rafmacalaba/datause-encoder-data huggingface dataset:
pricing scheme could help relieve peak-hour traffic congestion and lead to annual welfare gains of CNY 1.5 billion.

With a similar focus but a different method, Kreindler (2018) uses GPS data on more than 100,000 commuter trips in Bangalore, India, to conduct a randomized experiment for the morning commute. He compares two congestion charge policies that impose fees for driving through certain areas during peak hours. Based on the experimental price variation, he estimates commuters’ preference for scheduling flexibility relative to their value of time. He concludes that the costs of rescheduling travel to inconvenient times will almost entirely offset the benefits of the saved travel time, resulting in only a small consumer welfare gain.

# **_2.4 Ride-Sharing Services_**

Ridesharing services have become increasingly prevalent in many parts of the world. As travel demand and vehicle ownership increase in developing countries, encouraging ridesharing could potentially help combat severe air pollution and congestion. By increasing the flexibility of travel and providing a new travel mode, ridesharing could increase consumer surplus (Cohen et al. 2016). However, the impact of ridesharing on the environment hinges critically on the emissions of the substituted travel modes and on total travel demand.<sup>19</sup> Leard and Xing (2020) estimate a discrete choice model of household mode choice using nationally representative data on travel behavior in the United States and simulating household mode choices in a setting where ridesharing was not available. They find that the availability of ridesharing has led to modest increases in total vehicle miles traveled and greenhouse gas emissions. Using survey results on Uber use by residents of Santiago, Chile, and Monte Carlo simulation, Tirachini and GomezLobo (2017) find that unless ridesharing substantially increases the average occupancy rate of trips, ridesharing is like to increase vehicle kilometers traveled.

By exploiting the spatial and temporal variation of Uber entry and Uber penetration in the US market, Hall et al. (2018) estimate the impact of ridesharing on public transit ridership. They

> 19 If ridesharing mainly encourages carpooling and reduces private car driving, it may reduce overall onroad emissions. If the introduction of this new travel mode increases travel demand, or if ridesharing replaces walking or public transit trips, or if <mark>deadheading to search for customers (out-of-service movement) accounts for a significant component of vehicle miles,</mark> ridesharing may increase on-road pollution and impose new challenges to pollution reduction in the transportation sector.

20
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<!-- Start of picture text --> Long Term Female Marriage Trends<br>by District Conflict Intensity<br>Wester Calendar Year of Birth<br>1968-1969 1973-1974 1978-1979 1983-1984<br>25 eee TS Se eee le . ena = |<br>to | o- ee TT .<br>2. | | | |<br>ao ! <--Contral-> ! «--Excluded--> |< Tiested-> |<br>ae ere? iri 1996 | 18-22 in 1996 | 13-1¥ |<br>5 in 1996<br>Bay | | | |<br>2025 2030 2035 2040<br>Nepalese Calendar Year of Birth<br>——— Low Intensity Tercile<br>ses [nterrnediate Intensity Tercile<br>——-——- High Intensity Tercile<br><!-- End of picture text -->
where _yihcs_ are private inputs to the education of child _i_ in household _h_ from community _c_ and school _s_ . We consider three financial inputs (informal payments to teachers, tutoring, and transport expenditures) and two non-financial inputs if the child is enrolled in school and the frequency with which the caregiver spends time supporting the child in educational activities. The main explanatory variable of interest, Mig _hc_ is a household-level dummy variable taking the value one if the child lives in a migrant household and zero otherwise; _Xihcs_ is a vector of childand household-level control variables; and _εihcs_ is the error term.

