rafmacalaba/data-use-mentions-extended huggingface dataset:
the underlying value for area B increases. Unless it increases enough to surpass area A the ranking will not change even though the increase in the value of the ranked characteristics may directly affect fertility.

There are two major advantages to this approach. First, the instruments are easy to create from readily available secondary data like a census or, possibly, even from the primary data set itself. Secondly, the instruments are intuitive in that they mimic expectations about the underlying resource allocation process. In other words, ranks likely reflect what policy makers care about when distributing programs, but are not directly related to fertility.<sup>7</sup> Furthermore, the process is agnostic about which characteristics actually determine placement.

The paper makes three contributions to the literature. First, it uses a novel but widely available set of instruments to identify the effect of family planning on fertility. Second, it examines the effect of a family planning program of limited means in a very poor setting that experienced little economic growth during much of the period of study.<sup>8</sup> As seen above, the scant evidence in the literature so far comes from very ambitious, costly programs (Matlab) or dynamic macro-economics settings (Indonesia and Colombia). Yet, it is in more stagnant low-income settings that high fertility often poses the more important obstacle to accelerating development. Third, this study focuses on how the effect of access to family planning is critically dependent on the education level of women.

We find that access to family planning in Ethiopia has a statistically significant and economically large impact on fertility of women with no schooling, while there are no discernible effects of family planning on fertility for women who have ever attended school. The reduction in completed fertility at more than 1 child is large compared to other studies.<sup>9</sup> These insights have important policy implications as 65 percent of women 30 years or older never attended school in Ethiopia (Central Statistical Authority of Ethiopia 2007).
we nonetheless control for a wide array of variables that might correlate with trade policy and affect PTM. We start by adding interactions between the exchange rate and the destination countries’ characteristics such as per capita GDP, GDP, and market potential in columns (2) and (7). In columns (3) and (8) we include interaction terms between the RER variable and a measure of market size varying by product and destination (total imports), as well as a set of standard gravity controls. In columns (4) and (9) we control for interaction terms between the real exchange rate and several product-specific characteristics: dummies for consumer goods (defined according to the Broad Economic Categories classification), manufacturing goods and differentiated goods (defined according to Rauch classification). Finally, in columns (5) and (10) we perform a more extreme exercise and include sets of interactions between the exchange rate and (i) destination group dummies; (ii) origin country dummies; as well as (iii) 2-digit sector dummies. This allows controlling for any time-invariant unobserved destination, origin or product factors that could be correlated with our trade policy variables and affect pricing-to-market.

Overall, our main results are largely unaffected throughout. We find weak evidence that pricing-to-market tends to be less prevalent in more competitive markets, where firms’ market power is lower (column (7)). Regarding product characteristics, when significant, the coefficients on the interaction terms tend to be negative (column (4)), but this result is not robust to the inclusion of firm _×_ product _×_ year FE (column (9)), to alternative currency specifications (online appendix Section C.8), nor to the use of the full sample of countries, i.e without the NTM data restriction. Taken together our results suggest no significant differences across categories of goods.<sup>31</sup> Finally, when including a full set of origin, destination group
develops a methodology that helps analyze these data and identify deficiencies. The examples of Pakistan and Cambodia are used illustratively to showcase how such an analysis could be done and inform the reform process. The foundation of expenditure data comes from the transaction profile. Harvesting and analyzing this provides critical insight into the governing political economy and an opportunity for a more targeted engagement.

# **2.Materials and Methods**

All expenditure transactions that are routed through the FMIS are reflected in the general ledger without exception. Each transaction originates from a spending unit and must be executed against the full chart of accounts, and specify the amount, payee including recipient account number, and the time of transactions. Depending on the detail of the chart of accounts, the transaction may capture source of funds, the organizational code, the purpose of the expenditure (economic classification / line item), in which jurisdiction the transaction happened and for what program or sub/program it was intended. Each line of a typical budget execution report that is produced by the FMIS would be an aggregate of multiple transactions against a particular charge code. The format that the data structure of financial transactions in an FMIS typically takes is given in table 1 below.

