rafmacalaba/data-use-mentions-extended huggingface dataset:
our risk index is -0.264 in our preferred speci...cation. Therefore, if Tanzania’s level of ...nancial development reached Senegal’s, then the hazard rate of shrimps exports would decrease by � � Risk � �FinDev = �0:264 � 2:97 � (0:145 � 0:051) � 7%.

# 6 Robustness Checks

## 6.1 Alternative Measures of Financial Development

In Table 2 we report results using alternative measures of risk (column 2) and ...nancial development (column 3 to 5). Column (1) reports our preferred baseline speci...cation for the sake of comparison. In column (2) we use an alternative measure of risk (alt_sanitary_riskk)–constructed by Jaud and al (2009a) using a Poisson model instead of a Negative Binomial. Results remain qualitatively the same<sup>21</sup> . Column (3) and (4) report results using alternative measures of access to ...nancing. Local ...nancial markets are not the only source of ...nance for exporters.

Firms operating in countries with poorly developed ...nancial markets may rely on trade ...nancing provided by institutions in the destination country. The interaction term between our risk index and the measure of short term credit from the BIS banks (TCc) is negative and statistically signi...cant at the 10% level. The coe¢ cient is negative but not signi...cant when using the ratio of trade insurance to GDP (ICc) interacted with risk. Column (5) reports results when using the Ease of Getting Credit index (EGCc): The coe¢ cient on the interaction term with the risk index comes out negative and signi...cant at the 5% level, suggesting that the quality of domestic ...nancial institution increases survival of "risky" products in foreign markets.

>
whether or not long-term unemployment is primarily a result of ineffective job search. These two questions will be dealt with in subsequent sections.

As far as the composition of long-term unemployment is concerned, 72 percent of long-term unemployed belong to the prime-age group (25-54). Non-Latvians constitute 58 percent of the long-term unemployed. 28 percent of the long-term unemployed have basic education or less, and another 21 percent have professional basic education (ISCED 2C) or vocational (professional after basic, possibly followed by a fast-track general secondary) education (ISCED 3C, ISCED4A).

# **F. REGIONAL DISPARITIES AND COMMUTING**

There are four major dimensions of geographical disparities in Latvia: (i.) urban – rural; (ii.) Riga region – other territory; (iii.) inter-regional disparities between the five NUTS 3 regions (Riga region, Vidzeme, Zemgale, Kurzeme, Latgale), and (iv.) between 33 NUTS 4 regions (7 main cities and 26 districts).

Income and employment prospects are better in cities than in the countryside and in Riga compared to other cities. The division between urban and rural residents (and also between metropolitan and other areas) reveals itself most clearly as income inequality. According to Household Budget Survey results, per capita income in rural areas in 2000 (as well as per capita consumption expenditure in 2002) was on average just 69 percent of that in cities. This is a very significant fall since 1996, when the rural-urban income ratio was 90 percent.

Although unemployment is higher in Latvia’s urban areas compared to the countryside (13.2 versus 9.0 percent in 2002), intuition that higher wages in cities are being balanced out by lower employment possibilities (as in the classic Harris-Todaro model) is misleading. In fact, according to LFS 2002, the number of paid jobs located in the rural areas is just 50 percent of number of economically active residents, while this ratio is 95 percent in Riga and 79 percent in other cities
are linked to changes of goods prices through the production technology and the functioning of the factor markets. Different production technologies are approximated by different factor and intermediate inputs intensities across sectors, as shown in Table 6, and factor markets function so as to mimic short-term adjustment possibilities. Capital is sector specific, and the farm and non-farm sectors constitute two segmented markets for skilled and unskilled labor.

