rafmacalaba/datause-encoder-data huggingface dataset:
is worse than predicting growth in the mean, and that trying to predict inequality as well does not help.

# **6 Conclusion**

In this paper, we have analyzed how best to nowcast poverty around the world. We applied statistical learning techniques to the World Bank’s collection of international poverty estimates utilizing more than 1,000 development indicators as features. We looked at whether predicting poverty rates directly had a higher accuracy than predicting poverty indirectly, by, for example, predicting changes in poverty from the last survey, by predicting growth in mean welfare and applying this growth to scale up the past distribution, or by predicting growth in the mean and in the Gini and applying growth incidence curves to scale and stretch the past distributions.

Our findings revealed that a model which simply uses a fraction of growth in real GDP per capita since the last observed household survey to shift the entire distribution performs better than any model that predicts poverty rates directly using all the variables mentioned above, and nearly as well as all models trying to predict growth in the mean and in the Gini using all the variables mentioned above. This suggests that conditional on knowing growth in real GDP per capita, no other variable can substantially increase the predictive accuracy.

On the one hand, given the decades long literature documenting the importance of growth for poverty reduction (Kraay, 2006; Ferreira and Ravallion, 2009), this is not surprising. Partially as a result of this literature, variants of the model we prefer have been used both in the literature and for the World Bank’s official global poverty monitoring. On the other hand, several papers have documented important gaps between national accounts data and household survey data, suggesting that GDP might not be that connected to welfare as measured in household surveys (Ravallion, 2003; Deaton, 2005; Ferreira _et al._ , 2010; Pinkovskiy and Sala-i Martin, 2016; Prydz _et al._ , 2021; Deaton and Schreyer, forthcoming). What can explain these seemingly contradictory statements?

First, although the model using growth rates in GDP per capita performs better than nearly all other models, it does not imply that the model’s error is low.<sup>4</sup>

> 4Note that this pertains to the error of country-level nowcasts. The results do not directly assess the accuracy of the World Bank’s regional and global nowcasts, since a large portion of the country-specific errors will average out when aggregating across many countries. However, the results do suggest that there are substantial discrepancies between nowcasts and survey-based measures for individual countries, and that the country nowcasts should be interpreted with ap-

27
# **3.1. Sample and Regression Specification**

Our sample consists of an unbalanced panel dataset that comprises 82 countries. For each of them, the dataset includes at most 8 observations, consisting of nonoverlapping 5-year averages spanning the 1960-2000 period. The sample includes 22 developed countries and 60 developing ones. Among the latter, 18 are from Sub-Saharan Africa, 12 from Asia, 9 from the Middle East and North Africa, and 21 from Latin America and the Caribbean. Appendix 1 provides the full list of countries in the sample. The basic regression equation to be estimated is the following where the subscripts _i_ and _t_ represent country and time period, respectively; _y_ is the log of GDP per capita, _CV_ is a set of control variables, and _OP_ represents trade openness; μ _t_ and η _i_ denote unobserved time- and country-specific effects, respectively; and ε is the regression residual.

