rafmacalaba/datause-encoder-data huggingface dataset:
that direct actions to achieve (or not) these goals or outcomes.<sup>18</sup> For example, countries may have similar institutional backgrounds, yet, adopt different macroeconomic policies and have different development outcomes. As another example, a policy goal may be to have stable inflation and another to raise corporate tax payments. In the first case, examples of relevant institutions would be the Central Bank and the rules defining its mode of operation, while in the second case, among the relevant institutions would be the rules that govern information sharing (what accounting systems are used, audits of company accounts and disclosure, disclosure of personal income and property etc.), property rights (what taxes can be levied on and by how much, how ownership of taxable assets is determined, how shared assets are treated, who can collect the taxes etc.).<sup>19</sup>

# **Relationship to Governance**

Institutions _govern_ economic, social and policy interactions. These may be interactions between individuals or groups (e.g. firms and other organizations, clubs, religious communities) or nations (such as the WTO and the rules governing trade or the WIPO and rules governing intellectual property, or the Basel rules for financial institutions), or the organs of the state and the rules by which they conduct themselves. Thus, countries are well governed‐ _a normative concept_ ‐ when they have institutions that support such governance (or have good institutions), however defined. Like “policies”, “governance” is often used together with institutions and sometimes, in lieu of them.

# **Part 2. How and When Do Institutions Change?**<sup>**20**</sup>

> 18 This definition may still leave ambiguity in some cases.

> 19 Henry and Miller (2009) provide an interesting comparison of two countries with similar institutions following different macroeconomic policies.

> 20 Drawing on the WDR 2002 and some representative studies/research conducted since then, this section addresses institutional change. The attached list of references is not comprehensive, but illustrative.

12
**Figure 1**

Timeline of data sources, with count of villages receiving new roads <!-- Start of picture text --> 11107 10972 10758<br>9668<br>9333<br>8088 7922<br>6935 6890<br>6424<br>5760 5712<br>5152<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>Beginning Below Poverty Population Economic<br>of PMGSY Line Census Census Census<br>Population Socioeconomic<br>Census and Caste<br>Census<br><!-- End of picture text --> Notes: The figure shows the timing of the population, economic and poverty censuses of India used as principal data sources. Note that while the Socioeconomic and Caste Census (SECC) was intended to be conducted exclusively in 2011, and it is often referred to with this year, it was conducted primarily in 2012. The bar graph above represents the number of villages receiving PMGSY roads in each year in our full village-level dataset. Exact counts are also listed.

39
Fig. 5 Sectors’ contribution to annual value added growth in 2014-16 and 2017-19 (in %) Fig. 6 Firms’ annual employment growth (in %) <!-- Start of picture text --> 6 6<br>5<br>4<br>4<br>3<br>2<br>1 2<br>0<br>-1<br>0<br>-2<br>-2<br>EGY JOR LBN MAR PSE TUN<br>-4<br>Other sectors<br>Manufacturing and services - ES comparable<br>Source: the authors’ calculations based on UN National Source: the authors’ calculations based on EBRD-EIB-WB<br>Accounts Main Aggregated Database (2015 USD). Enterprise Survey.<br>2014-16 2017-19 2014-16 2017-19 2014-16 2017-19 2014-16 2017-19 2014-16 2017-19 2014-16 2017-19<br><!-- End of picture text --> Many productive firms might not be able to reap the scale and efficiency benefits from trade and innovation activity because of the weak business environment and state dominance in the economy. According to the latest WEF Global Competitiveness index, MENA countries perform worse than peer economies in terms of macroeconomic environment, labor market efficiency, technological competitiveness and business dynamics (Figure 7). Under this challenging business environment, the traditional channels of development and performance might be blocked or narrowed considerably.

Fig. 7 Global competitiveness Index <!-- Start of picture text --> Upper Middle Income (average) MENA (selected 6 economies)<br>Institutions<br>120<br>Innovation Infrastructure<br>80<br>Business Macroeconomic<br>dynamics environment<br>40<br>Market size 0 Health<br>Technological<br>Skills<br>competitiveness<br>Financial market Commodity<br>development market efficiency<br>Labour market<br>efficiency<br><!-- End of picture text --> _Source_ : WEF 2020 Global Competitiveness Report. _Note:_ A lower value means a better rank.

