rafmacalaba/datause-encoder-data huggingface dataset:
Financial inclusion can help drive development by helping people escape poverty. Financial services can facilitate investments in health, education, or businesses and make it easier to manage financial emergencies. The ability to manage financial risk is especially important for people earning their living through agriculture. Many farmers only get paid once or twice a year and households need to stretch their earnings across the year by saving or borrowing money. Moreover, agricultural production faces a variety of risks related to both production and markets because of their exposure to weather and disease shocks. Households engaged in agriculture may thus especially benefit from financial inclusion – access to and use of formal financial services.

This paper explores the topic of financial risk management in agriculture – how adults who rely on growing crops or raising livestock as their household’s main source of income manage financial risk and use financial services. The data draw from a module on agricultural risk management added in select economies to the 2017 Global Findex questionnaire.<sup>2</sup> Collected by the World Bank in coordination with Gallup, Inc., the data are based on a nationally representative survey of about 15,000 adults in 15 lowermiddle- and low-income Sub-Saharan African economies.<sup>3 4</sup> All averages, including for the 15 economies, are population weighted.

In the 15 surveyed economies in Sub-Saharan Africa, on average 35 percent of adults reported that their household relied on agriculture by growing crops or by raising livestock as the main source of their household’s income. But there is wide variation among economies (map 1). Ethiopia has the highest share of adults reporting relying on crops and livestock as their household’s main source of income, at 63 percent. Mali is a close second at 55 percent. In Malawi, Mozambique and Tanzania, about 40 percent of adults reported agriculture to be their household’s main source of income, followed by about a third to a quarter of adults in most other economies. The Democratic Republic of Congo has the lowest share of adults reporting relying on crops and livestock as their household’s main source of income, at 17 percent. See detailed country-level data on adults who rely on crops and livestock as their household’s main source of income in Annex 1.

> 2 The Global Findex database measures how adults around the world save, borrow, and make payments. For the complete methodology, see <u>https://globalfindex.worldbank.org/.</u>

> 3 Data were also collected in three additional upper-middle-income economies in Sub-Saharan Africa: Botswana, Gabon, and South Africa. However, the low rate of adults who reported that their household relied on agriculture by growing crops or by raising livestock as the main source of their household’s income does not allow for meaningful analysis because of the small number of relevant observations.

> 4 The module on agricultural risk management was also added to the 2017 Global Findex questionnaire in select non-Sub-Saharan African economies. Key summary statistics are reported in Annex 2.

2
**Table 3: Estimation results based on Equation (2) with the triple interaction using IV approach**

||(1)<br>Sales per<br>worker<br>(log)|(2)<br>Sales (log)|(3)<br>Employm<br>ent (log)|
|---|---|---|---|
|Energy intense|0.873|0.808|0.266|
||(0.923)|(0.936)|(0.301)|
|Energy inefficient IV1|-0.745|-1.103|-4.469***|
||(1.141)|(1.165)|(0.419)|
|Price oil, log|12.08***|11.54***|-1.974***|
||(0.486)|(0.488)|(0.134)|
|Price electricity, log|-2.385***|-2.353***|0.217|
||(0.507)|(0.517)|(0.166)|
|Explicit petroleum subsidy (% GDP)|-1.996***|-1.918***|-0.0712|
||(0.679)|(0.679)|(0.179)|
|Explicit electricity subsidy (% GDP)|6.943***|6.690***|-1.092***|
||(0.868)|(0.871)|(0.243)|
|Energy intense*Energy inefficient IV1|-1.337|-2.146*|-1.542***|
||(1.249)|(1.275)|(0.495)|
|log Price oil*Energy intense|0.555***|0.594***|-0.110**|
||(0.191)|(0.191)|(0.0505)|
|log Price elect*Energy intense|1.368***|1.421***|-0.141|
||(0.322)|(0.327)|(0.118)|
|log Price elect*Energy inefficient IV1|-0.833|-1.008*|-0.835***|
||(0.548)|(0.559)|(0.192)|
|log Price elect*Energy intense*Energy inefficient IV1|-0.401|-0.839|-0.702***|
||(0.601)|(0.612)|(0.229)|
|Constant|-48.26***|-43.55***|10.10***|
||(2.628)|(2.656)|(0.787)|
|Observations|19,237|19,237|19,237|
|R-squared|0.233|0.335|0.571|
|CountryFE<br>|YES<br>|YES<br>|YES<br>|

