rafmacalaba/data-use-mentions-extended huggingface dataset:
- _tech_ = domestic firm’s technology indicator as defined in previous section, where _tech_ ε {0, 1, 2, 3, 4}. The technology indicator serves as a proxy for R&D intensity which is unavailable;

- _skills =_ domestic firm’s share of high-skilled labor in firm’s total labor force;

- _size_ = domestic firm’s total number of permanent and temporary employees in natural logarithms;

- _aggl_ = region’s total number of manufacturing and services firms as percentage of a country’s total number of manufacturing and services firms. This measure is a proxy for urbanization economies (locational advantages) and covers both domestic and foreign firms<sup>15</sup> ;

- _exp_ = domestic firm’s share of direct or indirect exports in firm sales.

# **3.5 Measures of National Characteristics and Institutions**

In a third step, we turn to mediating factors at the national level. We include the following country-level variables into the model.

- _labor_ = measure of labor freedom in natural logarithms from the Heritage Foundation and captures labor market institutions. The variable ranges from 0 to 100 (highest labor freedom) and includes various aspects of the legal and regulatory framework of a country’s labor market, such as minimum wages; laws inhibiting layoffs; severance requirements; and measurable regulatory burdens on hiring, hours, etc. The measure is mainly based <mark>on data from the World Bank’s Doing Business study;</mark>

- _finance_ = measure of financial freedom in natural logarithms from the Heritage Foundation. The variable measures banking efficiency as well as a measure of independence from government control and interference in the financial sector with scores ranging from 0 to 100 (highest financial freedom). This measure relies on various underlying data sources, including (in order of priority) the Economist Intelligence Unit, the International Monetary Fund, the Organisation for Economic Co-

> 15 We included services and foreign firms in this
the bank, we subtract the disbursal amount from the discounted stream of repayments.<sup>12</sup> To achieve as representative a sample as possible, we also include 26 credit files from clients who applied, but were turned down by the Lender. For the incentive payouts in our study, these rejected loans are treated as non-performing<sup>13</sup> . Throughout the analysis, we report results disaggregated by non-performing and declined loans and show that our results are unaffected by the classification of loans declined ex-ante by the Lender. Importantly, the final columns of Table III show that loan files contain information that may be useful in distinguishing good loans and bad loans, suggesting there are returns to effort in this setting.

# **4.3 Loan Officers and Experimental Procedure**

Loan officers were recruited from the active staff of several leading private and public sector commercial banks. Participants first attended an introductory presentation, completed a practice session, and then participated in up to 15 sessions of the experiment, in which they evaluated six loan applications per session. Working conditions and presentation of information were designed to closely resemble the actual work environment of the representative loan officer.<sup>14</sup> Experimental sessions were scheduled to last one hour, although participants could finish early or late if they so chose. Incentive treatments, as described in Section 2, were randomly and individually assigned at the loan officer and session level, such that officers evaluated a batch of six loans under a given scheme.<sup>15</sup> The experiments took over a year to complete, and not all incentive schemes were eligible to be assigned in any given session.

> 12We estimate the Lender’s net profit per loan as the net present value of the disbursal plus repayments including interest, discounted by 8%, the approximate rate on Indian commercial paper between January 1 and December 31, 2009, and assuming a 10% recovery on defaulted loans.

> 13However, we
the previous section, we conducted two rounds of household surveys, one immediately prior to the intervention (baseline) and one a year

> 33For intent-to-treat estimates, these households are considered treated. Compliance is discussed below.

> 34Survey enumerators made at least four attempts to survey the household. If enumerators were informed that the previous respondents had moved, then the new residents were surveyed. A total of 835 original respondents were re-interviewed for the follow-up survey.

> 35A tariff reform was introduced in late 2014 (see McRae and Meeks (2016) for details of reform) and therefore we end our analysis in September to avoid conflating the CFL intervention with the tariff change.