_Table 1 Example of Data Capture by Transactions in FMIS_

|**Transaction ID**|**Time**<br>**Stamp**<br>**(date)**|**Source**<br>**of**<br>**Funds**|**Full ch**<br>**Org**<br>**Code**|**art of acco**<br>**Purpose**<br>**Code**<br>**(Line**<br>**Item)**|**unts segment**<br>**Location**<br>**Code**<
measure of services liberalization is multiplied by the proportion αjk of inputs sourced by the manufacturing sector _j_ from the services sector _k_ , as with the services index in equation 1. In this way we create two instruments: (i) pertaining to China's commitments and (ii) pertaining to Indonesia's commitments. Each instrument varies by time, manufacturing industry and services sector. An alternative specification, using instead the commitments of all WTO members yielded similar results (available upon request).

The results from IV regressions are reported in Table 7. As expected, the first stage results indicate that Indian services reform responded to services liberalization in China and Indonesia. The F-statistics suggest that our instruments perform well. The Sargan test does not cast doubt on their validity with the exception of the specification focusing on the transport sector. The second stage confirms our earlier finding that services reforms have improved manufacturing performance. This gives us confidence that reverse causation is not driving our results.

# **_Alternative Measure of Service Reform_**

While the construction of our services reform index was undertaken with great care and confirmed by extensive consultations with sector experts in India, a composite index is by its very nature always prone to measurement imperfections. We therefore wish to check the robustness of our findings to more parsimonious approaches to measuring services reform. Although a “true” measure of policy reform does not exist, it may be possible to identify the key structural break points in policy regimes with greater objectivity than is involved in the construction of a composite index that reflects a judgment of the relative importance of specific reforms. Hence we check the previous findings by using a simpler measure of structural breaks for each services sector.<sup>24</sup> This is done by identifying the year in which a service sector experienced the most transformative policy reform and generating a simple indicator variable that divides years into “before” and “after” this structural break. These policy cornerstones in services sectors are then weighted by the input-output coefficients linking
the HF analysis does not cover the post-2011 period. To focus the analysis on fragility arising from violent political clashes over control of irrigated territory, this section focuses on a narrower definition of conflict which captures only the occurrence of “battles” in ACLED, which is defined as “a violent interaction between two political organized armed groups at a particular time and location”, with a grid-cell being coded as a dummy equal to one if a battle occurred in year . Figure A2 in the appendix plots the spatial distribution of the frequency of battle ii events in the countries of Western Africa, and for the whole African continent, at the 50km x 50 km tt resolution, which is the resolution of the main analysis in section 5.2. Additionally, to measure the share of each grid-cell that is irrigated at the start of the analysis period gridded, gridded data on the average area equipped for irrigation between 1990 and 2005 is taken from the FAO. The construction of this data set is described in Siebert et al (2015). Figure A2 also plots this data at the 1km x 1km resolution at which the raw data is available. To conduct the analysis, this raw data is aggregated to 50km x 50km by taking the average percentage of area irrigated in all smaller cells that fall within a larger grid-cell.

In addition to these main data, the analysis detailed in section 5.2 also employs various other time-invariant grid-cell level characteristics to control for possible confounds of climate and irrigation availability that may also be correlated with conflict. These include: the share of area with cropland and pastureland taken from Ramankutty et al (2008); population density in the year 2000 derived from the Gridded Population of the World data set (GPW v4); the presence of wetlands from the Global Lakes and Wetlands Database (GWLD v3) from WWF; and the presence of ethnic homelands of ethnicities whose historic economic activity is identified as being agricultural (see Michalopoulos and Papaioannou, 2013 for data description).