As is standard in the literature, the dependent variable is the average rate of real per capita GDP growth (i.e., the log difference of GDP per capita normalized by the length of the period). The regression equation is dynamic in the sense that it includes the initial level of per capita GDP as an explanatory variable. Our measure of trade openness is the (structure-adjusted) ratio of real exports and imports to real GDP. We select the set of control variables considering both their importance as growth determinants _per se_ and their potential for affecting the growth response of trade opening. The control set includes variables that vary both across countries and over time, as well as variables that vary only across countries (that is, assumed constant over time). Among the former, we have the average rate of secondary school enrollment to account for human capital investment, the average ratio of private credit to GDP as a measure of financial depth, the average inflation rate to account for macroeconomic price stability, and the average number of main telephone lines per capita as proxy for public infrastructure. Among the variables that vary only across-countries, we have a governance index from _International Country Risk Guide_ (Political Risk Services), labor-market and firm-exit flexibility indices from _Doing Business_ (the World Bank), and a firm-entry flexibility index from _Doing Business_ (the 16
|**Table A12: Base Regression **|**Results fo **|**r Large Fir**|**ms: Margin**|**aleffects fro **|**m logitestim**|**ation **|
|---|---|---|---|---|---|---|
|Dependent variable: R&D Activity|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Informal Competition (Country-|-0.036|-0.043|-0.057|-0.054|-0.059|-0.091|
|level cell average)|(0.118)|(0.120)|(0.121)|(0.118)|(0.120)|(0.125)|
|GDP per capita (lagged, logs)||-0.004|0.003|-0.003|-0.003|-0.025|
|||(0.027)|(0.025)|(0.025)|(0.025)|(0.041)|
|Firm Size|||0.077***|0.064***|0.064***|0.064***|
||||(0.022)|(0.023)|(0.023)|(0.023)|
|HHI|||0.068|0.077|0.061|-0.021|
||||(0.131)|(0.141)|(0.132)|(0.127)|
|Age of Firm (logs)||||0.043*|0.040|0.038|
|||||(0.026)|(0.026)|(0.025)|
|Manager Experience (logs)||||0.022|0.021|0.030|
|||||(0.026)|(0.027)|(0.026)|
|Exports (proportion of sales)||||0.018|0.019|-0.003|
|||||(0.047)|(0.047)|(0.046)|
|Female Top Manger Y:1 N:0||||<br>-0.018|<br>-0.020|<br>-0.019|
|||||(0.062)|(0.062)|(0.057)|
|Multi-firm Y:1 N:0||||0.123***|0.121***|0.127***|
|||||(0.038)|(0.039)|(0.038)|
|Power Outages (day hours)|||||0.005|0.004|
||||||(0.004)|(0.005)|
|Finance Obstacle (0-4 scale)|||||-0.008|-0.008|
||||||(0.013)|(0.013)|
|Overdraft Y:1 N:0|||||0.062*|0.060*|
||||||(0.034)|(0.034)|
|Courts Obstacle (Country-level cell||||||0.089**<br>(0.036)|
|Population (lagged, logs)||||||-0.028**<br>(0.014)|
|Inflation (%, annual)||||||0.002<br>(0.002)|
|Gross Tertiary Enrollment Rate||||||<br>0.147<br>(0.183)|
|Gross Primary Enrollment Rate||||||<br>0.111<br>(0.128)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of observations|5,721|5,721|5,721|5,721|5,721|5,721|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on the country. Significance is denoted by *** (1%), ** (5%), * (10%).

80
**Table 22: The effect of baseline socio-economic status (using district SES)**

||Math|English|
|---|---|---|
|WSD|-0.04|0.00|
||(0.08)|(0.09)|
|Grant|-0.08|-0.08|
||(0.07)|(0.08)|
|District 2004 SES|0.14**|0.30***|
||(0.07)|(0.09)|
|WSD`×`District 2004 SES|0.02|-0.10|
||(0.10)|(0.11)|
|Grant`×`District 2004 SES|0.00|-0.13|
||(0.10)|(0.11)|
|6th Grade Dummy|0.70***|0.77***|
||(0.04)|(0.04)|
|Constant|-0.26***|-0.29***|
||(0.17)|(0.06)|
|Observations|3659|3659|
|R Square|0.13|0.16|

Notes: Robust Standard errors in parentheses. *** 1% Significance Level, **5% Significance Level, *10% Significance Level. District 2004 SES is the district level composite measure of the socio-economic background as measured in 2004 – Prior to the interventions. The variables included in the factor analysis are the quality of the housing (floor, roof, walls, electricity), the assets (phone, motorcycle, fridge, car, TV, fan, generator, livestock), and the expenditure on educator the past 12 months – Higher values of the factor associate with higher economic status of the district.

38
- Stokey, N. L., R. E. Lucas, and E. C. Prescott (1989). _Recursive Methods in Economic Dynamics_ . Harvard University Press.

- Taheripour, F., C. Hurt, and W. E. Tyner (2013). Livestock Industry in Transition: Economic, Demographic, and Biofuel Drivers. _Animal Frontiers 3_ (2), 38–46.

- Tian, X., B. Sohngen, J. B. Kim, S. Ohrel, and J. Cole (2016). Global Climate Change Impacts on Forests and Markets. _Environmental Research Letters 11_ (3), 035011.

- Tsionas, E. G. and S. C. Kumbhakar (2004). Markov Switching Stochastic Frontier Model. _The Econometrics Journal 7_ (2), 398–425.

- Tsur, Y. and A. Zemel (2014). Dynamic and Stochastic Analysis of Environmental and Natural Resources. In M. Fischer and P. Nijkamp (Eds.), _Handbook of Regional Science_ , pp. 929–949. Springer-Verlag Berlin Heidelberg.

- Verma, M., T. Hertel, and N. Di↵enbaugh (2014). Market-Oriented Ethanol and Corn-Trade Policies can Reduce Climate-Induced US Corn Price Volatility. _Environmental Research Letters 9_ (6), 064028.

