rafmacalaba/datause-encoder-data huggingface dataset:
PESA. Users are more likely to receive remittances via M-PESA for credit or in response to an emergency, while the fraction of total M-PESA transactions for regular support declines. The patterns suggest that M-PESA enables households to more easily draw on their social networks for support in trying circumstances.<sup>12</sup> Our work is also therefore related to a body of research showing that monetary transfers within networks of family members and friends are pervasive and crucial for household risk mitigation in developing countries. A number of papers have shown that informal financial arrangements provide insurance for households that experience illness (Gertler and Gruber, 2002; DeWeerdt and Dercon, 2006; Genoni, 2012). For example, Genoni (2012) shows that in Indonesia household members who do not personally experience an illness increase their supply of labor, and transfers increase from other households to the affected household. A related literature tries to uncover the motives for participation in informal financial and insurance arrangements and finds evidence consistent with the notion that reciprocity (rather than altruism) motivates most innetwork transfers (Fafchamps and Lund, 2003; Blumenstock, Eagle, and Fafchamps, 2011).<sup>13</sup> The remainder of the paper is organized as follows. Section 2 describes FINCA DRC and its place with the financial context of the DRC. Section 3 describes our data and our approach to estimation. Section 4 lays out our econometric specification and provides hypotheses about how our explanatory variables are expected to affect agent transactions. We present our main results in Section 5. In Section 6, we perform a series of robustness checks. Section 7 offers conclusions.

> 12 Relatedly, Blumenstock, Eagle, and Fafchamps (2015) show that Rwandan households affected by earthquake received increased amounts of cellular “airtime” (a simple precursor to mobile money) from members of their social network, especially those with whom they had already established reciprocal relationships.

> 13 The reciprocity motive for participation in informal financial networks appears to be a pervasive one in the developing world. For example, using historical loan contract data from low-income rural villages in China in the 1930s, Brandt and Hosios (2010) show that informal loans were predominantly used for consumption purposes and that a large share of these loans carried no interest rate. They argue that these arrangements should be interpreted as “long-term reciprocal insurance or patronage relationships between households.”

6
**<u>Table 2. Sample means and variances of household demographics and initial conditions</u>**

|**Variable**|**A**|**ll**|**Con**|**trol **|**Tre**|**ated**|
|---|---|---|---|---|---|---|
|Household lived in hamlet|0.20|(0.40)|0.17|(0.38)|0.21|(0.40)|
|Scheduled caste|0.13|(0.34)|0.18|(0.38)|0.12|(0.32)|
|Scheduled tribe|0.16|(0.37)|0.05|(0.22)|0.19|(0.39)|
|Backward caste|0.54|(0.50)|0.57|(0.50)|0.53|(0.50)|
|Other caste|0.17|(0.38)|0.20|(0.40)|0.16|(0.37)|
|Household size|4.94|(2.07)|4.56|(1.93)|5.05|(2.10)|
|Household female headed|0.11|(0.31)|0.12|(0.33)|0.10|(0.30)|
|Some member can write|0.84|(0.37)|0.82|(0.39)|0.84|(0.36)|
|Poorest of poor|0.25|(0.43)|0.24|(0.43)|0.26|(0.44)|
|Poor|0.27|(0.44)|0.27|(0.45)|0.27|(0.44)|
|Not very poor|0.24|(0.43)|0.27|(0.45)|0.24|(0.43)|
|Not poor|0.23|(0.42)|0.22|(0.41)|0.24|(0.43)|
|Land ownership|0.72|(0.45)|0.67|(0.47)|0.73|(0.44)|
|Located in Telangana|0.30|(0.46)|0.35|(0.48)|0.29|(0.45)|
|Located in Rayalaseema|0.38|(0.49)|0.29|(0.45)|0.40|(0.49)|
|Located in Coastal AP|0.32|(0.46)|0.36|(0.48)|0.30|(0.46)|
|Number of observations|24|06|5|26|18|80|

_Source:_ Own computation from 2004 and 2006 SERP/CESS impact evaluation surveys. Sample weights applied.