# 5. Empirical Approach
relationship between remittances and unemployment in the United States, we use unemployment data from the CPS, which comprise a monthly sample survey of 60,000 eligible households conducted by the US Census Bureau for the Bureau of Labor Statistics. We use quarterly (seasonally adjusted) unemployment rates of Hispanics of Mexican origin living in the United States at the county level for the 2013–2020 period. As we explain in the next section, we can estimate the exposure measure by merging this dataset with MCAS.

_D. Municipality’s proximity to the nearest border or airport:_ To measure closeness between each Mexican municipality and the United States, we implement the following approach. First, we obtained addresses for the 50 US–Mexico border crossings and Mexico’s 60 international airports from the Ministry of Foreign Affairs. Second, we used the HERE geolocation API to calculate driving distances (in kilometers) and driving times (in minutes) from the center of every Mexican municipality to each access point.<sup>12</sup> Given that informal remittances are also conveyed through air travel, we used an alternative measure of distance from

> 12See https://developer.here.com/ and Weber and P ́eclat (2017) for details on the methods used.

6
First, it is important to recognize that the ordering of economies above are not one-for-one matches with economic size. Indeed, several large developed economies—such as Germany and France—appear several notches lower than their economic sizes alone would suggest; likewise, several emerging economies, in particular South Korea and Singapore, assume a greater weight than one would expect by GDP alone. Moreover, some regional heavyweights—such as Egypt and South Africa—do not appear in Table 3, likely because of their relatively small economic size (when measured at the global level), coupled with the fact that their growth spillovers are likely contained within their respective regions.

Finally, the uneven distribution of growth polarity is evident: The top two economies typically account for as much as 80 percent of the world total. This result has an interesting factors.

> 12Migration is problematic for three reasons, which justifies our selective inclusion. First, the data are for the stock of migration, as opposed to the flow measures for the other indicators. Second, migration data are currently only available for one year, 2005. Third, as alluded to earlier, migration may capture not only positive spillover effects from a sending nation, but also the possibility of negative shocks in the sending nation, such as war, natural disasters, or economic crises.

14
Table 3. Regression Estimation of the Impact of Women’s Decision-making power on Budget Share

|Theta<br>Ln (# of peple in HH)|Food<br>-0.074<br>[0.056]<br>-0.010***<br>[0.004]|
|---|---|
|Ln (per capita consumption expenditure)|-0.134***<br>[0.005]|
|Observations|5,104|
|**Rural**||
|Theta|0.082|
||[0.082]|
|Ln (# of peple in HH)|-0.001|
||[0.005]|
|Ln (per capita consumption expenditure)|-0.149***<br>[0.009]|
|Observations|2,546|
|**Urban**||
|Theta|-0.195***<br>[0.062]|
|Ln (# of peple in HH)|-0.021***<br>[0.005]|
|Ln (per capita consumption expenditure)|-0.128***<br>[0.005]|
|Observations|2,558|

27
Controlling for characteristics of jobs, including occupational compositions, locations, type of contract and shifts, requirement of qualifications such as years of experience and gender specification, and characteristics of the employing firms, the analysis revealed historical growth trends by occupation and location as well as a unique seasonal patterns of fluctuations in wage offers. Figure 9 presents the stylized trend of real wage offers for all jobs in 20 cities. Taking the index value of 100 for the real wage of January 2011, the wage offers grew steadily over time. However, the wage offers are subject to a consistent pattern of seasonal fluctuations every year. The wage offers tend to grow towards summer months, such as July and August, and tend to face a sudden fall around in October and November. The trend of wage offers is considered to reflect the demand and supply of skills. Considering the fact that Babajob provides many entry-level job opportunities for fresh graduates, this annual cycle shows a correlational pattern with the timing students’ graduation and of job hunting, as well as with changes in skills demand associated with seasonal events.

