rafmacalaba/datause-encoder-data huggingface dataset:
that protect property rights and provide a minimum of certainty; on an environment of trust among economic agents; and on the ability of policy-makers to provide stable macroeconomic conditions. Fragile states, where these elements are in short supply, can be expected to have less developed financial institutions and markets—although the evidence on this is mixed (Barajas et al., 2021).

Numerous studies have empirically investigated the links between financial development and economic growth, often uncovering positive and significant correlations (Levine, 1997; Beck et al., 2000; Valickova et al., 2015), with causality running from finance to growth. Financial development has also been shown to reduce income inequality and is strongly associated with poverty alleviation (Beck et al., 2007). However, the relationship between financial development and state fragility (or its opposite, resilience), which may be heterogeneous across groups of countries and measures of financial development, has not been extensively addressed in the existing literature (Barajas et al., 2021). This leaves a gap that this paper aims to fill.

There are at least two major limitations of the current study that we want to mention upfront. First, measuring state fragility is not undisputed; see for instance Grimm et al. (2014). Similarly, proxying financial development is a difficult exercise given its multifaceted nature; see, for example, Svirydzenka (2016). In this paper we use World Bank and IMF measures for state fragility and financial development, respectively. For state fragility we use the Country Policy Institutional Assessment (CPIA) score, a widely used metric to classify fragile countries. This picks up aspects related to the level of institutional capacity and policy environment of a country. For financial development, we use the financial development (FD) index, introduced by Sahay et al. (2015), which is a relative ranking of countries on depth, access, and efficiency of financial institutions and financial markets.

Second, there is an obvious endogeneity issue with the current analysis. The relationship between financial development and state fragility may work both ways. The current data set does not allow us to solve this endogeneity 3
related to the provision of public services.<sup>8</sup> From 1992 to 2009, five local elections have been held (1992, 1998 and 1999, 2004, and 2009). The next local elections, which were initially planned for 2014, are finally expected to take place in 2015 and, for the first time, they will be coupled with regional elections in the new context of regionalization,<sup>9</sup> under which all the deliberating bodies of local authorities (regions, cercles, and commune councils) must be elected directly by their governed citizens.

Despite some progress in the administrative and financial empowerment of local authorities, they are still to a large extent under the authority of the central government with regard to resources, and even acts and decisions related to their respective territories. The government seems to be more concerned about national unity and territorial integrity than the enhancement of the transfer of capabilities and public resources to communities. Importantly, the Malian decentralization framework includes an element called “deconcentration,” whereby agents<sup>10</sup> paid by the central government are relocated within local authorities to provide technical assistance or even approve budgets.

# _<u>Flows of resources to communes</u>_

From 2011 to 2014, central government transfers to local authorities accounted for an average of 2 percent of GDP. While the transfers to local authorities account for only 9 percent of the government’s budget expenditures, local authorities additionally benefit from other public resources within the framework of “deconcentration.” Data on transfers to local authorities in the last four years show that their share in the government total budget has varied significantly from year to year (table 4). In particular, there was a very sharp fall in direct transfers in 2012, whereas “deconcentration” transfers grew moderately each year. These transfers to all communes are much smaller than the contribution of the five mining communes to the national budget. In 2013, revenue from mining areas represented 25 percent of total government revenue. Tracking these transfers to individual communes is only a recent phenomenon in the budgeting framework of the central government. There are no clear rules for the transfers, and therefore it is not obvious whether they benefit certain groups of communes to the detriment of others.

> 8 Responsibilities transferred are in the health, education, and water sectors.

> 9 In previous elections, commune councils were directly elected by citizen’s votes, while cercles and regional councils were indirectly elected.

> 10 In practice, the agents interfere with local authorities even within their responsibilities in the areas of health, education, and water.

14
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the costs they must pay for land and labor and the risks they will face at a given location. Taking the hedonic land and labor market pricing gradients as given, firms will chose a profit maximizing location. New firms will take into account the competitive effects of whether they want to locate close to or far from incumbent firms.