_<u>Table 5: Factor price changes due to full trade liberalization</u>_

||<br>Δ**P**|Δ(**P/CPI)**|
|---|---|---|
|**_Non-Farm Segment:_**|||
|**Skilled Labor**|-0.3|2.1|
|**Unskilled Labor**|0.6|2.9|
|**Capital**|-0.6|1.8|
|**Sk/Unsk wage gap**||-0.9|
|**_Farm Segment:_**|||
|**Skilled Labor**|-4.0|-1.6|
|**Unskilled Labor**|-6.3|-3.9|
|**Capital**|2.7|5.1|
|**Sk/Unsk wage gap**||2.5|
|**_Price indexes:_**|||
|**Food price index**|-3.6||
|**Non food price index**<br>**CPI**|-1.5<br>-2.4||

Sources: Author calculations from model results. Notes: The first column, Δ **P** , represents the percent variation of the price of each factor with respect to the initial levels, and Δ
budget. Because social assistance represents about three-quarters of income protection spending, we refer to this variable as the share of social assistance spending. The share of job protection measures in the total social protection budget is the complement of this variable. The pandemic response budget corresponds to governments’ COVID-19-related spending between March 2020 and June 2021.

A second data set, compiled for this paper, provides information on national elections in 205 countries. We collected data on the date the authorities in place as of March 2020 were elected, the terms of office, the anticipated election date after March 2020, the date of legislative elections after March 2020, whether elections were postponed, and whether 2020 was an election year in each country. The information for this data set comes from the National Democratic Institute <u>(NDI n.d.),</u> the Association of World Election Bodies (AWEB n.d.), Freedom House (Freedom House <u>n.d.), the International Institute for Democratic and Electoral Assistance (IDEA n.d), government</u> websites, news sources, and Wikipedia.

The main variable we use to measure the electoral cycle is the number of months that have _elapsed_ since the last election as of March 2020. The theory of political budget cycles tends to focus on the electoral cycle for upcoming elections, which would suggest using the number of months _until_ the next election. However, many political regimes allow the incumbent to alter the electoral schedule under extraordinary circumstances <u>(James and Alihodzic 2020). Indeed, during the</u> pandemic, 24 countries in our sample changed the national election schedule. The number of months until the next election could thus be endogenous to the development of the pandemic. On the other hand, the number of months elapsed since the last election is completely orthogonal to the start of the pandemic. Therefore, this measure provides us with an exogenous, cross-country variation in the period of the electoral
0.245|0.405|0.872|
||[35]|(377.935)|[36]|(186.063)|[42]|(225.469)||||
|Home station crimes / year / 100,000 people|101|1545.080|112|1323.169|112|1292.011|0.040|0.051|0.620|
||[38]|(127.419)|[38]|(82.366)|[41]|(70.072)||||
|Home station violent crimes / year / 100,000 people|101|48.433|112|46.456|112|44.170|0.303|0.018|0.059|
||[38]|(1.814)|[38]|(1.482)|[41]|(1.349)||||
|Home station theft / year / 100,000 people|101|307.821|112|254.343|112|245.993|0.040|0.051|0.596|
||[38]|(30.396)|[38]|(20.283)|[41]|(17.978)||||

_Note:_ Unit of observation is an individual. Groups are terciles of takeup of the reserved space in the revealed preference experiment, where 1 is the lowest and 3 is the highest takeup. Demographic characteristics (age, education, marital status and employment) are observed for the subsample who responded to the demographic survey; home station characteristics are observed for the full sample of participants. Standard errors are clustered at the level of the participant’s home station.
to assess the quality of service delivery and outputs. For some areas, the study includes an aspect of accountability by collecting information on available redress mechanisms in cases of bureaucratic errors.

_Living Life_ attempts to fill the knowledge gap on interactions between citizens and governments. The pilot stage of the project has produced and tested a data collection methodology that could eventually be refined and scaled to a larger number of countries. Refining the methodology and expanding the data collection coverage can lead to the creation of a relevant set of indicators, can generate a global public database on bureaucratic quality, and can foster policy dialogue around key areas of public service provision.

