rafmacalaba/data-use-mentions-extended huggingface dataset:
forms of financial innovation, including new products, new providers, and new delivery channels. However, for innovative approaches to financial service delivery to be adopted, a competitive environment and a flexible regulatory approach are needed.

While there are documented achievements in deepening and broadening financial systems in the region, it is important to note that long-term finance remains a challenge. The dominance of banks and their focus on short-term transactions has prevented the financial system from playing a stronger role in funding the infrastructure Africa needs so urgently, the housing needs that the ongoing urbanization and population growth require, and the long-term investment by firms.

Although Africa’s banking systems have gained in resilience over the past decades, they are not immune against global financial cycles and sovereign debt fragility. The turn of the global financial cycle towards tightening has contributed to increasing sovereign distress in many developing countries, including in Sub-Saharan Africa. Ultimately, this distress will have negative repercussions for banks. Similarly, increasing geopolitical tensions will have implications for the geographic footprint of global banks and will induce an additional element of international politics into regulatory decisions.

As discussed in this survey, reseach into banking in Africa has expanded rapidly over the past years, using increasingly micro-level data, including credit registry data, thus following a trend from other regions of the world. Close cooperation between researchers, central banks but also commercial banks and MFIs can help produce critical insights into what practices and policies work to deepen, broaden and lengthen the term structure of banking in Africa. There is an exciting and policy-relevant research agenda going forward.

41
Middle East and North Africa Economic Update. Washington D.C., Washington D.C., USA. Retrieved 02/29/2024, from https://elibrary.worldbank.org/doi/abs/10.1596/978-1-4648- <u>1974-2</u> Gavin, W., & Mandal, R. (2002). Predicting inflation: food for thought. The Regional Economist,, Jan, 4-9.

Green, R., Cornelsen, L., Dangour, A., Turner, R., Shankar, B., Mazzocchi, M., & Smith, R. (2013). The effect of rising food prices on food consumption: systematic review with metaregression. BMJ, 346(f3703). doi:10.1136/bmj.f3703 Headey, D., & Ruel, M. (2023). Food Inflation and Child Undernutrition in low and middle income countries. Nature Communications, 14(5761). doi:10.1038/s41467-023-41543-9 Hirvonen, K., Minten, B., Mohammed, B., & Seneshaw, T. (2021). Food prices and marketing margins during the COVID-19 pandemic: Evidence from vegetable value chains in Ethiopia. Agric Economics, 52(3), 407-421. doi:10.1111%2Fagec.12626 Joutz, F. (1997). Forecasting CPI Food Prices: An Assessment. American Journal of Agricultural Economics, 79(5), 1681-1685.

Kalkuhl, M., von Braun, J., & M., T. (2016). Volatile and Extreme Food Prices, Food Security, and Policy: An Overview. In M. Kalkuhl, J. von Braun, & M. Torero (Eds.), Food price volatility and its implications for food security and policy. Cham, Switzerland: Springer. doi:10.1007/978-3-319-28201-5 Kisan, G. (2015). Review of global food price databases:
of picture text --> Percent of EMDEs<br>100<br>80<br>60<br>40<br>20<br>0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text -->

## **D. Five‐year‐ahead forecasts of investment growth**

<!-- Start of picture text --> Percent<br>EMDEs AEs World<br>10<br>8<br>6<br>4<br>2<br>0<br>F. Share of world investment<br>Percent EMDEs excluding BRICS<br>Other BRICS<br>50 China<br>40<br>30<br>20<br>10<br>0<br>1993-1999 2000-08 2010-15<br>2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Sources: Consensus Economics; Haver Analytics; International Monetary Fund; Oxford Economics; World Development Indicators, World Bank.

A. Weighted averages. Long‐term average starts in 1991 for EMDEs due to lack of earlier data.

B. Long‐term averages are country‐specific and refer to 1990‐2008. Latest year is 2015.

C. Number of years needed to catch‐up with 2015 real per capita GDP level in the United States, assuming average growth rates over each period denoted for each group.

