rafmacalaba/datause-encoder-data huggingface dataset:
household consumption levels can be explained by household characteristics for survey round _j_<sup>_th_</sup> in the following equations where j= 1,..., k. We are interested in knowing such quantities as where zj is the poverty line in period j and the relation sign (~ ) indicates either the larger sign (>) or smaller sign (<).

It is rather straightforward to see that the formula to calculate such quantities in (16) on data from the j<sup>th</sup> survey round is the generalized version of (4) as follows where  _k_  . stands for the k-variate normal cumulative distribution function, and _d j_ , with _j=_ _1,...., k_ an indicator variable that equals 1 when<sup>_y_</sup> _ij_<sup>is smaller than the corresponding poverty</sup> line in the same period (i.e., household _i_ is poor in period _j_ ), and equals -1 otherwise. Note that the matrix  of partial correlation coefficient is symmetric and is represented by rounds under consideration and all the elements on the diagonal are 1s.  _djl_ stands for the 20
while only basic metal, chemicals, oil derivatives, and coal demonstrated higher productivity.

|Variable||**1**|Table22:<br>**993**|Sectorofa|ctivity|1|999||
|---|---|---|---|---|---|---|---|---|
|Div|Wage|P>**zl**|**Productivity**|P>**zl**|Wage|P>Izl<br>|Productivity|**P>Izl**|
|div922|0.09|0.00|<br>-0.41|0.00|0.05|0.03|-0.45|0.00|
|div923|0.00|0.92|<br>-0.73|0.00|-0.02|0.49|-0.47|0.00|
|div924|0.08|0.00|<br>-0.43|0.00|0.05|0.10|-0.01|0.90|
|div925|0.08|0.00|-0.09|0.02|0.08|0.00|0.30|0.00|
|div926|0.11|0.00|-0.71|0.00|0.04|0.18|-0.58|0.00|
|div927|0.07|0.07|0.21|0.00|0.06|0.05|0.64|0.00|
|div928|0.03|0.05|-0.33|0.00|0.07|0.00|-0.04|0.49|
|div929|0.06|0.29|-0.60|0.00|0.04|0.29|-0.37|0.00|

_Source:_ Author's calculations based on ENTRAM-ENESTYC.

# **v. Location**

The following regions (1) the North, (2) the Center, (3) the South, and (4) Mexico City were considered in terms of their influence on wages and productivity.

In 1993, workers employed in southern firms earned lower wages by an average of 21 percent than those employed in northern firms located near the border with the United States. In 1993, employees in Mexico City earned 6 percent higher wages than northern workers; however, in 1999 this distribution shifted dramatically. Established firms in the central, southern, and Mexico City regions paid lower wages than their northern counterparts by 12; 32; and 10 percent, respectively. The southern regions have always paid the lowest wages. However, the northern region has surpassed Mexico City in terms of the wages it pays. This is possibly due to its proximity to the United States and the favorable conditions that has brought for the region since the initiation of NAFTA (Esquivel et. al. 2002).

With respect to productivity, firms in Mexico City were 6 percent less productive than northern firms in 1993. In 1999 firms located in the central region were 13 percent more productive than northern firms, while southern firms were 25 percent less 23
# **Table 1: Financial Literacy Questions**

(Correct answers are shown in grey)

# **_Panel A: Interest Question_**

̳ _Let’s assume that you deposited 100,000 rubles in a bank account for 5 years at 10% interest rate. The interest will be earned at the end of each year and will be added to the principal. How much money will you have in your account in 5 years if_ _<u>you do not withdraw either the principal or the interest?</u>_ ‘

||Whole sample|Age 25–65|
|---|---|---|
|**More than 150k rubles**|**36.31%**|**38.96%**|
|Exactly 150k rubles|24.08%|26.42%|
|Less than $150k rubles|6.73%|8.08%|
|I cannot estimate it even roughly|32.87%|26.53%|
|N. of obs.|1,366|965|

