rafmacalaba/data-use-mentions-extended huggingface dataset:
in the form of statistically justified confidence intervals. We encourage users to take these into account when comparing governance across countries and over time.

We also note that comparisons of governance across countries and over time will often involve comparing estimates of governance that are based on different sets of underlying data sources. This reflects the reality that not all data sources that feed into the WGI cover all countries, which in turn allows the WGI to rely on many rich data sources with less than global coverage. Comparing estimates of governance for two countries based on differing underlying data sources poses no conceptual problems: for both countries, the conditional mean in Equation (2) is the best possible estimate of governance given the available data for that country, and these estimates can be compared (of course with due regard to margins of error). The same point is true when comparing governance at two points in time for the same country. However, for some purposes users may also wish to consider how comparisons of governance might look based on a common set of data sources, or the extent to which differences in the set of data sources contribute to differences in scores. To enable this further analysis, the WGI website makes all the source data and the weights assigned to different indicators readily available to WGI users at www.govindicators.org.

Finally, we briefly remind the reader that when comparing estimates of governance over time, it is important to bear in mind that the global averages of the indicators have been normalized to zero in every period. This means that changes over time in the WGI estimates of governance capture countries’ movements relative to a global average that is assumed to be constant. Whether these relative changes correspond to absolute changes of course depends on whether the global average of governance is constant or not. We take this issue up in Section 5.3 where we show that there is little evidence of trends over time in global averages of governance as measured by the WGI data sources, implying that the relative changes observed in the
Trends in employment figures are also informative. The ILO<sup>8</sup> offers a modeled estimate (using labor force surveys) of the “vulnerable employment” rate, the share that are self-employed, own-account workers, or contributing family members (ILOSTAT, 2019).<sup>9</sup> Figure 1 reports this measure in three series: for men, women, and overall, respectively. Each series reports vulnerable employment as a share of each category’s total employment (i.e., as a share of female (male) employment). These trends track with two salient breaks affecting Zimbabwe’s economy. First, in 2000, after a contentious election and political fallout—including widespread political violence—the country’s economy entered freefall (Figure 2). Following a series of events over the next several years, including the large withdrawal of international credit, the economy entered hyperinflation. In 2009, the government abandoned its currency and dollarized, which contributed to stabilized prices and was followed by rocky, but far less tumultuous periods of economic performance. Together, both figures show a picture of substantial movement into informal employment after the 2009 dollarization, providing at least a partial pattern of the informal sector as a fallback source of income under dire circumstances. Particularly relevant for Zimbabwe’s poor and vulnerable, coming in between these two periods, was the widespread Operation Murambatsvina in 2005 (literally translated as “trash removal”) program, which involved widespread and forceable clearing of several slums, including in Harare. These forces, collectively, indicate this period as one of displacement and raised insecurity among the country’s poor.

> 8 International Labour Organization.

> 9 Via the World Bank’s _World Development Indicators_ .

(8)
%)<br>2<br>4<br>1<br>2<br>0<br>0 -1<br>-2<br>-2<br>-3<br>Growth in state GDP per capita, 2008Q2 (%)<br>Growth in labor income per capita, 2001Q3 (%)<br><!-- End of picture text --> Notes: Panel A: Each point is the mean growth rate residual or mean Social Security increase residual (share of labor income) of 50 quantiles (controlling for state and time FEs). Quantiles with tiny transfer residuals are not shown. Panels BD: each point in the scatter plot is the growth in per-capita labor income or GDP per capita (GDPPC) (y-axis) in the quarter, plotted against the size of the contemporaneous transfer (x-axis). Sources: BEA, Romer and Romer (2016).

Figure 1. Scatter Plots: Transfers and Economic Growth signicant at the 1% level. This relationship is not driven by a couple of bins: an outlier-robust regression generates a similar slope and t-statistic.

