rafmacalaba/datause-encoder-data huggingface dataset:
- Lisboa, M., Menezes Filho, N.; Schor, A. (2010). The effects of trade liberalization on productivity growth in Brazil: Competition or technology? _Revista Brasileira de Economia_ 64 (3): 277-289.

- Ministry of Science and Technology - MCT. (2015). Terceiro inventário Brasileiro de emissões e remoções antrópicas de gases de efeito estufa. Relatórios de Referência. Setor Uso da Terra, -

- Mudanças do Uso de Terra e Florestas. Available at: <u>https://www.gov.br/mcti/pt br/acompanhe-o-mcti/sirene/publicacoes/relatorios-de-referenciasetorial/pdf/invetario3/rr_lulucf_mudanca_de_uso_e_floresta.pdf ;</u>

- Nepstad, D., Soares-Filho, B. S.; Merry, F. (2008). Interactions among Amazon Land Use, Forests, and Climate: Prospects for a Near-Term Forest Tipping Point. _Philosophical Transactions of the Royal Society_ B. DOI: 10.1098/Rstb.2007.0036, p1-10.

- OECD (2021) Evaluating Brazil’s progress in implementing Environmental Performance Review recommendations and promoting its alignment with OECD core acquis on the environment. https://www.oecd.org/environment/country-reviews/Brazils-progress-in- <u>implementing-Environmental-Performance-Review-recommendations-and-alignmentwith-OECD-environment-acquis.pdf .</u>

- Richards, P. (2021). A key ingredient in deforestation slowdowns? A strong Brazilian economy. _Frontiers in Forests and Global Change._ Vol. 4(2). February. Available at: <u>https://www.frontiersin.org/articles/10.3389/ffgc.2021.613313/full .</u>

- Samuelson, P. A. (1964). Theoretical notes on trade problems. _The Review of Economics and Statistics_ , 145–154.

- Schons, S. 2019. “Smallholder Land Clearing and the Forest Code in the Brazilian Amazon.” Environment and Development Economics 24: 1–23.

- Souza-Rodrigues, E. 2019. “Deforestation in the Amazon: A Unified Framework for Estimation and Policy Analysis.” The Review of Economic Studies 86 (6).

- Vasconcelos, R. Misallocation in the Brazilian manufacturing sector. (2017). _Brazilian Review of Econometrics,_ 37 (2): 191-232.

- Vergara, W., Scholz, S. (2010). _Assessment of the Risk of Amazon Dieback_ . Washington DC: The World Bank.

- Vidal Luna, F, Klein, H. S. (2014).The economic and social history of Brazil since 1889. _Cambridge University Press_ .

- World Resources Institute - WRI. 2019. Creating a Sustainable Food Future. Washington, DC: WRI.

- Wunder, S.; Sunderlin, W. D. (2004). Oil, macroeconomics, and forests: Assessing the linkages.” _The World Bank Research Observer_ 19 (2): 231–257.

14
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Compared with its regional peers, Niger has among the largest potential area for small-scale irrigation projects and an attractive rate of return (figure 11), using an IRR cutoff of 12 percent.

**Figure 11. Irrigation potential, small-scale projects**

<!-- Start of picture text --> 80 1800<br>70 1600<br>60 1400<br>1200<br>50<br>1000<br>40<br>800<br>30<br>600<br>20 400<br>10 200<br>0 0<br>Area increase (thousand ha) Internal Rate of Return (%)<br>Internal rate of return<br>Area Increase (thousand ha)<br><!-- End of picture text --> _Source_ : Derived from You and others (2009).

_Note_ : Based on 12 percent cut-off estimates, at which the estimated area increase for southern African countries not included in the figure is zero. Water for irrigation can be collected in two ways: through large, dam-based schemes or through small projects based on collection of run-off from rainfall. The investment costs of large-scale irrigation development reflect only irrigation-specific infrastructure, such as distribution canals and on-farm system development. The potential for small-scale irrigation is assessed not only on the basis of agroecological conditions, but also in terms of market access, since irrigation is typically viable only if the increased yields can be readily marketed.

