rafmacalaba/data-use-mentions-extended huggingface dataset:
Area, and Yields**

We rely on both administrative data and remote sensing estimates.

## **A.C.1 Administrative Data**

For rice, we obtain data on production and cultivated area in each district from the ‘Paddy Statistics’ produced by the DCS for each growing season from 2012-2013 Maha to 2022 Yala.<sup>99</sup> Crucially, this time frame includes the 2019-2020 Maha and 2020 Yala seasons, which we use as the pre-ban baseline to estimate the model in Section VI.B.

For non-rice, non-tea crops (maize, groundnuts, potatoes, onions, cinnamon, and cloves), we obtain data on production and cultivated area at the divisional secretariat (DS) level from the DCS for each growing season from Maha 2019-2020 to Yala 2022, which we aggregate up to the district level. For tea, the DCS data includes information on cultivated area but not on production, so we

> 97Namely: “dhl express parcel delivery”, “d h l”, “fits aviation”, “cargo logistics worldwide aramex courier freight” “non commercial xpress by air freight” “Skynet shipping setrans” “sky international”.

> 98Such firm names have the structure “Person Name+Institution+PERMIT”. For example: “KADAWARAGE C V WING COMMANDER (DENTAL DOCTOR) SRI LANKA AIR FORCE MIN.OF DEFENCE PERMIT”.

> 99Paddy Statistics were accessed at: http://www.statistics.gov.lk/Agriculture/StaticalInformation/Paddy ~~S~~ tatistics.
Alesina, Hausmann, Hommes, and Stein (1999) and Fabrizio and Mody (2006) include MTEFs in aggregate indexes of budget institutions using data from Latin America and Eastern Europe, respectively.<sup>7</sup> Our results complement this empirical literature by providing evidence of MTEF impacts from a worldwide sample. While previous studies rely on small samples and either cross-sectional or static panel models our empirical methodology takes advantage of the time variation to estimate dynamic panel models. We also propose a new classi...cation of MTEF phases, based on the level of disaggregation of the central government’s ...scal objectives.

Our paper is also related to the broader empirical literature on budget institutions.<sup>8</sup> The most studied institutions have been balanced budget amendments, debt ceilings, tax and expenditure limitations, and supermajority requirements for tax increases. Examples include: Bayoumi and Eichengreen (1995), von Hagen and Eichengreen (1996), Stein, Talvi, and Grisanti (1999), Hallerberg and von Hagen (1999), Perotti and Kontopoulos (2002), Fatas and Mihov (2003), von Hagen and Wol¤(2006), and Hallerberg and Ylaoutinen (2010), all using country-level data; and Poterba (1994), Bohn and Inman (1996), Kiewert and Szakaly (1996), Poterba and Rueben (1999), Knight (2000), Knight and Levinson (2000), and Fatas and Mihov (2006) using state-level data. Important lessons from this literature are that numerical constraints have limited e¤ectiveness because they can be circumvented, that the e¤ect of reduced ...scal discretion on macroeconomic volatility remains an open question, and that the political environment matters for the e¤ectiveness of budget institutions.

We contribute to this literature by proposing a dynamic panel approach (di¤erence GMM) that models the ...scal adjustment process while at the same time addressing the issue of institutional endogeneity
stringent regulation better maintained previous trends in earnings without incurring large employment costs._ Figure 9 shows how earnings and employment are associated with each set of country characteristics after accounting for the declines in countries’ GDP growth.<sup>22</sup> The most striking results are those associated with exports and firing regulations. High export countries were much better able to maintain earnings, at negligible cost to employment. Countries with higher redundancy costs were better able to protect employment, on the order of 1 to 2 percentage points. However, this may have partially come at the expense of greater earnings reductions. Countries with more costly and cumbersome firing regulations had two to three percentage point greater declines in earnings. The relationship between firing regulations and earnings declines is only suggestive and not statistically significant, however, due to the imprecision of the earnings measures.

