have reinforced investor expectations of government support for financial institutions, thus reducing the long-term incentives of depositors to monitor and discipline banks.

Finally, bank supervision became stricter and more complex after the crisis. But supervisory capacity did not improve to match the greater complexity of bank regulations. These capacity constraints can significantly hinder the enforcement of new reforms that were put in place after the GFC. The availability and quality of information disclosed as part of bank supervision has also not improved significantly. As information is key to effective public and private monitoring, greater information asymmetries may undermine market discipline in the long run.

36
effects and the time fixed effects. Standard errors will be clustered at the Boma level to allow for within-cluster correlation.<sup>6</sup> β1 is the difference-in difference estimator.

# **Household Resilience: Triple Difference**

To identify factors that make households resilient to the inflation shock, we estimate the following triple difference equation where hi is a potential resilience factor. i is a potential resilience factor. is a potential resilience factor.

triple difference equation where hi is a potential resilience factor. i is a potential resilience factor. is a potential resilience factor. i = β0 + β1 (post) + β2 (postt *inflationst) + β3 (postt *hi) + β4 (postt *inflationst *hi) + βX i + γs +δt + i _(3)_ βYY3 is the triple difference estimator. In this triple difference setting, βii 1 is the diff-in-diff estimate XX ii εε ii for the reference group (h=0). It captures average differential change in _y_ from the pre- to posttreatment period for the reference group in the treatment group _relative_ to the change in _y_ for the reference group (h=0) in the untreated group. The total treatment effect for both groups is β1 + β3.

# **Conflict indicator**

Given the ongoing conflict in South Sudan, conflict will likely be one of the confounding factors affecting household livelihoods. We control for this by including a conflict variable in the regressions. We construct an exogenous conflict variable based on conflict event data from the Armed Conflict Location & Event Data (ACLED)<sup>7</sup> for the period of our study. The data set codes the exact location of all political violence incidents that were reported during this time. We use proximity to a deadly conflict event to generate a continuous conflict exposure variable (i.e. the number of fatalities).

> 6 Default standard errors can greatly overstate estimator precision. Instead, if the number of clusters
term, ∆ _hp_ .

> 34At: https://oxfordtax.sbs.ox.ac.uk/cbt-tax-database.

> 35The exceptions are Cyprus, Malta, Latvia, and Lithuania.

> 36Centre d’Etudes Prospectives et d’Informations Internationales, see Head and Mayer (2014).

> 37Capital account openness is measured using the Chinn-Ito index, which is standardized between zero and one (Chinn and Ito, 2006), with higher values indicating greater openness. Trade openness is defined as exports plus imports as a share of GDP. The inflation rate is the annual percentage change in the consumer price index.

> 38Additional summary statistics (by host country) are provided in Appendix tables C.1 and C.2. The former summarizes the main tax variables, while the second summarizes the average, minimum, maximum number of parent countries per host country in a given year. The first and last years in the dataset with available FAI data are also listed.

18
still practice subsistence agriculture: the rates of market participation among farming households in general and maize-producing households in particular are 42 and 15 percent, respectively.<sup>16</sup> The inconsistent agricultural performance has direct implications for living standards, given the predominantly rural nature of the country and its heavy reliance on agriculture.

Poverty remains widespread and persistent, particularly among female headed households. Based on the data from the Second Integrated Household Survey (IHS2) 2004/05 and the Third Integrated Household Survey (IHS3) 2010/11, the national absolute poverty rate of 52.4 percent in 2004/05 declined only marginally to 50.7 percent in 2010/11. The trends in rural poverty followed a similar pattern: a rate of 55.9 percent in 2004/05 vs. 56.6 percent in 2010/11.<sup>17</sup> Focusing on the gender dimensions of poverty, while the absolute poverty rate among male-headed households was estimated at 49 percent in 2010/11, the comparable figure among female-headed households was 57 percent. In an effort to combat poverty and boost national food security, the Malawian Government has embarked on an ambitious annual fertilizer and seed subsidy program known as the Farm Input Subsidy Program (FISP), starting with the 2005/06 agricultural season. During the 2009/10 agricultural season (the reference agricultural season for over 75 percent of our sample), close to 50 percent of the farming household population is estimated to have participated in the program.<sup>18</sup> Stagnant poverty levels raise questions on the effectiveness of the FISP in alleviating poverty and food insecurity in a sustainable fashion, which should be subject to further empirical investigation.<sup>19</sup>

# 3.2.  DATA

This study uses data from the Third Integrated Household Survey (IHS3), collected from March 2010 to March 2011 by the Malawi National Statistical Office, with support from the World Bank Living Standards Measurement Study - Integrated
would find that FDI has a _negative_ effect on growth (the point estimates would be between -0.25 and -0.3).

