rafmacalaba/data-use-mentions-extended huggingface dataset:
considerable heterogeneity across crops. Columns (5) and (6) show that crop exports fell across the board but also with substantial variation across crops. The most fertilizer-intensive crop, maize, saw yields fall 53.7% and exports 36.1%. Less fertilizer-intensive crops like cinnamon saw decreases of only 12.2% in yields and 7.3% in exports. Rice yields decline by 37.9%, which is comparable to the 31.6% drop observed in the 2022 data relative to the historical average (see Section IV.B). Further illustration of the ban’s effects on agriculture is provided by Figure V, which shows a strong negative correlation (-0.986) between crops’ fertilizer intensity coefficients _γk_<sup>_f_and the ban’s effects on</sup> export value. The three most fertilizer-intensive crops (rice, potatoes, and maize) suffer the largest percentage losses in exports.

A crucial benefit of using a quantitative GE model is the ability to provide estimates of the ban’s welfare effects. Column (1) of Table VI displays cross-district average welfare effects for mobile workers, estate workers, farmers, and a (weighted) average of these three agent types.<sup>72</sup> The overall average welfare loss is equivalent to a 7.33% decrease in income.<sup>73</sup> However, there

> 71The intuition for this finding is that subsidies drive farmers to above-optimal fertilizer use. In the absence of subsidies, the cost and marginal revenue product of fertilizer are more closely aligned, so a marginal decrease in fertilizer use leads to reductions of roughly similar magnitude in fertilizer imports and agricultural exports.

> 72Appendix Table A8 displays the full set of welfare changes for all agent types and districts.

> 73Our average welfare loss metric puts zero weight on middlemen (since we assume their population has measure zero), as explained in Appendix F. However, our main results are robust
18 <!-- Start of picture text --> Figure 4: Weighted Average Unit Values, Euro/KG<br>EU Imports of HS 61 and 62<br>1998m1 2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1<br>time<br>Cambodia Sri Lanka<br>4<br>2<br>0<br>-2<br>-4<br><!-- End of picture text --> _Source:_ Eurostat data retrieved on 12/20/2012.

_Note:_ Data represent the weighted unit values (euro per 100 kg) of EU-27 imports of HS 61 and HS 62 of each country. Data have means removed, are deseasonalized using monthly dummy variables, and are then smoothed using a six-month moving average.

The declining trends in unit values were expected. After the MFA phase-out, the competitive pressure in apparel exports increased, which inevitably led to a decrease in apparel prices. According to the simple model described in section 2, the decline in apparel prices should have resulted in apparel manufacturing wages decreasing in the short run and in female wages in all industries declining in the long run (in approximately three to five years). The next subsection provides empirical evidence for these theoretical results.
India‘s economy is undergoing dramatic structural changes (Desmet et al. 2011). From a starting point in the 1980s when the government used licensing to promote industrial location in regions that were not developing as quickly, the economic geography of India has been in flux as firms and new entrants shift spatially (e.g., Chari 2008, Fernandes and Sharma 2011). One feature for a district that is important in this transformation is its link to major cities. We thus include a measure from Lall et al. (2011) of the driving time from the central node of a district to the nearest of India‘s ten largest cities<sup>6</sup> as a measure of physical connectivity and across-district infrastructure. This is calculated based on data on India‘s road networks using GIS software.

We next model local labor regulations using state-level variation in policies. Several studies link labor regulations in Indian states to economic progress (Besley and Burgess 2004, Aghion et al. 2008), and strict labor regulations have been found to hurt entrepreneurship beyond self-employment in advanced economies (e.g., Bozkaya and Kerr 2011). This effect may occur through reduced likelihood of wanting to start a new firm, or through reduced likelihood of opening new facilities to avoid regulations. There may also be reduced ̳push‘ into entrepreneurship with more protected employment positions. Fallick et al. (2006) model how labor mobility affects industry structure, with implications for entrepreneurship rates. Our measure is taken from Ahsan and Pages (2007), who break down the labor regulations index proposed by Besley and Burgess (2004) into separate components affecting labor adjustment and labor disputes legislation. Using these separate measures, we create a composite labor regulations index by state.