**Figure 9**

> 22 Figure 9 reports coefficient estimates from 7 regressions. Each bar represents the coefficient from a regression of employment or earnings growth on the change in GDP growth, its square, cube, and quartic, and one set of dummy variables.Stars indicate significance level. For earnings and employment equations, the regression also controlled for whether the data was derived from a firm or a household survey.

10
Covid-19); and in columns (3) and (6), from January 2015 to December 2023 (Invasion of Ukraine). Robust standard errors, clustered at the source-time level, are reported in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

# **5. Extensions to jobs**

This section expands the analysis to assess the impact of recent shocks on estimated jobs created directly through greenfield investment projects. Given the identified reshoring trend, we test for similar trends using information on the number jobs in the US associated 16
|0.003|0.021**|-0.002|0.018|
||(0.008)|(0.012)|(0.009)|(0.011)|(0.012)|
|0-1 quarters after|-0.003|-0.015*|-0.011*|0.018**|0.020**|
||(0.006)|(0.009)|(0.006)|(0.008)|(0.009)|
|2+ quarters after|-0.003|0.001|0.004|-0.007|0.003|
||(0.005)|(0.008)|(0.005)|(0.007)|(0.007)|
|Average|0.772|0.326|0.121|0.552|0.329|

_Source_ : Author’s calculations from Cambodia SES 2007-2015. _Note_ : The table is identical to Table 1 except that it restricts the observations to those surveyed within 6 quarters of the minimum wage hike. Standard errors, clustered by survey year _×_ psu shown in parentheses. ***: p _<_ 0.01; **: p _<_ 0.05; *: p _<_ 0.1.
RTA signed between South Asian countries is captured by _βrta_ + _βSAR_ + _βSARintra_ , while _βrta_ + _βSAR_ captures the RTA effect when signed with the rest of the world.

There is one particular RTA effect that is worth estimating, that is the South Asian Free Trade Area (SAFTA). This agreement is the main intra-regional agreement and entered into force in 2006 for most countries in the region. To refine this estimation I model the bilateral trade costs part as follows: where the _βsa f ta_ , _t_ coefficients capture the SAFTA effect on trade over time, allowing us to observe the dynamics of its implementation.

# **3 Data**

This paper studies the trade creation effects of trade agreements in South Asia. Important aspects of the effect of trade agreements are only properly captured when using data that cover as many countries as possible and when including domestic trade flows as the literature emphasizes. For this reason I use Eora multi-region input-output tables (Lenzen et al., 2012, 2013).

The Eora database consists of multi-region input-output (MRIO) tables that provide a time series of IO tables for 190 countries. I use the version at the common 26 sectors. In this model, all countries have been aggregated to a common 26-sector classification (see table (8)) and the supply-use tables have been converted to symmetric product-byproduct IO tables using the Industry Technology Assumption. This MRIO thus contains only symmetric product-by-product and industry-by-industry IO tables. This data is basic prices and covers the period 1990-2015 for 190 countries.

While the empirical strategy presented in this paper is very demanding in terms of the data required, it does not require using Eora in full detail. I need bilateral trade flows at the sector level including domestic trade flows and differentiating by type of good (final and intermediate). Note that Eora provides sector-country to sector-country trade flows 6
men in the same period.

The negative employment effects among females are also confirmed by similar results from simulation exercises using pre-pandemic data. For example, Lavado et al. (2022) find that, during the onset of the pandemic, female workers in the Philippines experienced a 4.5-percentage-point

> 10 Whereas in the United States and Canada previous studies have found that employment had dropped by only 21 percent and 15 percent, respectively, in April 2020. One should cautiously interpret the magnitude of the differences, as they could arise due to the different respondent types and sampling methodologies between the HFPS and nonHFPS studies. HFPS studies, such as Khamis et al. (2021) and Kugler et al. (2021), are typically only representative at the household level.