# **_Panel B: Inflation Question_**

̳ _Let’s assume that in 2010 your income is twice what it is now, and that consumer prices also grow twofold. Do_ _<u>you think that in 2010 you will be able to buy more, less, or the same amount of goods and services as today?</u>_ ‘

|More than today|Whole sample<br>4.39%|Age 25–65<br>4.25%|
|---|---|---|
|**Exactly the same**|**50.81%**|**53.89%**|
|Less than today|18.67%|19.38%|
|I cannot estimate it even roughly|26.13%|22.49%|
|N. of obs.|1,366|965|

# **_Panel C: Risk Question_**

― _Which is the riskier asset to invest in?_ ‖

||Whole sample|Age 25–65|
|---|---|---|
|**Shares ina single company stock**|**12.81%**|**14.72%**|
|Shares in a unit fund|6.73%|6.84%|
|Risks are identical in both cases|45.02%|47.98%|
|Don‘t know|35.43%|30.47%|
|N. of obs|1,366|965|

**_Panel D: Answers across questions_**

||Whole sample|Age 25–65|
|---|---|---|
|Interest & inflation|21.82%|23.94%|
|All correct|3.07%|3.42%|
|No correct|31.84%|27.98%|
|At least 1 DK|53.73%|48.19%|
|All DKs|12.52%|9.02%|
|N. of obs.|1,366|965|

8
TABLE D.3: Comparison of rent variables, ANTANANARIVO

||Rent value||Self estim|ated rent|Combined s|elf estimated rent|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Directly affected by food|-0.156||-0.058||-0.140||
||(0.100)||(0.249)||(0.107)||
|Flood risk perception||-0.067||-0.092||-0.044|
|||(0.062)||(0.233)||(0.064)|
|Job density|0.174***|0.171***|0.236|0.245*|0.164***|0.164***|
||(0.057)|(0.056)|(0.148)|(0.147)|(0.057)|(0.057)|
|Number of rooms|0.567***|0.555***|0.505***|0.502***|0.640***|0.640***|
||(0.107)|(0.106)|(0.169)|(0.166)|(0.052)|(0.052)|
|Number of rooms squared|-0.048**|-0.046**|-0.030**|-0.030**|-0.038***|-0.038***|
||(0.021)|(0.021)|(0.012)|(0.011)|(0.007)|(0.006)|
|Unimproved house|-0.088|-0.082|-0.052|-0.044|-0.117|-0.114|
||(0.064)|(0.065)|(0.193)|(0.203)|(0.077)|(0.077)|
|Unimproved roof material|0.110|0.116|0.075|0.078|0.002|0.001|
||(0.102)|(0.100)|(0.174)|(0.171)|(0.088)|(0.087)|
|Unimproved wall material|-0.423***|-0.419***|-0.223|-0.262|-0.536***|-0.537***|
||(0.149)|(0.144)|(0.418)|(0.391)|(0.146)|(0.142)|
|Unimproved foor material|-0.083|-0.087|0.160|0.143|-0.006|-0.009|
||(0.062)|(0.063)|(0.245)|(0.239)|(0.081)|(0.081)|
|Unimproved drinking water source|-0.565***|-0.568***|-0.712***|-0.710***|-0.556***|-0.561***|
||(0.079)|(0.079)|(0.199)|(0.202)|(0.085)|(0.085)|
|Unimproved toilet facility|-0.051|-0.044|-0.213|-0.218|-0.078|-0.077|
||(0.094)|(0.097)|(0.239)|(0.231)|(0.094)|(0.095)|
|Not collected solid waste|-0.179***|-0.192***|0.116|0.120|-0.139**|-0.152**|
||(0.054)|(0.052)|(0.228)|(0.243)|(0.061)|(0.059)|

