rafmacalaba/datause-encoder-data huggingface dataset:
nonbeneficiary children ages 24 to 36 months, the point estimates of impact also suggest a worsening of nutritional status but to a lesser degree—there is no difference in wasting, for example—and the difference is not as precisely estimated.

If children in nonbeneficiary households suffer growth deficits because of the cash transfer, then we would expect to see a divergence in growth only for those children exposed to the program at critical ages when they are most vulnerable to a nutritional deficit. We do indeed see this for children younger than 36 months, and especially for those ages 6 to 24 months at the time of the survey.

# **_Relative Sizes of Direct and Indirect Effects of the Transfer on Child Growth_**

The estimated indirect effect on nonbeneficiary children’s height-for-age (-0.397) is larger than the direct effect on beneficiary children’s height-for-age (0.257). This result may seem counterintuitive. To understand how a difference of this magnitude may arise, we first note that in highly saturated villages, there are many more beneficiaries than nonbeneficiaries, creating a relatively large price shock and concentrating its effects only on the smaller segment of the population without an income transfer.

Regarding the observed changes in height-for-age per se, while perhaps surprising at first glance, these impacts on child growth translate to estimated elasticities in line with the broader literature. Unfortunately, we do not have the data to estimate price or income elasticities for individual food items at the household level, as we only observe food quantities consumed by young children. However, using observed household consumption expenditure for aggregated food groups, we estimate a price elasticity of demand for dairy and eggs to be -1.3 and an income elasticity of egg demand, estimated for beneficiaries in highly saturated areas, of 0.15. These point estimates are similar to those reported in the literature: Friedman and Levinsohn (2002) estimate a price elasticity of demand for eggs in a nationally representative survey from Indonesia to be -1. Estimates of income elasticity of egg demand in the United States range from 0.04 to 0.11, which are somewhat lower than our estimate of 0.15, although not surprising given the difference in income between the two populations (Okrent and Alston 2012). Finally, using a sample of poor rural households in Kenya, <mark>Almås,</mark> Haushofer and Shapiro (2019) estimate income elasticity for all protein to be 1.29, which is considerably larger than our estimate for eggs alone and might suggest even greater impacts on child growth than we observe in our setting.

25
**Table 2: Maximum Likelihood Estimates for Total Non-Fuel Importsa**

|Model||(I)||(II)||
|---|---|---|---|---|---|
|Equation|Probit|Gravity|Probit||Gravity|
|_Intercept_|-7.000|-10.228<br>|-6.960<br>||-10.9:56<br>|
||(-27.40)|(-29.02)|(26.28)||(-30.58)|
|_GDPi_|0.541<br>|1.109<br>|0.545<br>||0.949<br>|
||(31.47)|(51.73)|(29.90)||(34.98B)|
|_GDPj_|0.567|1.341|0.566||1.339<br>|
||(32.36)|(61.89)|(32.33)||(62.12)|
|_Populationi_|-0.194|-0.233|-0.198||-0.08:2|
||(-9.80)|(-8.53)|(-9.17)||(-2.64)|
|_Populationj_|-0.058<br>|-0.333<br>|-0.058<br>||-0.336<br>|
||(-3.03)|(-12.76)|(-3.03)||(-12.97)|
|_Distance_|-0.435<br>|-1.109<br>|-0.437<br>||-1.060<br>|
||(-12.17)|(-23.87)|-12.15)||(-23.20)|
|_Border_|-0.376|0.179|-0.378<br>||0.239<br>|
||(-2.32)|(0.91)|(-2.33)||(1.27)|
|_Language_|0.592<br>|0.861<br>|0.591<br>||0.867<br>|
||(8.67)|(9.50)|(8.66)||(9.62)|
|_EC_||-0.264<br>|||-0.305<br>|
|||(-0.94)|||(-1.08s)|
|_EFTA_||-0.393<br>|||-0.415<br>|
|||(-0.81)|||(-0.86)|
|_LAIA/LAFTA_||0.713|||0.951|
|||(3.27)|||(4.37)|
|_ASEAN_||2.269<br>|||2.476<br>|
|||(4.64)|||_(5.1 0)_|
|_CACM_||2.133|||2.414|
|||(4.32)|||(4.91)|
|_IPRsb_|||-0.014||0.369|
||||(-0.53)||(9.59)|
|||2.100|||2.08:3|
|_obs._|7304|5492|7304||5492|
|||-0.034||-0.043||
|-2 In_A(p=_0)'||0.853||1.346||
|-2nX_({,_<br>_A_<br>_I}_= O)||8874.433||8965.67|7|

_a_ t-statistics in parentheses b Park and Ginarte index of the destination country of the trade flow, that is countryj in the case of exports and country i in the case of imports.