Firm-level adoption of efficiency measures instrumented by average adoption of the measures by other firms in the same sector, of similar size and location. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Weighted by country sample size Controls for size clusters<sup>[1]</sup> , age, wave, and subsector [1] Constructed using employment in columns (1) and (2), and sales in column (3) 21
30 <!-- Start of picture text --> 4<br>3.5<br>3<br>2.5<br>teta**(tow=0)<br>2<br>teta**(tow=0.05)<br>1.5<br>teta**(tow=0.1)<br>1<br>teta**(tow=0.2)<br>0.5<br>0<br>0.88 0.9 0.92 0.94 0.96 0.98 1<br>Rm<br>A. R  0.985,  0.001,  0<br>2.5<br>2<br>1.5<br>teta**(tow=0)<br>teta**(tow=0.05)<br>1<br>teta**(tow=0.1)<br>0.5 teta**(tow=0.2)<br>0<br>0.88 0.9 0.92 0.94 0.96 0.98 1<br>Rm<br>B. R  1,  0.001,  0<br><!-- End of picture text --> Figure 9: The effect of income tax on the social cutoff point

# 4.2 Welfare Cost Calculations

Welfare is given by:
relationship between investment and inflation for a sample of high inflation countries (Argentina, Bolivia, Brazil, Chile, Israel and Mexico) but the value of the coefficient is very small. The highest coefficient they find is -0.00016, which implies that a going from zero to 1000 percent inflation per year would reduce investment by only 0.2 percentage points. They find, however, an economically significant effect in a sample of OECD countries. where a 10 percent inflation would reduce total investment by 0.9 percentage points.<sup>2</sup> 1 Corbo and Rojas (1993) find that Latin American countries with inflation above 50 percent have 1.3 percentage points of lower investment than those countries with low inflation. Their coefficients are, however, only marginally significant. They also run separately equations for growth and investment and find that reducing inflation by 10 percentage points increases both growth and the rate of investment by 1 percentage point. Therefore, their results confirm that most of the effects of inflation on growth are due to a decline in efficiency.

There are some studies that do not find robust effects of inflation on growth. As already mentioned, Levine and Zervos (1993), using World Bank data on output growth, find that the relationship is not robust. Moreover, they argue that Nicaragua and Uganda are two outliers that change dramatically the results. McClandess and Weber (1994) examining the partial correlations between inflation and growth in a sample of 110 countries, with all data taken from IFS for the period 1960-90, find that they are not correlated. One explanation for the lack of correlation of some studies offered by Bruno and Easterly (1995) is that the declining path of growth during inflation crises is offset by the strong recovery after stabilization.

In the remaining of this section I complement the empirical evidence reviewed previously. Figures 1 and 2 present simple cross-section correlations of inflation, and growth and investment, respectively. The figures show that there is indeed a negative correlation between inflation and growth, and a weaker negative correlation between infiation and investinent. In tables 1 and 2 I present cross-country regressions for infiation and growth. I use the data from Barro (1991) and completed them with inflation from IFS. Based on the previous discussion I use the log of the inflation rate. Following Levine and Zervos (1993) I exclude Nicaragua and Uganda from the sample.<sup>2 2</sup> There is some evidence of heteroscedasticity, thus the standard deviations

> **2 1 This is of the order of magnitude of the findings of Fischer (1991, 1993).**

> **22 1n addition I experimented with the log of 1+inflation and the results do not change significantly.**

24
born people may indeed have been migrants at some stage, the actual move to the present country may have taken place quite some time back, with the result that many classified as migrants using the relatively broad “country of birth” criterion do not in fact conform to the notion many people have in mind when they think of migrants. After considering various possible alternatives, we used the information from both sets of variables to divide the sample population into five main groups of interest: (i) native-born population, (ii) citizens born in other EU countries, (iii) citizens born outside the EU, (iv) non-citizens from other EU countries, and (v) non-citizens born outside the EU.

The distribution of the EU-SILC sample into these five main groups is presented in Table 2. Using a relatively narrow definition of migrants—classifying as migrants all non-citizens in any given country—migrants comprise less than 5 percent of the population of the EU13 (less in the EU4). Even using a relatively broad definition—classifying as migrants all those (i.e. regardless of their citizenship) born outside the country where they currently reside—migrants constitute less than 10 percent of the population of EU13 countries. By contrast, the foreign-born population constitutes about 13 percent of the total population of the United States (i.e. not including the much larger share of internal migrants within the country).<sup>6</sup> Overall, about 2 percent and 9 percent of the population of EU4 and EU13 countries respectively were born outside their current country of residence. As illustrated in Figure 1, this share varies considerably across individual EU countries, from more than 15 percent in Austria to less than 2 percent in Poland and Slovakia.