Table 1 highlights business environment barriers that affect traders and innovators and also looks at firm performance indicators as dependent variable. We find that traders are more likely to mention customs and trade regulation and access to finance as a major obstacle to their operations. Noninnovative firms are more likely than innovators to report corruption as a major obstacle, indicating that corruption discourages considerably innovation. Finance constraints and political uncertainty 7
# 2 Is Sharecropping Less Productive?

## 2.1 Testing for moral hazard

As a backdrop for our empirical analysis, we make a few standard theoretical assumptions that will later be incorporated into the tenancy model of section 3. Consider a constant returns to scale technology (plot size normalized to one), in which yield y is produced according to y = f(e, x)+ ε, where e is tenant effort, x is a purchased input, and ε is a random output shock. Net productivity is π = y − px, where p is the normalized input price. The tenant’s disutility of total effort is given by the convex function v(e). Sharecropping contracts specify an output share β ∈ (0, 1) and possibly also a fi xed component α, which may be negative. We assume that the cost of the purchased input is shared between landlord and tenant at the same rate as is output; in the model we consider later this assumption turns out to be innocuous. Suppose further that tenant is noncontractible.

A risk neutral tenant chooses (es, xs) to maximize βπ(e, x) − v(e) − α, giving fi rstorder conditions βfe = v� and fx = p.<sup>3</sup> Since any tenancy contract must be incentivecompatible, moral hazard delivers the unambiguous prediction<sup><u>de</u></sup> dβ<sup><u>s</u>>0.</sup> The marginal effect of share-tenancy on yields is then which, in general, is ambiguous. A sufficient condition for dβ<sup>dy>0,whichwehenceforth</sup> assume to hold, is

### Assumption A.1 fxe > fefxx/fx.

Tests for moral hazard based on yield data, therefore, have power only to the extent that tenant effort does not have very close purchased substitutes.<sup>4</sup> If fxe is indeed large and negative, then yield differentials between sharecropped and owner-cultivated plots may be negligible even with moral hazard. However, the only input that could conceivably

> 3 Given the additivity of the shock, precisely the same fi rst-order conditions would hold for a risk-averse tenant. Since we later model share-tenancy as arising from fi nancial constraints, tenant risk aversion is an unnecessary complication.

> 4 Since <u>dπdβs</u><sup>=fe</sup> <u>dedβs</u><sup>isunambiguouslypositive,itmightseempreferabletoperformthetestusingnet</sup> rather than gross productivity. In practice, however, there are several purchased inputs, each measured with considerable noise, so that net productivity tends to be less precisely measured than gross productivity.

3
three separate component measures, from surveys covering different sets of donor countries and using somewhat different question wording. Coefficients are positive and significant using GI (equation 5.2) and WVS (equation 5.3), but not for Eurobarometer (equation 5.4). Each 10-point increase in the percentage of GI respondents (or 5-point increase for WVS respondents) is associated with an increase in use of country PFM systems of more than 4 percentage points.

Equation 6.1 shows that results for the index of public support are robust to the inclusion of sector shares and other donor-level control variables from Table 4. Public support is weakest for the USA, providing a possible explanation for its extremely low usage of country systems as shown in equation 4.4. The USA dummy coefficient remains strongly significant, however, when it is tested together with public support for aid in equation 6.2. The coefficient magnitude of -34 is not so different from the -37 in equation 4.4, so the outlier status of the USA is largely unexplained by the substantive variables in the analysis.<sup>19</sup> The USA is an influential observation however in testing the link between public support for aid and use of country systems. The public support index is significant only at the .06 level in equation 6.2, with a coefficient only half as large as in equation 6.1.

Public support for aid is potentially endogenous to its effectiveness, which may in turn be influenced by donor‟s use of country systems. The opinion surveys were all conducted between 1995 and 2004, prior to the 2005-2010 period for which the PDMS measures use of country systems. This timing sequence reduces but does not eliminate the potential for reverse causation. We therefore instrument for public opinion in equation 6.3, with first-stage results shown in

> 19 The large negative USA coefficient reflects its relatively tepid support for some of the PD principles.  Due to U.S. opposition the PD omitted targets for budget support, and weakened targets for use of country procurement systems (Mokoro Ltd., 2008b: 23).  In contrast, the U.S. led the effort to include a provision on “managing for results” in the PD because “reporting on results is critical to demonstrating aid effectiveness and to sustaining public and Congressional support for U.S. assistance” (USAID, 2006).  However, USAID‟s own reporting system “focuses mainly on physical deliverables” such as number of schools or clinics, instead of outcomes (OECD, 2006).

