rafmacalaba/data-use-mentions-extended huggingface dataset:
we find that adding an estimate of the proportion of population in poverty leaves our income estimate unaffected.'<sup>3</sup> In interpreting estimates of the impact of income on health there is a potentially serious econometric problem of reverse causation, as better health status might cause higher average income. This is related to the problem of the attribution of effect between income and health policy, as better policy might cause better health which might lead to higher income. While it is almost certainly true that better health leads to higher income at the individual level (Strauss and Thomas, 1995), the effect is less clear at the aggregate level. Moreover, it is unlikely that the mortality rate of children under five would effect higher incomes contemporaneously. Pritchett and Summers (1996), using instrumental variables and fixed effects estimation on a panel of data show that wealthier is _causally_ healthier for the cases of infant mortality, under-5 mortality, and life expectancy. While not going to the same level of detail in the estimation, we use two-stage least squares estimation (column 3 of Table 1) using as instruments for income whether or not a country's primary export is oil, and the percentage of years since 1776 that a country has been independent. Other than the much larger standard errors the results are largely unaffected, suggesting that whatever role health might have in causing higher incomes it does not affect the estimation of the cross-national impact of income on child mortality.

The results on female education are consistent with both aggregate and household level studies (King and Hill, 1993, Subbarao and Raney, 1995, Caldwell 1986, 1990, and Hobcraft, 13 In any case, the difference in the two results is not about whether income affects health status- but about the particular functional form of the specification for estimating the income effect.

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infrastructure, weather patterns, and exchange rates, among many others. For these reasons, food dependency ratios will not reveal the extent to which international food price movements are transmitted to domestic food prices.

Importantly, with substantial increases in international food prices in the 2000s and fast-growing domestic food demand due to high population growth rates, fiscal and inflationary pressures have grown as well. Fiscal pressures vary by country and reliable data is generally patchy, though estimates suggest that most MENA countries may be spending several percentage points of their GDP on food subsidies.<sup>4</sup> These are costly and, if targeting is poor, arguably not the most effective way of protecting vulnerable populations. Recognizing this dilemma, over the past two decades, countries in the MENA region have attempted to reform their food subsidy systems. Some governments have been more successful than others in cutting subsidies and improving targeting. Measures such as self-targeting, increasing prices by stealth, subsidy rationing and replacing subsidies with cash transfers, sometimes succeeded in reducing the government‟s fiscal burden. But many other reforms fell short or needed to be reversed after public pressure. The result has been partial reforms, with all countries still offering at least some food price subsidies, while social assistance schemes have in general not adequately channeled sufficient resources to the needy.

During political transitions, distributional and public policy challenges are exacerbated in countries dependent on imported food. A World Bank (2011a) report found that many MENA governments responded to discontent with economic and social problems at the onset of the Arab spring by extending food and fuel subsidies. Countries that increased food subsidies and/or imposed price controls during the first half of 2011 include Bahrain, Kuwait, Oman, UAE, Algeria, Yemen, Jordan, Egypt, Tunisia, and Morocco (World Bank, 2011b).

High international food prices can complicate macroeconomic management. Subsidy costs take away fiscal resources that can be used to finance growth-enhancing investments. Because of the high food shares in the consumption basket for many
But a pre-condition to being able to measure this is the existence of nationally representative data that allow us to observe the same household at two points in time. Unfortunately for Nepal, the panel data that have been collected with the three rounds of the living standards survey have suffered from a high degree of attrition between rounds and are thus not ideal for this analysis. Instead, we use what is referred to as the synthetic panel methodology (Dang and Lanjouw, 2013).

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# **B. Confronting History and Theory: A General Equilibrium Analysis of Dutch Disease in Mount Lebanon of 1912**

With the mono-export of silk and net transfers from the Americas accounting respectively for 41 and 54 percent of GDP, the economy of Mount Lebanon was both particularly globalized and highly vulnerable to external shocks. The data set available for the year 1912 is suitable for a simple general equilibrium analysis.

# **_1. Dependence and Vulnerability to External Shocks: The Case of Mount Lebanon through a Simple General Equilibrium Framework_**

In the following, I use the simple general equilibrium framework of the 1 2 3 Model developed by Devarajan et al ( _Simple general equilibrium modeling – 1997 & The simplest general equilibrium model of an open economy - 1998_ ) to assess the effect of external shocks on the economy of Mount Lebanon.

