rafmacalaba/datause-encoder-data huggingface dataset:
Figure 16 also demonstrates that direct taxes and direct transfers are more effective at reducing inequality than are, for example, subsidies, in-kind services, or indirect taxes. This is not uncommon—see Figure 17, which presents the range and mean of the Inequality Impact Effectiveness Indicator for different classes of instruments in 11 African countries between 2010 and 2015 as well as the value of the same indicator in Egypt in 2015. Egypt’s inequality impact effectiveness indicators for direct transfers and direct taxes are below the mean, but still within the range shown in figure 17. Subsidy spending is approximately at mean effectiveness, while indirect taxes (and also total taxes) are outside the range and actually contribute to an _increase in inequality_ . This is due to the steep excises on alcohol and, especially, tobacco.<sup>51</sup> Figure 17 also presents Egypt’s fiscal quandary: while fiscal policy (circa 2015) was indeed reducing inequality as well as poverty, most Egyptian expenditures are not targeted and are therefore _larger_ than they need to be in order to have positive welfare impacts. That in turn indicates that the 2015era profile of expenditures and revenue collections is likely not sustainable (see also tables 1 and 2).

# **Figure 17. Inequality Impact Effectiveness Indicator: Egypt (red square), Range (gray bars), and Mean (black square) and 11 Low- and Middle-Income African Countries**

<!-- Start of picture text --> Total benefits<br>Total taxes<br>Hospital (Inpatient Health)<br>Health<br>Tertiar y education<br>Education<br>Indirect taxes<br>Subsidie s<br>Direct transfers<br>Direct taxes<br>‐1.00 ‐0.75 ‐0.50 ‐0.25 0.00 0.25 0.50 0.75 1.00<br><!-- End of picture text --> _Source:_ Egypt: based on HIECS 2015 and budget figures. All others: - de la Fuente, Jellema and Lustig 2018. _Note:_ Hospital/inpatient health in Egypt includes outpatient services at public hospitals.

The fiscal gains to the poor effectiveness indicator (Figure 18) provides a similarly intuitive statistic for the effectiveness of direct and indirect transfers in protecting poor and vulnerable households from further impoverishment. In other words, the Karama direct cash transfer was the most effective at protecting poor households _specifically_ because it was targeted to households in the poorest 20 percent of the population. The Takaful program, meanwhile, delivered far greater amounts through direct transfers, but it delivered

> 51 With such “sin taxes” Egypt may create incentives for better long-term public health outcomes and therefore long-term increases in human capital and incomes in all households and thereby also long-term reductions in income inequality (see for example Fuchs, Hasbun, and Mukong 2018). Such long-term dynamics are not captured here.

35
In certain countries, the subjective measure of crisis impact is less consistent with the overall size of the economic contraction but reflects the broader experience of households. In Serbia, for example, the large fall in employment during the crisis may explain why over two-thirds of respondents report being affected (making it the second most subjectively affected country in the region) despite a smaller GDP contraction than many other countries (-3 percent in 2009). However, differences in subjective perceptions of the crisis impact cannot be fully accounted for by the objective consequences of the crisis for households; nor can they be fully explained by differences in government and household responses. The case of Latvia for example (with a lower subjective impact than the size of the objective shocks experienced would suggest) implies that there may be important cross-country cultural differences in reporting an impact, suggesting that it is difficult to compare subjective perceptions across countries.

# **_Comparing subjective and objective measures of crisis impact across households_**

We now turn to comparisons of subjective crisis impact across households, within a given country. The subjective measure of crisis impact generally corresponds closely to shocks objectively experienced by households. Among households that declared being affected “a great deal” or “a fair amount” by the crisis, 90 percent experienced at least one labor market shock or income loss (such as job loss, closure of family business, wage reduction or lower remittances). Yet, in a few countries, when asked how they were affected, a non-negligible proportion answered "Don't know". This was the case in Bulgaria (32%), Georgia (31%), Mongolia (23%), Poland (21%), Estonia (12%) and France (11%).