> 36Because electricity consumption data are monthly, we cannot use them to calculate timing of outages.

13
difficult to modify in the short term. This definition, which is comparable to what is used in EU member states for agriculture sector monitoring, allows fields to be identified using machine learning algorithms trained on EU data using medium resolution satellite imagery, in our case stacks of sentinel imagery,<sup>11</sup> rather than a possibly outdated or incomplete cadastral map.

Access to field boundaries allows us to compare if field, rather than cadastral parcel, size affects land prices. Moreover, for a limited subset of fields, we were able to access registry information by using the commercial registry API to obtain information on registered rights to all the cadastral parcels in the field. This allows to analyze the impact of different types of informality (i.e., not only the presence of cultivated area not mapped in the cadaster but also area mapped but not registered or the number of registered owners or users) within a field. Finally, we use cadastral numbers to construct the number of lease and sale transactions in each village and year to identify if the volume of market transactions affects prices.

> 7 Full reporting of prices for sales transactions started only in January 2023 following legal changes in mid-2022. Before that, sales prices were reported for less than 50% of transaction, a threshold that remains to be crossed for leases. Data on contract terms is missing for about 7% of lease transactions with price data. For transacting parties that are not legal entities, only synthetic IDs are available to protect personal data.

> 8 Files for transactions can be accessed at <u>https://land.gov.ua/monitorynh-zemelnykh-vidnosyn/. We exclude a total of 47,515 sale and 8,317 lease</u> transactions with reported prices for ‘gardens’ (land use categories 01.05, 01.06 and 01.07) that may have other unobserved infrastructure attached to it throughout.

> 9 Data for personal and commercial use used in the regressions are winsorized prices
resources for their states, however, depends at least in part on their ties to the president. The emphasis is on membership in either the President’s party or coalition on the part of Senate delegations from each state. For each state’s senate delegation we incorporate a measure of the proportion of the federal senators from each state that are members of the PSDB, and similarly the proportion that are members of parties included in the president’s coalition. The relationship between these measures of political closeness to the Cardoso government and PROES decision, however, is not unambiguous. On the one hand, one could expect states whose senators are politically closer to the federal government are better able to negotiate to keep their banks or at least privatize themselves. On the other hand, one could expect states whose leaders were more closely tied to the ruling coalition in the federal government to more easily agree to liquidation or federalization in return for other political or fiscal benefits.

We assess the explanatory power of political economy and developmental theories of government-owned banks by regressing _Transformation_ on an array of economic, social and bank-level financial indicators.<sup>30</sup> Given the ordinal character of _Transformation_ , we employ an ordered probit model.<sup>31</sup> We use data bank-level data for 1996 and state-level averaged over 1995 to 2000, assembled from different Brazilian data sources.<sup>32</sup>

> 30 We do not include the two states without state banks and the Federal District, which opted out of the PROES, and did not privatize its bank. We do, however, include Paraiba, since it privatized its state bank, though outside the PROES; our results, however, are robust to its exclusion. We also include São Paulo as a case where bank privatization happened under the auspices of the federal government.  For the regressions including bank-level performance indicators as explanatory variables, we lose observations since we do not have observations for most of the liquidated banks.  In the cases
ten equal groups and is expressed in logarithms. All monetary values were in real terms in August 2014. Model with fixed effects by the municipality and by year for 2005, 2010, and 2014, also using the FAIS formulated as the instrumental variable. The nonmonetary dimensions are expressed in 0–100, understanding 100 as 100% of coverage.

∞ Fiscal revenue items in the logarithm of the average monthly per capita value three previous years. ‡ Occupied per inhabitant, employees of the economic units or indirect workers, reported in the economic census of the circa year. ‡ ‡ Productivity by the total of occupied and active of the economic units of each municipality. Standard error in parentheses. *** p < .01 ** p < .05 * p < .1 33
percent. Corporate tax – at a 30 percent rate – is levied on profits<sup>12</sup> and is mostly payable by resident limited companies and cooperatives.