**Figure 9: Stylized Trend of Real Wage Offer Index (January 2011=100), 2011-2015**

<!-- Start of picture text --> 150<br>139.3 140.6<br>140<br>130<br>117.3<br>120<br>110 100.0 115.0 113.0<br>108.2<br>100<br>101.6<br>90 93.6<br>80<br>1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11<br>2011 2012 2013 2014 2015<br>2011 2012 2013 2014 2015 Linear ()<br><!-- End of picture text --> Source: Authors’ analysis using Babajob database Note: The
outcomes.<sup>32</sup> At the same time, data provided by countries remain an integral part of monitoring the SDGs, thus further statistical and analytical capacity activities, particularly for low-income countries, should perhaps receive more attention.

# **V. Conclusion**

We offer in this paper a review of various challenges regarding identification and measurement methods related to the SDGs. We place an emphasis on the data angle, and we focus on poorer countries. Our findings point to the need to further refine the SDG indicators in terms of their wordings—while we acknowledge that it can be a difficult process to make (even) minor changes to indicators—as well as to clarify their underlying objectives. We also bring attention to potential pitfalls with interpretation of progress on the SDGs, where different evaluation methods can lead to different conclusions. One particularly demanding challenge is the severe shortage of data for tracking progress across countries and over time.

We also propose relatively simple solutions to identify and interpret progress. We propose a three-step process to measure progress on the SDGs, with each subsequent step offering more granularity than the previous one. In particular, this process can well consist of tracking an overall index, some major groups, and then all the SDG indicators. We also consider imputation-based statistical methods to be cost-effective alternatives to addressing the missing data challenge. Furthermore, we view international organizations as playing a most relevant role in producing and curating data to track progress on the SDGs, which should be implemented in close collaboration

> 32 For example, the OECD produces an annual life index that aims to go beyond GDP figures (OECD, 2017). See also the recent annual world happiness report by Helliwell et al. (2018).

34
# **Table S1.1.** Continued.

|**Dependant variable:**||**A**|**gricultural production**|||
|---|---|---|---|---|---|
||**Caste**|**Caste & occup**|**Jati & occup**|**WHEAT**|**RICE**|
|**Village characteristics**||||||
|Village population (in log)|0.084<sup>*</sup>|0.090<sup>** </sup>|0.093<sup>** </sup>|0.11<sup>** </sup>|0.11<sup>*** </sup>|
||(0.044)|(0.044)|(0.044)|(0.051)|(0.036)|
|Village land|−0.000040<sup>*</sup>|−0.000039<sup>*</sup>|−0.000044<sup>*</sup>|−0.000081<sup>*</sup>|−0.000070|
||(0.000022)|(0.000022)|(0.000026)|(0.000042)|(0.000056)|
|Observations|4405|4405|3333|1165|1362|
|_R_<sup>2</sup>|0.819|0.819|0.829|0.846|0.848|

_Source_ : Author’s analysis based on the 2006 round of the Additional Rural Incomes Survey and Rural Economic & Demographic Survey (ARIS-REDS) from the National Council of
18 with a corresponding increase in the share of the urban population (this is the trend observed over the last five years). Using the fact that the overall population for Bangladesh is expected to grow by 1.5 percent per year over the next four years, and by 1.2 percent thereafter, we compute accordingly the growth in per capita GDP over time in the two sectors. What is still missing is an estimate of the change in the average propensity to consume, for which we need to use the World Bank's RMSM-X model.

From the RMSM-X model, given the limited availability of foreign financing, we find that private consumption as a share of GDP is expected to decline nationally by four percentage points to help finance investments (in the model, aggregate savings are measured residually as the difference between aggregate gross investments and the current account deficit). For simplicity, we assume that this drop in average propensity to consume affects all households in a similar way, in both urban and rural areas. Using the elasticities of poverty to growth computed with household data in the previous section, one finds the poverty forecasts given in Table 7 by sector and nationally using sectoral population shares (Table 7 uses the upper poverty lines; the trends obtained with the lower poverty lines are similar).

According to Table 7, poverty will decrease in both urban and rural areas, but more so in urban areas due to higher growth there (which more than compensates for higher urban population growth and rising inequality). Nationally, the headcount index with the upper poverty lines would be in 2008 at 29.05 percent, versus 53.08 percent in 1995-96. The reductions in the poverty gap and squared poverty gaps are even larger (proportionately to their 1995-96 level), indicating that growth would not leave the worst off behind even though inequality may be expected to rise over time (at least in urban areas).