# Water supply and sanitation

## **Achievements**

In a context of high population growth, Niger has made important progress in reducing its reliance on surface water, in particular in urban areas. Reliance on surface water declined from 9.8 percent of the population in 1998 to 1.8 percent in 2006, a level well below the average for LICs and even MICs (table 10). In spite of high population growth (3.4 percent per year between 1998 and 2006), around 0.3 percent of the population has been moving away from surface water, and 1.1 percent of the urban population has been moving to some other form of water supply every year (figure 13).

Niger has managed to move its population up the water supply ladder by means of extending low-cost technologies, such as standposts and wells and boreholes. Access to standposts increased from 12.5 percent in 1998 to 16 percent in 2006 (table 10), which implies that at the national level 2.1 percent of the population was gaining access to this mode of water supply (figure 13). The number of wells and boreholes increased from 73 percent to 76.4 percent of the population between 1998 and 2006. Compared to other LICs, Niger’s rate of access to wells and boreholes is twice as high. But only 20 percent of these wells can be considered safe.

22
Setting _πK_ = 0 _._ 825, (1) then yields Although there is some uncertainty about costs at market prices, these estimates of the average social cost of connecting a habitation are surely not too far off the mark.

As a preliminary to Section 4, observe that to any estimate of _PV_<sup>_∗_</sup> ( _I_ ; _ρ_ ) there corresponds a flow of annual benefits, growing at the constant rate _gb_ over the lifetime of the project following completion, such that the project just breaks even at the chosen discount rate. Denoting the said flow by _Bt_<sup>+= (1+</sup><sup>_gb_)</sup><sup>_tB_</sup> 1<sup>+, where</sup><sup>_B_</sup> 1<sup>+denotes the level</sup> of the flow in year 1, we require For the limiting case in which the flow fails to grow, we have _B_ 1<sup>+(</sup><sup>_ρ_=0</sup><sup>_._055</sup><sup>_,_0)=</sup> $17 _,_ 000 and _B_ 1<sup>+(</sup><sup>_ρ_=0</sup><sup>_._10</sup><sup>_,_0)=$23</sup><sup>_,_180.Sincesuchroadsareeffectivelypurepublic</sup> goods when viewed as isolated elements, their social profitability depends heavily on the number of beneficiaries. Denoting by _H_ the number of households in the habitation, the ‘representative’ household must enjoy a benefit of at least _B_ 1<sup>+</sup><sup>_/H_ayearforthe</sup> project to pass muster at the chosen social discount rate. With the hurdle’s height thus established, we now investigate whether the project will clear it.

# **4 Estimating the Benefits**

The participating states under the SWAp loan vary greatly in their levels of economic and social development. Jharkhand is poor and backward, most of its villages are but little commercialized, and traffic volumes are low. Punjab has the highest state domestic product per head in the whole Union, its agriculture is highly commercialized, rural traffic is heavy, and its social indicators are now well above average. A new all-weather road in Jharkhand is therefore unlikely to generate the sort of commercial benefits flowing from one in Punjab; but such roads may also be valued quite differently by their respective beneficiaries. These forms of heterogeneity demand careful treatment.

Viewed in isolation from the rest of the network, each road affects only those who live within its catchment area. The villagers enjoy benefits in three broad spheres, namely, 9
|<br><br>_i_<br>_INSD_<br>_INS_<br><br>|domestic institutions|hhd, gov|
|---|---|---|
|<br><br>_i INSDNG_<br>_INSD_<br><br>|domestic non‐government institutions|hhd|
|<br><br>_i_<br>_INSNG_<br>_INS_<br><br>|non‐gov’t institutions (rest of world and<br>elements in_INSDNG_)|hhd, row|
|<br><br>_h H_<br>_INSDNG_<br><br>|households|hhd|
|_t_<br>_T_<br>|timeperiods(simulationyears)|2015‐2030|
|_t_<br>_TMIN_<br>|baseperiod(first simulationyear)|2015|
|_tac_<br>_TAC_<br>|transactions (distribution) types<br>(domestic,import,export)|‐‐|
|<br><br>_tacd_<br>_TACD_<br>_TAC_<br><br>|transactions (distribution) for domestic<br>sales|‐‐|
|<br><br>_tace_<br>_TACE_<br>_TAC_<br><br>|transactions (distribution) for exports|‐‐|
|<br><br>_tace_<br>_TACM_<br>_TAC_<br><br>|transactions (distribution) for imports|‐‐|