**Table 2: Population Breakdown by Derived Migration Status and Country of Residence**

|||**Percent of**<br>**1.**<br>|**thepopulation**<br>**2.**<br>|**16+years:**<br>**3.**<br>|**4.**<br>|
|---|---|---|---|---|---|
|**COUNTRY**|**Native-born**<br>**Population**|**Citizens born in**<br>**other**<br>**EU countries**|**Citizens**<br>**born outside**<br>**the EU**|**Non-citizens**<br>**from other EU**<br>**countries**|**Non-citizens**<br>**born outside**<br>**the EU**|
|Austria|85|3|4|2|6|
|Belgium|88|2|3|4|3|
|Denmark|95|0|1|1|3|
|Spain|95|1|1|1|3|
|Finland|97|1|1|0|1|
|France|88|2|4|2|3|
|Greece|93|1|2|0|4|
|Ireland|89|4|1|4|2|
|Italy|94|1|1|0|4|
|Netherlands|94|1|4|1|1|
|Portugal|97|1|1|0|1|
|Sweden<br>|88|3|5|2|2|
|United Kingdom|89|0|6|1|4|
|**Total EU 13 countries**|**91**|**1**|**3**|**1**|**3**|
|Czech Republic|96|2|1|0.5|0.7|
|Hungary|97|1|2|0.1|0.5|
|Slovakia|98|1|0.3|0.2|0.1|
|Poland|99|1|1|0.0|0.1|
|**Total EU 4 countries**|**98**|**1**|**1**|**0.1**|**0.3**|

**Note:** Totals across rows may not add to 100 because of rounding.

> 6 US Census Bureau: 2006 American Community Survey, as cited on www.migrationinformation.org.

7
- 16 -

**<u>Table 3. SUR estimation results with backward-looking expectations</u>**

||SUR(1)||SUR(2)||
|---|---|---|---|---|
||Cost overrun<br> <br>|Project delays<br>|Cost overrun<br>|Project delays<br>|
||Coef.<br>Std. Err.|Coef. Std. Err.|Coef. Std. Err.|Coef. Std. Err.|
|ln_rain_<sup>_t_</sup>|-0.316 (0.297)|0.326 (0.117) ***|||
|ln_rain_<sup>_e_</sup>|||0.069 (0.551)|0.493 (0.169) ***|
|ln_rain_<sup>_a_</sup>_/ rain_<sup>_e_</sup>|||0.392 (0.936)|0.051 (0.288)|
|ln_rain_<sup>_t_</sup>_/ rain_<sup>_a_</sup>|||0.347 (0.566)|1.633 (0.174) ***|
|ln_cost_|0.945 (0.758)|1.449 (0.298) ***|<br>0.723 (0.818)|1.235 (0.251) ***|
|ln_length_|-1.899 (0.644) ***|-0.851 (0.254) ***|<br>-1.793 (0.668) ***|<br>-0.849 (0.205) ***|
|ln_lane_|2.028 (3.758)|1.500 (1.479)|2.608 (3.843)|0.202 (1.181)|
|ln_thickness_|-0.372 (0.483)|-0.393 (0.190) **|-0.240 (0.557)|-0.495 (0.171) ***|
|ln_gravel_|-0.038 (0.277)|0.022 (0.109)|-0.144 (0.347)|-0.094 (0.107)|
|ln_bitumen_|0.966 (0.218) ***|-0.123 (0.086)|0.860 (0.261) ***|<br>-0.024 (0.080)|
|ln_earth_|0.505 (0.275) *|-0.262 (0.108) **|0.567 (0.301) *|-0.248 (0.092) ***|
|ln_brick_|0.103 (0.304)|0.042 (0.120)|0.206 (0.359)|-0.020 (0.110)|
|ln_gabion_|-0.097 (0.195)|-0.135 (0.077) *|-0.065 (0.202)|-0.170 (0.062) ***|
|ln_exavate_|-0.168 (0.146)|0.061 (0.057)|-0.165 (0.149)|0.070 (0.046)|
|ln_cement_|0.049 (0.234)|-0.280 (0.092) ***|<br>0.033 (0.249)|-0.160 (0.076) **|
|D(_postqualify_)|4.056 (1.812) **|0.474 (0.713)|6.001 (2.072) ***|<br>0.717 (0.637)|
|Constant|-13.976(8.664)|-17.007(3.410)***|-16.143(9.170)*|-14.836(2.818)***|
|Obs.|155|155|145|145|
|R-squared|0.540|0.562|0.546|0.726|
|Chi-2|182.20|198.79|174.20|384.74|
|No. of dummy variables:|||||
|Project districts|18|18|18|18|
|Breusch-Pagan test of<br>independence|6.899 *||7.210 *||

