rafmacalaba/datause-encoder-data huggingface dataset:
The most direct measure of the quality of country PFM systems, and the one with the most comprehensive data coverage, is question 13, titled “Quality of Budgetary and Financial Management,” from the World Bank’s Country Policy and Institutional Assessments (CPIA). For brevity we re-name this variable “PFM Quality.” Higher ratings reflect a comprehensive and credible budget linked to policy priorities, effective financial management systems to ensure that the budget is implemented as intended, and timely and accurate accounting and fiscal reporting. In a robustness test, we use a second CPIA indicator (question 16), titled “Transparency, Accountability and Corruption in the Public Sector.” For brevity, we name it “Transparency.” It assesses the extent to which the executive can be held accountable for its use of funds and the results of its actions by the electorate and by the legislature and judiciary, and the extent to which public employees within the executive are required to account for the use of resources, administrative decisions, and results obtained. Weaker country systems as measured by lower scores on PFM Quality and Transparency reflect increased risks to donors of corruption scandals, diversion of funds to lower priority uses, or inability to account for how funds were spent. The Paris Declaration explicitly asserts that corruption “inhibits donors from relying on partner country systems.” We therefore expect coefficients to be positive for both PFM Quality and Transparency, in our use-of-country-systems regressions.

The CPIA indicators are produced annually by World Bank staff for aid allocation purposes, for approximately 135 developing countries. Assessments are on a 1 to 6 scale, including half-point increments.<sup>5</sup> We use a one-year lag of the CPIA ratings in our tests, to reduce the potential for capturing reverse causality. In our main sample PFM Quality ranges from 2 to 5.5, with a mean of about 3.4. The mean value of Transparency is about 3.0, with a

> 5 For example, if a country meets some of the criteria for a rating of 3, but also meets some of the criteria for a rating of 4, it will be assigned a rating of 3.5.

9
# **Development, Modernization, and Son Preference in Fertility Decisions**<sup>♦</sup>

Deon Filmer Jed Friedman Norbert Schady Development Research Group The World Bank

> ♦ We thank Monica Das Gupta, Peter Lanjouw, Cynthia Lloyd and T. Paul Schultz for valuable comments and suggestions, and Ryan Booth and Nicholas Ingwersen for outstanding research assistance. We are grateful for financial support from the Hewlett Foundation's Trust Fund on "Fertility, Reproductive Health and Socioeconomic Outcomes (TF058184).
Figure 2 illustrates WDI export indicators for MENA and non-MENA countries in 2010, by economy size. The logistics performance index scores a country on a range of aspects related to trade: customs, trade related infrastructure, price, tracking, etc. (Panel A). With regard to this indicator, most MENA countries underperform or perform near the expected level given in their level of GDP per capita. Inefficiencies in exporting in MENA are more apparent in terms of the number of documents necessary to export (Panel C), while costs to export in MENA are low for the majority of countries, except Iraq.

|Table1.EDDSumm|ary Statistics (2005-2010)<br>||
|---|---|---|
||Non-MENA|MENA|
|_Country-level averages_|||
|Average number of exporters|13,258|5,552|
|Mean Value per Exporter (USD2000)|$2,413,509|$1,321,472|
|Median Value per Exporter (USD2000)|$40,625|$59,289|
|Mean # of products per exporter|6.6|5.3|
|Mean #ofdestinations perexporter|2.93|2.6|
|N(country)|26|6|

_Notes: Unweighted averages. Low-income countries are excluded. Averaged at the country-year level Sources: Exporter Dynamics Database_

# **2.2. FINANCIAL SECTOR DEVELOPMENT**

The financial sector in the MENA region is characterized as undiversified, uncompetitive, underexposed, and concentrated (Al-Ississ, 2012). The region exhibits low levels of financial inclusion and the world’s highest loan concentration ratio, reflecting a bias towards lending to large established firms and excluding young and small firms (World Bank, 2011). The financial sector is comprised largely of traditional banking institutions with a low presence of non-bank financial institutions; the insurance sector is small, leasing, factoring and other methods of external financing—i.e., funding sourced outside the firm—are limited. Figure 3 illustrates GCI’s Financial Market Indicator score for all countries by income. It shows that the majority of MENA countries have lower scores than expected given the level of GDP per capita. Above average performers are Bahrain and Jordan, neither one a large high-income country nor ones characterized by significant Islamic financing activity.