_Continued on next page_
|**Table 13**|**: Average**<br>|**Money’s**<br>|**Worth R**<br>|**atios In S**<br> <sup>**1**</sup>|**elected Co**<br>|**untries**<br>|<sup>**2**</sup>|
|---|---|---|---|---|---|---|---|
||**Australia**<br>(James al)|**Canada**<br>(James al)|**Switzerl.**<br>(James al)|**UK**<br> (Can/Ton)|**UK**<br> (James al)|**UK**<br> (Brown al)|**US **<br> (Brown al)|
|**Nominal Annuities**||||||||
|Male, Age 55|-|-|-||-|0.921|0.934|
|Male, Age 65|1.013|0.981|1.046||0.977|0.908|0.927|
|Female, Age 55|-|-|-||-|0.928|0.937|
|Female, Age 65|1.002|0.976|1.037||0.979|0.907|0.927|
|Joint|0.988|0.980|0.985|0.981|0.987|-|0.929|
|**Indexed Annuities**||||||||
|Male, Age 55|-|-|-||-|0.867|-|
|Male, Age 65|-|-|-||0.887|0.854|0.822|
|Female, Age 55|-|-|-||-|0.876|-|
|Female, Age 65|-|-|-||0.877|0.857|0.782|
|Joint|-|-|-||0.880|-|-|

Notes and Sources: Rocha and Thorburn (2007), Annex 1 Market performance can also be measured by the relationship between the annuity rate (defined as the internal rate of return on the annuity) and the risk free rate. As shown in Figure 8, the average annuity rate measured by the RV-04 mortality table tracked the interest rate on 10 year Central Bank bonds reasonably well during the 1990s, with the difference between the two rates averaging 0.7 percent p.a. in this period.<sup>33</sup> It would be tempting to conclude that retired workers could have obtained a better deal by investing directly in risk-free bonds, but this conclusion would need to be modified considering the costs and the risks to retirees, especially their exposure to longevity risk.

In 2001, the difference between the two rates inverted and the annuity rate has exceeded the riskfree rate since then. This negative difference between the risk-free rate and the annuity rate is unusual. For example, Brown et al (2001) calculate the internal rates of return on US annuities and obtain rates ranging from 5.9 to 6.5 percent p.a., lower than the yields of 10 and 30-year Treasury bonds—which were 7.1 and 7.3 percent p.a. in the same period. James, Song and Vittas (2001) performed the same exercise for several countries and obtained similar results.

> 33 Until 2005, insurance companies had to report their average annuity rates using an outdated mortality table, the RV-85.  During 2005, annuity rates were calculated and reported with both the RV-85 and the recently built RV-04. Past annuity rates were recalculated with the RV-04 on the basis of the relationship between the two rates in 2005. This resulted in an average increase of 60 basis points in the annuity rate. The interest rate is the PRC-20 for the period 1993-2005 and BCU-10 for 2003-2009. Both instruments have the same duration.

22
negatively affected productivity. Between 2000 and 2008, the gains from structural change turned positive but the Great Recession, by slowing the growth of more productive activities, has erased the benefits from the transfer of workers to the urban sector.

As a first approximation, SSA is not competitive in any sectors that are not based on natural resources. Exports classified as industrial comprise less than 20 percent of total exports (as against 60 percent for fuel, ores and minerals) and not infrequently, such exports are little more than “modestly processed primary products” (Gelb 2010). And Gelb (2010) goes on to add that “barely one quarter of industrial exports are true manufactures and the two major categories, automotive products from South Africa and clothing exports from low income countries, are both supported by special incentive programs”. Besides, most of the exports of manufactures are being traded intra-regionally (Balchin et al 2016).<sup>7</sup> With growth sourced from industry being low and trending downward, African countries may need to increase the contribution of agriculture by moving it closer to the production frontier, which would raise factor productivity, and increasing domestic value added in downstream activities. Achieving a turnaround of agricultural productivity is urgent because yields per hectare in SSA have barely improved between 1961 and 2010; the 2.6 percent average annual increase in agricultural output during this period was largely because more land was brought under cultivation – nearly 800,000 sq. km of land has been added to the 1.5 million sq. km that were farmed in the 1950s (Figure 1). However, because the agricultural workforce also expanded, per capita agricultural output inched downwards by 0.17 percent annually between 1961 and 2011 (Alston and Pardey 2014).<sup>8</sup>

> 7 Approximately a third of African manufactured exports are to other African nations. Exports of manufactures from countries in SSA doubled between 2005 and 2014 to $100 billion and intraregional trade rose from 20 percent to 34 percent.