_Figure 3. Tax structure in different countries a. Indirect taxes as percent of GDP b. Direct taxes as percent of GDP_ <!-- Start of picture text --> 25% 20%<br>20% South<br>15%<br>Africa<br>15% Tanzania<br>Kenya<br>Tanzania 10%<br>10% Ethiopia Ghana<br>South<br>5% Uganda Africa 5%<br>Ghana Uganda<br>Kenya<br>Ethiopia<br>0% 0%<br>1,000 10,000 1,000 10,000<br>GDP per capita ( 2011 PPPs) GDP per capita (2011 PPPs)<br>% of GDP % of GDP<br><!-- End of picture text --> _Source: CEQ Institute database, World Bank WDI for GDP 2011 PPP per capita numbers._ _Note: The data for Ethiopia corresponds to 2010, for Ghana to 2012, for South Africa to 2010, for Tanzania to 2011, for Kenya 2015._ In the past 15 years, major reforms have been implemented to increase the revenue from the VAT and PAYE taxes, such as an increase of the standard VAT rate from 17 percent to 18 percent in 2005 (SEATINI, 2017). Even so, the fiscal authority has consistently missed tax revenue targets by 0.5 percent of GDP in the last few years (IMF, 2019).

# 2.4. Expenditures

Public expenditures in Uganda reached 19 percent of GDP in fiscal year 2016/17, a slight decline from 20.1 percent in 2015/16. This share is slightly higher than that of Ethiopia (18.6 percent) but considerably lower than that of Tanzania (31.4
<sup>3</sup> Traditional indicators are also often produced with delay, or not at all, particularly during crises when economic variables deteriorate rapidly.<sup>4</sup> Recognizing these limitations, international institutions have invested heavily

> 1The significance of price inflation as a signal of deteriorating food security can be highlighted by noting that the recent crises in South Sudan and the Republic of Yemen are characterized by high inflation. Historically, the Bengal famine of 1943 followed after a period of hyper-inflation, starvation in the Weimar Republic occurred alongside record hyper-inflation, the 1980 Uganda famine occurred after double to triple digit inflation rates, the 1992 famine in Southern Somalia was preceded by a year of triple-digit inflation, and the 1998 famine in southern Sudan was preceded by years of double digit inflation. The use of food price indicators to track famine risks is documented more substantively by Seaman and Holt (1980); Cutler (1984); Khan (1994); Andr ́ee et al. (2020); Wang et al. (2020).

> 2Naturally, there are other critiques around inflation calculations, some as old as the methodologies themselves. Some accusations about both up- and downward biases are discussed by Reinsdorf et al. (2009). Often, conflicting views on inflation can be traced back to differences in (baskets of) goods and data sampling locations, and so for some applications improved clarity can be gained by more narrowly defining indexes, which the results of the paper may also help enable.

> 3See also the recent economic update for South Sudan (World Bank, 2021). The work compares food prices subnationally and finds that increasing prices are the most significant factor driving recent food insecurity but documents strong spatial heterogeneity in both market price dynamics, as well as relations to food insecurity, and provides examples of markets where prices are more sensitive to localized shocks.

> 4For instance, the International Finance Statistics (IFS) data base of the IMF reports monthly price data at Consumer Price
geneous impact of DTAs on firms with different characteristics is confirmed by 2SLS estimations - see Table 9 columns 2-7.<sup>23</sup> The interaction terms are instrumented by simply interacting the firm indicators _I_ ( _kf > k_<sup> ̄</sup> ) with the IV discussed above. The bottom part of Table 9 shows the first stage results of the 2SLS approach. Our IVs (one for the RTA depth and the other for its interaction with the firm type dummy) are good predictors of the endogenous variables (i.e. significant first stage coefficients). Also, the joint F-stat statistics well above 10 support the absence of a weak instrument problem.