29
the estimates are strongly statistically significant, almost certainly the combination of

> attenuation from the reduced signal in aggregated data plus the much smaller number of

observations.

|TableI1:Regressi|onresults <br>|ofvoicev<br>|ariables|on_desa_av<br>|erages (O<br>|LSestim <br>|a<br>)ion)<br>|||
|---|---|---|---|---|---|---|---|---|---|
||Protestac<br>|tivity<br>||Existsa <br>expressio<br>(positive<br>voice)<br>|problemb<br>n<br> signis_les_<br>|utno<br>_s_<br>|Effective<br>|voice<br>||
||Coeff.<br>(p-|%chan<br>one uni|ge,<br>t|Coeff.|%chan<br>unit|ge,one|Coeff.<br>(p-|%change<br>unit|,one|
||level)|Agg..|Total<br>HH|level)<br>_|Agg|Total<br>HH|level)|Agg.|Total<br>HH|
|Sociability|.003<br>(.813)|2.5||-.027<br>(.164)|-14.6<br>_||.0039<br>(.924)|0.4%||
|Networkactivity|.015<br>(.434)|12.4||.0024<br>(.941)|1.3||.026<br>(.685)|2.9||
|_Desa_government<br>organizations|-.062<br>(.081)*|-51.2|-42.2|.053<br>(.350)|28.6<br>_|39.1|-.162<br>(.150)|-18.1|-1.8|
|Social<br>organizations|.022<br>(.642)|18.2|44.9|-.029<br>(.704)|-15.7|-29.1|.094<br>(.524)|10.5|4.8|
|Control variables|None|significa|nt||F+,A-|||Y-,F-,||
|Regions||Ngada+||Included,|none sign|ificant|Include|d, nonesig|nificant|
|N||42|||42|||41||
|R2||.612|||.452|||.569||
|AdjustedR2||.388|||.135|||.311||
|Source:L|LI2data.|||||||||

# _P9 Interpretation and Implications: Literatures, theory_

The _desa_ government organizations imposed by the Indonesian central government, which were ostensibly designed as channels of "participation" to improve local governance, are apparently less effective than social organizations at producing desirable governance outcomes-in fact greater participation appears to _worsen_ aggregate outcomes. Less rigidly structured groups (even if sponsored by government) and those that are locally initiated are better able to facilitate broad participation, information-sharing, responsiveness and accountability measures than the "uniforn blueprint" groups introduced in the creation of the official _desa_ structure.

34
# Social Impacts of Climate Change in Bolivia: A Municipal Level Analysis of the Effects of Recent Climate Change on Life Expectancy, Consumption, Poverty and Inequality<sup>*</sup>

by Lykke E. Andersen Institute for Advanced Development Studies, La Paz, Bolivia Dorte Verner The World Bank, Washington D.C.

**Keywords:** Climate change, social impacts, Bolivia. **JEL classification:** Q51, Q54, O15, O19, O54.

> * This paper forms part of the World Bank research project “Social Impacts of Climate Change and Environmental Degradation in the LAC Region.” The authors greatly appreciate the comments, suggestions and inputs received from Pablo Fajnzylber, Kirk Hamilton, Jacoby Hanan, Jens Hesselbjerg Christensen, Jakob Kronik, Andrea Liverani, Joergen Eivind Olesen, Claus Pörtner, Tine Rossing, Olivier Rubin, Emmanuel Skoufias, Fabián Soria and Addy Suxo. The findings, interpretations, and conclusions expressed in this paper are those of the authors and do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent.
**Table 5. Survey response rates**

||**Baseline**|**Follow‐up**|**Follow‐up**<br>**rate**|
|---|---|---|---|
|_2010 cohort_||||
|Treatment|1184|1047|88.43%|
|Control|372|330|88.71%|
|**Total**|1556|1377|88.50%|
|_2011 cohort_||||
|Treatment|1237|1113|89.98%|
|Control|349|306|87.68%|
|**Total**|1586|1419|89.40%|
|_2012 cohort_||||
|Treatment|1044|889|85.15%|
|Control|491|417|84.93%|
|**Total**|1535|1306|89.40%|

Notes: This table uses “ITT” treatment status‐ those whose scores qualify them for training.