# **Table A.1.3. Variables**

|**Name**|**Description**|
|---|---|
|_t_<br>_CPI_|consumer price index|
|, ,<br>_f a t_<br>_DKA_|change in capital stock_f_allocated to activity_a_|
|, ,<br>_i f t_<br>_DKINS_|investment by institution_i_(in_INS_) in capital stock_f_|
|_t_<br>_DPI_|domestic producer price index (PDS‐based)|
|_t_<br>_EG_|total current government expenditure|
|,<br>_h t_<br>_EH_|consumption expenditure for household_h_|
|_t_<br>_EXR_|exchange rate (local currency per unit of foreign currency|
|,_i t_<br>_INV_|value of investment (including stock change) for institution_i_(in<br>_INSNG_)|
|_t_<br>_INVG_|value of investment (including stock change) for government|
|,_i t_<br>_MPS_|marginal propensity to save for domestic non‐government<br>institution_i_ (in_INSDNG_)|
|_t_<br>_MPSSCAL_|_MPS_scaling factor|
|_t_<br>_NFFG_|net foreign financing of government (FCU)|
|,_i t_<br>_NFF_|net foreign financing for non‐government institution_i_(in_INSDNG_)<br>(FCU)|
|,_a t_<br>_PA_|output price for activity_a_|
|,_c t_<br>_PDD_|demand price for commodity_c_(in_C_) produced and sold<br>domestically|
|,_c t_<br>_PDS_|supply price for commodity_c_(in_C_) produced and sold domestically|
|,<br>_c t_<br>_PE_|price for export of_c_(in_C_) (LCU) (net of export taxes and distribution<br>margin)|

‐32‐
Table 8: Program employment effects, difference-in-differences IV estimates

|Outcome:|**Employed [0/1]**||
|---|---|---|
|Sample<br>All registrants|MDIC registrants<br>Unemp. Benefc.|All others|
|(1)|(2)<br>(3)|(4)|
|**Panel A: Em**|**ployment at any frm**||
|Post-course * completed<br>0.086***|-0.029<br>0.146***|0.023*|
|(0.014)|(0.032)<br>(0.032)|(0.013)|
|Mean of outcome<br>0.565|0.748<br>0.604|0.533|
|St. dev. of outcome<br>0.496|0.434<br>0.409|0.499|
|_R_<sup>2</sup><br>0.41|0.45<br>0.37|0.43|
|**Panel B: Employ**|**ment at requesting frms**||
|Post-course * completed<br>0.009***|0.032<br>0.005|0.009***|
|(0.002)|(0.022)<br>(0.003)|(0.002)|
|Mean of outcome<br>0.041|0.237<br>0.009|0.025|
|St. dev. of outcome<br>0.198|0.425<br>0.094|0.156|
|_R_<sup>2</sup><br>0.76|0.82<br>0.42|0.69|
|**Panel C: Employm**|**ent at nonrequesting frms**||
|Post-course * completed<br>0.077***|-0.062*<br>0.140***|0.014|
|(0.014)|(0.036)<br>(0.032)|(0.013)|
|Mean of outcome<br>0.524|0.511<br>0.595|0.508|
|St. dev. of outcome<br>0.499|0.5<br>0.491|0.5|
|_R_<sup>2</sup><br>0.42|0.60<br>0.37|0.44|
|_N_<br>10,243,541|912,053<br>1,872,837|7,458,651|

Notes: Table presents second-stage coefficients capturing effects of course completion induced by receiving a course offer. Heteroskedasticity-consistent robust standard errors two-way clustered by class and month*year reported in parentheses. All specifications include an unreported constant term and vectors of individual and month*year fixed effects. Significance indicated by: _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01.