29
— Actual PM2.5 PM2.5 if growth was 50% of observed <!-- Start of picture text --> 3000 ><br>fa)<br>M 2500~<br>i)<br>ive)<br>oO<br>N 2000 -<br>fe)<br>w<br>Cc<br>2<br>= 1500~<br>a<br>&<br>a<br>oO 1000 — _<br>500<br>B Ge) & % C) ><br>Cy WS S 3) S Sv<br>SNX wvv Ssv NZv X\v vyv<br>S s és & & é<br>Fiscal year<br>— Actual GDP GDP with reduced PM2.5<br><!-- End of picture text -->
- 24 -

# **JAPAN**

Japan is accelerating its development aid program and plans to disburse over **_S50_** billion during the period 1988-1992. Development aid is channelled through the Ministry of Foreign Affairs, the Japan International Cooperation Agency, and the Japan Overseas Economic Fund. Health sector aid ranges between 3 and 4 percent of ODA, or about S360 million for 1987. These statistics often exclude large grants to UN organizations, including 1987's S36 million to UNFPA and S18 million to UNICEF.

While Japan supports international global priorities on primary health care, its policy is to respond to official development requests. In this sense, Japan provides one of the best examples of the importance for developing countries to articulate their own health priorities. Since Tokyo does not preallocate funds, trends are not readily identifiable. Currently Japan is cooperating in the support of eight hospitals, including professional and laboratory training. Through its UNFPA contribution (S36 million in 1987), and through separate maternal and child health programs, there is substantial ongoing support for SM components. There is favorable potential for cooperation to provide support for critical areas of hospital-based care for high-risk births.

With 60 percent of all Japanese assistance going to Asia, geographic preferences should be kept in mind for global planning.

|Estimated Financial Flows to Developing Countries<br>|
|---|
|S millions|

||1985|1986|1987|1988|
|---|---|---|---|---|
|Total Health Sector<br>lill|||||
|(mutatera, bilateral,<br>non-governmental)|412|288|360|400|
|Safe Motherhood<br>|||||
|activities|40|30|40|45|

Ministry of Foreign Affairs 2,3 - chome Kasumigasekei Chiyoda-ku Tokyo Ministry of Health and Welfare 1-2-2 Kasumigaseli Chiyoda-ku Tokyo Japan International Cooperation Agency Shinjuku Mitsui Building 2-1-i, Nishi-Shinjuku Shinjuku-ku Tokyo
Figure 7: Bootstrapped GIRFs for a one percent decline in China’s growth using the trade weights of 2013 <!-- Start of picture text --> indns kor mal nzld phlp sing thai<br>.05 .08 .04 .08 .08 .08 .04<br>.00 .04 .00 .06 .06 .04 .00<br>-.05-.10 -.04.00 -.04-.08-.12 .04.02 .04.02.00 -.04-.08.00 -.04-.08-.12<br>-.15-.20 -.08-.12-.16 -.16-.20-.24-.28 -.02.00 -.02-.04-.06 -.12-.16-.20 -.16-.20-.24<br>-.25 -.20 -.32 -.04 -.08 -.24 -.28<br>-.30 -.24 -.36 -.06 -.10 -.28 -.32<br>.20 .02 .04 .06 .02 .01 .04<br>.16.12 -.02-.04.00 -.02.02.00 .04.02.00 -.02.00 -.01-.02.00 -.02.02.00<br>-.04 -.04<br>.08.04 -.06-.08-.10 -.06-.08-.10 -.02-.04-.06 -.06-.08 -.03-.04-.05 -.04-.06-.08<br>.00 -.12 -.12 -.08 -.10 -.06 -.10<br>1 1.0 1.2 1.6 1.0 1.2 1.0<br>0.5 0.8 1.4 0.5 0.8 0.5<br>-0.50.0 0.40.0 1.21.0 0.0 0.40.0 -0.50.0<br>0 -1.0 0.8 -0.5<br>-1.5-2.0 -0.4-0.8 0.60.4 -1.0 -0.4-0.8 -1.0-1.5<br>-2.5 -1.2 0.2 -1.5 -1.2 -2.0<br>-1 -3.0 -1.6 0.0 -2.0 -1.6 -2.5<br>1.2 1.2 .35 .7 .3 .20 .2<br>1.00.8 1.00.80.6 .30.25.20 .6.5.4.3 .2.1 .16.12.08 .1.0<br>0.6 .15 .0 .04<br>0.40.2 0.40.20.0 .10.05.00 -.1.2.1.0 -.1-.2 -.04-.08.00 -.1-.2<br>0.0 -0.2 -.05 -.2 -.3 -.12 -.3<br>y<br>dp<br>eq<br>reer<br><!-- End of picture text --> Note: Please refer to Table A1 in the Appendix for a glossary of acronyms. The lines correspond to the path of median ( _blue_ ), 16th and 84th percentiles ( _red dash_ ) of the distribution. The horizontal axis is now shortened to three years, and two vertical lines correspond to four quarters and eight quarters after the shock, respectively. For Indonesia’s equations, the real equity price is not included due to data unavailability. Thus the corresponding GIRFs are not calculated.