Firms are not tied to any one location on the map. If certain areas experience repeated horrible weather, then rents of such land should decline and the most productive firms should be less likely to locate there (Rentschler et al., 2023). Such areas would likely be populated by less productive firms. In this sense, the equilibrium hedonic land price gradient sends signals about land quality.

Given that it is costly for a firm to migrate, firms have strong incentives to research the risks and challenges that each location faces. In the United States, there has been a growth in climate science information firms that provide location specific “climate report cards” for different geographic areas regarding their flood and fire risk. Such sufficient statistics are based on academic models and recent geocoded natural disaster realization data. In the short run, we expect that if such tailored risk report cards are introduced in the developing world this will accelerate the learning process and that geographic site selection will be better informed about the recent shocks and the expected future shocks (Burlig et al., 2024).

Migration in response to climate change induces reallocation of activity. For example, weather change induced migration of labor leads to reallocation of factors across space (Cai et al., 2016; Cattaneo and Peri, 2016; Mueller et al., 2014), as evidenced among localities in rural India that experienced higher-than-normal local rainfall. Such migration shifted labor towards non-agricultural non-tradable sectors (e.g., construction, retail, and education sectors) and consequentially increased productivity in the agricultural sector and led to an increased demand for non-tradables (Emerick, 2018). Higher temperature in India also led to reallocation of agricultural employment towards local manufacturing and services (Colmer, 2021). Such cross-sectoral labor movements are bounded within districts due to severe liquidity constraints (Cattaneo and Peri, 2016).

Firms differ with respect to where they locate their supply chains. If one firm chooses a set of suppliers who are located in riskier locations (because these areas are located in areas facing greater shocks), then this firm can benefit in the short run from having lower costs. When disasters strike in the risky areas, other firms who have ”played it safe” by sourcing their supply chains in safer areas have an edge. One recent study used a structural discrete choice model to study how car manufacturers respond to the risks posed by local natural disaster on supply chains (Castro-Vincenzi, 2022). Severe floods close to a car assembly site is associated with an economically significant reduction in subsequent production at that 8
**Table 1: Characteristics of Microenterprises and Verification of Randomization**

|||Fu|ll Sample|||Trimm|ed Sample||
|---|---|---|---|---|---|---|---|---|
||N|Control|Cash|In‐kind|N|Control|Cash|In‐kind|
|_Variables Using to Stratify or Match_|||||||||
|Monthly profits in January 2009|781|128|132|131|753|103|99|115|
|Female|793|0.60|0.60|0.61|765|0.62|0.62|0.62|
|High Capture|793|0.58|0.58|0.57|765|0.58|0.58|0.57|
|High Baseline Capital Stock|793|0.49|0.49|0.49|765|0.48|0.48|0.48|
|Male in Male dominated industry|793|0.18|0.19|0.18|765|0.18|0.18|0.18|
|Male in Mixed industry|793|0.21|0.21|0.21|765|0.20|0.20|0.20|
|Female in Female dominated industry|793|0.29|0.29|0.29|765|0.30|0.29|0.30|
|Female in Mixed industry|793|0.31|0.31|0.31|765|0.32|0.32|0.32|
|_Other Variables_|||||||||
|Monthly profits in October/November 2008|729|124|133|104|704|93|129|99|
|Monthly sales in January 2009|790|724|463|630|762|412|402|595|
|Number of hours worked in last week|785|58.82|60.55|57.13|757|59.03|60.64|56.64|
|Total Capital Stock in January 2009|784|468|454|418|757|446|438|410|
|Inventories at end of January 2009|791|258|213|201|763|239|203|198|
|Uses a Susu Collector|791|0.49|0.46|0.49|763|0.49|0.46|0.51|
|Business operated out of home|793|0.76|0.78|0.82|765|0.77|0.78|0.83|
|Age of Firm|788|7.87|7.13|7.22|761|7.88|7.11|7.14|
|Ever had bank or microfinance loan|793|0.11|0.10|0.07|765|0.10|0.09|0.07|
|Business has a tax number|786|0.15|0.14|0.13|758|0.14|0.14|0.13|
|Owner is Married|791|0.65|0.64|0.67|763|0.65|0.63|0.68|
|Owner's Years of Education|775|8.87|8.75|9.05|749|8.81|8.70|9.00|
|Owner's Digitspan Recall|768|5.11|5.07|5.03|740|5.07|5.10|4.99|
|Owner is Akan Speaker|793|0.45|0.41|0.43|765|0.46|0.41|0.43|
|Owner is Ga/Dangme Speaker|793|0.28|0.27|0.31|765|0.29|0.27|0.32|
|Owner's Age|791|36.39|35.43|35.74|763|36.36|35.37|35.79|