The state of collateralization in the MENA region provides some insight as to why the percent of firms with loans or lines of credit is so low. Collateral lending in MENA is not common and when it does occur, it is heavily based on immovable (physical or tangible) assets such as land and buildings. However, in the developing world, immovable assets comprise 22 percent of capital stock (Fleisig Safavian, and De La Pena, 2006). In some areas of MENA, lending is also influenced by practices of Islamic financing, much closer in spirit to relationship banking rather than the armslength market-based financing having greater presence in more developed countries. In Islamic financing cases, lending becomes a risk-sharing venture between the bank and firm; hence lending is more likely to be approved on the basis of personal acquaintance of borrowers (Alvarez, 2011). Islamic financing includes principles such as no interest rates, which turns lending into a risk6
# Supplementary information

Supplementary Table 1. Directory of identifiers (present in all datasets)

|**Variable name**|**Label**|**Data processing notes**|**Additional notes**|
|---|---|---|---|
|_country_|Country name|||
|_wave_|Wave number|||
|_household_id_obs_|Household ID<br>(panel<br>identificator)|Variable created by assigning<br>a numeric code to each<br>unique household. The first<br>number of the code<br>determines the country.||
|_household_id _merge_|Household ID<br>(to merge with<br>raw data)|ID to merge with raw LSMS<br>data|Can be merged with the following<br>variables in the raw data<br>(concatenated variables are separated<br>by hyphens):<br>ETH:_household_id_(wave 1)_,_<br>_household_id2_(wave 2)_,_<br>_household_id2_(wave 3)_,_<br>_household_id_(wave 4)_, household_id_<br>(wave 5)<br>MWI:_case_id_(wave 1),_y2_hhid_<br>(wave 2)_, y3_hhid_(wave 3)_, y4_hhid_<br>(wave 4)<br>MLI: concatenation of_grappe_and<br>_menage_(wave 1), concatenation of<br>_grappe_and_exploitation_(wave 2)<br>NER:_hid_(wave 1)_,_concatenation of<br>_GRAPPE, MENAGE_and<br>_EXTENSION_(wave 2)<br>NGA:_hhid_(wave 1)_, hhid_(wave 2)_,_<br>_hhid_(wave 3)_, hhid_(wave 4)<br>TZA:_hhid_(wave 1)_, y2_hhid_(wave<br>2)_, y3_hhid_(wave 3)_, y4_hhid_(wave<br>4)_, sdd_hid_(wave 5)<br>UGA:_Hhid_(wave 1)_, HHID_(wave<br>2)_, HHID_(wave 3)_, HHID_(wave 4)_,_<br>_HHID_(wave 5)_, hhid_(wave 7)_, hhid_<br>(wave 8)|
|_season_|Season ID<br>(UGA)|Variable created to<br>distinguish between seasons<br>in UGA, which has a bimodal<br>seasonal pattern, and<br>households are therefore<br>observed twice within each<br>wave|<br>This variable is set to 1 by default in<br>all other countries.|
|_ea_id_obs_|EA ID|Variable created by assigning<br>a numeric code to each<br>unique EA. The first number<br>of the code determines the<br>country.|Longitudinal ID to track units<br>through time|
|_ea_id_merge_||ID to merge with raw LSMS<br>data|Can be merged with the following<br>variables:|