# 10.<sup>16</sup>

The responses of the “oil-only” model appear to overestimate the impact of China’s slowdown for the US, Australia, New Zealand, the Philippines, and Singapore. For example, the response of Australia was significant with the “oil-only” model, which now becomes marginally insignificant with three-commodity model. According to Table 5, the VARX* model of Australia includes all three commodity prices. This suggests the usefulness of adding two commodity prices to the GVAR model. On the other hand, for Indonesia, the

> 16For Argentina and Chile, the VARX* lags selected by the AIC were _p_ = 3, _q_ = 2, and _r_ = 1, or (3,2,1). However, when the GIRFs are calculated, we could not obtain the stable solutions with them. After examining the AICs for different combinations of lags for all the countries, we noticed that the increments of AIC for Argentina and Chile from (3,1,1) to (3,2,1) were almost negligible. Thus we impose (3,1,1) for these two countries. Figure 10 is calculated under these restrictions.

31
well as liquidity hoarding by corporate customers as part of the “dash for cash.” The G-SIB deposit ratios increase from mean and P90 values of 78.18 and 85.31 percent in 2019:Q4 to 81.45 and 88.41 percent in 2020:Q4, respectively.

# **4.2 Additional Statistics That Do Not Depend on Accounting Data**

We evaluate whether bank failures during the crisis may have distorted the accounting data and masked significant problems in the U.S. banking industry. The FDIC reports only four of the over 5,000 U.S. banks failed in 2020, including one failure on February 14 before COVID-19 slowed the U.S. economy. The failed banks had combined assets of just $458 million in an industry with assets over $17 trillion.<sup>17</sup> These very mild failure data provide additional evidence that the banking system was not in significant distress, and also suggest that missing Call Reports from failing banks are not obscuring our view of any substantial industry problems.

We consider as well the possibility that accounting data may generally be less accurate indicators of bank financial distress than market indicators. The KBW Bank Index, which covers stock market values for 24 banking organizations, did fall from 113.4 to 97.9 from December 31, 2019 to December 31, 2020, and has since risen to 134.5 as of this writing on May 17, 2021.<sup>18</sup> Thus, there were some market losses during the calendar year, but the index is currently higher than before COVID-19, not revealing any market value indications of forthcoming financial distress.

Another market-based measure is SRISK for the U.S. financial system, an indicator of systemic risk that is employed in many research studies. It rose from 286 to 562 from December 2019 to December 2020, but then fell to 247 in May 2021.<sup>19</sup> Again, there was some market

> 17 <u>https://www.fdic.gov/bank/historical/bank/bfb2020.html</u>

> 18 - <u>https://www.nasdaq.com/market activity/index/bkx/historical</u>

> 19 <u>https://vlab.stern.nyu.edu/welcome/srisk</u>

25
**Table 5 - Basic results for Development Outcomes . (Instrumental Variables - Share of Tropical Land)** **<u>Human Opportunity Index</u>**