The assumptions outlined above, as well as many others which have not been mentionedl<sup>
- _Debt issue size:_ The debt issue size captures the importance of the issuer in its market and its access to a broad range of financing sources. For the same borrower, though, a larger debt may carry higher risk and hence result in a lower rating. The average logarithm of issue size is 18.9, about 163 million in constant 2010 US dollars.

- _Maturity:_ Bonds with longer maturity may receive a lower rating than shorter maturity debt as long-term bonds accompanies larger uncertainty than short-term bonds. Average maturity is about 7 years in the sample.

- _Fixed vs. floating rate:_ Fixed rate bonds typically carry a higher yield than floating rate bonds to compensate the bond-holder for holding the debt to maturity at a given interest rate. Fixed rate notes account for the bulk (82 percent) of sub-sovereign bonds.

- _Callable bond:_ Callable bonds allow the issuer to prepay the principal prior to the maturity of the bond. About 25 percent of bonds are callable.

- _Collateralization and securitization:_ Bonds backed by collateral tend to have lower credit risk, which can potentially result in a weaker relationship with sovereign risk. About 5 percent of bonds are securitized by future-flow receivables. A further 3 percent of debt in is issued by Special Purpose Vehicles (SPVs), typically backed by an existing asset. Bonds backed by other types of collateral, other than future-flow and SPV structures, constitute another 3 percent of the total.

# _Firm balance-sheet variables:_

To test the robustness of our main results obtained from bond-level data to inclusion of balance sheet variables (see section 5), the data on international bond issuance was matched with firmlevel balance sheet data from Bloomberg based on International Securities Identification Numbers (ISINs). The inclusion of balance-sheet data reduces the sample size to a subset of bonds for which such matched data are available. The balance sheet variables include the logarithm of total assets (in thousands of constant 2010 US dollars), net profit as a percent of assets, financial leverage
- 29 Bradley VC, Kuriwaki S, Isakov M, Sejdinovic D, Meng X-L, Flaxman S. Unrepresentative big surveys significantly overestimated US vaccine uptake. _Nature_ 2021; **600** : 695–700.

- 30 Bloland P, MacNeil A. Defining & assessing the quality, usability, and utilization of immunization data. _BMC Public Health_ 2019; **19** : 380.

- 31 Sandefur J, Glassman A. The Political Economy of Bad Data: Evidence from African Survey and Administrative Statistics. _J Dev Stud_ 2015; **51** : 116–32.

14
Policy Research Working Paper 10306

# **Abstract**

Understanding how e-commerce platforms are affecting the small, informal firms that sell on them is a question of growing importance to researchers and policy makers in developing countries. This paper examines this question using data from surveys of firms selling on two e-commerce platforms in South Asia. The businesses selling on these platforms range widely in terms of size, degree of formalization, and other characteristics. However, these firms—even the micro and small ones, which tend to be informal— are from a selected group, being owned and managed by individuals who are more educated and younger than the owners and managers of more typical firms in this setting. The sellers’ main reason for joining the platforms is to access more customers. Most of the sellers report an expansion of their business after joining the platforms. They also report an increase in their incentive to register their business and their visibility to tax authorities. Other, less widespread channels of impact reported by the firms include the adoption of new or improved business practices and technologies, better access to finance, and greater flexibility in balancing home and work life. In general, these reported impacts do not vary significantly by firm size or degree of formalization, suggesting that even informal, small firms that have (selectively) joined e-commerce platforms can benefit from the greater market access facilitated by the platforms. Finally, given size and age, firms that have been selling on the platform for a longer period are more likely to experience these impacts, suggesting that firms learn how to use the platform more effectively over time.

This paper is a product of the Office of the Chief Economist, South Asia Region. 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 mbussolo@worldbank.