18
# Annex 2. Seasonality in the Sahel

Rainfall varies dramatically across the year in Sahelian countries. The rainy season typically lasts from June to September, although rains may begin in April or May and continue until October or November (see Figure 7). The remainder of the year is extremely dry, underlining the importance of the rainy season for agricultural and pastoralist activities.

**Figure 7. Seasonal variation in rainfall and temperature in Sahelian countries**

<!-- Start of picture text --> Panel A: Rainfall<br>50<br>40<br>30<br>20<br>10<br>0<br>Panel B: Temperature<br>40<br>30<br>20<br>10<br>0<br>Burkina Faso Mali Niger Senegal Chad<br>(milimeters)<br>Rainfall over five-day period<br>Jul-15 Oct-15 Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Jan-18 Apr-18 Jul-18 Oct-18 Jan-19 Apr-19 Jul-19 Oct-19 Jan-20 Apr-20 Jul-20<br>Celsius)<br>Jul-15 Oct-15 Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Jan-18 Apr-18 Jul-18 Oct-18 Jan-19 Apr-19 Jul-19 Oct-19 Jan-20 Apr-20 Jul-20<br>Daily mean temperature (degrees<br><!-- End of picture text --> Note: Monthly averages taken for penta-daily rainfall and daily mean temperatures. Source: Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data for rainfall, Climate Forecast System Reanalysis (CFSR) data for temperature, and World Bank estimates.

The agricultural lean season corresponds to the rainy season. The harvest season for millet and sorghum, the main coarse grains on which Sahelian households rely, ordinarily centers around October and November of each year, coming directly after the rainy season (FAO, 1995; FEWS NET, 2018). This may be complemented by off-season cultivation of rice, maize, and other crops – primarily between January and March – which may be supplied with water through irrigation or undertaken in areas where flood waters recede (FEWS NET, 2017; FEWS NET, 2017). By the time the rains arrive again, agricultural households face a lean season in which they will not only have drawn down their main season and off-season harvest but also face the physical demands of planting their principal rainfed crops.

27
**Table 3: Correlation matrix of inflation measures**

|||**A: 1997 Q2**|**-2008 Q4**<br>||||
|---|---|---|---|---|---|---|
|Inflation<br>measure|WPI<br>Headline|WPI Food|WPI<br>Core|CPI<br>Headline|CPI<br>Food|CPI<br>Core|
|WPI Headline|1||||||
|WPI Food|0.34*|1|||||
|WPI Core|0.82*|0.20|1||||
|CPI Headline|0.32*|0.90*|0.09|1|||
|CPI Food|0.41*|0.93*|0.26|0.94*|1||
|CPI Core|-004|063*|-032*|081*|058*|1|
|<br>|.|.<br>**B: 2009 Q1 **|.<br>**-2019 Q4 **|.<br>|.<br>|<br>|
|Inflation<br>measure|WPI<br>Headline|WPI Food|WPI<br>Core|CPI<br>Headline|CPI<br>Food|CPI<br>Core|
|WPI Headline|1||||||
|WPI Food|0.55*|1|||||
|WPI Core|0.93*|0.27|1||||
|CPI Headline|0.58*|0.91*|0.39*|1|||
|CPI Food|0.34*|0.89*|0.10|0.92*|1||
|CPI Core|077*|074*|067*|086*|061*|1|
|<br>Note:*denotes sig|.<br>nificance at 5%|.<br>level.|.|.|.||

**Table 4: Level and volatility of CPI inflation and its components (quarterly data from 1997q22019q4)**

||Mean|Standard deviation|Coefficient of<br>Variation|
|---|---|---|---|
|CPI|6.41|3.31|0.52|
|CPI, Food|6.29|5.01|0.80|
|CPI,Fuel|6.96|4.96|0.71|
|CPI, Core|6.47|2.76|0.43|

# **Persistence**

Contrary to popular presumption, food price inflation has not been higher than core and headline inflation (Table 4).<sup>17</sup> Neither is it more persistent. We estimate first order autocorrelation coefficients for rolling windows of 20, 30 and 40 quarters. The coefficients for core and headline inflation are higher than for food price inflation.<sup>18</sup> Nor has inflation persistence declined over time (in contrast to evidence for the United States – e.g. Fuhrer 2010).