10
# **5. Demand system estimation**

The challenge of estimating demand functions is to maintain empirical applications coherent with microeconomic theory, especially with respect to assumptions and restrictions from demand and consumer behavior theories. Since the seminal work of Stone in 1954, who developed the Linear Expenditure System (LES), the evolution of empirical demand models progressed significantly. The highest coherence with demand theory has been achieved so far by considering the non-linearity of the Engel’s curves and including in the demand function a quadratic term of the relationship between total expenditure and prices. The quadratic model that follows the demand and consumer theories most closely is the Banks et al. (1997) QUAIDS, which is employed in this paper.

Although QUAIDS can effectively deal with the issues related to the coherence with microeconomic theory, a remaining issue is the potential endogeneity of prices and expenditure when using survey data and unit values as proxies for market prices. That is, expenditure and/or unit values can be correlated with the errors, resulting in inconsistent estimations and in biased shapes of the Engel curves. This endogeneity can be caused by a variety of factors, such as measurement errors, unobserved commodity quality and unobserved prices and expenditure shocks (Blundell and Robin, 1999).

In Section 3, we discussed the two main approaches that are used in the literature to address this endogeneity bias when market prices are not available – i.e. Deatons’ and CW’s approaches. Both approaches assume that the variation in unit values that cannot be explained by the household characteristics is caused by quality differences, correcting the unit values. Moreover, Deaton's method necessitates weak separability restrictions, which overstate quantity responses over quality responses, and it uses the geographical clustering structure of data assuming that households in the same cluster share the same market price. The CW's approach has the advantage of being easier to implement, but it does not deal with measurement error problems.

However, if there is correlation between prices/expenditure and the
We expand the types of transportation projects analyzed. (iii) We use a structural gravity general equilibrium approach

> 1A related but distinct line of research studies the impact of infrastructure development on allocative efficiency in developing economies. Asturias, Garc ́ıa-Santana & Ramos (2019) use plant-level data and focus on domestic sales in India, while we use multi-country aggregate data and consider domestic and international sales.

> 2Examples of recent applications of New Quantitative Trade Model (NQTM) and Computable General Equilibrium models (CGE) to the Africa region are presented in the following studies Abrego, Amado, Gursoy, Nicholls & Perez-Saiz (2019) (for NQTM) and Bengoa, Mathur, Narayanan & Norberg (2021), World Bank (2020), African Development Bank (2019) (for CGE).

3
# **Supplementary Information**

# **SI-1 Materials and Methods**

## **SI-1.1 Data**

We use data on the tracks of every Atlantic basin hurricane that has struck the continental United States from 1988 to 2018.<sup>1</sup> These data are based on information tracked by the NOAA Storm Events database and the National Hurricane Center. They provide us with information on the track, rainfall totals, and wind speeds associated with the storms, as well as the counties that experienced a flood event as a consequence of each storm.

We define counties as exposed to a hurricane in three ways. In our primary analysis, a county is considered exposed if it experienced a flood warning within one day of the track of the storm passing through it OR if it experienced winds greater than 21 m/s in the same time period. In alternative analyses, we use the flood warning definition and the wind speed definition individually to assign exposure. In Figure SI-1, we show the counties effected by hurricanes under these alternative definitions in our sample. We choose 21 m/s as our wind threshold because this is (a) the wind speed at which NOAA indicates structural damage to buildings will begin to occur and (b) approximately the lower bound for wind speed for a storm to be considered a tropical storm. It is substantially below the minimum wind speed on the Saffir-Simpson scale for a Category 1 hurricane. We note, however, that our results are robust to using higher wind speed thresholds and that, as one increases the wind speed threshold to the Category 1 threshold, the set of impacted counties collapses to those that would be included based on the flooding definition. We show in the supplementary materials that our results are robust to using only those counties that meet this flooding threshold.

We supplement these data with data from FEMA on the total payments made to individuals for every disaster declared by FEMA since 1954.