**c** For a definition of the likelihood ratio test statistics, see Appendix A.

14
As shown in **Figure 7** , the Gini index for Pre-fiscal income was higher in 2009 than in 2017 as depicted in Panel A. In both years, the Gini index decreased through income concepts. In Panel B we show that the magnitude of the reductions is similar, but it was slightly higher in 2009. Indeed, in 2009 the reduction from Pre-Fiscal to Adjusted fiscal was 8.3 percentage points, while in 2017 it was 7.8.

**Figure 7.** Evolution of the Gini index through different concepts. 2009 and 2017. **Panel A.** Gini index

<!-- Start of picture text --> 0.600<br>0.505<br>0.492<br>0.500 0.461 0.445 0.467<br>0.429 0.422<br>0.383<br>0.400<br>0.300<br>0.200<br>0.100<br>0.000<br>Pre-fiscal income Net market income Disposable income Adjusted income<br>2009 2017<br>Panel B. Change of Gini index<br>From pre-fiscal to Net From Net to Disposable From Disposable to From pre-fiscal to<br>income income Adjusted income Adjusted income<br>0.000<br>-0.010<br>-0.020 -0.013 -0.016<br>-0.017<br>-0.030<br>-0.025<br>-0.040<br>-0.050<br>-0.045 -0.046<br>-0.060<br>-0.070<br>-0.080<br>-0.078<br>-0.090 -0.083<br>2009 2017<br><!-- End of picture text --> Source: Authors’ estimates based on ECH (2017, 2009), INE and Fiscal Accounts and CEQ Methodology. Note: This paper scales-down the in-kind transfers, but Bucheli et al. (2013) did not because they used a previous version of the methodology, so the 2009 figures were recalculated using the updated methodology for comparability. Therefore, figures presented here for 2009 might not coincide with those from Bucheli et al. (2013).

In turn, poverty before fiscal interventions was higher in 2009 than in 2017 ( **Figure 8** ). And in both years, fiscal interventions (particularly direct transfers) have a reducing effect on poverty for either 24
response to budget processes and managerial needs than in connection with the analytical agenda. As a result, the relevant information is currently scattered across the World Bank.

A contribution of this paper is to painstakingly match entries from three self-standing digital platforms to assemble a consolidated database of knowledge-related World Bank work. One of the platforms is a filing system for documents of all sorts, another one contains information on each of the budget codes the World Bank uses, and the third one hosts all the publications it has put in the public domain.

For each of the pieces of knowledge work identified through this matching process, the consolidated database brings together the information available on a broad set of indicators. On the output side, it may report the internal quality rating at completion and the number of downloads over time. On the input side, it may include the name of the task team leader (TTL), the financial resources and time devoted to the task, and the identity of the units in charge of producing it and supervising it. Important gaps remain, as information is not available on all indicators for all entries, but the result is probably the most comprehensive database of World Bank analytical work produced to date.

The analysis in the paper is conducted for knowledge-related documents completed between July 1<sup>st</sup> , 2015, and June 30, 2019. This choice is determined by the stability of the rules guiding the preparation of analytical work during that four-year period. Indeed, most knowledge-related tasks in the World Bank were by then under the management of central units. These included the research department – or DEC, for Development Economics – as in the past, but also 14 Global Practices introduced on July 1, 2014, as part of a major institutional reorganization. A significant departure from this centralized approach to knowledge generation was six relatively small regional Chief Economist offices, which continued preparing analytical products for the country groupings they served.

This period was also characterized by a relative stability of other relevant criteria, such as the administrative classification of tasks, the identification of “core” knowledge products that all country units are supposed to deliver, the typical budget envelopes by task, and the rules guiding the use of trust funds (TFs). The stability of the context over the four years considered reduces the risk that statistical results may be biased by structural breaks. It also helps address concerns about reverse causality, whereby similar tasks could have been subject to idiosyncratic processes.