Our final primary measure is the strength of household banking environments. A large body of work considers the link between financial constraints and entrepreneurship, as surveyed in Kerr and Nanda (2011). Our measure of local financing conditions is the percentage
000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Female|0.004|0.007***|-0.003***|0.004|0.007***|-0.003***|
||(0.002)|(0.002)|(0.001)|(0.002)|(0.002)|(0.001)|
|arcsinh(income)||||-0.001*|-0.000|-0.000**|
|||||(0.000)|(0.000)|(0.000)|
|Wage worker mean|0.101|0.092|0.008|0.101|0.092|0.008|
|Sample size|64,911|64,911|55,671|64,766|64,766|55,526|

_Notes:_ The sample is the set of individuals aged 18-65 surveyed in the CFPS 2010 and 2016, ELMPS 1998 and 2006, IHDS 2005 and 2011/12, IFLS 2000 and 2007/08, MxFLS 2002 and 2005/06, LSMS-ISA 2010/11 and 2012/13, KHDS 1991/94 and 2004, and PSID 2001 and 2007. We drop respondents who are not selfemployed or paid workers in the baseline wave of each survey. Refer to online appendix section B for more details on variable construction in the different surveys. Each column reports marginal effects from a probit regression of an indicator for migration on an indicator for self-employment at baseline. Each regression controls for country fixed effects. Columns (4)-(6) additionally control for the inverse hyperbolic sine (arcsinh) of the respondent’s income in the baseline wave.

40
average bias is 17.6 percent. Again, the corresponding average bias in the IGC estimates is much smaller at 10.4 percent.<sup>9</sup> Considering estimates across different age ranges and gender, the average biases in IGRC and IGC are 24.4 percent and 6.5 percent respectively in Bangladesh, and the corresponding estimates in India are 14.12 percent (IGRC) and 7.6 percent (IGC). Moreover, we provide suggestive evidence that the bias in IGC estimate is less sensitive to the variations in the coresidency rate.

We discuss explanations and intuitions for the empirical findings that the IGC estimates suffer from much lower coresidency bias. A simple intuition can be provided by using the _σ_ _<u>p</u>_ following relationship between IGRC (denoted as _β_ ) and IGC (denoted as _ρ_ ): _ρ_ = _β_ , ( _σc_ ) where _σp_ and _σc_ are standard deviations of parental schooling and children’s schooling respectively. In other words, the IGC estimate is equal to the IGRC estimate multiplied by the ratio of standard deviation of parent’s schooling to that of children’s schooling. It is wellknown that truncation biases the estimate of _β_ downward in an OLS regression (Hausman and Wise (1977)). An equally important implication of truncation in our context is that it also affects the estimate of the ratio of standard deviations in schooling of parents to children. The IGC estimate cancels out part of the downward bias in IGRC by multiplying it with an upward biased estimate of the ratio of standard deviation of parental schooling to that of children’s schooling. We also provide a plausible rationale for the finding that the IGC estimates are less sensitive to the variation in coresidency rate.

The analysis and findings presented below have important and wide ranging implications for research on intergenerational economic mobility. First, our analysis implies that much progress in understanding intergenerational mobility can be made with the household surveys available in developing countries by focusing on IGC
, mostly limited to head counting. Refugees and IDPs are very different in that they have a different legal status, which leads to different access to public services, labor markets, government, and international assistance. Surveys for IDPs are often more difficult to conduct because the host country might be linked to the cause of displacement, and a registration system like UNHCR’s proGres is absent, making it more challenging to obtain a representative sample. Given the large number of IDPs (58 percent of all FDPs), the real challenge will be to collect microdata on IDPs systematically in all those countries that are home to large numbers of IDPs. So far, the only country that collects data on IDPs systematically is Colombia and this country has shown that, when quality microdata are available, research on IDPs flourishes.

While many of the discussed issues need more attention and research, some processes are in place that should lead to the establishment of guidelines that address some of these questions. A recent process

> 12 All these surveys can be found in the World Bank microdata library at: https://microdata.worldbank.org/index.php/catalog?sort_by=rank&sort_order=desc&sk=refugees.

20
*|0.139**|
||(0.053)|(0.058)|(0.053)|(0.061)|(0.036)|(0.056)|(0.037)|(0.055)|
|Education||0.041|<br>|0.049|<br>|0.045||0.025**|
|||(0.028)||(0.031)|<br>|(0.029)||(0.012)|
|(log) Household per capita<br>i||0.210***||0.221***||0.191***||0.169***|
|ncome||(0.063)||(0.085)|<br>|(0.062)||(0.060)|
|(log) Accumulated<br>||0.075**||0.072*||0.072**||0.063***|
|precipitation||(0.030)||(0.043)||(0.030)||(0.022)|
|Observations|735|735|580|580|702|702|432|432|
|Log likelihood|−355.21|−331.55|−282.16|−263.89|−340.11|−316.54|−198.54|−195.19|
|Sampleperiod|1986|–2009|1993|–2009|1986|–2009|1986|–2009|

_Source:_ Authors’ estimation using ECH and National Meteorology Office data. _Note:_ Estimates are obtained using a Papke & Wooldridge Fractional Logit Model. Marginal
sources as well as a survey of deposit insurers. One of the main improvements is the introduction of historical data on coverage and co-insurance, introducing a time series aspect to the data. Another major data source is the survey carried out by the International Association of Deposit Insurers in 2002-03. The main cross-country part of the database comprises readily usable data for empirical and statistical analysis where most variables are coded as indicators along with explanatory details. We present the main database in the appendix section A.1. The details of the data for each country with references to the sources are covered in the appendix section A.2 and the detailed data sources are given in the appendix section A.3.