**Table 6. Uptake of EF‐sponsored training in the year following baseline survey**

||**Participate**<br>**training co**|**d in an EF**<br>**urse**|**Did not par**<br>**EF training **|**ticipate in an**<br>**course**|
|---|---|---|---|---|
||Number|Percent|Number|Percent|
|2010 Cohort (n=1372)|||||
|Assigned to Treatment (n=1040)|671|64.52%|369|35.48%|
|Assigned to Control (n=332)|86|25.90%|246|74.10%|
|2011 Cohort (n=1415)|||||
|Assigned to Treatment (n=1110)|826|74.41%|284|25.59%|
|Assigned to Control (n=305)|110|36.07%|195|63.93%|
|2012 Cohort (n=1306)|||||
|Assigned to Treatment (n=889)|597|67.15%|292|32.85%|
|Assigned to Control (n=417)|127|30.46%|290|69.54%|

Notes: There are four individuals from the 2011 cohort and five individuals from 2010 whose status in the EF database is unknown. For these individuals, we rely on the respondent’s self‐report of whether they took an EF training in the past year for the ATT results. The table only includes those individuals who were surveyed for the first follow‐up.

29
**Appendix Table 3:** Determinants of regional enrollment growth (1999-2003) -- Sample of older schools

||(1)|(2)|(3)|
|---|---|---|---|
|total female enrollment in 1999 in the region, grade 6-10||||
|(in logs)|-0.222|-0.230|-0.233|
||(32.11)**|(32.14)**|(32.16)**|
|female enrollment in 1999 in the region, grade 6 (in logs)||||
|female enrollment in 1999 in the region, grade 10 (in||||
|logs)||||
|# of madrasas in the region, 1999|0.017|0.022||
||(4.90)**|(5.95)**||
|# of schools in the region, 1999|0.051|0.058||
||(17.74)**|(19.31)**||
|Δin # of madrasa (1999-2003)|-|-|-|
|Δin # of school (1999-2003)|-<br>-|-<br>-|-<br>-|
|Mean age of schools|-<br>-0.000|-<br>-0.000|-<br>-0.000|
||(0.59)|(1.43)|(0.82)|
|Mean age of madrasas|0.002|0.002|0.002|
||(4.20)**|(3.67)**|(4.08)**|
|# of co-educational madrasas in the region, 1999|||<br>0.020<br>(4.70)**|
|# of girls-only madrasas in the region, 1999|||<br>0.036|
||||(4.02)**|
|# of boys-only madrasas in the region, 1999|||-0.002<br>|
||||(0.10)|
|# of co-educational schools in the region, 1999|||0.060<br>(18.13)**|
|# of girls-only school in the region, 1999|||0.064|
||||(10.91)**|
|# of boys-only schools in the region, 1999|||0.019|
||||(1.68)+|
|Govt (=1 if a government secondary school present)|0.018|0.019|0.038|
||(1.06)|(1.10)|(2.10)*|
|Urban|0.015|-0.009|-0.001|
||(1.14)|(0.61)|(0.07)|
|Constant|<br>1.381|<br>1.417|<br>1.427|
||(35.75)**|(35.40)**|(35.01)**|
|N|<br>4737|<br>4737|<br>4737|
|R-squared|0.20|0.22|0.22|
|Fixed-effects|District|Thana|Thana|

Note: Absolute value of t statistics in parentheses. + significant at 10%; * significant at 5%; ** significant at 1%.

18
The available evidence also examines the different circumstances and mechanisms through which lower-income households may be more prone to bear the brunt of inflation. First and foremost, the bottom part of the distribution is typically found to allocate a higher share of their expenditures to food. In Turkey, for instance, the 1st decile spends 7 of 10 Turkish lira of their incomes on food and housing, about twice as much as the richer decile. In addition to differences in consumption bundles, poor households may have more limited means to cope with rising prices, such as lower ability to substitute across goods (Argente and Lee 2015). Similarly, budget, information and bargaining constraints mean that lower-income households may end up paying higher prices per unit (Kaplan and Schulhofer-Wohl 2017; Orhun and Palazzolo 2018). They are less able to make bulk purchases and often, due to marked seasonality in agricultural production and credit market imperfections, farmers sell their produce at low prices at the height of the harvest season and buy food at much higher prices in the lean season (Burke, Bergquist and Miguel 2019). Higher exposure to the effects of inflation also stem from the fact that poor and vulnerable households are less likely to earn indexed wages –due chiefly to high levels of informality, hold a higher share of their income and wealth in cash and fewer savings –due to lower financial inclusion,<sup>4</sup> or receive public transfers whose values are not always tied to the evolution of inflation rates, as is the case in some of the social assistance programs in Turkey (Kahn 1997; Mulligan and Sala-i-Martin 2000; Erosa and Ventura 2002; Burdick and Fisher 2007, Cysne, Maldonado, and Monteiro 2005; Areosa and Areosa 2016).