The paper proceeds as follows. The next section reviews selected assessments of the knowledge work conducted by the World Bank, summarizing the methodologies they used, and the hypotheses articulated 5
# **Table S1.1 : Comparison of « Enquête de Suivi de la Pauvreté au Sénégal » (ESPS) and « Pauvreté et Structure Familiale » (PSF) surveys designs**

|||**ESPS**|||**PSF**||
|---|---|---|---|---|---|---|
||**Respondent**|**Number and list of items**|**Recall period**|**Respondent**|**Number and list of items**|**Recall period**|
|**Purchased food**<br>**(every day**<br>**spending)**|Ever|y day spending is not separate|ly covered.|Household<br>members in<br>charge of<br>preparing<br>meals|Respondent is asked about<br>the amount of every day<br>spending (“dépense<br>quotidienne”, DQ), she/he<br>receives from contributors.|Respondent is asked<br>about the length of<br>the period covered<br>by the DQ (from one<br>day to three<br>months).|
|**Food**<br>**contributions**<br>**received in kind**||Contributions received in<br>kind are not covered.||Household<br>head|Respondent is asked about<br>the amount of the<br>contributions received by<br>the household on a regular<br>basis.|Frequency is<br>reported by the<br>respondent who can<br>choose among<br>twelve modalities<br>from every day to<br>every year.<br>Respondent is asked<br>about the usual<br>contributions.|
|**Other purchased**<br>**food**|Household<br>head|Respondent is asked about<br>expenditure on 27 items:<br>Millet, maize, sorghum and<br>fonio; Sub-products from<br>millet, maize and sorghum;<br>Rice; Peanuts and their|Over the 30 last<br>days. The<br>respondent is then<br>asked about the<br>number of times it<br>spent the same|Household<br>head|The head of household is<br>asked about items that<br>he/she is in charge of<br>buying him/herself (this<br>does not necessarily means<br>that he/shepays for it and|Frequency is<br>reported by the<br>respondent who can<br>choose among<br>twelve modalities|

3
reserves held by the government helped the banks retain their confidence in the macro and micro fundamentals in Russia.

# **Figure 14. BRICs and the Development of NIM and I&R (1995-2009)**

<!-- Start of picture text --> 18 Brazil 25 Russia<br>16<br>20<br>14<br>12<br>15<br>10<br>8<br>10<br>6<br>4 5<br>2<br>0 0<br>1995 1997 1999 2001 2003 2005 2007 2009 1995 1997 1999 2001 2003 2005 2007 2009<br>NIM I&R NIM I&R<br>India China<br>10 3.5<br>8 3<br>6 2.5<br>4 2<br>2 1.5<br>1<br>0<br>1995 1997 1999 2001 2003 2005 2007 2009<br>0.5<br>-2<br>0<br>-4<br>1995 1997 1999 2001 2003 2005 2007 2009<br>-0.5<br>-6<br>NIM I&R NIM I&R<br>Percentage points Percentage points<br>Percentage points Percentage points<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data We depict the behavior of the NIM and I&R factors in all four BRICs in Figure 14. The immediate observation is that in all four countries micro-factors play a significant role in the determination of the spreads. The banking sector in all four countries is dominated by a few large banks and competition is limited. Moreover, the significant growth rate of all BRIC countries has pushed banks to take economic growth for granted when making their lending and borrowing decisions.

It would be interesting to see if this trend continues after the crisis. Despite the significant capital outflows from all four countries, banks seem to not have significantly changed their behavior in terms of NIM, micro or macro-factors. This fact speaks to the confidence in BRICs' economies and their growth potential.