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at the 10%, 5% and 1%, respectively.
In Latin America, the process of catching up to the standards of living in advanced economies has been a slow one. In 2017, Latin America’s real per capita income was only about a quarter of the one in the US, compared to a fifth in 1960 (Cavallo and Powell, 2018). Spatial convergence of mean household incomes between the mid-1990s and mid-2000s was also slow (Modrego and Berdegue, 2015). Unsurprisingly, within-country spatial inequality remained high in most Latin American countries (Acemoglu and Dell, 2010). Using census data mostly from the 2000s, Acemoglu and Dell (2010) show that the between-municipality differences in labor income within countries were twice the size of the between-country differences within Latin America. They attribute these differences to differences in human capital and inequality in proximity to paved roads. In a series of studies on Peru, Escobal and Torrero (2005) also empirically connect poor local road infrastructure to higher transaction costs, lower market participation, and reduced household income.

This regional context elevates the importance and policy relevance of the questions posed in the opening paragraph of this paper. To answer these questions, we adopt the state-of-the-art dynamic global spatial general equilibrium framework of Desmet, Nagy, and Rossi-Hansberg (2018). It is wellsuited for the task at hand as it differentiates the effects of mobility frictions from those of other forces that shape the spatial patterns of economic activity and welfare in a dynamic, global context. It also offers a way of estimating the mobility frictions and tracing the effect of changes in these frictions on economic activity in the long run. We follow Desmet et al. (2018), who identify trade frictions with transportation costs, and Allen and Arkolakis (2014), who compute bilateral transportation costs using a least-cost route algorithm between any two cells. This way the costs reflect the geography of locations, the availability of different types of connectivity infrastructure, and the prices of different transportation service modes. Migration frictions capture moving costs into and out of a location due to multiple factors, including institutional restrictions, imperfect information, moving costs, and social norms or idiosyncratic preferences for a given location. Desmet et al. (2018) estimate the cost of moving in and out of each location in 2000 using initial technology, amenities, and utility as well as the change in population between 2000 and 2005. Then, they identify migration costs between two locations as the product of an origin- and a destination-specific cost. They proxy initial utility in all locations in each country with the average Cantril Ladder score available for the country from the Gallup World Poll.<sup>2</sup> Thus, if productivity increases due to innovation in a high initial utility location,

> 2 Desmet et al. (2018) assume uniform utility within countries only in the initial period. After the initial period, utility varies both across and within countries.

3
# **3. An Application for Irrigated Rice Production**

The CH4 database has been designed to complement the CO2 database developed in Dasgupta, Lall and Wheeler (2022 a,b). Both are intended to assist global stakeholders with readily-usable, internationally-comparable and objectively-verifiable information about greenhouse gas emissions. We believe that 25-km resolution strikes a reasonable balance between robust estimation of cell means, given the available data, and the resolution required for analysis at subregional scales.

We illustrate with a global analysis of emissions from irrigated production of rice, the staple crop for the majority of the world’s population (Adhya et al. 2014). In 2013, rice was harvested on 165 million hectares of land in 100 countries, with 90 percent of global production in Asia. Lowland irrigated fields occupy about 80 million hectares and produce 75 percent of the global crop (FAO 2014; Fischer et al. 2014).

Figures 3 and 4 display the global distribution of irrigated rice production by field extent and yield, respectively, estimated by a team affiliated with the International Food Policy Research Institute (IFPRI) (Yu et al. 2020). Aside from the vast irrigated areas in South, Southeast and East Asia, Figure 3 reveals smaller areas under particularly intense cultivation in the United States (California and the Mississippi River Valley), southern Brazil and Uruguay, northern Italy, The Gambia, Mali, the Nile Delta and Madagascar. Figure 4 displays a yield pattern that is somewhat different, with evident high-yield areas in the United States, southern Brazil and Uruguay, southern Spain, northern Italy, the Nile Delta, central and northeastern China, and eastern Australia. South and Southeast Asia offer the most striking contrast, with widespread cultivation intensity in Figure 3 but relatively low yields in Figure 4.

The georeferenced IFPRI data have higher spatial resolution (0.083 degrees) than our spatial grid for CH4 anomalies (0.25 degrees). To incorporate them, we map each IFPRI pixel to a grid cell. Then we add IFPRI pixel information to obtain total production and irrigated rice land in each grid cell, and divide the former by the latter to calculate the yield.