The 1 2 3 Model builds on the Harrod-Samuelson-Balassa two-sector (tradable and non-tradable sectors) model to examine the effects of remittances, inflows, and external shocks. The model involves one country, two producing sectors and three goods. The country has a small open economy and is a price taker on the international market. The two producing sectors are (i) one sector that produces goods for export only and (ii) another sector producing goods for the domestic market only. The three goods are (a) export goods, (b) goods produced and consumed domestically (mainly non-tradable goods), and (c) imported goods. Remittances, export prices and import prices are set as exogenous variables. The model does not take factor markets into consideration. In this model, the RER is weighted by the shares of imports and exports in the total foreign trade of the economy, _PD M PD E_ according to the following equation: _RER_ = + where PD is the price of domestic _PM FT PE FT_

> goods, PM the price of imported goods, PE the price of exported goods, FT foreign trade, M

> imports and E exports _._ In this equation
2 impact without SGRY is expectedly heterogeneous across outcomes. However, in most cases, the program impact without controlling for SGRY is underestimated between 30-40%.

I use a novel dataset of annual program expenditure for every district in the country for SGRY and NREGA programs. The expenditure data for SGRY is from its implementation in 2001 until its last operational year in 2007. For NREGA, the expenditure data is between 2006 to 2010. To the best of my knowledge, this is the most detailed and disaggregated expenditure data used in any comparable analysis on public works in a developing country. The data reveal three salient findings. First, the two programs never coexisted in a district at any point in time. Next, NREGA was implemented in phases, covering 200, 330, and all the districts of the country by 2006, 2007, and 2008, respectively. The second finding shows that during the staggered roll-out of NREGA, SGRY continued to be present exclusively in the “non-NREGA” or “control” districts during 2006 and 2007. Third, the availability of public employment under the SGRY was not inconsequentially small relative to NREGA. Approximately 14 percent of the districts covered under NREGA during 2006 and 2007 spent less than the average per capita program expenditure incurred under SGRY in the control districts during both these years. These findings blur the distinction between treatment and control districts where NREGA substituted and not supplemented the older program.

I start by estimating the general equilibrium impact of large public workfares on short-term wages for comparable manual labor work in the private sector. The question itself is of policy relevance since wage income from casual labor is a principal source of income for the poor in a developing country like India (Banerjee and Duflo 2007). A consensus among the recent literature on NREGA is that the program did lead to modest increases in private wages (Azam 2012; Berg et al. 2018; Imbert and Papp 2015; Muralidharan et al. 2017; Zimmermann 2012). Notably, the income gain due to higher private wages is
is evidence that years since colonization affects development (Feyrer and Sacerdote, 2009), we control for the years since colonization in some of the analysis. Our measure of the year of colonization is presented in the last column of Table 1.<sup>13</sup>

> 12 We were not able to find a clear assignment for the Mapuche, and as such they are dropped from the analysis in this section. While the Mapuche relied heavily on agriculture to the west of the Andes (see for example Murdock, 1967), in their expansion to the east they incorporated hunter-gatherer groups and relied less on agriculture.

> 13 We use several data sources to construct the variable shown in Table 1 measuring the year of colonization of each group. Firstly, we define the ancestral area for each group based on the literature (Colombres, 2008; Ib ́a ̃nez, 2008; Lobos, 2011; Mandrini, 2008; Molocznik, 2011; Nesis, 2005; Nordenski ̈old, 2002; Outes and Bruch, 1910; Sacco, 2011; Mart ́ınez Sarasola, 2011, 2014; Serrano, 2012). Secondly, we use the 2010 Census to calculate the population density of Natives of each group across counties. Thirdly, for each group, we take into account the three counties with the largest concentration of people from that group among counties in the ancestral land of the group. Fourthly, we find the year of colonization of those counties based on historical records of arrival of the Spanish or foundation of the county or main city in

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and distance.<sup>9</sup> In this sense, municipalities closer to the border were more likely to receive a higher share of not only formal but also informal remittances than those farther away.