27
differences in maize prices that farmers are likely to face. The price of nitrogen, , is computed NN using the prices and nitrogen composition of _chitowe_ (NPK 23:21:0 +4S) and the urea fertilizer, PP the two main fertilizers used in maize production. Following Xu et al. (2009), let _f_ be the amount of each of _chitowe_ and urea required for a kilogram of nitrogen. Given the 1:1 application ratio of _chitowe_ and urea and their nitrogen components (23% for chitowe and 46% for urea), we have the following expression: ; and solving for _f_ results in . This means that a kilogram of nitrogen costs approximately 1.449 kilograms of each of 23%ff+ 46%ff= 1 ff= 1.449k _chitowe_ and urea; hence [ = 1.449 ∗(p . NN PP ppccee ooff cchppiiooiiee+ p ppccee ooff uuppeeaa)] Whether or not fertilizer is likely to be profitable depends on outcomes that are uncertain when fertilizer decisions are made, as well as costs associated with the use of fertilizer that are unknown to the analyst. If these uncertainties and unobserved costs are assumed to be zero, fertilizer use is deemed profitable if MVCR is at least one, an indication that an increase in the rate of fertilizer application will increase income from maize production. In the face of these uncertainties and unobserved costs however, an MVCR of at least 2.0 (meaning a risk premium of one) has been recommended in the literature to be required in order for fertilizer to be profitable (Xu et al., 2009; Sauer and Tchale, 2009; Bationo et al., 1992; FAO, 1985). We discuss the implications of our results when profitability is assumed to be obtained with an MVCR of 1.0 and 2.0.

# **Data and Sample Selection**

The data used in the analyses is a two-wave, nationally representative, panel dataset collected by the national Statistical Office of Malawi (NSO) with support from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) program.

15
is consistent with our finding that relative labor deprivation is higher for young males as compared to young females.<sup>6</sup> Next, in table 9 we present a probit regression of determinants concerning young people’s wish to migrate abroad. The dependent variable takes a value of one if a youth wishes to migrate and zero if not.

**Table 9:  Probit estimates of determinants of young people's (aged 15-29) wish to migrate abroad**

|Dependent variable: Yes =1; No=0|**All indi**|**viduals**|**Females**|**Males**|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Employed|-0.0236<br>(0.0243)||||
|Relative Labor Deprivation (reference: all||386.5|||
|individuals)||(377.3)|||
|Relative Labor Deprivation (reference:<br>|||-276.6|400.7***|
|individuals of same gender)|||(439.2)|(154.6)|
|Primary education|0.0125|0.00723|-0.0202|0.0666|
||(0.0301)|(0.0302)|(0.0286)|(0.0534)|
|Low secondary education|0.0937***|0.0884**|0.0800**|0.128**|
||(0.0349)|(0.0352)|(0.0395)|(0.0562)|
|Secondary education|0.0778*|0.0713|0.132**|0.0683|
||(0.0432)|(0.0434)|(0.0594)|(0.0681)|
|Tertiary education|0.0692|0.0584|0.0517|0.0987|
||(0.0632)|(0.0625)|(0.0696)|(0.0960)|
|Married|-0.148***|-0.147***|-0.0790***|-0.224***|
||(0.0229)|(0.0229)|(0.0222)|(0.0475)|
|Youth aged (20-24)|0.00895|0.00640|0.00664|0.0141|
||(0.0270)|(0.0268)|(0.0279)|(0.0444)|
|Youth aged (25-29)|-0.0283|-0.0329|-0.0250|-0.00942|
||(0.0284)|(0.0279)|(0.0294)|(0.0467)|
|Urban|0.0837***|0.0863***|0.0531*|0.0919**|
||(0.0239)|(0.0240)|(0.0272)|(0.0389)|
|Male|0.321***|0.322***|||
||(0.0237)|-0.0235|||
|Region dummies|Yes|Yes|Yes|Yes|
|Observations|2,128|2,128|1,085|973|
|PseudoR2|0.176|0.176|0.0905|0.0907|

Source: MHYS 2009-2010. Probit estimations. Marginal effects, evaluated at sample means for continuous variables, are shown. Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. The reference groups is rural unmarried youth aged 1519 without any education (for model 1 and 2 add female) We would expect youth who wish to migrate abroad to be unsatisfied with domestic labor market conditions and suffer from stronger feelings of deprivation. Interestingly, employment status in itself is not a significant predictor of the wish to migrate. However, relative labor deprivation among men is

> 6 Specifying the above regression as a probit with the dependent variable being defined as satisfied/dissatisfied

> (1/0) yields results that are similar in interpretation.