For temporary transfers, the line slope ranges from 0.31.2, depending on the quarter and income variable, and the scatter plots help us to understand the drivers of cross-state variation in transfers. Figure 2 Panel B plots 2008 transfers (share of labor income) against growth, which have a relative multiplier (line slope) of about 0.28 (t-stat = 2.0)remarkably similar to the pooled temporary transfer multiplier estimated later in regressions. The cross-state variation in transfers is striking: The 2008 ESP ranges from around 2.3% of 15
In the remainder, section 2 discusses the data. Section 3 presents evidence of the transmission of monetary policy to loan volumes and other credit terms, and on how this transmission depends on borrower creditworthiness. Section 4 considers the role of banking FDI and other country-level institutional and policy variables in the international transmission of monetary policy. Section 5 concludes.

# **2. Data**

Our cross-border lending data come from the Loan Pricing Corporation’s (LPC) Dealscan database, which contains detailed data on syndicated loans originated all around the world. The database contains information on individual loan volume, pricing, as well as other loan terms and conditions. LPC collects this information from SEC filings and public documents, loan syndicators and other sources. Our data set comprises lenders in 50 countries, and borrowing non-financial firms in 124 countries over the 1995-2015 period. Table A1 in the appendix shows the number of lenders and loans by lender country, while the number of borrowers and loans by borrower country is shown in Table A2.

A feature of the data is that loans are organized by packages and facilities. A package is a loan agreement signed by a borrower and one or more lenders, and each of them may contain one or more facilities. The basic level of observation in Dealscan is a facility. A further characteristic of syndicated loans is that lenders may assume different roles in a deal. Most importantly, lead arrangers are responsible for negotiating the terms with borrowers, and they are also responsible for monitoring borrowers. Several papers provide extensive information about the syndicated loan market as well as LPC’s Dealscan, see e.g. Chava and Roberts (2008).

As a first main independent variable we create Volume, which is the natural logarithm of the dollar amount of a bank’s share in syndicated lending aggregated at the borrower6
Asia, based on published statistics and a review of the empirical literature. The study also reviews the literature that examines actions by governments, the private sector, communities, and households that promote or undermine recommended feeding practices in the region.

Published statistics on the rates for recommended feeding practices are obtained from statistical reports for the latest rounds of relevant national household sample surveys, such as Demographic and Health Surveys. Except Maldives, all countries in the region have statistics on recommended feeding practices from surveys administered in the last five years. The reviewed literature includes articles identified using a search of PubMed, as well as reports, briefs, and other literature sourced from organizational websites and experts.

# **State of child feeding**

The Global Strategy for Infant and Young Child Feeding (WHO and UNICEF 2003) and guiding principles on complementary feeding (PAHO/WHO 2003; WHO 2005) recommend that breastfeeding be initiated within the first hour of birth (known as “early initiation of breastfeeding”), that breastmilk be fed exclusively to an infant for the first six months of life (“exclusive breastfeeding”), and that breastfeeding be continued until the child’s second birthday or beyond (“continued breastfeeding”). The guidelines also recommend that complementary foods be introduced at six months of age (“timely introduction of complementary foods”), that they be fed frequently according to the child’s age (meeting “minimum meal frequency”), and that they include a sufficient number of diverse food groups (meeting “minimum dietary diversity”).

2
# **Table 3 Drivers of fiscal procyclicality**

|**Dependent variable: Cor**|**relation bet**|**ween real g**|**overnment**|**spending a**|**nd real GDP**|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Financial openness|-0.25**||||-0.19|
|Corruption control||-0.12***|||-0.07|
|Political constraint index|||-0.47**||-0.08|
|GDP volatility||||1.58**|1.26|
|_F_-test|||||**|
|Observations|92|74|92|97|72|
|_R_-squared|0.05|0.13|0.05|0.06|0.18|

_Source_ : World Bank.

_Note:_ Cross-sectional OLS regressions for commodity exporters. For regression (5), the F-test evaluates the joint significance of financial openness, corruption control, and GDP volatility. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively.