The electronic version of the full dataset<sup>6</sup> is available online at the Finance Research website of the Development Economics Research Group, World Bank. The complete database includes the full coverage ratio data spanning 1960 to 2003 for all countries, where applicable. In the following sections we describe the dataset and the included variables and discuss main features of explicit deposit insurance systems around the world.

# **3.1 Explicit versus implicit deposit insurance**

EDISs differ from IDISs due to their reliance on formal regulation through central bank law, banking law, or the constitution and so on. The relevant law explains the main ingredients of the deposit insurance such as the beginning date, coverage limits, how (if any) they are going to be funded, and how bank failures will be resolved.

If such regulation is not present for deposit insurance, we assume that the DIS is implicit, relying on the observation that every country establishes a de facto insurance system for banks.

The variables related to the type of deposit insurance available in each country comprise of the following: **_a) Type:_** This variable identifies the form of the deposit insurance – explicit or implicit – present in each country. The variable takes the value of one for countries with EDIS, and zero otherwise
of large-scale irrigation development reflect only irrigation-specific infrastructure, such as distribution canals and on-farm system development. The potential for small-scale irrigation is assessed not only on the basis of agroecological conditions, but also in terms of market access, since irrigation is typically viable only if the increased yields can be readily marketed. The unit cost for large-scale projects is set at $3,000/hectare and for small-scale projects at $2,000/hectares.

# Water supply and sanitation

## **Achievements**

Since the end of the conflict, the CAR has made important efforts to improve the institutional and legal framework of the water supply and sanitation sectors. In 2006 the Water Code was promulgated and the Government’s Policy and Strategy Paper on the National Water and Sanitation Program (NESP) was approved, and in 2007 the National Agency for Water and Sanitation was established. But a clear definition of responsibilities among the different actors in the water supply and sanitation sectors is pending.

The CAR has managed to reach people with some form of water supply and sanitation, as shown by the relatively low reliance on surface water and open defecation. According to the results of the 2006 Multiple Indicator Survey (box 2), the CAR’s reliance on surface water is only one-third of the average for fragile states, which in fact do better than other low-income countries. In particular, due to the rapid expansion of utility connections in urban centers since the end of the conflict, it is expected that the use of surface water has decreased further in recent years. Reliance on open defecation in the CAR is at the level of other fragile countries.

19
(GHS 2018/19) Uganda (UNPS 2019)<br>Agriculture Manufacturing & Utilities<br>Construction, Transport & Professional Act. Commerce<br>Public Administration Services<br><!-- End of picture text --> _Source_ : pre-COVID-19 face-to-face LSMS-ISA surveys.

# **High-Frequency Phone Surveys**

We use data from HFPS in Ethiopia, Malawi, Nigeria, and Uganda. The sampling frame for these phone surveys was the most recent nationally representative LSMS-ISA-supported national longitudinal household survey. This enables a methodologically sound comparison with the pre-COVID-19 survey data. We chose these four countries because the latest face-to-face survey was conducted in 2019.

These surveys have been designed to track the social and economic implications, as well as household behavioral responses to the pandemic, with sections on Knowledge, Behavior, Access to Services, Employment, Income Loss, Food Security, etc. The LSMS-ISA COVID-19 HFPS were implemented in Ethiopia, Malawi, Nigeria, and Uganda. In each country, the aim has been to conduct monthly phone interviews over a period of 12 months.

Phone surveys have the advantage of creating opportunities and learning experiences for the direct and timely analysis of developments in dynamic labor markets; however, there are several drawbacks as well. These include (i) selection bias since households that do not own a phone are automatically excluded; (ii) non-response bias that may arise particularly in longitudinal studies; (iii) heterogeneity in phone coverage across population groups and regions; and (iv) a limited scope for verifying the accuracy of answers. Phone surveys face restrictions with respect to the questionnaire design; since interview times are limited, phone surveys must therefore rely on short, simple questions with similarly short response options. In addition, interviews have to rely on data provided by one representative household member, usually the “household head”, because it will be cumbersome to try to interview all household members. These drawbacks need to be actively considered when implementing the surveys and analyzing the data collected. However
how bilateral bargaining among supply chain partners alters the mapping from tariffs to prices, and therefore optimal trade policy. In contrast to their approach, we are agnostic about the nature of price determination within global supply chains; our results obtain even if prices are determined by market clearing conditions, as in conventional models. Our work also builds on Blanchard (2007, 2010), who shows that foreign direct investment and international ownership alter the standard mapping from prices to income, and thus optimal tariffs. Though similar in spirit, the mechanics and empirical implications of the model in this paper are different. Our theory links observable input trade patterns to bilateral tariffs, separate from ownership concerns.