Note: Trimmed Sample eliminates matched groups in which baseline profits for at least one firm in group exceed 1500 cedis per month The only differences between groups which are statistically significant at conventional levels are January 2009 sales and October/November 2009 profits in the trimmed sample.
2018.<sup>7</sup> Further, RGGI is one of the first market-based efforts in the United States aimed at reducing greenhouse gas emissions by auctioning emission allowances and investing the proceeds in clean energy technologies and the creation of green jobs in each of the states. The third auction occurred on March 18, 2009.<sup>8</sup>

# **European Efforts**

In Europe, interventions tend to combine local, national, and regional levels. The EU Emissions Trading Scheme (EU-ETS) was developed so as to reduce the economic costs of meeting the European Union’s Kyoto target of 8 percent CO2 reduction by 2012. The scheme includes around 10,000 large industrial plants in the power generation, iron and steel, glass, brick, and pottery industries, but not the transport sector. Operators of these facilities receive emission allowances that are good for a one-year period. If an allowance is not fully used by the assigned operator (after verification), the unused portion may be sold to other facilities that cannot yet meet their assigned target. The official data issued by the European Environmental Agency (EEA) in 2006 show that the EU members that had signed the Kyoto Agreement were able to achieve in 2005 a 2 percent cut in CO2 emissions compared with 1990 levels and that greenhouse gas emissions were projected to decline further by 2010 compared with 2004 levels (EEA, 2006: sections 8 and 9).

While a considerable accomplishment has been achieved, some concerns have been raised—which could also be raised regarding the RGGI approach in the United States—that some energy-intensive sectors may relocate to regions that have less stringent policies on climate change or none at all.

> 7. http://rggi.org/home/ (accessed February 7, 2009).

> 8. http://www.climatetrust.org/solicitations_RGGI.php.

21
# <u>German Flood Act 2005</u>

National policies can contribute towards flood management. Legislation forms a vital part of that policy. The flood control sections of the 2005 German Water Act are an example of good practice in this area which has many features in common with other water and flood protection acts. The act holds to three core principles which place stringent flood control obligations on government and individuals to manage flood risk in advance of flooding, and also on how flood zoning is managed and how warnings are issued:

- Surface waters have to be managed in such a way that as far as possible floods are held back, non-harmful water run-off is ensured and flood damage is prevented. Areas that may be inundated by a flood or where an inundation may help to alleviate flood damage have to be protected.

- Within the bounds of possibility and reasonability, any person potentially affected by a flood is obliged to undertake adequate measures to prevent flood-related risks and to reduce flood damage, particularly to adjust the land use to a possible risk created for humans, the environment or material assets through floods.

- A land law shall stipulate how the competent state authorities and the population in the areas affected are informed about flood risks, on adequate preventive measures and rules of behavior and on how they are to be warned of an expected flood in a timely manner.

In practice this means that (Government of Germany 2005):

- Extensive improvements to flood zoning and mapping are occurring in accordance with this Act as well as the European Water Directive. Importantly, public consultation is built into the process.

- New building in the floodplain is forbidden in most cases. Where it is permitted the design of new construction is strictly controlled, for example the placement of oil heating systems and computer control centers.

- Flood protection plans have to be drawn up for the 100 year flood and must consult with upstream and downstream riparian owners.

- Flood zone maps are to be integrated into all spatial maps and plans such as land use plans and development plans.