Clearly, migrants are not a random population group but rather self-select into migration. Thus, it can be expected that they systematically exhibit distinct unobservable characteristics relative to non-migrants that might bias OLS estimates of equation (1). To overcome this problem, we estimate an instrumental variable approach by two-stage least squares (2SLS).<sup>19</sup> Our instrument for migration status is the interaction between pre-existing migration networks at the local-level and destination-specific economic conditions. Formally, we use the growth rate of per capita GDP for each destination country between 2004–2010 and weight it with the share of migrants that, by 2004, had migrated from the community to that destination.<sup>20</sup> The data for the migrant-destination share at the community level are derived from the 2004 Moldovan Census.<sup>21</sup> The variable has already been employed as an instrument for migration in other studies of the Moldovan context (e.g., <u>Lücke et al. 2012, Böhme et al. 2015). The rationale behind the use of</u> _Network-Growth_ is twofold. First, migrant networks are known to be very important in 19. The most common approach in the literature is instrumental variable strategies exploiting exogenous aggregate factors at the origin or destination: past migration rates (McKenzie and <u>Rapoport 2010, Antman 2011, Zhang et al. 2014), financial infrastructure (Calero et al. 2009), and political unrest</u> (Bansak and Chezum <u>2009) at the origin-level; employment conditions (Antman 2011, Cortes 2015) and exchange</u> rate crises (Yang <u>2008) at the destination-level.</u> 20. Analytically: where _c_ is the Moldovan community; _j_  1,2,3,..., _J_ is the migration destination countries and _t_ = 2004, 2005, ..., 2010.

21. An advantage of our setting is that migration has been a relatively recent phenomenon in Moldova, and, thus, there is little scope for the non-migrant population to be influenced over time due to spillovers and long-term confounding developments that might have arisen over time. As a robustness check, we exclude for the analysis the migrant households which already had a migrant in 2004 or before as they might be included in the Census migration rates. The main results do not change qualitatively (available upon request).

10
Table 4: ITT Effects on Employment Outcomes

||Empl|oyment||
|---|---|---|---|
||(1)<br>Any|(2)<br>Salaried|(3)<br>Outside<br>Wage|
|_Pan_|_el A: Midl_|_ine (6 mon_|_ths)_|
|ITT|0.08<sup>_∗∗_</sup>|-0.08<sup>_∗∗∗_</sup>|36870.754|
||(0.03)|(0.03)|(26375.090)|
||[0.020]|[0.010]|[0.149]|
|Observations|547|547|539|
|Control Mean|0.72|0.16|3.95e+05|
|Adj. R<sup>2</sup>|0.175|0.140|0.164|
|_Panel _|_B: Endlin_|_e (18–24 M_|_onths)_|
|ITT|0.03|-0.05<sup>_∗_</sup>|6154.552|
||(0.03)|(0.03)|(23810.000)|
||[0.624]|[0.178]|[0.782]|
|Observations|545|545|535|
|Control Mean|0.81|0.14|3.60e+05|
|Adj. R<sup>2</sup>|0.064|0.026|0.153|

_Note:_ We winsorize Outside Wage at the 99th percentile. Coefficients are ANCOVA estimates that control for the outcome at baseline, the respondent’s location, marital status, household size, number of children, age, network size and level of education. ITT shows traditional intent to treat effects. Any Employment is a binary variable equal to one if the woman reports being either employed by someone else or being self-employed. Salaried Employment is a binary varibale equal to one if the woman reports being employed by someone else. Outside Wage is the salary a woman requires to prefer working in an important private company instead of having her own shop. Index is an Anderson index of all outcomes using the swindex command in Stata. We report White robust standard errors in parentheses. Romano-Wolf multiple hypothesis test q-values are presented in brackets. Q-values include the same controls as above and are computed within families of outcomes.

27
Table 5: Regional welfare impact

||Percent|US$ billions|
|---|---|---|
|South Asia|-4.2|-125|
|East Asia and Pacifc|-2.0|-77|
|Latin America and the Caribbean|-1.1|-70|
|Middle East and North Africa|-1.0|-42|
|China|-0.8|-91|
|Sub-Saharan Africa|-0.7|-14|
|Other Advanced Economies|-0.5|-65|
|United States|-0.4|-74|
|European Union|-0.4|-76|
|Europe and Central Asia|-0.1|-2|
|**Total**|**-0.8**|**-634**|

Source: Authors’ simulations. Note: Results are reported relative to the baseline in 2020.