To analyze differences in subjective perceptions of crisis impact across households, we estimate an ordered probit regression of the perceived impact of the crisis on selected household characteristics, with country fixed effects. The regression is estimated for all ECA countries (pooled) and for each transition sub-region taken separately, using ordered probit regressions. Using the LiTS II housing and expenses module (with seven expenditure items, including durables), we construct a consumption aggregate. The per capita consumption aggregate is then used to divide respondents into three consumption terciles in each country (lower, middle and 8
|**Tabl**|**e 8: Incidence**|**of Petty Corr**|**uption and F**|**irm Performa**|**nce Interactio**|**n terms**|||
|---|---|---|---|---|---|---|---|---|
|Dependent variable: Financially<br>Constrained (dummy)|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
||**Annua**|**l sales**|**Labor Pro**|**ductivity**|**Sales gro**|**wth rate**|**Exp**|**orts**|
|Incidence of Petty Corruption (Cell<br>average)*Annual Sales (2009 USD,|0.196*<br>(0.102)|0.248**<br>(0.099)|||||||
|logs)|||||||||
|Incidence of Petty Corruption (Cell|||0.219**|0.277***|||||
|<br>average)*Labor Productivity (logs,<br>2009 USD)|||(0.104)|(0.105)|||||
|Incidence of Petty Corruption (Cell|||||0.024***|0.024***|||
|<br>average)*Sales growth rate (%,|||||(0.007)|(0.007)|||
|annual)|||||||||
|Incidence of Petty Corruption (Cell|||||||1.814*|2.055**|
|<br>average)*Exports (proportion of<br>sales)|||||||(0.961)|(0.967)|
|Incidence of Petty Corruption (Cell||-0.741**||-0.725*||-0.974***||-0.590|
|average)*GDP per capita (logs)||(0.378)||(0.397)||(0.369)||(0.379)|
|Incidence of Petty Corruption (Cell|-1.520|4.120|-1.218|4.370|0.780**|8.931***|0.728**|5.703*|
|average)|(1.263)|(3.462)|(1.009)|(3.417)|(0.336)|(3.135)|(0.328)|(3.212)|
|Annual Sales (2009 USD, logs)|-0.180***|-0.197***|-0.344***|-0.342***|-0.171***|-0.175***|-0.123***|-0.124***|
||(0.042)|(0.043)|(0.072)|(0.072)|(0.035)|(0.035)|(0.037)|(0.038)|
|Age of Firm (logs)|0.050|0.052|0.076|0.076|0.009|0.005|0.050|0.050|
||(0.072)|(0.072)|(0.072)|(0.072)|(0.078)|(0.078)|(0.072)|(0.072)|
|Exports (proportion of sales)|-0.496|-0.471|-0.364|-0.345|-0.496|-0.439|-0.965**|-1.006**|
||(0.362)|(0.359)|(0.370)|(0.367)|(0.372)|(0.372)|(0.463)|(0.465)|
|Firm Has Female Owners Y:1 N:0|-0.188|-0.188|-0.175|-0.175|-0.200*|-0.209*|-0.190|-0.192*|
||(0.117)|(0.116)|(0.119)|(0.118)|(0.118)|(0.117)|(0.118)|(0.117)|
|Foreign Ownership (proportion)|-0.030|-0.031|0.029|0.027|0.019|0.010|-0.032|-0.034|

50
the highest risk of dispossession in case of widowhood. We therefore expect a stronger fertility response from them. Table 12 shows that their fertility response is indeed stronger: these women have on average almost one more child than women in the control group. Women’s fertility response when they do not already have a son is twice as large when there are rivals.

Table 12: Heterogeneity by Son and Rivals (Benin)

||Nu|mber of child|ren|
|---|---|---|---|
||No rivals<br>(1)||Has rivals<br>(2)|
|Treatment follow-up 1|-0.017||0.243|
||(0.123)||(0.384)|
|Treatment follow-up 2|0.427***||0.883**|
||(0.128)||(0.441)|
|Had son with head before PFR|0.286*||-0.050|
|X Treatment follow-up 1|(0.149)||(0.464)|
|Had son with head before PFR|-0.465***||-1.523***|
|X Treatment follow-up 2|(0.161)||(0.500)|
|Observations|2,963||534|
|Control group mean|5.031||3.743|
|Difference test p-val: Treatment<br>follow-up 1||0.489||
|Difference test p-val: Treatment<br>follow-up 2||0.275||

Notes: This table displays heterogeneous treatment effect of the land titling intervention on the number of children at each follow-up round depending on whether the woman’s husband had at least one son from a previous union. Controls include: number of children at baseline, highest education level completed, woman’s age, age square, whether woman is household head, and polygamy status. Standard errors are clustered at the randomization level. *, **, *** denote significance at the 10, 5 and 1 percent levels respectively.