47
<mark>We evaluate frm performance using three metrics: labor productvity (sales per worker) and, separately, sales and employment. Labor productvity is used due to the unavailability of value-added data</mark> (Bartelsman et al., 2004) <mark>.</mark><sup>6</sup> <mark>We hypothesize that frms with a higher degree of exposure to energy price increases may experience a reducton in sales, are likely to downsize and cut jobs, and could experience a drop in productvity because frm costs may rise faster than revenues, and some supply chain disruptons may reduce frms’ ability to make proft-maximizing decisions. Additonally, we hypothesize that frms operatng in energy-intensive (CPRS) sectors will be disproportonately afected, with the efect of energy prices on their performance further amplifed.</mark> <mark>The baseline regression to assess the efect of energy prices on frm performance is described in equaton (1) where we focus on the energy price efect associated with fuctuatons in electricity prices that we observe at the country level and can beter identfy and use the global prices of oil only as a control variable:</mark> p (1) where pppii,jj,kk,tt = αα∙p is the performance of firm ppkk,tt + ββ∙ccp ccjj + γγ∙p _i_ in sector ppkk,tt ∙ccp _j_ in country ccjj + ρρ∙XX _k_ ii,tt at time + ππjj + θθ _t_ , pkk + μμ is the price of electricity kk,tt + ωωtt + εεii,jj,kk,tt that varies over time at the country level, is the sector-specific classification according to energy ii,jj,kk,tt pp pp usage intensity, is a vector of firm control characteristics, including age and size clusters (micro, small, ccp cc medium, large)<sup>7</sup> as well as the monthly global price of oil, are sector-specific dummies to account
**Table 3. 1  Channels of receiving remittances**

|||Domestic r<br>|emittances<br>||R<br>|emittances f<br>|rom abroad<br>||
|---|---|---|---|---|---|---|---|---|
|Channels|Banked|%|Unbanked|%|Banked|%|Unbanked|%|
|Cash|20,175|28%|32,740|56%|7,067|11%|17,716|43%|
|Remittance Agency|7,337|10%|5,798|10%|28,232|45%|19,199|47%|
|Deposit in Account|25,186|35%|9,244|16%|2,909|5%|0|0%|
|Transfer in Account|18,054|25%|10,821|19%|15,747|25.%|0|0%|
|Other|1,271|2%|0|0%|4,237|7%|3,954|10%|
|Total|72,023|100%|58,603|100%|62,707|100%|40,869|100%|

_Source: DANE household study of Bogotá , 2003_ 17. **The cost of sending and receiving remittances is high.** Receiving hand carried cash remittances is not secure and takes time for the unbanked. In order to have access to more secure alternatives such as deposit and account transfer, the unbanked frequently have to pay an obligation or cost for using someone else’s account<sup>45</sup> . Remittance agencies regularly charge up to 25% of the received amount in fees.
# _2005 Slowdown_

GDP growth in 2005 was projected to be in the range of 6 to 7 percent, although the data for the first half of the year suggest growth of now more than around 4 percent. Year-onyear quarterly growth fell consecutively from 7.7 percent in the last quarter of 2003 to 3.3 percent in the first quarter of 2005, before recovering to 4.4 percent in the second quarter of 2005 (Figure 4.A). A number of idiosyncratic factors play a minor role in this reduction: the tsunami in early 2005, its impact on tourism, and a severe drought which counterbalances the buoyancy of agricultural prices. More important is the rise in oil prices that have fueled the costs of imports. But the key change is the performance of exports whose year-on-year growth dropped close to zero in the first six month, compared to expanding by 8 percent in the first half of 2004. The combination of higher import and lower export growth has contributed to turning the current account negative. This slowdown is linked to lower import growth in China and underlines the three principal risks to Thailand’s growth: rising costs of oil and capital import, a “hard landing” in China and a “disorderly unwinding” of global current account imbalances.