## **2.2. Existing Evidence and Knowledge Gaps**

### **2.2.1. Literature on bureaucracy, regulations, and individual outcomes**

Citizen interactions with governments are regulated and managed by administrative bodies or bureaucracies, whose role in distributing government resources and administering policy can have a relevant impact on inequality. Low institutional or bureaucratic quality is often associated with higher inequality (Chong and Calderon 2000). Although very limited evidence exists, some studies have found that “bureaucracy is prone 3 | P a g e
# **5.2 Philippines**

We next analyze our survey in Central Mindanao, Philippines.<sup>17</sup> The Philippines data enables us to evaluate two aspects of our theory of IDP perceptions of engagement: first, we compare IDPs and those who are not displaced in Central Mindanao in Section 5.2.1, second, we analyze variation within IDPs in Section 5.2.2.

## **5.2.1 Displacement and Empowerment**

To analyze whether experiences with displacement change perceptions of empowerment, we compare IDPs in our sample to respondents do not have a history of displacement. Because these categories are unlikely randomly distributed, we used propensity score matching to estimate the Average Treatment Effect on the Treated (ATT).<sup>18</sup> Matching enables us to approximate the causal effect of displacement on perceptions within the case by maximizing the suitability of the comparisons we use in our data.

We create two matched samples based on displacement status: one using current displacement as our treatment and the other using previous history of displacement as the treatment. The propensity score was estimated using a probit regression of the treatment on the covariates. We used full matching on the propensity score, which yielded adequate balance, as indicated in the Appendix, Section E.

Using these matched samples, we run a series of regressions. We fit a linear regression model with the given empowerment variable as the outcome and treatment status and the covariates as additive predictors and included the full matching weights in the estimation. The coefficient on the treatment (displacement status) was taken to be the estimate of the treatment effect of being an IDP. We create matches with the following controls: age, gender, education, violence witnessed, and Province. Models based on the matched sample control for the same variables. We use clustered standard errors with matching stratum membership as the clustering variable. We plot the results from these models in Figure 4, with the estimates using
characteristics of the household head; employment status of working-age members; household size and dependency ratio; ownership of toilets, electricity, or farm plots; recipient of MASAF or education aid; rural-urban indicator; and distance to the closest town, while _μt_ are time fixed effects (season and year), _νd_ are district fixed effects, and _ict_ is the error term. Standard errors are clustered by districts.

The main identification challenge in assessing the impact of shocks is endogeneity. When shocks are self-reported, one may argue that poor households with limited coping mechanisms are more likely to report them than the non-poor households, who tend to have good mitigation and coping strategies in place. The use of weather shock indicators derived by exogenously measured temperature and precipitation addresses this endogeneity problem. These indicators are aggregated at the TA level, the lowest administrative division in Malawi.<sup>8</sup> As shown in the annex, there is low correlation between self-reported weather shocks and exogenously measured shocks at the TA level.<sup>9</sup> This discrepancy may stem from the endogeneity of self-reporting and/or the difference in spatial coverage of the shock indicators, that is, TA average versus specific location of individual household.

Even when measured exogenously, the probability of occurrence of a shock is not exogenous to consumption per capita across households. Indeed, arid areas are more likely to be poor and hit by frequent droughts due to their geography. We use the fact that the timing of a shock is exogenous to welfare status conditional on the likelihood of its occurrence (Anttila-Hughes and Hsiang, 2013). We account for the probability distribution of weather events by including variables related to the TA-specific historical distribution of these shocks.

It is difficult to measure idiosyncratic shocks exogenously. In the annex, we present the correlation between self-reported shocks and household characteristics.<sup>10</sup> Households planting crops are more likely to report droughts or floods, and they are less likely to report food price shocks.
moderate, high, very high and hazardous air pollution concentrations throughout the year. We offer these exposure headcount maps as global gridded layers with a resolution of 90 meters. In addition, we aggregate them to administrative units, including for each country and subnational unit. These estimates are further aggregated to yield regional and global estimates.