# **5.2.2 Investing in Assets in a Matrilineal Setting**

In Ghana, we also find suggestive evidence that the positive impact observed results from the old-age motive for fertility. A large share of our sample is Akan, a matrilineal ethnic group. In matrilineal ethnic groups, a father does not belong to the same lineage as his wife and children. Among the Akan, under customary land tenure systems, when a male household head dies, his property is transferred to a male member of his lineage. This means that his wife and children cannot inherit from the household head and are at high risk of dispossession. In 1985, Ghana enacted the ‘Intestate Succession Law’, aimed at protecting widows’ rights over the property of a deceased husband, especially over assets acquired during their marriage, and children’s rights over the property of a deceased father. While the law exists, it is not widely enforced, especially due to lack of land formalization. Ghana is thus an interesting setting to study the effect of a land titling intervention: when the husband’s plot 27
aspects of cooperative behavior. The protocols are common across societies. For example, Marlowe et al. (2008) use data from such a large-scale, multi-country experimental study to show that larger and more complex societies engage in greater levels of third-party punishment of uncooperative or unfair behavior in order to enforce social norms. Heinrich et al. (2010) use data from the same experiment to show that market integration and fair behavior in the games are correlated, indicating the importance of institutions for individual behavior and social norms. In an earlier study, Heinrich et al. (2006) also find that altruistic behavior and costly punishment are correlated, again reflecting differences in social norms across societies. In a different study using more developed countries, Hermann et al. (2008) find that a weak rule of law and norms of civic engagement yield greater levels of anti-social punishment.

When we refer to culture, we employ a composite term which may include both formal and informal institutions. However, at the very least, it includes the informal rules of behavior; formal rules may, or may not be included. For example, to say that a society has a patriarchal culture is to say that _at least_ the informal rules have a male as the primary authority figure. Formal rules may reinforce males as authority figures (such as laws prohibiting women to work in Saudi Arabia), or may not weigh in on the matter at all. In some cases, formal rules may be in the opposite direction (for example, quotas in India for female representation in parliament) and may even exist to overturn informal rules of behavior. In addition, the extent to which norms and/or institutions govern behavior is yet another aspect of culture. Experimental methods allow us to unpack the influence of various aspects of culture on corruption.

# **Culture and Corruption: Theory and Empirics**

Culture interacts with corruption through formal institutions and social norms, both of which can differ across countries. For a government that seeks to inhibit corruption, the goal is to devise formal institutions that can reinforce existing social norms. Formal and informal rules may not be in congruence with each other. As an illustration of this, Wade (1982) found that Indian villagers defined a corrupt act as one where the official demanded a bribe that was higher than the market level of a bribe, conflicting with formal rules that prohibited bribes of any size. In other countries, such as the US, any demand for a bribe would be considered corrupt.

Researchers have identified four main informal enforcement mechanisms that reinforce adherence to informal contracts: trust, reputation, hostage-taking, and reciprocity (Rose7
12 and sentences, and then to activities<sup>designedto foster readingcomprehensionskill would be</sup> particularly beneficial to adults. These<sup>techniques could be integratedinto existing instructional</sup> methods, thereby maintaining the cultural<sup>relevanceof theinstructional procedures.</sup> To recommend that these methods become<sup>a routine part of literacy coursesin other countries,</sup> more tnals must be carried out in environments<sup>differentfrom those of rural BurkinaFaso. Issues to</sup> study further in future research and tryouts<sup>might be:</sup>

- A brief neuropsychological assessment should<sup>be carried out beforeand after the teaching</sup> program (digit span, verbal memory, workmng<sup>memory conceptualknowledge, visuospatial</sup> abilities), as shown by the Mexican Neuroalfa<sup>literacy program. Thus,it would be possible to</sup> find out the extent to which the cognitive<sup>processesof thegraduateshave improved.</sup>

- The phonological awareness exercises should<sup>be more extensiveandgraduallyincreasein</sup> complexity. They could include syllable matching<sup>and rhyme comparisonswith wordsand</sup> non-words. It is also important to do pair discriminations<sup>and manipulatethe phonemic</sup> distance between the consonants and the vowels.

- Improvements must be made in the low-level<sup>reading task logistics. Thereading packalways</sup> contained the same words, so even with<sup>shuffle, learnersmight havememonzedthe rangeof</sup> words contained therein, since there were only<sup>20, and these weresoundedout as a reader</sup> and as a reader-timer. Perhaps longer packs<sup>should be tned.</sup>

- O The amount of practice needed for the<sup>sound-letterlinkages to stabilizein the memoryof</sup> neoliterates must be assessed. Clearly, 3-<sup>and 4-monthcoursesare too short for rural</sup> populations with limited daily exposure to<sup>print.</sup>