> 8 Agricultural productivity in SSA is analyzed by Fuglie and Rada (2011) whose findings point to slow growth in productivity with most of the additional output coming from an expansion in the cropped acreage.  Block (2014) and Nin-Pratt and Yu (2011) show that agricultural TFP accelerated from the mid-1980s through the early 2000s.

4
Policy Research Working Paper 10999

# **Abstract**

This study examines the effects of climate and weather shocks on Afghanistan’s agricultural economy, with an emphasis on food security, prices, and wages. By utilizing a dynamical model and a unique data set that includes monthly global and local food prices, agricultural wages, unofficial exchange rates, and local climate data, the research provides econometric estimates of the impacts of droughts and floods. The findings reveal that both flooding and drought significantly increase food insecurity, directly and indirectly. Directly, these climatic shocks are linked to heightened risks of food insecurity in the following months, even when controlling for price and wage fluctuations. Indirectly, droughts and floods drive up food prices and depress agricultural wages, further exacerbating food insecurity. The study suggests that enhancing climate resilience in the agriculture sector could mitigate these risks, stabilize local food prices and wages, and strengthen food security and the broader agricultural economy. The results also show that price data effectively capture food security shocks from various non-economic sources, and can serve as a versatile monitoring tool in situations where detailed data on food security are unavailable.

This paper is a product of the Agriculture and Food Global Department and the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at bandree@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Table 10.** **<u>Community monitoring</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||<br>Index for<br>Intensity of<br>Project<br>Community<br>Monitoring|<br>Index for<br>Intensity of<br>Social<br>Accountability<br>Committee<br>Project<br>Monitoring|<br>Reporting<br>NUSAF-related<br>issues to LC1|<br> <br>Reporting<br>NUSAF-related<br>issues to<br>Subcounty|<br>Reporting<br>NUSAF-<br>related issues<br>to District|<br>Reporting<br>NUSAF-<br>related issues<br>to IG|<br>Reporting<br>NUSAF-<br>related issues<br>(total)|
|Training|-0.047|0.404***|0.058|0.069|0.075*|0.139***|0.340**|
||[0.139]|[0.112]|[0.051]|[0.048]|[0.043]|[0.044]|[0.162]|
|Scorecard|||0.095*|0.063|0.102**|0.155***|0.415**|
||||[0.055]|[0.056]|[0.049]|[0.050]|[0.180]|
|Training x scorecard|||-0.038|-0.023|-0.041|-0.063|-0.163|
||||[0.071]|[0.067]|[0.059]|[0.063]|[0.224]|
|Training + Scorecard + interaction=0|||0.018|0.023|0.002|0.000|0.000|
|Training=Scorecard|||0.443|0.910|0.511|0.712|0.616|
|Control means|3.174|0.959|0.681|0.540|0.341|0.256|1.816|
|N|559|574|3,850|3,848|3,850|3,849|3,852|
|R-squared|0.380|0.454|0.168|0.172|0.168|0.185|0.210|

Notes: This table reports the OLS regression results for the treatment effect on project- and individual-level outcomes for the training and scorecard information samples. The sample comprises all individuals, including those that are not direct beneficiaries. Columns 1 and 2 are at the project level and were collected during the first assessment survey. Columns 3 to 7 were collected at the individual level at the final household survey. Column 7 is an aggregate measure of columns 3 to 6. Standard errors are reported in brackets below the coefficients. Regressions include region controls. All analysis is clustered at the project level. *** p< 0.01, ** p< 0.05, *p< 0.1.