## **6.1 IV validity**

As discussed above, the validity of our instrumental variable is based on the absence of a direct effect of the depth of RTAs signed with third-country on _fij_ -specific exports. In this section we test the robustness of our baseline results to a deviation from the perfect validity of the exclusion restriction (Conley et al., 2012). A degree of deviation from the exclusion restriction can be obtained by regressing the exports of firms _Xfijt_ on our main _DTAijt_ and IV variable. The coefficient associated to the IV represents an approximation of the direct effect of the IV on the outcome variable (i.e. degree of deviation from the exclusion restriction) – van Kippersluis and Rietveld (2018). We obtain a small and not statistically significant direct effect of IV on firm exports - see parameter _ν_ in Table 10. We therefore plug such a degree of deviation from exclusion restriction in the plausible exogeneity test à la Conley et al. (2012), and obtain a lowerand upper-bound coefficients that do not cross the zero. Thus, we can safely argue that the depth of RTAs has an unambiguous positive causal effect on the export of firms in developing countries.<sup>24</sup>

# **7 The dynamic effect of deep RTAs: An event
# **Figures**

Figure 1: District-wise risk-index of land use <!-- Start of picture text --> 1998 2000 2002 2004 2006 2008 2010<br>Anantapur Cuddapah<br>Karimnagar Mahaboobnagar<br>Srikakulam West Godavari<br>.5<br>.45<br>.4<br>Risk Index .35<br>.3<br>.25<br><!-- End of picture text --> Source: Author’s estimation based on the Land Use Statistics and District-wise Crop Production Statistics, Ministry of Agriculture, GoI.

33
LMICs (Mitra, Posarac and Vick (2013) find a significant difference in household per capita expenditures across disability status in only three countries.

This paper’s result needs to be contextualized with the well-established literature on the socioeconomic determinants of health inequalities (Marmot 2005) and with growing evidence of the causal impact of disability on earnings (Mani et al 2018), consumption expenditures and assets (Takasaki 2020).

Several issues should also be noted with regard to using household expenditures as a dimension of economic well-being in the context of this study. First, there is a possibility that the intrahousehold distribution of expenditures is unequal across disability status, which is not captured here.

Second, if poverty is measured through overall or non-health per capita expenditures against a poverty line as done in Table 4.3, the comparison of households with a disability to other households may be biased because households with disabilities may have additional expenditures due to the disability (for example, transportation, personal assistance). Consumption-based poverty indicators used above do not account for extra costs related to disability in order to provide a more accurate assessment of poverty among persons with disabilities (Mont and Nguyen 2017).

More research is needed on the links between disability and monetary poverty, including on extra costs of living with a disability and the dynamics of household expenditures, which may be affected by different patterns of household formation or mortality in relation to disability (Mitra 2018).

# **Social Protection, Economic Insecurity, Living Conditions and Assets**

Table 4.4 presents several household indicators for households with and without any difficulty. Appendix 8 gives results on the same indicators but at the individual level, i.e. whether persons with and without any difficulty live in households with specific household level outcomes such as receiving social protection or having safely managed drinking water. It should be noted that in Appendix 8 a given household will be counted as many times as it has adult household members with or without functional difficulty. Results are overall consistent with the
Table A3: Correlation between platform observations data and rider reports

|Platform observations|Rider reports<br>Share of men in reserved space<br>(1)|High crowding<br>(2)|
|---|---|---|
|Average share of reserved space riders who are male|0.636***<br>(0.055)||
|High crowding||0.246***<br>(0.029)|
|Constant|0.158***|0.490***|
||(0.017)|(0.018)|
|Observations|4846|20742|
|Riders|327|363|

_Note:_ Unit of observation is one ride. Sample includes all rides and riders with matching platform observations across all study phases. Rider report variables are collected for each ride and reported only with respect to the space chosen by the rider. As a result, column (1) is restricted to reserved space rides. Platform observations are taken on different days, then aggregated by time and line segment to be merged with rides. Standard errors in parentheses, clustered at rider level.