With literacy software available to supplement<sup>the fast reading tasks, anexperimental use of</sup> computers could be applied in various centers.<sup>Reactiontimedatacould be collected.Additional</sup> software, if it becomes available, could<sup>offer practicein visualscanningtasks, discnminationtasks,</sup> reading, lexical decision tasks. Data would<sup>giveinformationabout learning processand development</sup> of automaticity. Neoliterate adults may achieve<sup>automatic readingonly after averylarge number of</sup> pairings of visual and auditory stimuli. Such<sup>alargenumber of trials is not feasible inclasses, but itis</sup> feasible through computers.
# _Table 1.8: Institutional Cost to Deliver the DTS and RPs – Private Large TVI (PHP)_

||201|0|20|11|201|2|20|13|2|014|
|---|---|---|---|---|---|---|---|---|---|---|
||DTS|RP|DTS|RP|DTS|RP|DTS|RP|DTS|RP|
|Personnel|4,076,854|6,115,282|4,062,720|6,094,080|4,349,036|6,523,553|4,598,261|6,897,391|4,564,733|6,847,099|
|Non-personal|788,827|1,183,241|718,294|1,077,441|766,643|1,149,965|884,732|1,327,099|858,922|1,288,382|
|Materials and supplies|536,810|805,214|631,245|946,868|627,401|941,101|863,881|1,295,822|849,476|1,274,213|
|Maintenance|1,122,428|1,683,642|966,613|1,449,920|989,040|1,483,561|1,245,198|1,867,796|1,275,914|1,913,871|
|Administrative|2,416,143|3,624,215|2,678,024|4,017,035|2,761,933|4,142,900|2,490,532|3,735,799|2,289,950|3,434,925|
|Capital expenses|377,764|566,646|637,905|956,857|705,252|1,057,877|789,213|1,183,820|196,299|294,449|
|Contingencies|—|—|—|—|—|—|—|—|—|—|
|Total|11,520,334|17,280,502|11,374,233|17,061,350|11,078,754|16,618,130|10,588,509|15,882,765|8,543,849|12,815,771|
|Enrolled|398|682|455|695|478|702|694|551|638|520|
|Total per trainee|28,946|25,338|24,998|24,549|23,177|23,673|15,257|28,825|13,392|24,646|
|DTS/Non-DTS|1.1|—|1.0|—|1.0|—|0.5|—|0.5|—|
|Personnel cost per trainee|10,243|8,967|8,929|8,768|9,098|9,293|6,626|12,518|7,155|13,167|
|DTS/Non-DTS|1.1|—|1.0|—|1.0|—|0.5|—|0.5|—|
|MMC per trainee|5,118|4,480|4,914|4,825|4,857|4,961|4,176|7,890|3,639|6,697|
|DTS/Non-DTS|1.1|—|1.0|—|1.0|—|0.5|—|0.5|—|

Source: DTS Tracking Survey, 2015, World Bank. World Bank calculations. Note:

||DTS|RP|
|---|---|---|
|Average per-trainee cost|21,154|25,406|

MMC = Material/supplies, Maintenance, and Capital Expenses 39
# **Reviewing Assessment Tools for Measuring Country Statistical Capacity**

Hai-Anh H. Dang, John Pullinger, Umar Serajuddin, and Brian Stacy<sup>*</sup>

# **Summary**

We review assessment tools for measuring country statistical capacity and offer further thoughts on their future development.

**Key words** : statistical capacity, statistical performance, statistical indicators, statistical capacity index, national statistical system

**JEL** : C8, H00, I00, O1

* Dang (hdang@worldbank.org; corresponding author) is senior economist with the Living Standards Measurement Study  Unit, Development Data Group, World Bank and is also affiliated with IZA, Indiana University, and London School of Economics and Political Science; Pullinger (johnpullingerltd@gmail.com) is former head of the UK National Statistical Service; Serajuddin (userajuddin@worldbank.org) is manager of the Indicators and Data Unit, Development Data Group, World Bank; Stacy (bstacy@worldbank.org) is economist with the Indicators and Data Unit, Development Data Group, World Bank. We would like to thank Lisa Bersales, Mustafa Dinc, James Foster, Haishan Fu, Craig Hammer, Johannes Hoogeveen, Dean Jolliffe, Christoph Lanker, Hiroko Maeda, Daniel Mahler, Jimmy McHugh, Yongyi Min, Johan Mistiaen, Yusuf Murangwa, Juan Oviedo, Viveka Palm, Carolina Sanchez Paramo, Francesca Perucci, Martin Rama, Walter Radermacher, Tara Vishwanath, and participants at the 63<sup>rd</sup> ISI World Statistics Congress, the 9<sup>th</sup> International Conference on Agricultural Statistics, and seminars at the World Bank for useful discussions. All remaining errors are ours alone and do not reflect in any way the viewpoints of colleagues who we discussed with. We would like to thank Juderica Dias and Dereje Ketema Wolde for helpful support with the data. We are grateful to the UK Foreign Commonwealth and Development Office (FCDO), formerly the Department of International Development, for funding assistance through various Knowledge for Change (KCP) grants.
Dijtinnov ∙ Insj is the interaction between a country’s institutional variables and a firm’s innovation. We ultimately are interested in μ.