21
|P143841|2013|2019|9/7/2018|11 Active|Afghanistan|
|---|---|---|---|---|---|
|P143841|2013|2019|4/15/2019|12 Active|Afghanistan|
|P143841|2013|2020|9/23/2019|13 Active|Afghanistan|
|P143841|2013|2020|5/14/2020|14 Active|Afghanistan|
|P143842|2015|2016|12/3/2015|1 Closed|Nigeria|
|P143842|2015|2016|6/28/2016|2 Closed|Nigeria|
|P143842|2015|2017|12/30/2016|3 Closed|Nigeria|
|P143842|2015|2017|6/23/2017|4 Closed|Nigeria|
|P143842|2015|2018|12/30/2017|5 Closed|Nigeria|
|P143842|2015|2018|6/25/2018|6 Closed|Nigeria|
|P143842|2015|2019|1/7/2019|7 Closed|Nigeria|
|P143842|2015|2019|6/28/2019|8 Closed|Nigeria|
|P143842|2015|2020|12/31/2019|9 Closed|Nigeria|
|P143842|2015|2021|7/5/2020|10 Closed|Nigeria|
|P143843|2014|2014|6/14/2014|1 Closed|Togo|
|P143843|2014|2015|12/11/2014|2 Closed|Togo|
|P143843|2014|2015|6/1/2015|3 Closed|Togo|
|P143843|2014|2016|12/22/2015|4 Closed|Togo|
|P143843|2014|2016|6/29/2016|5 Closed|Togo|
|P143843|2014|2017|12/28/2016|6 Closed|Togo|
|P143843|2014|2017|6/27/2017|7 Closed|Togo|
|P143843|2014|2018|12/27/2017|8 Closed|Togo|
|P143843|2014|2018|4/9/2018|9 Closed|Togo|
|P143843|2014|2019|10/22/2018|10 Closed|Togo|
|P143843|2014|2019|12/31/2018|11 Closed|Togo|
|P143843|2014|2019|6/29/2019|12 Closed|Togo|
|P143843|2014|2021|7/21/2020|13 Closed|Togo|
|P143844|2014|2015|11/7/2014|1 Active|Bosnia and Herzegovin|
|P143844|2014|2015|4/28/2015<br>|2 Active|Bosnia and Herzegovin|
|P143844|2014|2016|10/31/2015|3 Active|Bosnia and Herzegovin|
|P143844|2014|2016|4/19/2016|4 Active|Bosnia and Herzegovin|
|P143844|2014|2017|10/14/2016|5 Active|Bosnia and Herzegovin|
|P143844|2014|2017|4/7/2017|6 Active|Bosnia and Herzegovin|
|P143844|2014|2018|12/15/2017|7 Active|Bosnia and Herzegovin|
|P143844|2014|2018|6/29/2018|8 Active|Bosnia and Herzegovin|
|P143844|2014|2019|12/21/2018|9 Active|Bosnia and Herzegovin|
|P143844|2014|2019|6/25/2019|10 Active|Bosnia and Herzegovin|
|P143844|2014|2020|12/20/2019|11 Active|Bosnia and Herzegovin|
|P143844|2014|2020|6/12/2020|12 Active|Bosnia and Herzegovin|
|P143849|2014|2014|4/5/2014|1 Closed|Congo,Republic of|
|P143849|2014|2015|9/29/2014|2 Closed|Congo,Republic of|
|P143849|2014|2015|4/22/2015|3 Closed|Congo,Republic of|
|P143849|2014|2016|8/17/2015|4 Closed|Congo,Republic of|
|P143849|2014|2016|2/29/2016|5 Closed|Congo,Republic of|
|P143849|2014|2017|9/6/2016|6 Closed|Congo,Republic of|
|P143849|2014|2017|3/16/2017|7 Closed|Congo,Republic of|
|P143849|2014|2018|9/12/2017|8 Closed|Congo,Republic of|
Table 6. Random effects logit model Dependent variable: Crisis indicator (1 = crisis, 0 = no crisis)