D. Each line shows five‐year‐ahead Consensus Forecasts as of the latest available month in the year denoted. Unweighted averages of 21 EMDEs and 25 advanced economies. World sample includes 46 countries. Last observation is for October 2016. E. The sample includes 100 EMDEs and 34 AEs. Difference between EMDEs’ and AEs’ weighted average investment growth rates.

F. Each column shows the period average of the share of global investment contributed by each
<mark>in Saleempur to 66.7% in Gauspur, with slightly over 50% of households owning improved structures with septic tanks. 34.7% households had pits with lining and slab, while 11.4% had simple pit latrines. 12% of the households in the sample received some amount of SBM subsidy to cover the cost of latrine construction.</mark> <mark>We conducted multiple focus group discussions with four distinct groups of latrine owners—female users, female non‐users, male users, and male non‐users. Each focus group discussion was conducted with a group of 6‐8 individuals from a particular village or</mark> _<mark>tola</mark>_ <mark>(neighborhood within the village), and discussed</mark> — <mark>the current state of latrine access and use, barriers structural, attitudinal, and normative—that influence or impede latrine use (own and for others), and notions of cleanliness (and its importance) and</mark> _<mark>gandagi</mark>_ <mark>.</mark> <mark>The baseline survey was administered at the household level, across 204 households that owned latrines, between January to February 2017. Households were identified by enumerators with the help of key individuals in the village and through a snowball sampling method. Standard in‐field randomization (following a “right‐hand” rule) was initially used to select a sample of eligible households for the survey. However, given the difficulty in finding households with latrines where at least one male or female adult was available, this method of selection was adapted when necessary. Table 2 summarizes key demographic characteristics of households and respondents (one from each household) at baseline.</mark>

**<mark>Table 2. Demographic Characteristics</mark>**

|**Variables**|_A_|_verage_|_Observations_|
|-
disappointing trend in employment is related to the fortunes of the Ghanaian private manufacturing sector in the post adjustment period and will be discussed further at a later point in the paper.

The average real earnings in the private formal sector seem to have increased at the same rate as real wages in the public sector in the post-ERP period until 1988 ( World Bank, ibid.). But did private sector wages fall to as low levels as they did in the public sector in the years of accelerating inflation before the ERP? Unlike the public sector, the private employer could be expected not to let wages fall below a threshold which would actually lead to a proportionately greater fall in efficiency than wages per worker. Nor is the latter obliged to follow a policy which favors retention of employment more than falling wages. In fact, we have already seen that private formal employment fell sharply while the public sector work force was expanding in the years prior to the ERP. Aggregate earnings data do suggest that in 1988, private sector average earnings were higher than those in the public sector; the private-public differential was 1.46. This differential in favor of the private sector seems to have been whittled down in the years after 1988 as public sector wages increased further. The -over-all private-public earnings ratio stood at 1.07 in 1991 (ISSER, Table 8.14, p.153). The massive wage increase of 80 per cent in the public sector in 1992 must have pushed this ratio much further down although the data are not available for more recent years from this source.

10
# **Figure 6-3 All transfers are progressive, except for fuel subsidies and pension income**

Progressivity of transfers, measured by the Kakwani Index at Market Income <!-- Start of picture text --> a. Progressivity of transfers<br>Fuel subs. (direct effect) -23.0<br>Pension income -14.0<br>Fuel subs. (total) -8.3<br>Fuel subs. (indirect effect) -4.7<br>Elec. subs. (direct effect) 33.4<br>Secondary educ. 39.0<br>Basic educ. 45.2<br>Primary healthc. 47.9<br>Hospital healthc. 47.9<br>Near-cash transfers 73.0<br>Cash Transfer Program (CTP) 100.3<br>-50 0 50 100<br>Kakwani Index w.r.t. Market Income<br><!-- End of picture text --> Source: Authors' estimates based on PECS 2016/2017; LFS 2017. Notes: [1] The Kakwani Index is a summary statistic of progressivity [>0 = progressive]. [2] It is calculated for transfers by subtracting the concentration coefficient from the Gini coefficient.