Note that since the data are collected by the stratified random sampling method, all the regressions are weighted accordingly to restore representativeness. In addition, we cluster the standard errors at the country level to capture potential correlations between the error terms, and allow for heteroscedasticity (i.e. having robust standard errors).

Dependent variables:

- _Log of real sales_ (i.e. sales divided by country’s price level)

-

- _Log of real sales per full-time employee_

Explanatory variables:

- _Innovation:_ whether a firm introduced products or services or upgraded its product or services in the last 3 years. This is problematic for our regressions if the two rounds of survey are less than 3 years apart. For this reason, we only keep countries that have surveys more than 3 years apart.

- _Changing manager:_ if a firm changed its manager between the two waves of the survey. This is to capture potential other restructuring activities besides innovation. There is no direct way to know if a firm changes its manager. We indirectly guess by using the manager’s years of experience (as the firms are asked about the manager’s experience). We identify if a firm changed it managers by comparing the change in the experience years of the managers and the years between the two surveys. If the change in the experience years is different to the change in years, we conclude that the firm changes its manager. For example, at the first round of the survey in 2005, a firm’s manager has 10 years of experience; at the second round of survey in 2009, the firm’s manager has 20 years of experience. Since ΔyearEXPERIENCE is greater than ΔyearSURVEY , we conclude that the firm must have changed its manager between the two rounds of the survey. We acknowledge that there is a possibility that the manager might not remember exactly his or her years of experience. As a robustness check we allow for that possibility by loosening the restriction: only when yearEXPERIENCE is greater than yearSURVEY+1 or smaller than yearSURVEY-1 we can conclude the firm changes its manager. The variable is quite robust: if we follow the original criteria, we find that 3,495 out of 6,447 (54.2%) firms change their managers; if we follow the less restrictive criteria, we

8
- b) This is also one of the first papers to juxtapose absolute and relative ratings and reveal their comparative impacts on capital flows in the pre- and post-crisis periods.

This study relies on a comprehensive data set of sovereign ratings, relative ratings, and private capital flows, and other potential correlates of capital flows to analyze the influence of sovereign and relative ratings on both net and gross capital inflows with a particular focus on possible shifts following the global financial crisis in 2008-09. The post-crisis years are a unique period characterized by highly accommodative monetary policies in advanced economies and great heterogeneity in the performance of EMs and FMs.

Using dynamic fixed effects estimation, we control for other factors that can influence capital flows particularly growth and interest rate differentials and global risk conditions. We find evidence that while absolute ratings were an important determinant of capital inflows, especially in the pre-2008 period, the influence of relative risk ratings increased in the postcrisis period. The observed relationship between absolute ratings, relative risk ratings, and capital flows holds even after accounting for the effects of quantitative easing (QE) by the U.S. Federal Reserve in the post-crisis period. This finding suggests that in a post-crisis environment characterized not only by abundant global liquidity but also by greater investor caution, the relative creditworthiness of EMs and FMs compared to their peers has become a more relevant factor in attracting private capital flows. The baseline results are robust to the use of fixed GDP weights in calculating relative risk ratings, to an alternative measure of capital flows (gross capital flows instead of net capital flows), and to the potential endogeneity of absolute and relative ratings as described below. In a combined specification with both absolute ratings and the residual of relative ratings, the relative rating residual retains its positive sign and significance in the post-crisis period indicating an independent effect of relative ratings, which is not driven by sovereign ratings. Disaggregating the flows into foreign direct investment, portfolio flows and other investments (largely commercial bank lending) reveals that the postcrisis effect of relative ratings is driven mostly by portfolio flows.

This paper is organized into eight sections. Following this introductory part, section 2 reviews the relevant literature, and section 3 describes the data and methodology. Section 4 describes the empirical strategy, section 5 discusses the main results and their implications, and section 6 tests the robustness of the results. Section 7 examines the effects of ratings for the different components of capital flows, while section 8 concludes with some implications for policy.

4