Commentators have noted that for German flood risk management this Act is a shift away from the “protection” mentality towards “adaptive risk management.” However, the success of this shift in practice has yet to be realized (Garrelts and Lange 2011).

44
Table 4: Impact of Conflict Intensity Measured by Casualties on Primary Schooling Attainment

||(1)<br>Overall<br>Exposure<br>Female|(2)<br>Overall<br>Exposure<br>Male|(3)<br>Exposure before<br>and after 10<br>Female|(4)<br>Exposure before<br>and after 10<br>Male|(5)<br>2001-2006<br>Change<br>Female|(6)<br>2001-2006<br>Change<br>Male|
|---|---|---|---|---|---|---|
|Outcome variable|primary|primary|primary|primary|primary|primary|
|Specification|Eq. (1)|Eq. (1)|Eq. (2)|Eq. (2)|Eq. (3)|Eq. (3)|
|=1 if 5-9 in 1996 x total|0.0555**|0.0241|||||
|district casualties|(0.0272)|(0.0251)|||||
|District casualties while|||0.1067|0.0325|||
|age 6-10|||(0.0773)|(0.0684)|||
|District casualties while|||0.0221|0.0015|||
|age 11-14|||(0.0457)|(0.0334)|||
|District casualties until|||||0.0776*|0.0006|
|survey|||||(0.0433)|(0.0364)|
|Constant|0.3271***|0.6703***|0.3271***|0.6711***|0.0070|0.0277**|
||(0.0260)|(0.0246)|(0.0261)|(0.0248)|(0.0175)|(0.0132)|
|Panel Variable|District|District|District|District|District|District|
|Region x Year of Birth<br>dummies<br>|Yes|Yes|Yes|Yes|No|No|
|Age at Interview<br>dummies|No|No|No|No|Yes|Yes|
|DHS 2006 x Region x<br>Age dummies|No|No|No|No|Yes|Yes|
|Observations|3823|3055|3823|3055|9595|9267|
|No. of Clusters|75|75|75|75|69<sup>a</sup>|69<sup>a</sup>|
|R-Squared|0.1106|0.0368|0.1105|0.0367|0.2028|0.2975|

All specifications are estimated using the panel fixed-effects estimator. District casualties are expressed per 1000 inhabitants. Columns (1) to (4): Sample only includes individuals surveyed in the Nepal DHS 2006 and aged 5-9 (treatment group) or 16-19 years old (control group) at the start of conflict in 1996. Columns (5) and (6): Sample only includes individuals surveyed in Nepal DHS 2001 and 2006, aged 10 to 18 at the time of the survey.<sup>a</sup> DHS data collection was somewhat affected by the conflict in 2001, and so a number of districts were not covered, namely Dolpa, Jajarkot, Rolpa and Rukhum. The small districts of Manang and Mustang were not surveyed either, but these districts did not experience any casualty during the conflict. Standard errors clustered at the district level are in parentheses. *p<0.10, **p<0.05, ***p<0.01.

40
# **Appendix C. Additional System GMM regression**

Table C.1: Additional System GMM regression

|equation no.|(1)|(2)|
|---|---|---|
|log of number of cumulative loans|.389|.|
||(.10)<sup>_∗∗∗_</sup>||
|log of number of cumulative conditions|.|.146|
|||(.034)<sup>_∗∗∗_</sup>|
|log GDP per capita (PPP)|.278|.251|
||(.054)<sup>_∗∗∗_</sup>|(.053)<sup>_∗∗∗_</sup>|
|aid over GDP|-.485|-.481|
||(.524)|(.510)|
|Political Rights|-.089|-.099|
||(.025)<sup>_∗∗∗_</sup>|(.026)<sup>_∗∗∗_</sup>|
|_country fxed efects_|_yes_|_yes_|
|_year fxed efects_|_yes_|_yes_|
|Observations|1761|1748|
|Number of instruments|24|24|
|Wald statistic|194.05|178.86|
|_p-value_|0.0001|0.0001|
|Hansen J-test|7.31|5.77|
|_p-value_|0.293|0.449|
|Dif-in-Hansen test|0.66|2.73|
|_p-value_|0.417|0.10|

Note: cluster-robust standard errors are reported. Coefficients estimated with forward orthogonal deviations and level equations for IV style instruments. Collapsed instrument matrix. Lags 5 to 10 used for loans and 10 to 15 for conditions. * significance at 10%; ** significance at 5%; *** significance at 1%. Argentina excluded from the sample when the number of conditions is used as variable of interest.