> 17 See Appendix B for details.

> 18 Unit root tests conducted on all inflation measures provide mixed results. While we cannot reject the null of unit root for core inflation at 5% level using both ADF and PP tests (implying high persistence), but for food, fuel and headline inflation we reject the null of unit root when lag length considered for ADF and PP tests is less than 4.

9
# Figure 9: ECA: Financing needs and constraints

# A. Bond issuance and yield spreads

<!-- Start of picture text --> US$, millions Bond issuance Basis points<br>2,000 Bond spread (RHS) 1,600<br>1,500 1,200<br>1,000 800<br>500 400<br>0 0<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text -->

# C. GDP per capita relative to EU-27

<!-- Start of picture text --> Percent of EU-27 2021 (2019 for TUR)<br>75 2012<br>50<br>25<br>0<br>ECA CE WBK TUR<br><!-- End of picture text -->

# B. Efficiency gaps in public infrastructure investment

<!-- Start of picture text --> Percent<br>Minimum-maximum range<br>75<br>Median<br>50<br>25<br>0<br>ECA Euro area<br><!-- End of picture text --> D. Cumulative absorption rates, 2014-20 EU <!-- Start of picture text --> spending program<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>100 Bulgaria<br>Croatia<br>75 Poland<br>Romania<br>EU<br>50<br>Best absorber<br>25<br>0<br>2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text --> <!-- Start of picture text --> E. Planned EU investments in transport F. State-owned enterprise activity and<br>and green projects in the Western assets ,<br>Balkans 2014-16<br>Percent of Western Balkans GDP Percent of GDP Percent of GDP<br>3 6 120<br>5 100<br>2 4 80<br>3 60<br>1 2 40<br>1 20<br>0 0 0<br>Transport investment Green investment SOE value added SOE assets (RHS)<br><!-- End of picture text --> Sources: Bartlett, Bonomi, and Uvalic (2022); Dealogic; European Bank for Reconstruction and Development (2020); Eurostat; IMF (2021); World Bank.

Note: CE = Central Europe; SOE = state-owned enterprise; TUR = Türkiye; WBK = Western Balkans. A. Unweighted average for an unbalanced sample of 16 ECA economies for bond issuance and 11 ECA economies for bond spreads. B. Efficiency gap is the percent difference between a country’s spending efficiency and that of the best performers. Higher values indicate greater inefficiency. Infrastructure spending efficiency is calculated using the volume and quality of infrastructure as the output
5 'Ihere are very likely to be measurement errors in our series on poverty lines, either because of the inrompleteness of our survey, or errors in the primary data. However, to the best of our knowledge, those errors are random and, in particular, uncorrelated with average incomes. Thus we do not belie:ve that these errcrs will bias our econometric estimates, and hence, our estimates of the absolute poverty line for cross-country comparisons.

In converting local poverty lines to a common currency, and indeed for all such comparisons in this study, we have used the estimates presented by Summers and Heston (1988) of the adjustments to official exchange rates needed to give purchasing power parity (PPP). Ideally one would like to construct new PPP rates for the prices most relevant to the absolute poor, in which the prices of food-staples would clearly carry a high weight.

The results of our survey of poverty lines are plotted agaicist mean incomes in Figure 1. Mean income is measured by private consumption per capita, and both variables are for 1985 and are measured at PPP, using local CPI's when necessary. Figure 2 gives a "blow-up" of the part of Figure 1 for developing countries only. India's poverty line is '4 per person per month.

There is a clear tendency for the local poverty line to increase with .mean income, though dispersion in poverty lines at most income levels is also evident, presuniably reflecting non-income factors and/or measurement errors. The poverty line is below the mean in all cases. The Figure also gives our fitted values of the poverty lines. In modelling the variation in poverty lines we assume the following semi-log functional form for equation (1):
The Mexican Pension Annuity Market rather quick to adjust the generosity of additional benefits but not as quick in adjusting the commission levels paid to agents. Such adjustments took place only between 2004 and 2005 that brought the overall underwriting expense ratio to 8.5 percent, a level lower than in 2001.