**All taxes are progressive relative to Market income. The PIT is the most progressive, followed by the total VAT, the fuel excises, and the customs tax (with a score of 14.1, 12.6, 11.9 and 3.7 on the Kakwani Index respectively).** The PIT is only slightly more progressive than the total VAT (direct and indirect effects combined). This is because of the flat design of the PIT in the West Bank and Gaza with relatively few income brackets.<sup>74</sup>

**The progressivity of the VAT is primarily driven by its direct effects, with a Kakwani
in parentheses. Controls chosen using pdslasso from demographic controls and baseline bargaining measures, their squares, and factory, enumerator and month dummies.

**Table C.15: Effects on other outcomes**

||Economi|cally active|Slapped o|r beaten by husband|Hiding money|Controlling money|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||Wife|Husband|Ever|In last 5 months|||
|selected|_−_0_._02|_−_0_._03|0_._03|_−_0_._03|0_._03|_−_5_._32|
||(0_._03)|(0_._04)|(0_._06)|(0_._04)|(0_._08)|(17_._90)|
|N|363|350|363|363|181|182|
|Nominated mean|0_._96|0_._97|0_._30|0_._09|0_._28|146_._77|
|Nominated SD|0_._21|0_._17|0_._46|0_._29|0_._45|147_._72|

Notes: Selected is a dummy variable indicating whether the female sewing-line operator is quasi-randomly selected for the promotion program. Nominated is a dummy variable indicating whether the female sewing-line operator is an eligible, shortlisted runner-up that was quasi-randomly not selected for the promotion program, and represents the comparison group for the selected group. Standard errors clustered at the individual level in parentheses. Controls chosen using pdslasso from demographic controls and baseline bargaining measures, their squares, and factory, enumerator and month dummies. Column (2) uses data from the husband’s surveys.

81
|4.23|19.94|0.00|5848.00|
|Insp_t−_1|2431641|2.98|14.53|0.00|100.00|
|ln UV_t_|2431615|7.77|2.08|-6.93|22.80|
|AV tarif_t_|2431641|0.07|9.13|0.00|8612.07|
|Multi-goods|2431454|7.53|11.57|1.00|212.00|

Table notes: Summary statistics for Serbian customs data from 2013-16. Annual data organized at the level of HS10 product, origin country and importing firm. _Bin_ variables are dummy variables indicating the frequency of shipments in the product-country-firm triplet in the previous year. Subscripts on the bin dummies indicate ranges of the number of lagged shipments associated with the dummy variable for that bin.

# **Appendix B: Theoretical framework**

## **General Framework**

It is assumed that the inspecting agency’s uncertainty can be described by a non-empty finite set of states, Γ. There is a set of events, _ε_ , associated with the set of states, which are the power set of Γ. It is assumed that for each _γ_ in Γ, _{γ}_ is in _ε_ . Let _X ⊂_ R be a non-empty, finite set of outcomes and let _G_ = _{g_ : Γ _→ X}_ be a simple set of functions from 33
embodied in the March 7, 2019 widespread power outage in the República Bolivariana de Venezuela that turned out to have massive spillovers on Bitcoin markets in several other Latin American currencies.

The first part of the paper describes our novel core data set, with details provided in the Data Appendix. The second section describes the probabilistic matching methodology we develop. In a significant number of cases, the individual trades we pair are like matching snowflakes that occur nowhere

> 3 The 1 percent fee charged by the exchange we study makes the platform impractical for high-speed arbitrage use.

2
- Home-based work: In the last 30 days, has the share of workers working from home increased or decreased? Increase/Decrease/No change.

# **A3. Sampling frame of the FAT and COV-BPS Surveys**

For both Firm-level Adoption of Technology Survey (FAT) and Business Pulse Survey (COVBPS), the sampling strategy was based on the latest establishment census available from national statistical agencies or administrative business register. The sampling frame for the state of Cear ́a in Brazil was the 2017 _Relac ̧ ̃ao Anual de Informac ̧ ̃oes Sociais_ (RAIS) managed by the Ministry of Economy (MoE); the 2018 Establishment Census from the General Statistical Office (GSO) for Vietnam; and the 2016 _Recensement G ́en ́eral des Entreprises_ (RGE) from the _Agence Nationale de la Statistique et de la D ́emographie_ (ANSD) in Senegal. Each database covers all registered establishments operating in each country.