However, we obtained this correction by assuming that the exclusion restriction is completely violated. If we relax this extreme assumption and assume that half of the real external shock affects GDP growth through our instrument, we get that _πγ_ 1<sup>=</sup><sup><u>1</u></sup> 1<sup>_<u>.</u>_</sup> _._<sup><u>1</u></sup> 2<sup>=0</sup><sup>_._45whichisexactlythe</sup> difference between the OLS estimates of columns (1) and (3) of Table 4 and the IV estimates of columns (2) and (4). We thus think that the OLS coefficients provide a reasonable estimate of the causal effect of FDI on growth.

# **4.3 Panel IV Regressions**

In our last exercise, we use our instrument to estimate a panel data model that uses overlapping 10-year growth spells. We estimate two types of models: one that only includes year fixed effects (Columns 1 and 2 of Table 5) and one that includes both year and country fixed effects (columns 3 and 4). As before, we estimate the model for the same sample using both OLS (columns 1 and 3) and TSLS (columns 2 and 4).

When we only include time fixed effects, we find that FDI inflows are associated with higher growth and, as in the cross-sectional estimates, the effect is about three times larger in the IV estimations. We also find high values for both the Cragg-Donald Wald F-Statistics and the Kleibergen-Paap rk LM statistics. We are thus confident that, for the model of column (3), weak instruments or underidentification are not an issue. The
examining the factors and social consequences of privatization. When analyzing the development of the housing market, the Section 2 uses waves two and four (1993 and 1996).

- **_The Vladitir_** _and Gorodets Longitudinal Household Survey on Housing Allowances_ was conducted for the Urban Institute, Washington, three times: in October 1994, November 1995 and December, 1996. Using random samples, 300 households in Vladimir and 500 households in Gorodets were polled annually. The samples differed each year but were constructed in a uniform fashion<sup>21</sup> . The September, 1994 study, which dealt with housing allowances, provided a framework for an analysis of the need and demand for housing under the technique developed for the USAID-

> 21 To draw the samples for Vladimir, we also used statistics obtained for EHDP Vladimir Household Survey, which was conducted as part of the preparations for the World Bank Enterpise Housing Divestiture Project
them found clustered near the large industrial park that transects the city (shown in purple in Figure 6). Upon closer inspection, small pockets of irregular settlements are detected directly adjacent to large warehouses and transit infrastructure.

In order to quantify these observed trends, the classified data layer was summarized by distance to the city center. Figure 6 shows how the coverage of residential (regular and irregular) and commercial/industrial land changes as one moves from the center to the periphery of the city. As expected, the first few kilometers of built‐up land around the CBD is dominated by commercial/industrial and residential regular, with a noticeable low percentage of irregular residential. It is not until 3 to 4 kilometers beyond the city center that residential irregular begins to appear, it then peaks between 6 to 9 kilometers with residential irregular occupying roughly 18% of the built‐up land.

In contrast, residential regular neighborhoods are spread throughout the city. The residential regular class captures neighborhoods with moderately dense housing units, some lawns/trees and gridded or well organized road network. Given this rather broad/encompassing definition, the class includes both attached homes (townhouses) within the city center that have a gridded road, as well as large development single family houses near the periphery. While there is a slight increase in residential

> 33 UN Habitat (2010) Dar es Salaam: A Rapidly Urbanising Informal City.

> 34 CIUP, 2004. Dar es Salaam Infrastructure Development Programme. Community Infrastructure Upgrading <u>Project.</u>

14
# _1.4_ Omnibus balance tests of localities’ sample

||T0 vs T1|T0 vs T2|T1 vs T2|
|---|---|---|---|
|Yucatán (dummy)|0.196|0.541|-0.060|
||(0.767)|(0.730)|(0.715)|
||[0.799]|[0.459]|[0.933]|
|Marginalized Locality Index 2010|-1.261|-0.886|0.793|
||(0.975)|(0.980)|(1.000)|
||[0.196]|[0.366]|[0.428]|
|Proportion of women 3 years or older that<br>|0.408|0.459|-0.082|
|speak indigenous language||||
||(0.491)|(0.479)|(0.413)|
||[0.406]|[0.338]|[0.843]|
|Grants awarded from CONAFOR between<br>|0.077|0.067|-0.015|
|2007-2017||||
||(0.105)|(0.090)|(0.070)|
||[0.461]|[0.456]|[0.829]|
|Proportion of adult women in adult<br>lti|-1.953|-6.956|-4.068|
|popuaon||||
||(6.780)|(5.770)|(5.381)|
||[0.773]|[0.228]|[0.450]|
|Adult Population|0.004
|YES|YES|YES|YES|
|Dependent variable<br>mean|0.057|0.057|0.087|0.087|0.028|0.028|

Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1 _Notes._ Each cell represents a coefficient estimate using OLS regression using a first difference model. The analysis is restricted to working age individuals in the years 2012 and 2018. Columns (1) and (2) report results on the full sample. Columns (3) and (4) report results on the sample of men. Columns (5) and (6) report results on the sample of women. In Panel A, the dependent variables in columns (1), (3) and (5) are the first difference in dummy variable indicators for participating in the labor force in the years 2018 and 2012, according to the market definition. The dependent variables in columns (2), (4) and (6) are the first difference in dummy variable indicators for participating in the labor force in the years 2018 and 2012, according to the extended definition. In Panel B, the dependent variables in columns (1), (3) and (5) are the first difference in dummy variable indicators for being employed in the years 2018 and 2012, according to the market definition of labor force participation. The dependent variables in columns (2), (4) and (6) are the first difference in dummy variable indicators for being employed in the years 2018 and 2012, according to the extended definition of labor force participation. The independent variable of interest Internet job search is the first difference in dummy variable indicators for Internet use for job search between the years 2018 and 2012. Regressions also include a vector of individual control variables first differenced between the years 2018 and 2012. The controls in first difference
-20-

**TABLE 2.3**

|**Czmpariaon**of Mexican<br>**Census Estimates**<br>**for Na**<br>|National<br>Accounts<br>**nufacturing**<br>**Gross**<br>|and Industrial<br>**Value Added**|**1975**|
|---|---|---|---|
|Census Estimate<br>(US Census)<br>Concept) of<br>Gross Value<br>Added<br>(million pesos)|Census<br>Estimate<br>(Present Nationa<br>Accounts Concept<br>of Gross Value<br>Added<br>(million pesos)<br>(|National<br>l Accounts<br>) Estimate<br> <br>(present<br>concept) of<br>Contribution<br>to GDP at<br>factor cost<br>million pesos|Ratio<br>of Value<br>Added (Present<br>National<br>Accounts Concept)<br>National Accounts/<br>Census Values<br>)|
|(1)|(2)|(3)|(4)|
|Food Products<br>28,963.6|22,111.8|50,794.0|229.7|
|Beverages<br>1 2<sup>,</sup>99 3<sup>.</sup>8<sup>a</sup>|8 <sup>,</sup>54 6 <sup>.</sup>3<sup>a</sup>|<br>12,635.4|147.8|
|Tobacco<br>1
et al. (2017) use a similar approach to estimate traffic emissions in the Boston metropolitan area. Their results suggest that only 10 percent of the road network accounts for 70 percent of traffic- related air pollution. Linking emissions data with privacy-preserving medical records can provide even better insights into the impacts of emissions on a variety of health outcomes (see, e.g., Kho et al., 2015). Reports or complaints about congestion or traffic-related incidents can also be tracked using social media data (Hasan et al., 2013). Combining traffic data from the California Department of Transportation with Twitter data, Georgiou et al. (2015) find a strong correlation between users’ complaints and the severity of traffic congestion. Using Twitter data, Wang et al. (2015) estimate aggregate congestion in Chicago with about 80 percent accuracy.

# IV. Combining Big Data and Economic Approaches for Prediction

Finally, an important trend in the big data literature is that it is not only used for past and real time measurement of phenomena, but increasingly used to make predictions. The of data can be useful to construct credible coun- predictive capability big analytics terfactual for causal inference using observational studies. In this section, we briefly review traffic flow prediction attempts that make use of big data, and detail how big data techniques can incorporate economic insights to evaluate the causal effect of driving restriction policies on traffic congestion.

# A. Big Data, Machine Learning, and Econometrics

Transportation experts can rely on big data to generate accurate and timely information for traffic flow prediction, a crucial step for improving real-time traffic management and mitigating urban congestion (Lv et al., 2015; Chen et al., 2017). Traffic flow prediction usually begins with mining vast amounts of unstructured locational data extracted from physical and social sensing technologies. The data are then analyzed through 12
_vX_ }, _X_ where G( ) is agriculture GDP, p is output price, and v is a vector of purchased input prices. The function G( ) is a (dual) revenue or value added function and must satisfy certain conditions. The most important, from our point of view, is that, apart from being increasing and concave in L and K, the agricultural value added function is also homogenous of degree one in K and L. This follows directly from the constant returns to scale assumption. We can thus express per capita agriculture GDP as

> 15 The assumption of constant returns to scale in agriculture has not often been rejected by empirical studies using farm household data. See, for example, López and Valdés (2000), who provide estimates for five countries in Latin America.

9