# **Investment performance**

The problem of combined ratios is that they are frequently negative (or equivalently, above 100 percent as shown in Figure 3). In other words, they are not very informative of the profitability of a company if not analyzed in conjunction with the investment performance. As a way of example, the following table reports a summary analysis of profitability of non-life markets in select industrialized countries. Notice how the combined ratio is almost constantly above 100 percent, meaning that markets are profitable only with good investment performance.

**Table 16: Evolution of Average Non-Life Loss Ratios in Select Countries (in Percent)**

||**USA**|**CAN**|**GBR**|**DEU**|**FRA**|**JPN**|
|---|---|---|---|---|---|---|
||**‘94 – ‘04**|**‘94 – ‘04**|**‘94 – ‘04**|**‘94 – ‘04**|**‘95 – ‘04**|**’96 – ‘04**|
|Loss ratio|78.7|73.3|73.0|71.3|80.8|61.2|
|Expense ratio /1|26.4|29.8|31.2|27.5|23.3|38.1|
|Polic holder dividend ratio<br>y|1.1|n.a.|n.a.|n.a.|1.1|0.1|
|Combined ratio|106.2|103.1|104.2|99.9|105.2|99.4|
|formance<br>Investment per|16.2|13.8|16.8|15.4|13.4|4.7|
|Profit margin<br>re tax)<br> (p|9.2|10.4|12.8|12.7|7.0|4.4|

**Notes** : 1/ this is comparable to underwriting expense and operational expense ratios for the Mexican pension annuity industry. **Source** : Holzeu Lechner (2006).

Hence, the final element of a company performance is given by the rate of return that can be generated from reserves. As previously mentioned, pension annuity companies invest reserves in government debt. With increased fiscal discipline, sovereign risk and inflation pressure decreased considerably. The next figure reports the evolution of inflation and sovereign risk for Mexico. This is measured by the JP Morgan EMBI+; i.e., the interest rate difference between Mexico and US Treasury Bills. As of November 22 2005, this was at historical lows of 112 basis points.

- 23 -
_Among working individuals, wage workers are the most likely to have moved or migrated within the previous 5 years_ The Labor Force Survey shows that among working individuals who moved within the previous 5 years, 64 percent are wage workers. Among the latter, over half of them report having moved for job related reasons (either to find work or to start a new job). Moreover, nearly half moved within Vietnam, while the other half reports having moved locally (i.e. within their province), a small minority having moved internationally.

# _International in-migration is a marginal phenomenon_

The findings suggest that international in-migration is a marginal phenomenon. The Labor Force Survey gives a pretty good picture of within-country migration and moves (see footnote 13 for caveat), but it is not adapted to look at international migration since those who have moved abroad are not interviewed. In fact most moves reported in the LFS are at the local or national level; very few individuals (0.5 percent of workers who have moved in the previous five years) report having migrated from another country. Among the employed, own-account workers are the most likely to have moved from another country, they represent 51.7 percent of all international in-migration. This suggests that international migrants may have saved enough money while working abroad to set-up their own business upon return to Vietnam. But it is noticeable that no employers report having moved within the 5 years preceding their interview. This may reflect a slow growth of firms that are set-up by international migrants, but it also suggests that few international migrants come back with large investment capacity. This is consistent with previous evidence that suggests that remittances are mainly used to purchase land and housing, and are received by relatively better-off households (Nguyen and Mont, 2011).<sup>14</sup>

# _Gender differentials are apparent_

Family tops the list of reasons for women’s moves, while labor market reasons dominate men’ moves. It is not surprising to find that men are more likely than women to have

> 14 Of course these interpretations have to be taken cautiously given the important caveat of a very small number of observations. Specific migration studies that contain labor market modules are best to understand these issues.