21
Policy ReseaRch WoRking PaPeR 4316

# **Abstract**

This paper investigates who is most affected by informal competition and how regulation and enforcement affect the extent and nature of this competition. Using newly-collected enterprise data for 6,466 manufacturing formal firms across 14 countries in Latin America, the authors show that formal firms affected by head-to-head competition with informal firms largely resemble them. They are small credit constrained, underutilize their productive capacity, serve smaller customers, and are in markets with low entry costs. In countries where the government is effective and business regulations onerous, formal firms in industries characterized by low costs to entry feel the sting of informal competition more than in other business environments. Finally, the analysis finds that in an economy with relatively onerous tax regulations and a government that poorly enforces its tax code, the percentage of firms adversely affected by informal competition will be reduced from 38.8 to 37.7 percent when the government increases enforcement to cover all firms.

This paper—a product of the Enterprise Analysis Unit, Financial and Private Sector Development Department—is part of a larger effort in the department to study and promote reforms in the business environment. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at flamanna@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 OF CONTENTS

|Introduction**.......................................**<br>1|
|---|
|Part I: The Existing System**.....................................**<br>3|
|Old-Age Security in the Command Economy .........................<br>3|
|Characteristics of EECA Systems Today .........<br>...................<br>4|
|Impact of the Transition ......................................<br>7|
|<br>Box 1: Are the Aging Poor in ECA? ........<br>...................<br>8|
|Reform Issues ...<br>10<br>Box 2: Should early retirement with full pension be used to resolve|
|unemployment problems in ECA? .<br>.........................<br>12|
|Part**IHt**Options for the Future**............**<br>**..................**<br>15|
|Overview of Proposed Reforms .<br>...............................<br>16|
|<br>Box3: Outline of Recommended New System ......<br>..............<br>17|
|Public Pillar Reforms ....................................<br>18|
|Benefit and Financing Reform Options ..........<br>.................<br>19|
|The Contributions Model ....................................<br>19|
|The Social Assistance Model ...................<br>21|
|Other Public PiUlar Reforms ...................<br>22|
|Funded Systems: Should EECA Follow Chile or Germany? ..............<br>24<br>Box4: Can public assets to be privatized be used to start funded pension<br>schemes? .........................................<br>26|
|Effects of Reform Program ...................................<br>30|
|Sequencing and Obstacles to Reform .............................<br>31|
|<br>Box 5: Transition Economies - Public Pension Reforms since 1989 ...<br>....<br>32|
|Conclusion**...............................................**<br>37|
|Biblogrphy..............................................<br>38<br>**Table**|
|Table 1:<br>Demographic arnd System Dependency Rates, Retirement Age<br>Table 2:<br>Financial Characteristics of Public Pension Schemes in Transition Economies,<br>1992|
|**Graphs**<br>Graph 1:<br>Growth in the Pension Share of GDP in Selected Transition Socialist Economies|
|Graph 2:<br>Pension Spending Share of Government Expenditure in Selected East European<br>Countries, 1980-1990|
shaded area in Figure 2 shows the 90-percent credible interval that is around 2 percentage points wide throughout the sample, declining slightly in the last two decades.

**Figure 1** The global equilibrium real interest rate

<!-- Start of picture text --> Percent<br>5<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 2019<br><!-- End of picture text -->

**Note:** The dark blue line shows the GDP weighted equilibrium interest rates. The light blue band shows the 90-percent credible interval.

# _c. Robustness_

We perform several robustness checks to validate the global aggregation and our main estimation procedure. We present the alternative results in Figure 2, together with the benchmark result. Adding China to the sample results in only a minimally higher global equilibrium rate after 1990. Next, we replace the estimated ex-ante interest rates entering the model with ex-post observed interest rates, calculated as a difference between nominal rates and observed inflation. While the main trends remain the same, there are some differences. The equilibrium rate based on ex-post rates was somewhat higher in the 1980s and 1990s. It peaked at 4.3 percent, compared to 3.5 percent for the equilibrium rate based on ex-ante rates. In an additional estimation, we do not replace outliers and missing data points. Instead, they are excluded from the analysis and not used to update the information within the Kalman filter.<sup>21</sup> This enables us to address potential concerns regarding the method used to replace missing values in the sample. But the results are nearly identical, and differences are not visible.

In our estimation we need to choose priors that then determine the amount of variation in our components. We experimented with different priors and found that overall trends are robust to a wide range of priors. Naturally, more diffuse priors allowing for substantially larger fluctuations in components result in more volatile equilibrium rates.<sup>22</sup>

> 21 This is based on the approach described in Mariano and Murasawa (2003).

> 22 Results available upon request.

10