Our migration data come from the IRS Statistics of
As to be expected, even though the OI interpolation performs better than other techniques, a cross-validation exercise shows performance to degrade significantly with increasing distance to nearest station (Chen et al., 2008). As a result, this data set may not be suitable for analysis in some parts of Africa, with high spatial variation and low density of stations.

Data and technical documentation are available for download from `https://psl.noaa.gov/ data/gridded/data.cpc.globalprecip.html` for precipitation and `https://psl.noaa.gov/data/ gridded/data.cpc.globaltemp.html` for temperature.

# **A.1.4 European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5**

ERA5, based on the European forecasting model ECMWF, is one of two assimilation model data sets used in this paper. The inputs are far too numerous to mention but include a range of satellite inputs as well as gauge data sets. There are a wide range of outputs as well, including 2-meter air temperature and rainfall, available at sub-daily intervals and differentiated vertically. ERA5 is coarser spatial resolution than the global and regional merged rainfall data sets, but more detailed than MERRA2.

The sheer number and complexity of outputs can be a deterrent to the use of weather variables from assimilation models. Uncertainty or lack of understanding about inaccuracies associated with individual output variables of assimilation models, compared to other types of models, is another reason to carefully consider their suitability for particular research (Parker, 2016). Nevertheless, reanalysis data sets are used in a broad range of applications and even outperform other gridded climate data sets in some settings (Zandler et al., 2020).

Data and technical documentation are available for download from `https://cds.climate. copernicus.eu` .

# **A.1.5 Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2)**

The second reanalysis data
percent in Other Cities, compared to the 15 percent decline in East Forest and the 21 percent cent decline in the Savannah. Despite the large fall in household expenditure in Abidjan, there was no increase in poverty, since even in 1988 mean household expenditure per capita was still three times greater than the poverty line of 75,000 CFAF per year. The decline in expenditures in rural areas was accompanied by a large increases in poverty in West Forest and in the Savannah. West Forest registered the largest fall in expenditure, almost 50 percent between 1985-1988.

Between 1985 and 1988 per capita expenditure fell by 33 percent, a much larger fall than the decline in real private consumption per capita of 6 percent. The large difference in expenditure trends between the household surveys and national accounts estimates has not been accounted for. The large decline in household expenditure per capita and the increase in poverty in the West Forest region-- a cocoa and coffee growing region like the East Forest-- is especially puzzling. Household expenditure per capita in the West Forest was 50 percent higher than East Forest in 1985, but fell about 20 percent year, bringing it to a level below that of the East Forest in 1988. In 1985 and 1986, West Forest had a 4 The 1985-1988 household survey data come from the four COte d'Ivoire Living Standards Surveys (CILSS) and are based on the expenditure aggregate described in Oh and Venkataraman (1992). The 1993 and 1995 data are taken from the Household Priority Survey (HPS). Changes to the cleaned 1993 and 1995 INS data files (as of March 1996) are described in Annex 1. The comparability of the methods used to estimate food expenditure in the CILLS and LPS surveys are discussed in Appendix B.

5 Grootaert chose the 75,000 CFAF per day poverty line as the index of extreme poverty because it corresponded to a headcount index of 10 percent in 1985, based on the set of deflators he used. This paper uses the CPI series published by the INS
23%)<br>|(0.18%)<br>|(0.12%)<br>|(0.27%)<br>|

~~Notes: Estimates of variance components using the method described in the text (Equations 9-11) on PWT9~~ data (excluding outliers). Established Democracies are defined as countries with an average polity score>7.5 (Autocracies/Transition Countries otherwise) *SD(iid) is the across-sample SD of the iid error, though in the estimation of leader effects, we allow this to vary across countries. Standard deviations of estimates based on a country-level block bootstrap are shown in parentheses.

21
, and the index value of amenities _A_ ( **x** ). The disutility parameter of informal housing ( _B_ ) is calibrated to reproduce the distribution between housing types generated by the model.