28
**<mark>Table 1: Government Support by Survey Round</mark>**

|**Variable**|**Rou**|**nd 1**|**Rou**|**nd 2**|**Roun**|**d 3**|
|---|---|---|---|---|---|---|
||**Obs**|**Mean**|**Obs**|**Mean**|**Obs**|**Mean**|
|**_Per round_**|||||||
|Received government support|3,647|0.42|3,647|0.23|3,647|0.25|
|Cash transfer|3,635|0.13|3,644|0.05|3,645|0.08|
|Payment deferrals|3,639|0.06|3,645|0.03|3,645|0.04|
|New Credit|3,638|0.05|3,645|0.02|3,646|0.02|
|Fiscal relief|3,634|0.06|3,644|0.04|3,646|0.05|
|Wage subsidies|3,643|0.32|3,643|0.17|3,645|0.19|
|**_Cumulative_**|||||||
|Received government support|||3,647|0.50|3,647|0.55|
|Cash transfer|||3,633|0.17|3,637|0.22|
|Payment deferrals|||3,636|0.09|3,635|0.12|
|New Credit|||3,636|0.07|3,635|0.08|
|Fiscal relief|||3,631|0.09|3,635|0.12|
|Wage subsidies|||3,638|0.40|3,640|0.44|

_Source_ : Enterprise Surveys COVID-19 Follow-up Surveys.

_Notes_ : Includes the 3,647 firms that reported whether they received government support in Rounds 1, 2, and 3. Some firms did not specify the type of support, which is why the number of observations varies.

**<mark>Table 2: Number of Government Support Rounds Received</mark>**

|**No. of Rounds**|**Rounds**|**Obs**|**Mean**|
|---|---|---|---|
|**_Considering all 3 Rounds_**||||
|3|All 3 Rounds|3,647|0.09|
|2|Rounds 1 and 2|3,647|0.07|
|2|Rounds 1 and 3|3,647|0.07|
|2|Rounds 2 and 3|3,647|0.05|
|1|Round 1 only|3,647|0.20|
|1|Round 2 only|3,647|0.03|
|1|Round 3 only|3,647|0.05|
|0|No Rounds|3,647|0.45|
|**_Considering Rounds 1 and 2_**|**_only_**|||
|2|Rounds 1 and 2|3,647|0.15|
|1|Round 1 only|3,647|0.27|
|1|Round 2 only|3,647|0.08|
|0|No Rounds|3,647|0.50|

_Source_ : Enterprise Surveys COVID-19 Follow-up Surveys.

_Note_ : Includes the 3,647 firms that reported whether they received government support in Rounds 1, 2, and 3.

14
(2) with different number of bins for both the developing and high-income sample. Columns (1) and (2) estimate models with _b_ =11, using all of the bins defined in Mu et al. (2013).

**Table 3 Impact of drought severity on GDP growth, various bins**

|Dep. Var. Δlog(GDP p.c.)|(1)|(2)|(3)<br>(4)|
|---|---|---|---|
|# bins|10|bins|5bins|
||impacts relative|to (-0.5<z<0.5)|impacts relative to (-0.5<z<0.5) and all wet<br>categories|
||High-income|Developing|High-income<br>Developing|
|**Dry shocks**||||
|Extreme(z<-1.6)|-0.3946*|-0.8595***|-0.3576*<br>-0.9069***|
||(0.194)|(0.207)|(0.169)<br>(0.202)|
|Severe(-1.6<z<-1.3)|0.1911|-0.5760**|0.2279<br>-0.6253***|
||(0.159)|(0.176)|(0.139)<br>(0.170)|
|High(-1.3<z<-1)|0.0037|-0.4691**|0.0419<br>-0.5187***|
||(0.141)|(0.153)|(0.128)<br>(0.147)|
|Moderate(-1<z<-0.8)|-0.0161|-0.3920**|0.0240<br>-0.4413***|
||(0.107)|(0.137)|(0.127)<br>(0.130)|
|Mild(-0.8<z<-0.5)|0.0465|-0.1106|0.0811<br>-0.1600|
||(0.140)|(0.128)|(0.096)<br>(0.140)|
|**Wet shocks**||||
|Mild(0.5<z<0.8)|0.4168+|-0.0731||
||(0.215)|(0.175)||
|Moderate(0.8<z<1)|0.0399|0.1972||
||(0.178)|(0.127)||
|High(1<z<1.3)|-0.1549|0.3282*||
||(0.147)|(0.158)||
|Severe(1.3<z<1.6)|-0.4733|0.2870||
||(0.391)|(0.194)||
|Extreme(z>1.6)|-0.4320|0.1184||
||(0.276)|(0.215)||
|Avg Temp(C)|0.2476*|0.7826*|0.2419*<br>0.7729*|
||(0.111)|(0.339)|(0.106)<br>(0.339)|
|Avg Temp Sq.|-0.0130***|-0.0245**|-0.0124***<br>-0.0246**|
||(0.004)|(0.008)|(0.004)<br>(0.008)|
|N|201466|667981|201466<br>667981|
|Cell FE|y|y|y<br>Y|
|Year FE|y|y|y<br>Y|
|Country Trends|y|y|y<br>Y|
|R-sq|0.395|0.305|0.392<br>0.305|