_Compute the number of poor people exposed to air pollution risk:_ While poverty estimates are not available at the pixel level, the World Bank’s Global Subnational Poverty Atlas (GSPA) of harmonized household surveys provides them at the subnational level for at least 153 countries. These poverty shares are multiplied with the population headcount that is estimated to be exposed to unsafe air pollution, in order to obtain an estimate of the number of poor people in each administrative unit exposed to air pollution risk.

7
ultimate information sources will be carried out by national statistical agencies (through censuses or household or enterprise surveys) or regulatory agencies or providers of other public services (through their administrative records). Examples of these are provided below.

# **2. Household users**

Surveys of households are at once the most direct and the most costly<sup>10</sup> way of discovering information about access to financial services. In general, members of the household do have most of the information, and not only on what financial services they use, with what frequency and at what cost. To be sure, the household member may not be willing to share that information, especially if probed about the size of their financial asset holdings, or indeed their indebtedness. And a single respondent (head of household) may not be fully informed about the services used by other members of the household. The desire for secrecy in these matters does complicate the information gathering exercise, and there can be significant reporting and response biases.<sup>11</sup> A key question is the relative merits of a stand-alone specialized financial services survey and the introduction of a financial module in a general household survey. The former has the attraction of allowing greater detail in the questionnaire instrument. Given the practical limitations on administering lengthy questionnaire instruments, a finance module embedded in an overall general purpose household survey will typically be limited to just a few questions. Also the sample frame can be adjusted to oversample groups or geographical areas of particular interest to the financial researcher.

On the other hand, a “finance-only” survey will naturally tend to be more costly than the finance component of a general survey, because it will be bearing all of the overheads. Furthermore, it will tend to offer a more limited range of non-financial control variables and correlates<sup>12</sup> , and as such the range of research questions that can be answered will be constrained and the reliability of those that can may be
WDI, World Bank 2021), covering 1990 _−_ 2015. For official development assistance (ODA) flows, we used the AidData Core

> 7This may explain the inability of previous researchers to explain more than 30 percent of the variance in outcome ratings. If every project defined objectives to achieve the highest rating, the correlation between ratings and project/country characteristics would be zero. IEG does assess the ‘Bank’s contribution’, defined as ‘the extent to which the services provided by the World Bank ensured quality at entry of the project and supported effective implementation through appropriate supervision.’ Its evaluation of project efficacy, or the extent to which outcomes were achieved, is also required to examine ‘whether the achieved outcomes can plausibly be attributed to the government program or project.’ But the first focuses largely on compliance with fiduciary and reporting requirements; while the second does not evaluate whether the objectives would have been achieved in the absence of the Bank’s involvement.

5
the population in the country, as reported by the National Bureau of Statistics.<sup>9</sup> The information collected is suitable for carrying out fiscal incidence analysis as it includes individual labor income, household agricultural and business operation income, household rental income, financial investment profits, private transfers, auto-consumption, household expenditure by category, coverage of social insurance programs, reception of cash transfers and pension, as well as utilization of education and health services. The 2014 round of the CFPS was used in a previous fiscal incidence analysis for China (Lustig and Wang 2018). For the current analysis, we use the 2018 round, which contains the most recent available survey data. The 2018 round has information on 45,310 individuals from 14,218 households in 31 provinces/municipalities/autonomous regions.<sup>10</sup> To model some of the interventions, we also rely on alternative surveys: the 2012 round of the CFPS and the 2019 China Household Finance Survey. Our second key source of information is administrative data. We use administrative data to validate the consistency of the survey-based estimates, as well as to conduct simulations and imputations when detailed information in the survey is not available. The main sources of administrative data are the Statistical Yearbook of China 2019, China Social Statistical Yearbook 2019, the 2019 Finance Yearbook of China, the 2018 Annual Report of Housing Fund, and 2018 Statistical Bulletin on Development of Human Resources and Social Security and information from the Ministry of Finance, the Ministry of Housing and UrbanRural Development, and The People's Bank of China.