||Debt crisis|Banking crisis|Currency crisis|
|---|---|---|---|
|Change in U.S. real interest rate|-0.067<br>(0.132)|0.015<br>(0.106)|0.253**<br>(0.100)|
|GDP growth|-0.095***<br>(0.025)|-0.020<br>(0.025)|-0.006<br>(0.020)|
|Short-term debt|0.026*<br>(0.015)|0.012<br>(0.012)|0.006<br>(0.011)|
|Debt service|0.028***|0.029***|0.010|
||(0.009)|(0.007)|(0.008)|
|Reserves cover|-0.573***<br>(0.116)|-0.163***<br>(0.063)|-0.115*<br>(0.062)|
|Change in government debt|0.014*<br>(0.008)||0.016**<br>(0.007)|
|Change in private debt||0.055**<br>(0.023)|0.052**<br>(0.026)|
|Change in government debt<br>x Change in private debt|||0.003***<br>(0.001)|
|Concessional debt|-0.033***<br>(0.009)|||
|Funding ratio||0.002*<br>(0.001)||
|Currency overvaluation|||0.165***<br>(0.015)|
|Currency mismatch|||0.014<br>(0.033)|
|FDI|||-0.101**<br>(0.046)|
|Constant|-2.678***|-4.161***|-3.617***|
||(0.616)|(0.371)|(0.395)|
|No. of observations|3,089|2,797|2,395|
|No. of countries|106|106|99|

Note: ***, **, * denote statistical significance at the 1 percent, 5 percent, 10 percent level respectively. Standard errors are in parentheses.

29
migration (Helliwell 1997; and Poncet 2006). Helliwell (1997), for example, suggests that inter-provincial migration in Canadian provinces is almost 100 times more likely than migration to Canadian provinces from the United States. But these studies explore the role of international borders rather than the internal ones.

Migration costs naturally impede internal migration flows in a country. Bayer and Juessen (2012) suggest that inter-state migration in the U.S. can cost a potential migrant up to two-thirds of an average household annual income. In her study of internal migration in China, Poncet (2006) suggests migration flows between two localities are negatively related to distance but positively related to contiguity (as well as with wage levels at the destination). More relevant to our paper, they too find that there is more intra-province migration in comparison to inter-province migration.

The next section of the paper presents the internal migration data, the geographic and linguistic distance variables as well as several empirical observations that motivate the analysis. Section 3 introduces the gravity model and our empirical specification, followed by empirical results in Section 4. We then discuss the results in Section 5 and end with the conclusions.

# **2 Data**

## **2.1 Data source and empirical observations**

The National Census of India for 2001 is the main data source in this paper.<sup>6</sup> The census has been conducted every decade since 1871 and is the responsibility of the Office of the Registrar General and Census Commissioner in the Ministry of Home Affairs. The national census, like those in many other countries, collects individual and household level information on various demographic and labor market characteristics for the entire population. We supplement the census with additional household and labor force data from the 55th Round (1999-2000) and the 61st Round (2004-2005)

> 6As of the date of drafting of this paper, the migration related sections of the 2011 Census have not been processed.

6
* iii -

Page No.