Europe and Central Asia experience relatively modest welfare losses. The decomposition of the aggregate effects indicates that this is due to considerable terms of trade gains which close to allocative losses. ECA from terms of trade as outweigh efficiency benefits gains the level of bound tariffs is relatively close to applied ones and thus, relative to other regions, the increase in the price of their imports is smaller than the increase in the price of exports.

# **5.2.2 Impact on consumers**

The worldwide increase in barriers to trade translates into a strong decline in the imports of goods and services for household consumption in all regions. Regions most affected are South Asia and Latin America and the Caribbean which experience a decline 47 percent and 35 percent, respectively, relative to the baseline after three years (Table 6).

Total household consumption also is expected to decline in all regions, as the increase in the consumption of domestically produced goods and services is not strong enough to compensate for the pronounced decline in the consumption of imported goods and services. In the case of the United States, the imports of goods and services for household consumption would decline by 5 percent or US$43 billion and as in the European 21
than when allocating them entirely to the North (yielding 1 and 0.9 percent, respectively) or when using a proportional criterion based on the currency breakdown of allocated reserves (yielding 1.8 and

- 1.6 percent, respectively).

We do not make any assumptions regarding missing values or zero-valued observations. As in the case of portfolio investment, we have no evidence on whether missing values are indeed missing or true zeros. Hence, we use the raw information provided in the databases.

39
# **1 Introduction**

Elevated food prices over the last half decade have provoked a rash of government interventions in agricultural markets across the globe, often in the name of protecting the poor. Of course, it is well recognized that many poor households in developing countries, especially in rural areas, are also food producers and hence net beneficiaries of higher prices.<sup>1</sup> Even so, there is another price-shock transmission channel, potentially more important to the poor, that has received far less attention in the literature: rural wages.<sup>2</sup> To what extent do higher agricultural commodity prices translate into higher wages? For rural India, home to roughly a quarter of the world’s poor (those living on less than $1.25/day), the answer to this question can have momentous ramifications. After all, the vast majority of India’s rural population relies on the earnings from their manual labor, most of which is devoted to agriculture.<sup>3</sup> Any thorough accounting of the global poverty impacts of improved terms of trade for agriculture must, therefore, confront rural wage responses in India.

Aside from direct income effects for consumers and producers, as in the textbook partial equilibrium analysis (e.g., Singh, Squire, Strauss, 1986, Deaton, 1989), higher agricultural prices, in principle, induce three types of indirect, or general equilibrium, effects concomitant with higher wages: (1) higher labor income; (2) lower capital (land) income due to higher labor costs; (3) higher prices for nontradables. To account for these channels in a manner that is both theoretically coherent and transparent, I integrate a standard three-sector, specific factors, general equilibrium model of wage determination (Jones, 1971,1975) into an otherwise conventional (first-order) household welfare change calculation.<sup>4</sup> I use this

> 1Ivanic et al. (2012), Wodon et al., (2008), and World Bank (2010a) provide recent multi-country assessments of the the welfare impacts of food price increases accounting for such producer gains.

> 2Ravallion (1990) surveys the debate in development on the nexus between the intersectoral terms of trade and poverty. Shah and Stiglitz (1987) provide an early theoretical treatment. In their cross-country study, Ivanic and Martin (2008) incorporate price-induced changes in wages for unskilled labor derived from nation-level versions of the GTAP computable general equilibrium model.

> 3Indeed, rising wages are seen as the major driver of rural poverty reduction in recent decades (Datt and Ravallion, 1998; Eswaran, et al. 2008; Lanjouw and Murgai, 2009).

> 4Porto (2006) (and subsequently Marchand, 2012) comes closest to my approach in the context of trade liberalization, but differs in some important details. Primarily, he keeps the general equilibrium model, in his case applied to the country as a whole, in the background of the analysis.