**Figure 4: Expenditure Composition of GDP by Quarter, Q1 2004 to Q2 2005**

## A. <u>Quarterly Real Growth, Year-on-Year</u>

<!-- Start of picture text --> 8<br>7<br>6<br>5<br>4<br>3<br>2<br>01Q3 01Q4 02Q1 02Q2 02Q3 02Q4 03Q1 03Q2 03Q3 03Q4 04Q1 04Q2 04Q3 04Q4 05Q1 05Q2<br>GDP<br>B. Value-Added, 1988 Constant Prices<br>2004Q1 | 2004Q2 | 2004Q3 | 2004Q4 | 2005Q1 | 2005Q2<br>18<br>16<br>14<br>12<br>10<br>8
panel to the administrative geography of India as of the 55<sup>th</sup> round survey in 1999/00. Consequently, the number of regions we have poverty estimates for is 59, with these available for four years, giving a balanced panel of _N_ =236.

The pattern of change in rural poverty over time is shown in Table 1. The headcount index halved over the two decades considered, falling from 50 percent in 1993/94 to 25 percent in 2011/12. The poverty gap index declined even faster, with a fall from 0.123 in 1993/94 to 0.045 in 2011/12. The reductions in poverty rates are spread fairly evenly between the four survey years, but since there is a ten-year gap between the first two surveys and then gaps of four years and two years, it is clear that the pace of poverty reduction is increasing.

> 7 These are the “Tendulkar” poverty lines, after Suresh Tendulkar who headed an expert group that revised India’s official poverty lines. Among several revisions were a smaller rural-urban cost of living differential than the original Planning Commission poverty lines. At 2011 purchasing power parity (PPP) the Tendulkar poverty lines have a value of $2.09 per day.

> 8 The NSSO follows the Census in defining areas as urban, based on criteria in terms of minimum population, minimum population density, and the percentage of the population working outside of agriculture, or whether an area is a Statutory town (e.g., has a Municipality, Corporation, Cantonment Board, etc.).

7
**Figure 15: Trends in EXPY for Togo and WAEMU countries (LHS), and other benchmark countries (RHS), 1980‐2006,.**

<!-- Start of picture text --> p<br>7,000 12000<br>Benin<br>6,000 Burkina Faso 10000 Malaysia<br>Cote D'Ivoire China<br>5,000 Cape Verde 8000 South Africa<br>Ghana India<br>The Gambia 6000<br>4,000<br>Guinea‐Bissau Mauritius<br>Liberia 4000<br>3,000<br>Mali TOGO<br>Niger 2000<br>2,000 Nigeria<br>Senegal 0<br>1,000 Sierra Leone<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006<br>Togo<br>Real US$ of 2000<br>Real US$ of 2000 per capita<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br><!-- End of picture text --> _Source:_ Export diversification data, PRMED, World Bank.

**Given the country’s income level, Togo’s EXPY remains below expectations.** As reported by Hausmann et al (2005) there exist an empirical relation between EXPY and a country’s income level that is explained not only by construction of the indicator. Once a set of controls ‐ as rationalized by the authors ‐ have been accounted for, the income value of Togo’s export basket remain below that predicted by the country’s per capita GDP (Figure 16).

**Figure 16: Residual of EXPY regression after controlling for independent variables suggested by Hausmann et al (2005).**

<!-- Start of picture text --> Measure of Unobserved Factors Affecting EXPY (
exercise essentially seeks to confirm that the MSE ratio for the true case is large relative to the distribution of the MSE ratio among the placebos.

We implement the placebo test by sequentially assigning every potential control state to the placebo treatment and verifying whether it is unusual to find MSE ratios as large as Rajasthan’s when the treatment is assigned at random to other states.

# **3.2 Difference-in-differences analysis with firm-level panel data**

In the main empirical analysis presented in this paper, we use firm-level panel data to conduct a difference-in-differences analysis of the impact of the main amendment, the repeal of IDA Chapter V-B for firms with fewer than 300 workers. Since firms with fewer than 100 workers were never subject to Chapter V-B in the first place, the amendment changed firing cost among firms that had between 100 and 300 workers at baseline (that is, the year the amendment was passed). We consider these firms to be our treatment group, and compare the post-treatment change in their outcome with that of firms employing more than 300 workers at baseline. The latter is an appropriate control group as it continued to be subject to IDA Chapter V-B even after the amendment. Moreover, we compare this differential change in the outcomes of the treated and control groups of firms across Rajasthan and other states (that is, we estimate a triple difference). As contract workers are not technically on the payroll of firms under IDA, we define the threshold based on regular workers alone, in line with Chaurey (2015).