# JULES (continued)

|Table 4- Estimates of the Parameters of the Cobb-Douglas<br>Production Function, with the without Real Money||
|---|---|
|Balances, Corrected for Autocorrelation, for the||
|Years 1958-1984. (Capital is Adjusted for Capacity<br>Utilization and Labor is Adjusted for Change in Sex||
|Composition and Education) . . . . . . . . . . . .|24|
|Table 5- Data for Colombia 1958-1984 . . . . . . . . . . . . .|25|
|Table 6- Natural Logarithmic Transformation<br>of the Variables .|26|
|Table 7- GDP of Colombia 1958-1984 . . . i. . . . . . . . . .|27|
|Table 8- Capital Stock for Colombia 1958-1984 . . . . . . . .|28|
|Table 9- Labor Force in Colombia 1958-1984 . . . . . . . . . .|29|
|Table 10- Monetary Balances in Colombia 1958-1984 . . . . . . .|30|
|Table11- Real Monetary Balances for Colombia 1958-1984 . .<br>.<br>.|31|
|Table 12- Indexed Real Money Balances for Colombia 1958-1984 .|32|
|Table 13- Regression with No Money Variable . . . . . . . . . .|33|
|Table 14- Data Used for Using Solow's Approach to Find||
|Components of Growth . . . . . . . . . . . . . . .|34|
|Table 15- Data Used for Predicting the Values . . . . . . . . .|36|
|Table 16- Ratio of M2 (Broadly Defined Money) Over GDP<br>. .<br>.<br>.|37|
|Table 17- Regression Results of Financial Deepening Over Time .|38|
|Table 18- Inflation Tax on Money Balances as a Share of Income.|39|

# FIGURE

- Figure 1 Inflation Tax on Monetary Balances as a Share of Income . . . . . . . . . . . . . . . . . . . . . 40 BIBLIOGRAPHY ......................... . 41
Flpre 5: Farm Size-Efficiency in the Vassharts Irriptin Area, 1990/91 <!-- Start of picture text -->
1.7<br>1.5<br>1.4<br>31.3<br>~1.2  ..<br>3g1.1-  C  *  *+  ..<br>g  I  C  *C  C  S*<br>0.9<br>0.9  1*----  1  I  I  I  I<br>0  10  20  30  40  50  60  70  80  90  100<br>Quality Adjusted Farm  Size  (ha)<br><!-- End of picture text -->

Figure 6: Managerial Ability and Farm Size, Vaalharls (1990/91) <!-- Start of picture text --> 100<br>- 90- C<br>e 80g<br>. 70<br>* *<br>60 * **<br>50 C *<br>= 40<br>30 I I I I I<br>0 10 20 30 40 50 60 70 80 90 100<br>Quality Adjusted Farm Size (ha)<br><!-- End of picture text --> Figure 6 establishes a strong relationship between scale efficiency and managerial ability. For example, managers with better managerial ability are generally more scale efficient. This rdationship is also statisticall highly significant: the correlation coefficient between farm size and invese scale efficiency is -0.334 (rO.062); 35
countries, respectively, in contrast to 71 percent in high-income countries and 80 percent in lower-middleincome countries.

**Figure 7. Countries adopting good regulatory practices on e-commerce/e-transactions by income group (in percentages)**

<!-- Start of picture text --> High income Upper middle income<br>Lower middle income Low income<br>Existence of e-<br>commerce law<br>100%<br>80%<br>60%<br>Legal equivalence of<br>Digital ID system to<br>40% paper based and<br>access e-government<br>electronic<br>services 20%<br>communications<br>0%<br>Technological<br>Legal recognition of<br>neutrality of electronic<br>electronic signatures<br>communications<br><!-- End of picture text --> _Source:_ Author’s calculation based on results from the diagnostic.

Digital authentication allows users to be verified, uniquely and securely, through digital technologies or biometrics.<sup>14</sup> Digital IDs allow users to effectively access sensitive applications, data, and e-services, which enhances equality and inclusion.<sup>15</sup> A digital ID system to access e-government services is found in 30 percent of lower-middle-income countries and only 6 percent of countries in the low-income group; the system is adopted by 93 percent and 62 percent of high- and upper-middle-income countries, respectively. Singapore’s SingPass is a well-functioning digital ID system.<sup>16</sup> Users can access SingPass on their smartphones, logging in through biometric data, using their digital identity to sign e-documents or access more than 200 e-government services, such as tax or property information.<sup>17</sup> Instruments like SingPass allow citizens to interact easily with government services and save on administrative costs and fees.<sup>18</sup>

> 14 See World Bank, _Identity for Development_ (ID4D), glossary, available at: https://id4d.worldbank.org/guide/glossary.