# **3. Inflation in Turkey**

Turkey has long experience dealing with fast and enduring periods of inflation. As shown in Figure 1, episodes of high inflation were frequent between the 1970s and the middle of the 2000s, significantly more than across other EMDEs. Local economic and political turmoil along with an challenging external context, including a major global oil shock and volatile financial markets, created the conditions for high inflation in the 1970s. Price pressures continued in the 1980s largely due to monetized fiscal imbalances. The 1990s marked another decade of high inflation. Average annual changes in the CPI exceeded 50%, reaching an all-time high of 105% during the 1994 crisis.

> 4 Findex data from 2017 shows that 56% of the bottom 40 had a bank account compared to 77% for the rest of the population. Global Findex 2017.

6
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA which is the ratio of the maintenance cost over investment expenditure. The government expenditure ( _G_ ) will be determined with the following equation (1.3):

- 1.3 _G_  _Go_   ( _Itp_  _Itpo_ )

A parameter (ω) is used to adjust for the type of infrastructure investment that will be simulated. The following parameters were computed from expected annual investment needs for 2005–2010 in SubSaharan Africa from Fay and Yepes (2003). We simply computed that ratio between investment needs and maintenance costs.

- Roads, 0.84

- Sanitation, 1.29

- Water, 1.38

- Telecom, 0.74

- Electricity, 0.90

This assumption for public expenditure is equivalent to establishing what it will be. As we fix, or establish, investment objectives, the public investment ( _Itp_ ) will also be exogenous; implicitly, the government savings will also be exogenous given the identity of equation 1.4.

- 1.4 _Itp_  _Sg_

Given these assumptions, only one element can be adjusted to balance out the government budget constraint (equation 1.2). The only variable we can use to adjust the budget constraint is government income _Yg_ . As this variable is not free in the model (it is determined by the income generated from all sources of income), one variable of this equation must be rendered endogenous. One option would be to leave the _Trg_ endogenous, which would mean the objectives for public investment will be met by more foreign aid. The other option would be to endogenize one of the tax rates (household income tax, firms’ income tax, production tax, or import duties). An intermediate option that could be simulated is to assume an exogenous rise in foreign aid ( _Trg_ ) and to let an internal tax rate adjust for the rest of the funds needed to meet public investment objectives.

The other important element is the externality equation (1.5), along with is its role in increased total productivity of factors of the value-added equation (1.6). For this, we draw on the vast literature linking public infrastructure to factor productivity in the private sector, such as that modeled by Dumont and Mesplé-Somps (2001) in a CGE context—although our externality function does not use private investment. The function defining the externality is defined with the following function:

> where _θi_ is the externality or sectoral productivity effect, which is a function of the ratio of new public

> investment ( _Itp_ ) over past public investment ( _Itpo_ ) with a sector-specific elasticity ( _ξi_ ). We do not model a direct private sector displacement or crowding effect tied to increased public investment. The externality

5
5 spreads o-er time, **and** geo~,tl.iphic region dummy variables are incorporated to capt-re differences accross reo,ions that mav not be captured by the other '.'ariaoes in(orporated it) fa;':, spreads varied considerablv over-time in the 1<sup>97</sup> 0s (Ozler 1990).12 Incorporating a dummv variable,<sup>D, that is uni y for a country</sup><sup>**that**</sup> had any prior repayment<sup>problems and zero</sup><sup>**otherwise, the equation we**</sup> estimate (using ordinary least squares methods) is as follows: _(2)_ where x - a vector of k variables relevant to the probability, **p.** A discussion of of the<sup>variables included in this vector</sup><sup>**will be**</sup> provided in the next section,

- C = region specific dummv variable,

- T time specific dummv variable.

- 2.2. Data

Equation (2) is estimated,<sup>employing data on commercial bank loans from</sup> Eurocurrency credit markets<sup>during the 1970s.</sup> 1 Tne a contain information on the month<sup>and the year of each loan contract as well as some</sup> characteristics of the<sup>contract. We use LIBOR (London Intetoank Offer Rate)</sup> as the base rate, and<sup>include only $U.S.-denominated loans that have</sup> variable interest rates<sup>to avoid complications that may arise from</sup> comparisons across different<sup>types of financial instruments.</sup><sup>**After these**</sup> restrictions, the data<sup>set contains information on 2184 loans to 70</sup> developing countries for<sup>the 1968-81 period.</sup> The 70 countries in our<sup>sample are of three tvpes in terms of their</sup> repav..er.t histories of sov7"n -n debt The first rain division<sup>is between</sup>