Second, we employ migration patterns of Mexicans from northern states (Fig. 2) to show they were more likely to migrate to southern US states between 2016 and 2019, whereas those from states in southern Mexico do not show a clear pattern.<sup>10</sup> However, geographic mobility declined dramatically in March 2020. As seen in panel B of Figure 1, the number of border crossings between Mexico and the United States dropped by 70 percent in April 2020 compared to April 2019. As a comparison, border crossings between 2007 and 2008 fell by only about five percent. The decline in mobility since March 2020—not only at the border but also within the United States and Mexico—implies that informal remittance channels suddenly shut down. As a result, municipalities closer to the US border that received remittances through informal networks would be disproportionately more likely to experience a shift from informal to formal channels, and recorded remittances would be more likely to increase. Descriptive patterns of remittance flows across municipalities since March 2020 align with the shift towards formal channels, as northern Mexican municipalities and southern US states experienced disproportionate increases in this type of remittance flow.<sup>11</sup> Several articles have assessed geographic mobility and distance as important variables that affect the level of remittances (Frankel, 2011; Docquier et al., 2012; Lueth and Ruiz-Arranz, 2008). However, to disentangle the role of distance, it is necessary to use subnational or individual-level data, which enable us to control for unobservable factors that might affect cross-country regressions. Evidence that uses such data is scarce. Exceptions include Ferriani and Oddo (2019), who use data on official remittances from institutions or other authorized intermediaries (MTOs, banks, and post offices) at the provincial level in Italy
to offer are actually more likely to stop buying from Agruppa after having tried it out, and less likely to be customers after one year. This is surprising and goes against the idea that customers will be more likely to stick around if given wider variety. While we are controlling for household socioeconomic status, owner characteristics, distance to Corabastos, and a linear time trend in time since Agruppa started in the first block, this may still reflect unobserved characteristics of the blocks phased in later rather than earlier.<sup>12</sup> Our six-month follow-up survey (described below) asked current users the most important reasons for using Agruppa, and those who had stopped using the service the most important reasons for stopping. The main reasons for continued use were reducing travel time (62%), lower prices (15%), and transporting heavy items (13%). The most important reasons given for stopping use were not not enough variety (28%), not satisfied with customer service (28%), and prices not being competitive (22%). Only 11% said the reason was product quality. Qualitative work by Agruppa led them to conclude that their most loyal customers were owners of smaller shops, who had strong practical reasons for not wanting to have to travel to the market, such as safety or caring for children or elderly family members; while other clients were seen as using Agruppa mainly for sporadic needs, stocking up between visits to the market. They also noted that for many shopkeepers, going to Corabastos remained “central to their identity as shopkeepers, to their social life, and even to their ego, while appealing to their primordial need to have selected and negotiated (or in good Colombian "bargained") the product themselves”. This suggests it may have not just been an economic consideration as to whether to use and continue using the service or not.

# **3.4 Data for Measuring Impacts**

IPA Colombia conducted five rounds of high-frequency
varies significantly and may change over time. For example, a gas field once seen as non-viable may become lucrative once the necessary infrastructure is in place to utilize it.

In this section, we review how much the perception of the 12 countries’ resource endowments changed in the period of study.

In five of the twelve countries—Ghana, Mauritania, Mozambique, Niger and Senegal—discoveries have attracted a final investment decision (FID). Three countries—Kenya, Tanzania and Uganda— are still awaiting an FID. The initial oil discoveries in the other four countries—Guinea-Bissau, Liberia, São Tomé and Príncipe and Sierra Leone—have already been deemed to be non-commercial and abandoned by the companies. Kenya has also suffered from disappointment in this area. While its oil discoveries may still attract an FID, discoveries of gas have already been abandoned. Current estimates of oil and gas reserves in the 12 countries are provided in the appendix.

## _Resource growth_

Initial discoveries in the East and Southern African countries of Kenya, Mozambique, Tanzania and Uganda led to further discoveries and production plans. For example, after Uganda’s first oil discovery in 2006, 20 subsequent discoveries were made in blocks 1, 2 and 3A. These blocks now comprise the Albertine Graben project planned by China National Offshore Oil Corporation (CNOOC), Total and Tullow. Similarly, offshore gas discoveries in Mozambique and Tanzania in 2010 were followed by multiple others, with LNG projects now planned in both countries.

Similar success has been experienced in the West African countries of Ghana, Mauritania, Niger and

> 9 For example, government revenue forecasts are more common than, for example, employment forecasts. It is therefore easier to determine whether government revenue has disappointed rather than whether job creation has disappointed.

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new investors then select similar strategies to their experienced peers. In turn, social interactions lead to high volatility portfolios even when new investors have no inherent demand for volatility.

# **6.2. SELECTIVE INFORMATION NEGLECT**

A potential explanation for our results is that experienced individuals accurately share their performance, but signal receivers ignore negative outcomes. Specifically, if inexperienced students believe they can replicate peers’ successful trades and avoid peers’ losses, only positive outcomes would impact market entry. In psychology, there is evidence of self-enhancing thought processes, such as the tendency of people to attribute wins to their own virtues but losses to external circumstances (Bem, 1972; Langer & Roth, 1975). Similarly, people might disregard negative peer outcomes, assuming that these result from lack of skill or from other unique circumstances that do not apply to them. At the same time, people might be encouraged to trade actively if they overestimate their ability to reproduce good peer outcomes.

To shed more light on this issue, we use the answers from the social interaction section of the electronic survey. We ask former students if they engaged in informal discussions about stock trading with classmates.<sup>29</sup> Among the respondents of our electronic survey, 760 students had no trading history prior to the course start date. Within this group, 80% of people reported engaging in investment conversations with others in the class. We classify our respondents in three groups based on their classmates’ experience: (i) those who attended courses with no experienced classmates (330 students), (ii) those in courses where peer returns were positive (252 students), and (iii) those registered in courses with negative peer returns (178 students).