35
|mates|All others<br>(4)<br>0.011<br>(0.014)<br>0.005***<br>(0.001)|0.533<br>0.499<br>0.42<br>5,985,104<br>-0.136***<br>(0.016)<br>0.012***<br>(0.001)<br>0.025<br>0.156<br>0.46<br>42,920,272<br>consistent robust<br>e an unreported<br>_∗∗p < ._05, _∗∗∗_|
|---|---|---|
|Table 15: Diferential efects by post-program employment growth, diference-in-diferences IV esti|Outcome:<br>**Employed [0/1]**<br>Sample<br>All registrants<br>MDIC registrants<br>Unemp. Benefc.<br>(1)<br>(2)<br>(3)<br>**Panel A: Demand-driven segment**<br>Main efect<br>0.075***<br>-0.040<br>0.155***<br>(0.015)<br>(0.034)<br>(0.034)<br>Main * standardized employment growth share<br>0.005***<br>0.007*<br>-0.000<br>(0.001)<br>(0.004)<br>(0.002)|Mean of outcome<br>0.565<br>0.748<br>0.604<br>St. dev. of outcome<br>0.496<br>0.434<br>0.409<br>_R_2<br>0.40<br>0.44<br>0.37<br>_N_<br>8,212,030<br>817,655<br>1,409,271<br>**Panel B: All other courses**<br>Main efect<br>-0.005<br>0.105***<br>(0.018)<br>(0.023)<br>Main * standardized employment growth share<br>0.011***<br>-0.000<br>(0.001)<br>(0.001)<br>Mean of outcome<br>0.041<br>0.009<br>St. dev. of outcome<br>0.198<br>0.094<br>_R_2<br>0.45<br>0.39<br>_N_<br>45,919,339<br>2,752,395<br>Notes: Table presents present diference-in-diferences IV estimates of course completion. Heteroskedasticity-<br>standard errors two-way clustered by class and month*year reported in parentheses. All specifcations includ<br>constant term and vectors of individual and month*year fxed efects. Signifcance indicated by: _∗p < ._1, <br>_p < ._01.|

42
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results with administrative data, which contains transaction-level information on the full universe of program accounts over time.

# **4 Empirical Strategy and Results**

Treatment was randomly assigned at the individual level, so that we can estimate the impact of payroll accounts using simple treatment effect regressions of the form: where _Yi_ is an outcome of interest measured post-treatment, _Ti,acct_ is a treatment indicator equal to one if an individual was assigned to the bank or mobile account treatment and _Ti,payroll_ is a treatment indicator equal to one if individual _i_ was assigned to the bank or mobile payroll account treatment condition.<sup>35</sup> _X_ is a vector of controls which, where we use survey data includes the value of the outcome variable at baseline _Yi_<sup>_BL_</sup> as well as strata dummies, and _εi_ is a stochastic error term. In this specification, the coefficient _γpayroll_ measures the impact of payroll accounts with direct deposit wage payments.

Where we use administrative data rather than survey outcomes to estimate treatment effects we use regressions where the treatment indicators _Tit,acct_ and _Tit,payroll_ are zero in the periods before the randomly-assigned account opening date and equal to one thereafter. This means that in these specifications, the treatment effects are estimated relative to a control group whose outcomes are zero by construction. Because we do not observe transactions for the control group, the comparison of interest in these specifications is between _γpayroll_ and _γaccount_ , the treatment effect estimates for the payroll account, compared to the account only treatment groups, respectively.

## **4.1 Account Use and Learning**

In this section, we examine the effect of payroll accounts on account use and learning. The main interest of our study is to understand whether consistent use of a formal account enables inexperienced consumers to learn how to navigate the financial technology more effectively and avoid common consumer protection risks. Examining this question requires variation on the _extensive margin_ of account ownership as well as the _intensive margin_ of how frequently the account is used. Our experiment is designed to generate variation along both dimensions by separately randomizing access to an account and automatic wage payments into the account. We begin our analysis by outlining patterns of account usage over time to confirm that the payroll account treatment was indeed successful at encouraging active engagement with the account, over and above the effect of account opening alone.