The main difference-in-differences specification is as follows: Here, _yit_ is an outcome of interest in firm _i_ and year _t_ . _Postt_ is a dummy for post-treatment years, _Treati_ is a dummy equal to one if firm _i_ had 100-300 regular workers in the baseline year, and _Rajasthani_ is a dummy equal to one if the firm _i_ is located in Rajasthan. The regression includes year-specific dummies _Y eart_ and firm fixed effects _μi_ . The equation is estimated for firms that had 100 or more workers at baseline
.nancial development itself depends on trade patterns. We argue that ...nancial development is endogenous, and that it will be determined in part by demand for external ...nance in each country. Comparative advantage in trade will a¤ect a country’s production pattern, and in turn its demand for external ...nance. Countries specializing in ...nancially dependent goods will have high demand for external ...nance and thus a high level of ...nancial intermediation. On the other hand, the ...nancial system will be less developed in countries that specialize in goods not requiring external ...nance. In this paper, we ...rst illustrate this point using a very simple model in which goods di¤er in their reliance on external ...nance. Comparative advantage implies that after trade opening, the ...nancially intensive sector expands in one country and disappears in the other. This change in production patterns in turn has implications for equilibrium ...nancial development in the trading countries.

We then demonstrate this e¤ect empirically. For a panel of 96 countries and 30 years, we use industry-level export data and information on each industry’s reliance on external ...nance from Rajan and Zingales (1998) to build a measure of the external ...nance need of exports. This measure, constructed following the methodology of Almeida and Wolfenzon (2005), summarizes the demand 2
18

# <u>Table 2: Health equity: Service coverage (treatment)</u>

|||Indicator|Definition|Main Data Source|
|---|---|---|---|---|
|||Births attended by skilled health<br>staff (% of total)|Percentage of most recent births in last 2 years attended by any skilled health personnel (women<br>age 18-49 at the time of the survey). Definition of_skilled_varies by country and survey but always<br>includes doctor, nurse, midwife and auxiliarymidwife).|DHS, MICS, WHS|
||**_MDG_**|Acute respiratory infections treated<br>(% of children under 5 with cough<br>_and_rapid breathing)|Percentage of children under 5 with cough and rapid breathing in the two weeks preceding the<br>survey (DHS, WHS) who had a consultation with a formal health care provider (excluding<br>pharmacies and visits to ‘other’ health care providers). MICS data points use sample of children<br>under 5 with cough and rapid breathing in the 2 weeks preceding the survey which_originated_<br>_from the chest_. The definition of_formal_health care providers varies by country and data source.|DHS, MICS|
|**_ENT_**||Diarrhea treatment (% of children<br>under 5 with diarrhea who received<br>ORS)|Percentage of children under 5 with diarrhea in the 2 weeks before the survey who were given<br>oral rehydration salts (ORS)|DHS, MICS|
|**_TREATM_**|**_SDG_**|Inpatient care use in last 12 months<br>(% of population age 18 and older)|Percentage of population age 18 and older using inpatient care in the last 12 months|DHS, ECHP,<br>ENAHO,<br>Eurobarometer,<br
: Authors’ estimates. International Labour Organization; Penn World Tables (database); WDI (database); World Bank.

Note: *** indicates significance at the 1 percent level, ** at the 5 percent level, and * at the 10 percent level. Robust standard errors reported in brackets. Country fixed effects and year dummies are included. Columns show the results from regressions of the change in aggregate, women’s, men’s, non-agriculture, and aggregate employment ratios. Sample includes 103 emerging market and developing economies (EMDEs) that are not small states during 2000–19.

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