> 9 CFPS-based estimates indicate that 54.1% of individuals are urban residents and 38.5% are rural residents, which is comparable to official reports showing a distribution of 59.6% individuals residing in urban areas and 40.4% in rural areas. Regarding province of residency, 44.2% individuals reside in the Coastal region, 31.4% in the Interior region and 23.
- 14 -

representing the poorest F families in the cumulative distribution measures the gain in real income experienced by the family at that income level. Similarly, the size of the DCC curve itself indicates the cumulative gain of the poorest F families over the range of incomes when the DCC curve is increasing. Losses, on the other hand, occur whenever the DCC curve is decreasing and, over the entire range of the DCC curve, there is no net gain or loss as long as a is one.

Since the value of a is unknown and difficult to determine empirically, we concentrate in the rest of the paper on the special case where a is one. For simplicity of exposition belov we will also assume that the income distribution is a continuous distribution.

Since household surveys of consumer expenditure normally include thousands of obeservations it would be extremely cumbersome to calculate the DCC for more than a few commodities. Clearly, some sort of screening mechanism would be desirable to distinguish promising candidates for welfare dominance. However, Yitzhaki (1987) has provided a fairly straightforward procedure for reducing the number of possibilities to a manageable total. In an earlier paper that author has established the following result: where F is the cumulative distribution of income, Gy is the Gini coefficient of income, Si is the share of the expenditure on Xi and (9) bi - cov (Xi, F(y))/cov (y,F (y)) Dominance **& the** Design of Excise Taxation in **the** Cots D'Ivoire/Wrhirsk:ab/I/18/8S Massll#4/Welfare
7|1|10|7|25|36|31|15|26|21|
|Number of households|226|184|410|186|263|449|1,998|1,923|3,921|2,410|2,370|4,780|
|Observations (number<br>of individuals)|1,254|970|2,224|1,009|1,426|<br>2,435|10,969|10,689|21,658|13,232|13,085|<br>26,317|

_Source:_ Estimated by authors using Somali HFS 2017-18. _Note:_ IDP stands for internally displaced persons. Notes HH = household head 6 Surveys were conducted by the World Bank in collaboration with the Somali statistical authorities. They can be accessed in the World Banks micro data library see https://microdata.worldbank.org/index.php/catalog/2738 and <u>https://microdata.worldbank.org/index.php/catalog/3181</u> 7 Rates of poverty and other markers of well-being may thus be different from those in different regions inaccessible to the HFS.

10
approach to consolation that, albeit (Loumeau 2022) and implying that reinforcing markets with appropriate policy measures may be more appropriate, equitable, and sustainable.

_Tenant size effects:_ Beyond economies of scale at field size, larger tenants (with more than 10 ha of total area) also tend to pay a substantial rent premium: moving from the 25<sup>th</sup> percentile to the median, from the median to the 75<sup>th</sup> or from the 75<sup>th</sup> to the 95<sup>th</sup> percentile (from 75 to 215, 450, and 1,054 ha, respectively) would, according to regression, be associated with rent premia of 7.2, 6.7, and 9.5 percent, other covariates held constant. While production data for different farm sizes will be desirable to assess the extent to which this price increase mirrors cost reductions in production or economies of scale beyond the production stage and the share of such benefits passed on to landowners, it suggests lease market are reasonably competitive.

_Limited evidence of market power:_ For the approximately 60% of the sample where information on tenant characteristics is available, including the share of registered land cultivated by the tenant in the village in which the parcel of interest is located can be used to more directly test for market power. Tenants that cultivate less than 50% of the land in the village indeed pay lease rates 11% lower than those paid by nonresident bidders. The fact that this discount decreases to between 6 and 7% for parties cultivating more than 50% of village land suggests that either land rental markets remain relatively competitive or that concentration of land use at village level is not the most appropriate indicator to measure market power.

_External effects of informality:_ The significant and negative coefficient on the share of a field’s area not mapped in the cadaster suggests that informality generates negative externalities as registered parcels in