Among students in courses where the _Experience Rate_ was zero, 72% indicated having investment conversations with peers. This number was significantly higher at 93% among students in courses with positive peer returns. Even in courses where peer returns were negative, students report more
variables. Table 3 displays the predicted climate change for the two selected basins by Palmer et al. (2008). Due to the constraints of obtaining natural flow data, the sample areas are upstream sections of the gauging station and represent a portion of the entire river basin. They are around 25% of the entire set of tributaries of the rivers that they represent.

# **Data**

Runoff data with location coordinates were collected from the Global Runoff Data Centre (GRDC). GRDC provided available monthly time-series gauging station data in the international river basins (approximately 3500 stations). While the GRDC is the source of comprehensive data on global gauging station data, the data are available in limited time periods and the distribution of gauge data is not even across the globe; gauges are mainly located on main stem rivers in middle and high income countries.

The model of precipitation data is from the CRU 3.0 Global Climate database downloaded from <u>http://badc.nerc.ac.uk/data/cru/ (accessed 2010) with data from 1901 to June 2006 (in</u> preparation, see Mitchell and Jones 2005). These data have a spatial resolution of 0.5 degree.

# **SSM/I derived surface wetness data description**

The BWI uses observations from the Special Sensor Microwave Imager (SSM/I). It is a seven channel passive microwave radiometer operating at four frequencies (19.35, 22.2, 37.0, and 85.05) and each channel has dual-polarization (except at 22.235 GHz which is V-polarization only). The frequencies flown on the SSM/I are used to dynamically derive the amount of liquid water near the surface. Data are available from 1988 to present except mid-1990 through 1991 and this analysis uses data until 2009.

# **Statistical procedures and calibration of the runoff models**

In order to relate the BWI to the flow gauge data, we use the
of these firms had invested in the previous. Not only is this 7.36% difference statistically significant at the 0.05 level, it is substantially large.

These results indicate a positive relationship between the level of firm investment and the firm’s providing potentially false responses to corruption questions. Unfortunately there are numerous interpretations of this result since our results do not speak to the causal relationship between corruption and investment. Firms that have invested in the past more may be more exposed to corruption (and thus be more prone to lie about corruption) or may simply be more sensitive to answering politically sensitive questions. Alternatively, the firms that have benefited the most from corruption may also be the firms that are most concerned with providing answers to corruption related questions.

While we can not definitely tell which of these theories explains this relationship, or results, at a minimum, lead to caution in the design of surveys measuring corruption and the interpretation on the relationship between corruption and investment. Firm level surveys are plagued by both nonresponse and false response bias that can have a dramatic impact on our empirical results and our substantive understanding of corruption.

# **6. Conclusion**

Understanding how corruption affects the business environment has important academic and public policy implications. Unfortunately, much of what we know about corruption is either through subjective evaluations of country experts or through the use of firm-level surveys that are plagued by nonresponse and possibly nonresponse bias. Using a firm-level survey of the business environment in Bangladesh, we include a series 24
American economies suggesting that antidumping and other TTBs allowed governments to manage domestic political pressures for import protection while maintaining a generally liberal trade policy.<sup>6</sup> In other words, some controlled access to new protection may be the price of maintaining an open trading system. Implicit is the idea that overall gains from trade in a particular sector must sometimes be sacrificed in order to appease domestic groups that are harmed by competing imports. Vandenbussche and Zanardi (2010) quantify this implied sacrifice by estimating the effect of antidumping laws on _aggregate_ trade flows between new adopters and their trading partners.<sup>7</sup> But the early history of antidumping reflects a completely different rationale—one based on the possible use by foreign suppliers of temporarily low export prices to achieve market power. National antidumping laws in the United States and other industrialized countries predate the original GATT by several decades, and in their earliest versions were closely linked to antitrust policy. In 1904, Canada became the first country to impose an antidumping law, closely followed by New Zealand (1905) and Australia (1906). The first U.S antidumping statute (1916) was very narrow in its scope, requiring not only a low price of imports relative to some standard but also evidence of _intent_ on the part of foreign suppliers to injure current or potential domestic producers and/or to achieve a monopoly position in the political-economy justification of antidumping, i.e., the same domestic political pressures that led to higher ex ante tariffs in particular sectors were later accommodated through antidumping protection.

6 However, Moore and Zanardi (2009) reach a contrary conclusion. They use data for a sample of 23 developing countries, including some that have become aggressive users of antidumping, to examine whether use of antidumping has contributed to tariff reductions. Their results do not support the “safety valve” argument that the availability of antidumping encourages countries to liberalize. In fact, use of antidumping may have led to less rather than more liberalization