1
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16
# Data Protection Laws

<!-- Start of picture text --> @8 Comprehensive Data Protection Law Enacted | @8 Funding Effort to Enact Law No Law<br><!-- End of picture text -->

<!-- Start of picture text --> IBRD 39566<br>— a nal eure ~~ SEPTEMBER 2012<br>, af +x<br>§a<br>\ a<br>a<br><!-- End of picture text -->
<!-- Start of picture text --> Share of the B40 population Share of World Bank funding<br>[_] 0.002 - 0.025 (—) 0.006 - 0.034<br>(* 0.025 - 0.048 [) 0.034 - 0.063<br>(9 (0.048 - 0.071 Ga 0.063 - 0.091<br>GM 0.071 - 0.094 GE 0.091 - 0.120<br>MH 0.094 - 0.117 GM 0.120 - 0.149<br><!-- End of picture text -->
# PRE Working Paper Series

||Dlie|Author||Contact<br>forgaper|
|---|---|---|---|---|
|WPS469|Modeling Economic Behavior in<br>Peru's Informal Urban Retail Sector|J. Barry Smith<br>Morton Stelcner|August 1990|M. Abundo<br>36820|
|WPS470|What Do Alternative Measures of<br>Comparative Advantage Reveal<br>About the Composition of Developing<br>Countries' Exports?|Alexander J. Yeats<br>|August 1990|J. Epps<br>33710|
|WPS471|The Determinants<br>of Farm<br>Investment and Residential<br>Construction in Post-Reform China|Gershon Feder<br>Lawrence J. Lau<br>Justin Lin<br>Xiaopeng Luo|August 1990|C. Spooner<br>30464|
|WPS472|Gains in the Education of Peruvian<br>Women, 1940 to 1980|Elizabeth M. King<br>Rosemary Bellew|August 1990|C. Cristobal<br>33640|
|WPS473|Adjustment, Investment, and the<br>Real Exchange Rate in Developing<br>Countries|Riccardo Faini<br>Jaime de Melo|August 1990|R. Sugui<br>37951|
|WPS474|Methods for Measuring the Effect<br>of Adjustment Policies on Income<br>Distribution|Anne Maasland|August 1990|P. Dixon<br>39175|
|WPS475|Does Divestiture Matter? A<br>Framework for Learning from Experie|Ahmed Galal<br>nce|August 1990|G. Orraca-Tetteh<br>37646|
|WPS476|Health Insurance in Sub-Saharan<br>Africa: A Survey and Analysis|Ronald J. Vogel|August 1990|K. Brown<br>35073|
ture like Uganda. Munshi (2003) was the first to find a strong negative impact of rainfall in the Mexican origin-communities (where rain-fed agriculture is the major occupation) on migration from these communities to the United States. Since then, a vast literature has confirmed that extreme weather conditions impose considerable strains on populations that depend on rain-fed agriculture (see Miguel, Satyanath and Sergenti (2004), Barrios, Bertinelli and Strobl (2006), Yang and Choi (2007), Gray and Mueller (2012), Beegle, De Weerdt and Dercon (2011), Miguel and Satyanath (2011), Marchiori, Maystadt and Schumacher (2012)).<sup>5</sup> Uganda is no exception and arguably constitutes a particularly good case study. According to FAOSTAT (2007), Uganda is among the countries in Sub-Saharan Africa showing the lowest share of irrigated cropland (less than 1%). Moreover, according to UN data (2005), a large majority of the Ugandan people (68.7%) make their living on rain-fed agriculture. Due to its heavy dependence on this sector, Uganda is widely considered as one of the most vulnerable countries in Sub-Saharan Africa to climate shocks.

The second advantage of relying on the weather-predicted determinants of the net inmigration rate in a given region as instrumental variables is that these variables are expected to be orthogonal to the unobserved correlates of the employment probability of the nonmigrants in that region. The weather-predicted determinants of the net in-migration rate indeed depend on the weather shocks that affect the _other_ regions, not on the weather shocks that affect the region under consideration. Put differently, the weather-predicted determinants of the net in-migration rate can be deemed as good instruments to the extent that they are not only correlated with the endogenous explanatory variable, but they also

> 5Note that extreme weather conditions can affect individuals’ conditions of living and therefore their decision to migrate in developed countries as well, as shown by Deschˆenes and Moretti (2009) and by Boustan, Fishback and Kantor (2010).

4