Table A4: Response to platform survey and IAT

||Dependent variabl|e:||
|---|---|---|---|
||Responds platform survey<br>R|esponds IA|T|
||(1)<br>(2)|(3)|(4)|
|Female respondent|0.045**<br>-0.055|||
||(0.020)<br>(0.034)|||
|Usually chooses reserved space||-0.036<br>(0.050)||
|Female family members usually choose reserved space|||-0.013<br
price impacts must be interpreted alongside nominal wage effects and vice versa. For instance, floods raise food prices by about 3.4% annually and agricultural wages by 1.8%, but the greater rise in food prices erodes purchasing power, making food less accessible—an effect not fully captured by wage data alone.

# 6.2 Food Security Monitoring

The interplay between environmental shocks and economic factors can create complex, counterintuitive outcomes—for example, rising food prices may slightly reduce food stress in agricultural regions due to increased income for producers, but when combined with inflationary pressures, they exacerbate overall food insecurity. Effective early warning systems must therefore target both direct and market-mediated food insecurity drivers.

Despite these complexities, the risks are well captured within a highly interpretable modeling framework. Our final estimates demonstrate that significant spatial and temporal variations in food insecurity can be tracked using readily observable data, making the model practical for real-time monitoring and early intervention. For example, the model captures widespread food stress across Afghanistan, with 28 out of 34 provinces exhibiting average probabilities of over 50% of being classified as IPC phase 2 or higher. In contrast, food crisis risks are more localized, with provinces like Ghor and Badakhshan showing probabilities exceeding 50%. These areas are likely more vulnerable due to structural factors such as geographical isolation, limited infrastructure, and greater exposure to climatic shocks.

The model's ability to reflect historical trends in food insecurity, such as through the global food price crisis of 2010–2011 and periods of increased conflict and climatic shocks, reinforces its practical proposition. Its capacity to detect early warning signals of escalating risks highlights the utility of this approach for continuous monitoring. By leveraging such models, policy makers and humanitarian organizations can anticipate food security crises, allocate resources efficiently, and implement timely interventions that address the compounded risks of climatic and economic factors. This ongoing monitoring is essential for managing the dynamic nature of food insecurity and improving resilience in vulnerable regions.

# 6.3 Limitations and Future
# **II Data**

In order to obtain estimates of welfare for 2014-15, we first identify variables that are common both to the three earlier consumption surveys and the 2014-15 round. The surveys have five demographic variables in common. They are household size, age of household members, gender, religion, and caste contained in the “household characteristics” section of each survey. The three common labor market variables are the household’s principal industry, principal occupation and principal means of livelihood.

The principal industry of a household is determined according to the National Industrial Classification (NIC) 2008 code for the NSSO72, NSSO68 and NSSO66 surveys and the NIC 1998 code for the NSSO61 survey.<sup>16</sup> Regarding the principal occupation, the NSSO72, NSSO68 and NSSO66 surveys categorize households according to the National Classification of Occupations (NCO) 2004 code and the NSS061 survey according to the NCO 1968 code. To ensure consistency, the occupation categories are harmonized across survey years. We further group households into high skill, middle skill and low skill occupation categories.<sup>17</sup> The household’s principal means of livelihood is determined from the “household type” variable. The categories are self-employed, regular wage/salary earning, casual labor and “other”.<sup>18</sup> In addition, the surveys ask questions regarding expenses on miscellaneous services such as household service (domestic help, barber, beauty, laundry, priest, grinding, tailor), recreation (cinema/theater, fairs/picnics, club fees, photography, VCD/DVD on hire) and transport (conveyance related expenses). We also include a district-level rainfall variable that accounts for deviations from the mean.<sup>19</sup> Second, we ensure that the wording and recall periods on the questions are comparable over time. Both different wording and/or recall periods would change the

> 16Households whose principal industry is agriculture, forestry or fishing are