Below, we describe in more detail data sources and the calibration process.

# _4.1. Data sources_

The spatial distribution of population is taken from National Censuses for the years 2001 and 2011. We define the four income groups by choosing income-group thresholds such that only the lowest income group is eligible for subsidized housing programs, and so that the two highest income groups are not observed to reside informally (see Appendix A for details).<sup>31</sup> We use the transport model used by the City of Cape Town to retrieve transport times between pairs of transport zones for each transport mode and job locations.<sup>32</sup> We also use aggregate statistics on modal shares and residence-workplace distances in Cape Town, that are derived from Cape Town’s 2013 Transport Survey.

Land availability is defined for each housing type. Areas of subsidized housing are identified from the cadastre of the City of Cape Town.<sup>33</sup> The area available for backyard housing is estimated as the yard size of these units. Informal settlement areas are obtained from the Enumerator Area

> 30For simplicity, in what follows, we do not index these variables with time.

> 31 The Census captures annual income, which we assume reflects the employment rate χ _i_ . Households eligible for subsidized housing are the ones with an annual income (χ _iwi,c_ in the model) below the threshold of R38,200.

> 32The origin-destination matrix is produced by the City of Cape Town’s four-step travel demand model, last updated in 2013 using the EMME/2 software. The model was designed by INRO Consultants at the University of Montreal and adopted by the City of Cape Town in 1991. The model implements an equilibrium route assignment based on the distribution of trip origins
diaries have often been used as a benchmark for collecting information on food consumption (Beegle et al., 2012). Despite the “gold standard” status of diary methods, discrepancies can occur due to compliance or supervision issues, diary fatigue,<sup>4</sup> education, and even changes in spending behaviors<sup>5</sup> (Beegle et al., 2012; Browning et al., 2014; Burke et al., 2011). These drawbacks are significant enough that there is some disagreement about the accuracy of diary measurements.<sup>6</sup> Because of this, diary methods need strict protocols to increase compliance and reduce response burden to yield accurate consumption data (Browning et al, 2014). Greater interviewer

> 3 In addition, the impacts of disaggregation on measures of inequality and welfare are still unclear (Beegle et al., 2012; Browning et al., 2014; Hurd & Rohwedder, 2013).

> 4 For example, Ahmed et al. (2006) find a drop off in diary records of around 10 percent between the first and second week of the diary. In this and other studies, respondents stopped measuring their consumption as time went on due to the burden.

> 5 In other contexts (weight loss, financial and health habits and instrument take-up), surveys have been found to have impacts on behaviors (Burke et al., 2011; Crossley et al., 2017; Zwane et al., 2011).

> 6 In national budget surveys, recall and interview survey measurements can sometimes work as well as or better than diary measurements (Bee et al., 2014; Browning et al., 2014).

6
|100|

Note : Figures in parentheses indicate the number of countries for which data are available. Source: USAID Demographic and Health Surveys; World Bank, _World Development Indicators_ , various years; International Telecommunication Union; Angel, 2000.

New investment also requires policy changes to ensure that service quality is improved and maintained at a reasonable level, and that assets are effectively operated and systematically maintained. Preventive maintenance lowers operating costs, reduces adverse external impacts, and extends asset life—savings that are generally underestimated when maintenance budgets are cut to meet fiscal targets. There is evidence that shifting resources toward maintenance can have positive consequences for GDP and welfare. For example, Rioja (1999), using data for seven Latin American countries, found that they allocate two-thirds over their resources to new infrastructure investment and only one-third to maintenance, a pattern that was exacerbated by international donors. Shifting just 20% of donor aid away from investment and toward maintenance would raise GDP and welfare by about 15% in the long run.

The only data available on the perceived quality of infrastructure services reflect commercial and industrial perspectives (table 6). No systematic effort has been made to collect comparable data from residential users—a clear information gap.

15