_Note: Dependent variable is change in gridcell log(GDP p.c). Observations are weighted by population. Standard errors in parentheses are clustered at the Administrative 1 level. Statistical significance is given by + p<0.1, * p<0.05, ** p<0.01, *** p<0.001._ Figure 5 demonstrates the coefficients in columns (1) and (2) of Table 3 graphically. In the developing sample, the estimated coefficients on the dry side of z-score are negative and statistically significant for conditions of moderate (−1 < z< −0.8 ) to extreme drought ( z< −1.6 ) compared to normal conditions. They are negative, but insignificant, for all of the other “non-normal” dry categories.

The magnitude of impacts appears to grow with drought severity. This is true for all of the coefficients moving away from normal on the dry side of z-score, though only the moderate, high, severe and extreme
of firms that experienced at least one bribe payment request in any of the above six transactions is called the bribery incidence indicator of the WBES.<sup>4</sup> An additional separate question asks whether an informal payment or gift was requested if the manager attempted to secure a government contract. Since all these seven questions are prompted only if managers have recently undertaken these specific transactions, the bribery incidence indicator plus the bribery indicator on public procurement provide the overall occurrence of bribe demands on the enterprises that have engaged in the included transactions. On the contrary, the last question I use is asked to all the enterprises in the sample. It asks: “It is said that establishments are sometimes required to make gifts or informal payments to public officials to “get things done” regarding customs, taxes, licenses, regulations, services etc. On average, what percentage of total annual sales, or estimated total annual value, do establishments like this one pay in informal payments or gifts to public officials for this purpose?” Managers have an option to answer this question either in percentages or in local currency value. I convert answers in local currency to the percentages of sales using the annual sales information, also available from the survey.

The WBES interviewed 1,344 top managers or business owners in Türkiye in 2013. In order to achieve a representative sample of the country, the WBES follows a stratified sampling strategy along dimensions of location, sector, and size. The WBES provide public access to the raw enterprise-level data. Using these data requires some functional choices, including the treatment of missing questions due to item non-response. In this case, all instances where bribery rates exceeded total revenues, as well as “Don’t Know” or “Refusal” values for the key variables of the number of permanent full-time workers, the total value of annual sales, or the estimated bribery payment, were removed. This process yielded a sample of 812 enterprises.

Tables 1 and 2 report the weighted summary statistics of bribery in Türkiye. The first column in the tables indicates enterprise size, which shows whether the statistics belong to small enterprises (enterprises with 5-99 employees) or big enterprises (more than 100 employees). Small plants constitute 95.1% of the sample. The second column of Table 1 shows the percentage of enterprises that have encountered a bribery request during a public transaction in the recent years. Of the enterprises that have done public transactions in recent years, 1.8% were asked to pay an informal payment or gift when they applied for a license, permit or during inspections. Among the small enterprises, 1.8% were asked to pay a bribe whereas, 2.9% of the big enterprises reported that they were asked to pay a bribe. The third

> 4This indicator is also being used by the U.N. Sustainable Development Goals (See SDG 16.5.2).

6