|Regression|Depen|dent Variable: Logaritm|of Per Capita Income,Average 200|5-2010|
|---|---|---|---|---|
||IV|IV|IV ExcludingAmericas|IV|
|Human Opportunity Index*|0.038***|0.050***|0.049**|0.039***|
||(0.01)|(0.01)|(0.02)|(0.01)|
|WDI Gini Index - Average for 1960-2010||0.039*|||
|||(0.02)|||
|Americas - LAC plus Canada and United States||||0.901**|
|||||(0.31)|
|Europe and Central Asia||||0.294<br>|
|||||(0.75)|
|Middle East and Africa||||0.133|
|||||(0.29)|
|constant|6.154***|4.126***|5.704***|5.797***|
||(0.29)|(0.66)|(0.32)|(0.37)|
|Number of Observations|70|68|53|70|
|F-statistics from first stage|11.776|9.648|9.19|6.055|
|**D-Index**|||||
|Regression|Depen|dent Variable: Logaritm|of Per Capita Income,Average 200|5-2010|
||IV|IV|IV ExcludingAmericas|IV|
|D-Index*|-0.067***|-0.084***|-0.075***|-0.083***|
||(0.02)|(0.02)|(0.02)|(0.02)|
|WDI Gini Index - Average for 1960-2010||0.039**|||
|||(0.01)|||
|Americas - LAC plus Canada and United States||||0.992**|
|||||(0.30)|
|Europe and Central Asia||||-0.053|
|||||(0.69)|
|Middle East and Africa||||0.379|
|||||(0.26)|
|constant|8.789***|7.485***|8.791***|8.734***|
||(0.41)|(0.80)|(0.49)|(0.50)|
|Number of Observations|70|68|53|70|
|F-statistics from first stage|18.386|15.004|17.007|8.216|
|* p<0.05, ** p<0.01, *** p<0.001|||||
|<br>*Completing Six Years of Education on Time - HHS|||||
|**Gini Index**|||||
|Regression|Depen|dent Variable: Logaritm|of Per Capita Income,Average 200|5-2010|
||IV|IV|IV ExcludingAmericas|IV|
|Gini*|-0.178***|-0.028|-0.265***|-0.487|
||(0.03)|(0.04)|(0.04)|(0.28)|
|Human Opportunity Index*||0.031***|||
|||(0.01)|||
|Americas - LAC plus Canada and United States||||7.026|
|||||(3.76)|
|Europe and Central Asia||||-1.133|
|||||(177)|
|Middle East and Africa||||.<br>2.467|
|||||(2.17)|
|constant|15.285***|7.576***|18.116***|26.001*|
||(1.12)|(1.65)|(1.62)|(10.52)|
|Number of Observations|142|68|116|142|
|F-statistics from first stage|78.67|6.656|49.503|3.01|
|* p<0.05, ** p<0.01, *** p<0.001|||||
|<br>*Average 1960-2010|||||

24
Policy Research Working Paper 10871

# **Abstract**

Consumption data from household surveys continue to be the main source for poverty and inequality statistics in lowand middle-income countries. Although recent research has demonstrated that the choice of diary- versus recall-based methods for consumption data collection can directly impact poverty measurement, the available evidence stems from small-scale, subnational survey experiments. This study uses data from a nationally representative randomized survey experiment in Tanzania to provide a comparative assessment of how household consumption and poverty measures may be impacted by relying on a 14-day food consumption diary versus two different variants of 7-day-recall-based food consumption data collection. The analysis reveals significant differences in food consumption expenditures across the diary and recall arms, and these differences result in differences in total consumption expenditures as well. The results further show that the diary method captures more diverse food consumption items, but the overall consumption expenditure appears significantly lower than in the recall arms, even at different percentiles. Despite these disparities, the paper finds little statistically significant difference in poverty rates between the diary and recall arms, even at different thresholds.

This paper is a product of 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 aamankwah@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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
with human resource (ability) constraints in implementing this type of budgeting and the underlying accounting systems.

Efforts to strengthen budgetary process are most likely to meet with success in developing countries and in localities that have some underlying (effective) legal and regulatory framework that is supportive of public involvement in governance processes. National, and local, governments that do not have effective laws and regulations allowing for public consultation and monitoring of budgets should consider enacting such legislation. Governments that have already created such a framework should be further encouraged to ensure that other parts of the budget rules and consistent with public participation process. These governments may also wish to consider issuing rules and guidelines for ensuring more effective public consultation in the budgeting development, oversight and monitoring process.

Demand side measures for financial accountability include publicly accessible local government financial information (including budgets and end-of-year financial statements); public involvement in budgetary process through participatory budgeting practices; gender-sensitive planning, budgeting, and resource allocation, reinforced by gender audits; independent budget analysis and participatory public expenditure tracking programs that monitor budget execution and leakage of funds.

Only when citizens attain not only effective involvement in the budget formulation process but in the expenditure management side via more direct auditing, more direct expenditure analysis and control, of financial systems will full public sector budget accountability be achieved. Community (demand-side) participation is necessary, but not an adequate condition, for enabling greater local government accountability. Community based schemes for enhancing local government accountability generally need to combine legal, political, and administrative mechanisms (more effective budgeting, public oversight of local government expenditures, enforcement) with proactive community involvement.

20 | P a g e