35
by comparable surveys within countries, over 3 to 7-year periods.<sup>13</sup> We use both the historical database which contains 467 spells for 121 countries, of which 88 countries were published in the most recent version in March 2021.<sup>14</sup> Since this database overlaps considerably with the PovcalNet database, we do not add these observations to our main data set, but rather create a complementary sample based on more recent, harmonized and comparable data with medium spell lengths useful for understanding how growth rates in surveys differ from those of national accounts. Results for this data set are presented as robustness checks in Section 4.

# **_Putting it all together_**

Of the 2,095 household survey estimates we compile; we match 2,082 to GDP per capita data and 2,020 to HFCE for the same country and year.<sup>15</sup> This sample is substantially larger and more recent than those included in past studies. Deaton (2005) presented results from 557 surveys for 127 countries between 1979 and 2000. Ravallion (2003) used a smaller sample of survey means from 90 countries, and a panel of 142 growth spells for 60 countries. We also have substantially better coverage of High-Income countries, than Ravallion (2003), Karshenas (2003) and Deaton (2005).

We generate growth spells with annualized growth rates by matching all observations for each country and type (income/consumption), which gives more than 15,000 possible spells (including overlapping spells) and 1,881 consecutive (non-overlapping) spells. In contrast, Ravallion (2003) generates 142 spells between successive household surveys for 60 countries in the 1980s and 1990s. Our practice of matching all observations with each other within each country-type panel maximizes our potential power in estimation, but we also conduct analyses of

> 13 Global Database of Shared Prosperity, circa 2013-2018, available at <u>http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity (accessed May 8,</u> 2021).

> 14 See Yang and Nguyen (2021) for a description of the data and methodology. Access to the data is available at: <u>https://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity.</u>

> 15 When a household survey spans more than one calendar year, we construct a weighted national accounts aggregate where the weights correspond to the distribution of months of survey fieldwork across the years. This is consistent with the practice of dealing with surveys in PovcalNet that run over multiple years, as described in Chen and Ravallion (2010).

9
start with the Britain and the First Industrial Revolution.

While the evidence is still being hotly debated, what we do have suggests that British capitalism did breed inequality, and the inequality drift seems to have been a product of the forces associated witb the industrial revolution. The rise in inequality can be dated from around 1760, and it was manifested throughout the full income distribution: the income sbares at the top rose, the shares at the bottom fell, real wages of the unskilled were relatively stable, the numbers in poverty were slow to fall, the premium on skills inc^eased, and the earnings distribution widened. British inequality seems to have reached a peak somewhere around the 1860s or shortly thereafter. While not spectacular, the egalitarian leveling up to World War I was universal: as Figure 3 suggests, the income shares at the top fell; in addition, the shares at the bottom rose, the relative pay of the unskilled improved, the numbers in poverty fell sharply, the premium on skills declined, and the earnings distributio **n** arrowed. A sample of the data wbich supports this interpretation can be found in Table 1.

Before leaving this evidence, one important finding must be stressed. Most of the changes in British (and American, as we shall see) inequality across the 19th century were driven by changes in factor rewards -- the wages of skilled labor relative to the wages of unskilled labor, rents on land relative to the wages of unskilled labor, the returns to capital relative to the wages of unskilled labor -- and mueb less by changes in the distribution of factor ownership. Changes in earnings inequality are explained primarily by changes in the structure of pay rather than by employment shifts from occupations with low skill content to those with high. While skills may well have become less equally distributed in the early 19th century, it was the inflation in the premium on those skills, a relative scarcity of those skills, and a relative glut of unskilled labor that did most of the work in augmenting

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