> 15 See, generally, World Bank, _Identity for Development_ (ID4D), available at: https://id4d.worldbank.org/global-dataset; and Sustainable Development Goals (SDGs), available at: https://sustainabledevelopment.un.org/sdgs. SDG 16.9 provides that, “By 2030, provide legal identity for all, including birth registration.”

> 16 The SingPass portal can be accessed at: https://www.singpass.gov.sg/.

> 17 For further information, see also: https://www.tech.gov.sg/products-and-services/singpass/.

> 18 For information about the benefits of SingPass, see also: https://www.smartnation.gov.sg/docs/default-source/press-releasematerials/factsheet---national-digital-identity.pdf?sfvrsn=ea1d279f_2.

20
Policy Research Working Paper 9607

# **Abstract**

The COVID-19 pandemic has highlighted the centrality of primary care in protecting people’s health and well-being during and beyond crises. It has also provided an opportunity to strengthen and redesign primary care so that it will better serve its purpose. However, to-date there is limited evidence on the quality of service delivery in primary care. Service Delivery Indicators surveys have attempted to fill this gap. Using Service Delivery Indicators surveys of 7,810 health facilities and 66,151 health care providers in nine Sub-Saharan African countries, this paper investigates the quality of care across five domains to understand a citizen’s experience of primary care in his/her country. The results indicate substantial heterogeneity in the quality of primary care service delivery between and within countries. The availability of basic equipment, infrastructure, and essential medicines varies—public facilities, facilities in rural areas, and non-hospitals are more lacking compared with private facilities, urban facilities, and hospitals. In terms of patient care, health care providers’ ability to correctly diagnose and treat common health conditions is low and variably distributed. COVID-19 has catalyzed a long overdue health system redesign effort, and the Service Delivery Indicators surveys offer an opportunity to examine carefully the quality of service delivery, with an eye toward health system reform.

This paper is a product of the Human Development Global Practice. 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 sdi@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
from 3 to 4 in a country corresponds to 0.7 percent reduction in _ad valorem_ transport cost. An upgrade of the _infrastructure index_ , consisting of paved road, railroads and telephone lines also reduces the ocean transport cost, while the index is a proxy of the port infrastructure.

# _Ad Valorem Transport Costs in East Asia_

The conclusion of this section outlines international transport costs in East Asia drawing on data from the United States and Japan since 2000. As noted above, U.S. official statistics report import charges aggregated at the detailed HS commodity classification. In addition, Japan, another major importer for the developing countries in East Asia, publishes official Balance of Payment (BOP) Statistics which include import amount on the FOB base<sup>16</sup> . Subtracting the FOB import in the BOP statistics from the CIF import in the customs statistics gives the estimate of transport cost of Japan.

The authorities in Japan, Ministry of Finance and the Bank of Japan, publish the data disaggregated by the exporting partners, but not in commodity subdivision. In the compilation of the official statistics, the authorities in Japan estimate the freight and insurance cost for the import from each country first, and then calculates the FOB imports by subtracting it from the reported customs values. With the ministerial ordinance, Japanese sea transport enterprises must report their revenues to the authorities, including import sea freight fare from the importers in Japan. Dividing the total amount of freight fare by the share of import sea cargo carried by the Japanese enterprises in the official maritime statistics, the authorities estimate the total amount of freight costs. This calculation is made on the exporter country-specific and modal-specific (liners, tramps and tankers) base, adding them up to country specific freight payments in total (Bank of Japan (2005)).

Table 3 summarizes the _ad valorem_ ratio of import charges over the amount of imports from selected East Asian countries in the United States and Japan, averaged for 1996-2000 and 2001-

> 16 Japan is one of the few countries which publish the FOB base import data in conformity with the _Balance of Payments Manual_ of the IMF.

16