In this exercise, we use the CH4/IFPRI data to address four related questions about the irrigated rice production areas displayed in Figures 3 and 4. First, do CH4 concentration anomalies from the database align with the scale of irrigated rice production areas? Second, what is the alignment between the same area scale and EDGAR’s “bottom-up” emissions estimates that combine detailed sectoral activity data with regionally-tailored emission factors? Third, what is the “value added” by the S5P CH4 data in identifying methane emissions from irrigated rice production that are not identified by area scale and EDGAR data in combination? Finally, what can the S5P data tell us about the global distribution of CH4 intensities, or methane emissions per unit of irrigated rice production? The following sections address these questions in turn.
effective participation in debt-equity<sup>swap programs generally</sup> requires a close working knowledge<sup>of the local economy that can</sup> be gained only through extensive<sup>operations in the country. Thus,</sup> the largest banks, with local<sup>branch networks, are best able to</sup> use the debt-equity swap programs<sup>to their advantage.35</sup> The large banks, and especially<sup>Citicorp, have blocked</sup> comprehensive debt reduction arrangements<sup>in tne past year (e.g.</sup> in Bolivia, Brazil, and Costa<sup>Rica), in order to press for</sup> expanded debt-equity swap programs.<sup>This is perfectly justifiable</sup> as a corporate strategy,<sup>but is highly distortionary from the</sup> point of view of a settlement<sup>on the debt issue. The main problem</sup> is that the debt-equity swaps<sup>by themselves are generally harmful</sup> to the countries undertaking<sup>them, since they imply an</sup> acceleration of debt reRayments rather<sup>than an easing of the debt-</sup> service burden.<sup>36</sup> There are only limited circumstances<sup>in which</sup> 35Extensive evidence from the income statements of the moneycenter and regional banks<sup>suggests that the largest banks</sup> (especially Citicorp) have been able to divest<sup>their LDC assets at</sup> a lower percentage loss than have<sup>the regional U.S. banks.</sup> Citicorp's stated losses in the<sup>past year have averaged between 20</sup> and 25 percent of the reduction<sup>in LDC exposure, while for the</sup> superregionals, the loss has<sup>been on the order of 50 percent of</sup> the reduction in exposure. These<sup>data are from Salomon Brothers,</sup> "Citicorp -- Developing Country Debt Reduction<sup>Is Ahead of</sup> Schedule", October 20, 1988;<sup>and Keefe, Bruyette & Woods, Inc.,</sup> 'In the Southeast, 'LDC' Stands<sup>for 'Less Dire Consequences",</sup> January 1989.

36In a debt-equity program, the debtor country repurchases the debt, usually at somewhere<sup>between the face value price and</sup> the secondary market price.<sup>Thus, instead of paying simply the</sup> interest on the debt (net<sup>of new lending), say a rate of 5 percent</sup> of exposure, the debtor government<sup>pays 70-80 percent of the</sup> exposure in order to repurchase<sup>the entire debt. This repurchase</sup> causes a worsening of the<sup>budgetary situation, and thereby</sup> contributes to inflation.<sup>Even if the monetary increase of the</sup> 41
_CHAPTER 6. UNIT LEVEL MODELS: GENERAL INDICATORS_ 46 6. The “naive bootstrap” estimator of the MSE of the EB predictor, ˆ _δd_<sup>_EB_</sup> , is given by If the model is correctly specified, the best predictor is exactly model unbiased and has minimum MSE under the model. The “empirical” version obtained by replacing the unknown model parameters with estimators are neither exactly unbiased nor optimal. Nevertheless, when using consistent estimators of the model parameters as the total sample size _n_ grows, EB is expected to be nearly unbiased and nearly optimal for large total sample size _n_ . The gains in efficiency of EB with respect to ELL may be remarkable when the nested error model assumptions hold and area effects are significant (equivalent to a poor explanatory power of auxiliary variables), as illustrated by Molina and Rao (2010) and corroborated by the results of many different authors, see, for example, Corral Rodas, Molina and Nguyen (2021).

The EB method under a homoscedastic model is implemented in the `sae` R package by Molina and Marhuenda (2015), in the `emdi` by Kreutzmann et al. (2019) and the more recent updating of the later, the `povmap` R package (Edochie et al., 2023). It is also implemented in the Stata `sae` command by Nguyen et al. (2018), see https://github.com/pcorralrodas/SAEStata-Package. The EB method has been applied, for example, to estimate poverty indicators in Spanish provinces by gender in 2006 (Molina and Rao, 2010), mean income in Mexican municipalities (Molina and Mart ́ın, 2018), mean income and (non-extreme) poverty rates for census tracts by gender in Montevideo, Uruguay, and poverty rates and gaps in Palestinian localities by gender (Molina Peralta and Garc ́ıa Portugu ́es, 2020).

Note that to estimate non-linear indicators, both ELL and EB methods require two data sources: a survey with unit-level data of the variable of