> 35We pool the bank and mobile money account treatments and provide disaggregated results in Appendix D.1.

15
19 Anderson, K. and E. Valenzuela (2008), _Global Estimates of Distortions to Agricultural Incentives, 1955 to 2007_ , database available at <u>www.worldbank.org/agdistortions</u> Anderson, K. and J. Swinnen (2008) (eds.), _Distortions to Agricultural Incentives in Europe’s Transition Economics,_ Washington DC: World Bank.

Anderson, K. and J. Swinnen (2009), ‘Eastern Europe and Central Asia’, Ch. 6 in K. Anderson (ed.), _Distortions to Agricultural Incentives: A Global Perspective, 1955-2007,_ London: Palgrave Macmillan and Washington DC: World Bank.

Anderson, K., R. Lattimore, P.J. Lloyd and D. MacLaren (2009), ‘Australia and New Zealand’, Ch. 5 in _Distortions to Agricultural Incentives: A Global Perspective, 1955-2007,_ London: Palgrave Macmillan and Washington DC: World Bank.

Croser, J. and K. Anderson (2010), ‘Changing Contributions of Different Agricultural Policy Instruments to Global Reductions in Trade and Welfare’, CEPR Discussion Paper 7748, London, March.

Croser, J.L., P.J. Lloyd and K. Anderson (2010), ‘How Do Agricultural Policy Restrictions to Global Trade and Welfare Differ Across Commodities?’ _American Journal of Agricultural Economics_ 92(3): 698-712, April.

Feenstra, R (1995). ‘Estimating the Effects of Trade Policy’, in G. Grossman and

- K. Rogoff (eds.), _Handbo_ o _k of International Economics, Vol 3_ , Amsterdam: Elsevier.

Gardner, B. (2009), ‘United States and Canada’, Ch. 4 in K. Anderson (ed.), _Distortions to Agricultural Incentives: A Global Perspective, 1955-2007,_ London: Palgrave Macmillan and Washington DC: World Bank.

Honma, M. and Y. Hayami (2009), ‘Japan, Republic of Korea, and Tawian, China’ Ch. 2 in K. Anderson (ed.), _Distortions to Agricultural Incentives: A Global Perspective, 1955-2007,_ London: Palgrave Macmillan and Washington DC: World Bank.

Irwin, D. (2010), ‘Trade Restrictiveness and Deadweight Losses from U.S. Tariffs, 1859–1961’, American Economic Journal: Economic Policy 2: 119-45, August.

Josling, T. (2009) ‘Western Europe’, Ch. 3 in K. Anderson (ed.), _Distortions to Agricultural Incentives: A Global Perspective, 1955-2007,_ London: Palgrave Macmillan and Washington DC: World Bank.
to provide an insight into a household’s economic ability to access a variety of foods (Kennedy _et al._ , 2011; Upton _et al._ , 2016). In this study, we calculate HDDS by first categorizing the food items consumed by household members over a 7-day recall period into 7 food groups, excluding sugar, alcohol and tobacco. Then we generate a binary indicator _Zig_ that takes value 1 for a food group _g_ , if the household of respondent _i_ had consumed a food item from that group during the recall period, and 0 otherwise. The HDD score for household _i_ (HDD _i_ ) is then calculated as in Equation 4 below:

# _•_ The Household Food Insecurity Access Scale (HFIAS)

This indicator of consumption behavior infers food security status from people’s responses to questions about consumption patterns that are considered to be correlated with food insufficiency and anxiety - for example, whether and how often the household members had to eat less preferred food based on a lack of resources (Carletto _et al._ , 2013).