Turning to the empirics, our first contribution is that we combine data on bilateral import protection and value-added contents to test key predictions of the theory. We focus our analysis on dimensions of policy over which governments have scope to implement discretionary levels of protection.<sup>2</sup> We first examine bilateral applied tariffs, where countries offer preferential tariffs to selected partners. We then examine the use of temporary trade barriers (antidumping, safeguards, and countervailing duties) in a separate, complementary set of exercises.

Our approach to analyzing bilateral tariffs is guided by both the theory and key institutional features that govern tariff setting in practice. Theory motivates the empirical specifications we adopt and our choice of controls. In a first specification, we focus on identifying the role of domestic value added in foreign production, using fixed effects to control for export supply elasticities, political economy, and foreign value-added effects. We then turn to a second theory-based specification to identify the role of foreign value added in domestic production. Throughout the analysis, we measure value-added contents using input-output methods and data from the World Input-Output Database.

Because institutional features of the multilateral trading system constrain policy, we are careful to incorporate them into our empirical strategy. While governments have discretion to offer preferential tariffs bilaterally via various trade preference programs (under the GATT’s Article XXIV
Moreover, to account for the possibility of opportunistic behavior by governments, we introduce the possibility that, when a new government is elected, there is a small chance that it expropriates the rents secured by the firm in the initial contract. If the probability of this outcome is not too large, no ratchet effect occurs and we have a model displaying both Pareto improving and rent shifting government-led renegotiations. If it is large, there is ratchet effect or no investment at all, so no renegotiation occurs.

As a result, we obtain an equation for the probability of government-led renegotiation that allows us to derive theoretical predictions for the main variables in our analysis. While the direction of the effects are unchanged for a number of variables, like shocks or the existence of a regulator, theory leads to expect opposite effects in the case of the variables affecting the status quo of the parties in an eventual renegotiation, namely the financing variables (investment requirements and private financing) and institutional quality.

We then present empirical results based on the same sample as in GLS, using as dependent variable the occurrence of a renegotiation at the initiative of the government. From the empirical viewpoint, a number of differences with firm-led renegotiations are also to be expected. One open question is the influence of price cap regulation. Firms’ calls for renegotiation were the intuitive outcome when the cap appeared to be too restrictive. Here, we could expect a reverse effect, in the sense that governments would like to renegotiate caps that proved too generous and leave excessive profits to the firms (the opportunistic calls). However, it could also be the case that governments concerned about the continuity of service or the realization of planned investments would step in when firms are making losses, resulting in a similar effect of price cap on firm-led and government-led renegotiations. Another aspect of interest is the behavior of variables affecting the status quo of the parties in a renegotiation, in particular the financing variables, which effect should be the opposite as with firm-led renegotiation. Finally, it
Malečková (2003) refute this notion as they find no evidence of a causal connection from poverty to terrorism. The authors claim terrorist activities are more likely to be associated with political conditions and social frustration, than with suboptimal welfare conditions. In line with this finding, Abadie (2004) shows that the risk of terrorism is relatively similar between developed and developing countries, after considering country-specific characteristics, and concludes that the level of political freedom is better at explaining terrorist incidents compared to economic and poverty conditions of the population.

Moreover, the consequences of a terrorist attack could be underestimated as they appear to affect only some parts of the economy. Becker and Murphy (2001) claimed that the attacks on the World Trade Center on September 11th in New York would barely affect economic outcomes since they only represented a loss of 0.06% of the stock of capital in the US. Yet, several studies indicate large effects on the economy. Abadie and Gardeazabal (2008) show that even if the threat of an attack only accounts for a small share of the overall risk, terrorism can have a substantial impact on the allocation of productive capital across nations. The risk of an attack increases uncertainty, reduces expected return to investments and induces a decline in net foreign direct investment (FDI).

Empirical research has documented the effect of terrorism on consumption and gross domestic product (GDP) due to increased uncertainty and disruption to the markets. Eckstein and Tsiddon (2004) use a long time series data and find a decrease in annual consumption per capita of around 5% after a year from a terrorist incident. Fielding (2003) describes how violence in Israel explains reductions on aggregate consumption and savings between 1987 and 1999. Using a synthetic control method, Abadie and Gardeazabal (2003) estimate a decline of 10% in GDP per capita over two decades as a result of terrorism in the Basque Country. Some authors have analyzed the consequences of terrorism on other outcomes. Nitsch and Schumacher (2004) associate terrorist actions with