46
paid and 25% of the PBF payment to the health facility was retained. Additionally, the District Medical Team in collaboration with the Fund Holder Agency assessed the quality of the health services provided by PBF health facilities. This assessment used a standardized checklist to verify that a minimum quality level is met, and to calculate a quality score for the health facility ranging from 0 – 100%. The quality score was used to calculate a quality bonus that is received by the health facility.<sup>1</sup>

# CALCULATION OF PAYMENTS

The validated quantitative data, and the quality assessment were used to calculate performance payments for PBF health facilities. The quality bonus provided an increase of up to 30% of the total payment based on health service quantity. This percentage depended on the health facility quality score. Quality assessments were conducted quarterly, and focused on facility management, hygiene and sanitation, as well as specific attributes of service delivery. The services delivery items included, among many other categories, listing user charges, privacy, the condition of the waiting area and consultation room, and the correct management of cases. For example, if the health facility received a 65% quality score, and their total payment amount based on the services they provided was 597,240 CFA, the quality bonus provided to the health facility would be calculated as follows: 597,240 x 0.30 x 0.65 = 116,461 CFA, and the total payment to the health facility would be 597,240 + 116,461 = 713,701 CFA.

Additionally, an equity bonus was included in the calculation of performance payments. The equity bonus was paid to health facilities that faced serious structural problems making service provision more challenging. Equity bonuses ranged from 0% to 80% of the basic subsidy. <mark>Each region applied the criteria listed below differently. North West and South West applied the same scoring; however, few facilities in South West received the equity bonus since almost all facilities in the region were located in urban areas. The East region had a slightly different scoring approach but also used the same criteria.</mark> The following issues were considered in the calculation of this bonus:

- Geographical inaccessibility (hard-to-reach) that makes it difficult to retain staff;

- The size of the health area and low population densities that create viability issues (high running costs)

- Extreme poverty

The facility management committee had the authority to decide on the allocation of PBF revenue. These decisions must have been clearly documented in facility business plans.

RESEARCH QUESTIONS OF THE IMPACT EVALUATION

> 1 An example of the quality checklist can be found here: Performance Based Financing Implementation Procedures Manual North-West Region of Cameroon North West Region, Bamenda-Cameroon: Performance Purchasing Agency; 2012. _www.fbrcameroun.org/cside/contents/docs/Procedure_Manual.pdf_ .

6
**Table 5.3. Consolidation and Internationalization of the Banking System**

||**Dec.1994**|**Dec.1998**|**Dec.2000**|
|---|---|---|---|
|Numberoftotalbanks|166|104|89|
|Foreignbanks||||
|Numberofbanks|31|39|39|
|Numberofbranches<br>|391|1,535|1,863|
|Shareoftotalassets(%)|15|55|73|
|Numberofpublicbanks|32|16|15|

**Source:** Central Bank of Argentina.

# B. VULNERABILITIES

As the policy intent was to reinforce the viability of convertibility, it made no sense for the authorities to issue prudential norms that would dissuade the use of the dollar in financial contracts _per se._ To be sure, the markets did not take the permanence of the Currency Board completely to heart-the peso problem continued throughout the 1990s, as evidenced by the always positive "currency risk" implicit in forward contracts, which showed spikes during turbulent times (Figure 5.1). But the authorities could not signal the possibility of a _nominal_ devaluation through prudential norms without undermining their own quest to raise the credibility of Convertibility above all doubts. The hard peg and prudential regulation thus contributed to the high and increasing share of dollar deposits and dollar loans in the domestic financial system (Figure 5.2). The share of dollar deposits increased after the Russian crisis and the Brazilian devaluation, and especially after mid 2001 when expectations of devaluation soared.