( _17_ ) drew a nationally representative sample of establishments with 5 or more employees in agriculture, manufacturing, and services. The sample was randomly selected based on three strata: region, size, and sector. The FAT data for the State of Cear ́a in Brazil, Vietnam, and Senegal were collected between August 2019 and February 2020, and most observations were collected before December 2019. For Cear ́a-Brazil, Senegal, and Vietnam, the BPS survey was implemented on a nationally representative sub-sample of FAT data. This data was collected between April and July 2020.

The FAT-BPS data covers around 1,000 businesses both before and after the COVID-19 pandemic crisis. Table A1 provides the sample size of the linked FAT-COV-BPS data and the interview periods in each country. Figure A2 shows the interview period of the COV-BPS survey in each country and the average mobility trend as a proxy for the severity of the COVID-19 shock over this period.

||Sample size|FAT|COV-BPS|
|---|---|---|---|
8 where _egst_ is defined as above and _pgst_ is the number of students promoted in that grade/school/year.

R166-SIMAT also includes a registry of public school teachers for the sub-period 20142018. We use these data to explore potential mechanisms (albeit for public schools only) related to key supply measures such as the teacher/students ratio and the quality of teachers.

The second administrative dataset contains information on the scores obtained by students in the official high-school exit exam (called _Saber 11_ ) for the period 2005– 2018. It comes from the Colombian Institute for the Promotion of Higher Education. From it, we computed math and language test scores. To facilitate the interpretation and make scores comparable across years, we standardized the test scores to have mean zero and standard deviation one each year.

Table 1 reports descriptive statistics of the main outcome variables.

3.2. **Other data.** We will also employ data on the total number of Venezuelans arriving annually in Colombia, available from the national migration agency ( _Migraci ́on Colombia_ ). These data come from the information recorded at official migration points and thus, it does not include any illegal or unregistered migration.

footnoteThis imply that the 2.5 million figure of Venezuelans in Colombia is likely underrepresented.

Finally, we have access to a large number of pre-determined municipal level controls (based on the 1993 and 2005 population censuses) that we use to control for differential trends interacted by time-invariant municipal characteristics that may help predict the evolution of educational outcomes. Table 2 reports the descriptive statistics on these covariates.
, Bangkok is reference), and for urban residence; monthly series on: unemployment rate; consumer price index for south, northeast, north, center, Bangkok; U.S. dollar value of exports of agricultural products, manufacturing, overall; index of baht value of export price, export volume, import price, import volume; terms of trade; business sentiment index; expected business sentiment index; leading economic index; no. of foreign tourists; inverse of price of oil; monthly wage in private sector; monthly wage in government sector; price of 5% broken rice (in baht); price of tapioca flour (in baht); price of tin concentrate (in baht). **Full regression:** All variables in Survey and Public Data regressions. Number of observations: 176,141 households for full sample.

# **Conclusions**

In this paper we ask a straightforward question: Who was hurt in Thailand by the global recession that spread from the world’s rich countries to Thailand in late 2008 and persisted for much of 2009?

Somewhat to our surprise, we find that the recession itself was not associated with a reduction in real spending for most groups in Thai society. Neither women, nor the poor, nor rural residents, nor children, were particularly hurt by the recession. The main exception is that young adults, especially those residing in Bangkok, and with a vocational education, were negatively affected in 2009. This is consistent with the observation that export-led manufacturing, which is concentrated around Bangkok, contracted sharply for several months.