The indicator has been adapted from the current US approach to estimate the national food insecurity prevalence.<sup>9</sup> . It uses a set of nine questions designed to capture the degree and anxiety associated with

> 9FANTA Project

16
**Table 6b:  Ecuador - Log hourly wages for the labor force as a whole and with Heckman correction***

||**Hou**<br>|**rly wage est**<br>|**imation**<br>|**He**<br>|**ckman corre**<br>|**cted**<br>|
|---|---|---|---|---|---|---|
||All|Men|Women|All|Men|Women|
|**_Wage earn_**|**_ers_**<br>||||||
|_Female_|-0.13<br>(-3.55)|---|---|0.33<br>(6.47)|---|---|
|_Age1525_|-0.43<br>(-8.27)|-0.41<br>(-7.0)|-0.44<br>(-4.46)|-0.19<br>(-3.18)|-0.27<br>(-4.04)|0.00071<br>(0.01)|
|_Age2645_|-0.031<br>(-0.7)|0.0044<br>(0.08)|-0.055<br>(-0.71)|-0.19<br>(-3.74)|-0.087<br>(-1.52)|-0.38<br>(-3.8)|
|_Nosch_|-0.57<br>(-5.48)|-0.7<br>(-4.55)|-0.51<br>(-3.37)|-0.54<br>(-4.68)|-0.57<br>(-3.49)|-0.64<br>(-3.27)|
|_Pri_|-0.23<br>(-6.17)|-0.2<br>(-4.61)|-0.28<br>(-4.16)|-0.28<br>(-6.66)|-0.22<br>(-4.93)|-0.37<br>(-4.28)|
|_Home_|-0.26<br>(-5.36)|0.042<br>(0.56)|-0.41<br>(-6.05)|-0.2<br>(-4.26)|-0.051<br>(0.69)|-0.21<br>(-3.41)|
|_Constant_<br>|7.3<br>(157)|7.24<br>(139.61)|7.25<br>(87.41)|7.65<br>(136.22)|7.46<br>(114.2)|8.64<br>(76.67)|
|R<sup>~~2~~</sup>|0.063|0.063|0.059|---|---|---|
|Sample<br>size|2597|1573|1024|4836|2163|2673|

* t- and z-values in parentheses

37
# <u>6.2 Networks</u>

Table 5 (Panel B) reports program impacts on a number of networking proxies, including whether the beneficiaries registered at employment offices, whether they knew an entrepreneur, an employment officer or a banker, and how often he/she interacted with him. Results show that the entrepreneurship training was effective in increasing the business and employment networks of participants. Participants are marginally more likely to be registered at employment offices, an indicator obtained directly from administrative data, not from the follow-up survey. Graduates that were assigned to the entrepreneurship track are also more likely to know an employment agent, even though they do not interact with the employment agent more frequently than graduates from the control group. Furthermore, there is evidence that the program increased the probability of knowing an entrepreneur, as well as knowing and interacting frequently with a banker. Interestingly, however, program participants were less likely to seek advice from a professor to develop a new project idea.

# <u>6.3 Behavioral skills</u>

As discussed in section 2, the entrepreneurship training contained a module “for the person” aiming to develop entrepreneurial traits and behavioral skills. These behavioral skills are likely most malleable among youth (Almlund et al., 2011). In Tunisia, during qualitative interviews, some training facilitators stressed that as part of this module one of their main objectives was to change students’ behavioral skills to contribute to “turn them into entrepreneurs.” We now consider whether the program impacted a range of behavioral skills often associated with entrepreneurship.

First, Table 6 (Panel A) presents estimates of program impacts on risk and time preference parameters. Indicators of willingness to take risk include a direct self-reported measure (“on a scale from 1-10, how willing are you to take risks?”), as well as the certainty equivalent for an imaginary lottery where the respondent would have a 50 percent chance of winning 0 and a 50 percent chance of winning 2000 TND. Based on this last question, we also create a binary variable taking the value of 1 if the individual is a risk-taker (i.e. with a certainty equivalent larger than 1000 TND). Finally, we create a proxy for “patience”, taking a value of 1 if respondents reveal a preference for 1000 TND in 6 months rather than to 800 TND now. Table 6 21