It was thus no secret that a disorderly breakdown of the rule of one-peso-onedollar would wreck the banking system, and this was the main reason why many Argentine economists and external analysts preferred an exit from the Currency Board towards full dollarization over an exit towards a flexible exchange rate regime and a monetary anchor - and some still hold this view.

44
**Figure 2: Average annual change in residential electricity tariffs in Latin America 1990/02**

<!-- Start of picture text --> 70%<br>60%<br>Constant<br>50%<br>LCU<br>40%<br>30%<br>20%<br>Current<br>10%<br>LCU<br>0%<br>-10%<br>Average annual growth rate<br>Panama Chile Bolivia El Salvador Argentina Guatemala Dominican Republic Ecuador Paraguay Honduras Costa Rica Mexico Jamaica Average Colombia Uruguay Venezuela Peru Nicaragua Brazil<br><!-- End of picture text --> Source: OLADE, 2004

**Figure 3: Real average annual increase in local currency residential electricity**

**tariffs by region 1995/00**

<!-- Start of picture text --> 0.16<br>10%<br>0.14<br>8% EAP<br>0.12<br>6% ECA<br>0.10<br>4% LAC<br>0.08<br>2% SA<br>0.06<br>0.040% OECD<br>OECD LAC EAP ECA SA<br>0.0- 2 %<br>0.00-4%<br>1994 1996 1998 2000 2002<br>Sources: OECD 2004, ERRA 2004, EIA 2004, OLADE 2004, UN-ESCAP 2004.<br>Notes: Data drawn from 84 countries worldwide, broken down as follows OECD<br>(23), LAC (19), ECA(18), EAP (8), SAS (3)<br>Average annual growth rate<br>Average residential electricity tariff (US$/kWh)<br><!-- End of picture text --> 10
2005 averages, the ‘Late 2000s’ is defined as the performance between the 2001-05 and 2006-10 averages, and the ‘Early 2010s’ comparing 2006-2010 averages and 2013 values.

**_Figure 2: Definition of time periods used in the study_**

<!-- Start of picture text --> 1996 2000 2005 2010 2013<br>Avg. 1996-2000 Avg. 2001-2005 Avg. 2006-2010 2013<br>t0 t1 t2 t3<br>Early 2000s Late 2000s Early 2010s<br> 2000-2013<br><!-- End of picture text --> It is also instructive to recap the implications and dynamics of this empirical neoclassical growth model. Levels equation (1) implies that the (log) level of GDP changes with the (log) level of the explanatory variables _X_ . Any change or innovation in the (log) level of _X_ will thus have a permanent effect on (log) GDP and the effect is intermediated through a temporary (transitory) effect on the growth rate. A one-time increase in human capital in the Early 2000s, for example, will thus be captured as _Δln(x)_ > 0 in equation (2) and impact the growth rate _in the same period_ with parameter Γ. This growth-enhancing effect will be echoed in the Late 2000s via the lagged dependent variable effect as _θ_ Γ < Γ (which is captured as persistence effect in our model) and eventually fade out over time.

# **3. Explaining Ethiopia’s Recent Growth Performance**

Our model is well specified and consistent with economic theory. Results are presented in Tables 1, 2 and Figure 3. Column 1 in Table 1 shows the baseline results of the model estimation, while columns 2-4 summarize robustness checks discussed in Section 4. Table 2 illustrates the baseline results for Ethiopian data for the four time periods (2010-13, Early 2000s, Late 2000s, and Early 2010s). Overall, there is no indication that the model is mis-specified and parameters show the expected signs, except from the institutional variable Policy2 which is statistically insignificant.<sup>11</sup> Parameter estimates are either statistically significant or at the borderline of significance, except for schooling which is a well-known issue in growth regressions (e.g. Pritchett, 2001) and should not lead to neglect of education policies. Figure 3 illustrates the more detailed results of Table 2.

> 11 Institutional quality usually does not vary as much over time, so it is difficult to identify the according parameter in this context.

10