There are a number of possible explanations for the benign, and for some people, even beneficial, effects of the recession of 2008-09. The first is that the prices of food – including the Losers and Winners in Recession: Thailand 2008-09 26
at different levels), and committed to one with the highest power. Simulations suggested that this could produce a 20 to 25 percent narrower confidence interval than in a simple benchmark specification. Comparing Table 3 to Appendix Table A.4, this was substantively borne out.<sup>18</sup> On the basis of this theory and analysis of blinded data, we settled on six primary tests: an outcome, a sample, a specification and associated test statistic, and an the data-blinding process. Results of the blinded analysis (for which IPA certified that we used only blinded data) are in our pre-analysis plan. Our RCT registry entry (AEARCTR-0002565) is accompanied by IPA’s letter specifying the date after which treatment was unblinded.

> 16Hypotheses II and III employ data that our team collected, so did not have power concerns associated with them; Hypothesis VI offered fewer degrees of freedom.

> 17The confidence interval for the KS test is roughly half the width of the corresponding OLS confidence interval: a gain in precision commensurate with more than tripling the sample size.

18Our pre-committed random effects model yields a confidence interval 86 percent as wide as that from OLS, commensurate with the power gain from increasing the sample size by a third.

13
# **3.3 Summary statistics**

The first two panels in Table 2 present pre-intervention sample means, by treatment group, for child and household characteristics respectively. The sample of children does not include those born after the pre-intervention survey (they do not have baseline data), and the sample of households reflects one observation per child that we observe in the followup survey even if they were born after the baseline survey. Consistent with the random assignment, we find that characteristics are balanced across the three groups, with three exceptions: in cash localities, the share of boys is lower than in both in-kind and control localities; households in control localities are more likely to have an unmarried head than those in cash localities; and control households are more likely to have running water at home than those in cash villages.

Individual consumption data was only collected for children aged 1 through 4 years. As the 24-hour food recall module could overstate or understate actual consumption, it is difficult to draw definitive conclusions about child health, however, comparing caloric and micronutrient intake to Recommended Daily Allowances (RDAs) suggests that most children consume too few calories and that for many, those calories do not contain enough essential micronutrients. In particular, 89 percent of children consume fewer than the RDA of calories, and 32, 46, and 41 percent of children are not consuming the RDA of iron, vitamin C, and zinc, respectively. Households in our sample are large and poor, with a total per-capita expenditure of about 360 pesos per month, or $36 US dollars, and about 5.5. household members. Approximately 30 percent of households have at least one member who speaks an indigenous language. The household head has on average 7 years of education, about 40 percent of households have a dirt floor, and around 50 percent do not have running water.

The bottom panel in Table 2 presents the characteristics of the closest school to each PAL community. The closest school is typically a general school and on average
To ensure equilibrium in the model, three sets of market clearing conditions are met. First, total production of each commodity equals the sum of domestic consumption and export so that the goods and services markets clear. Second, total investment equals total saving, where savings are composed of private (household) savings, public (government) savings and exogenously fixed foreign savings. Third, factor markets clear, which implies full employment.

The model is calibrated with GTAP version 7.0 data. However, not all data needed for the model are available in the GTAP database. For example, biofuels are not a proper sector in the original GTAP 7.0; therefore, we modified the database in a way that allowed us to introduce biofuels sectors in our CGE model. For this purpose, we collected detailed information on production, consumption and trade, a total of seven new biofuel sectors, which have been created by splitting existing GTAP sectors. The Splitcom<sup>14</sup> software was used to process the splits and keep the global social accounting matrix balanced. The land data are based on the GTAP 7.0 database and were derived analogously to Lee et al. (2008).

# **3. Simulation Results**

We first consider a range of globally uniform carbon taxes on all fossil fuels, starting from US$10/tCO2 up to US$100/tCO2. The taxes are introduced in 2012 and kept fixed in nominal

> scenarios and the baseline. Any value assumed for AEEI is common for both baseline and the carbon tax policy scenarios. Moreover, the role of AEEI would be significant only in the long-run; 50-100 years (e.g., models presented in Edenhofer et al. 2006,2010).  The version of the model used in this analysis considers a short time horizon, 10 years (2010-2020). Interested readers are referred to existing literature such as Edenhofer et al. (2006,2010), Kaufmann (2004) and  Edenhofer