rafmacalaba/datause-encoder-data huggingface dataset:
||||_Annual_<br>_Import_|_Gains from_<br>_Variety_|
|---|---|---|---|---|
|_Year_|_Bias_|(<br>1<br>_bias _<sup>_−_1)</sup> <sup>_×_ 100</sup>|_(% of GDP)_|_(% of GDP)_|
|2018|0.984|1.67|26%|0.42|
|2019|1.006|-0.55|26%|-0.14|
|2020|0.997|0.29|25%|0.07|
|2021|0.998|0.20|26%|0.05|
|**Average per-annum**|**0.983**|**3.69**|**21%**|**0.43**|
|**Total (1995-2021)**||**99.51**||**11.63**|

_Note:_ Authors’ calculation based on six-digit disaggregated data. See text for detailed explanation.

In column 1 of Tables 4 to 6, the ratio of the aggregate exact price index including variety and the aggregate conventional price index is reported as the yearly _bias_ measure as in equation (10). It is worth explaining the intuition behind this bias. If this fraction is lower than one, it means that the changing set of imported varieties has _lowered_ the import price index. In that case, the consumers benefit from lower unit costs of imports. Thus, these lower costs are the source of the welfare gains. On the other hand, if the bias is larger than one, this means that the import price index is _increased_ by the changing variety set. Thus, the disappearing varieties are more valuable to the consumers than the new varieties and it results in welfare loss.

Column 1 in Tables 4 to 6 shows that in most years, the bias is lower than one, meaning the variety change resulted in a lower import price index. On average, the bias measure is 0.970 in Bhutan, 0.986 in Mongolia and 0.983 in Rwanda, which means that ignoring new and disappearing product varieties in the conventional price index had led to an upward bias. We would like to show by how much this import price inflation is overstated yearly. To <u>1</u> show that, we calculated ( _bias_<sup>_−_1)</sup><sup>_×_100foreachyear,showingthepercentagechangeor</sup> 24
extent of the market, economists have also focused on concentration, the scope and the extent of the firm (Viscusi, Vernon, And Harrington, Jr., 1990).

**Concentration.** Two hypotheses about the implication of concentration lead to dramatically different policy recommendations. The first hypothesis is collusion. Viscusi, Vernon, and Harrington (1990) note that "the more concentrated an industry is, the less competitive are firms and thus the higher is the price-cost margin." In this case, it is desirable to break up highly concentrated industries. The second hypothesis is Demsetz's superior efficiency hypothesis. That is, superior firms have both higher market shares and larger price-cost margins. Thus, at the firm level, both profitability and price-cost ratios are positively correlated with market shares, and the relationship may be present in a weaker form in the industry level.<sup>8</sup> In this case, the relationship is association, but not causation. The policy implication then is not to break up concentrated industries; the government, after all, does not want to punish firms with superior technology.

The empirical evidence from industrialized countries so far tended to support Demsetz's hypothesis. The evidence is that a firm's profit is strongly correlated with its market share, and the positive association is still observed at the industry level, but in a weaker form (Salinger, 1990; Viscusi, Vernon, And Harrington, Jr., 1990). But what is the evidence in developing countries? To learn the answers and thus give useful policy advice, we need to collect information about concentration ratio, profitability, price/cost ratio, entry barriers as characterized by license fees to entry, sunk costs (the percentage of the value of equipment can be recouped if the firm is to quit), and the existence of exclusive patents.<sup>9</sup>

**The scope of the firm.** The scope of the firm is characterized by its mix of activities (Viscusi, et al., 1990). Several issues of interest to the Bank and to development more generally may be related to fimn scope. For example, firms may be more likely to be vertically integrated in countries with weak contract enforcement. Whether this is true is, again, an empirical question requiring much data to answer. Few World Bank surveys have paid much attention to

> 8 Weaker because one has to average profitability and price-costs ratios with those of the competitors, for which the profitability and price-cost ratios are lower

> **9** This measurement has some problems. It is hard to measure costs, especially because it is subject to accounting methods and may be easily manipulated.

12
they created the import price index adjusted for new and disappearing varieties and measured the value that consumers attached to these new product varieties. They found that the total gain from the introduction of new varieties in the United States was 2.6 percent of GDP between 1972 and 2001. This meant that to obtain the new set of varieties imported each year, consumers would be ready to pay on average 0.1 percent of their income.

Following Broda and Weinstein (2006), a body of country studies emerged, using the same methodology.<sup>1</sup> For instance, Chen and Ma (2012) found that the welfare gain in the Chinese economy as a result of new import variety amounts to 4.9 percent of GDP, or 0.4 percent annually between 1997 and 2008. Minondo and Requena (2010) investigated the welfare gains due to Spanish imports of new varieties over the period 1988-2006. They found that the total welfare gain is equal to 1.2 percent of GDP in 2006. In a comparative study of Switzerland and the United States, Mohler (2009) estimated a lower and an upper bound of the gains from variety. He found that during the period from 1990 to 2006, the gains from variety in Switzerland were between 0.3 and 4.98 percent of GDP and that in the United States the gains from variety were between 0.5 and 4.7 percent of GDP.

Mohler and Seitz (2012) applied the methodology to the 27 countries of the European Union for the period 1999 to 2008. Their results show that within the European Union, especially “newer” and smaller member states exhibit high gains from newly imported varieties. For instance, Estonia gained 2.80 percent of GDP (GDP of Estonia); the Slovak Republic, 2.37 percent; Latvia, 1.65 percent; and Bulgaria, 1.59 percent. They also found that interestingly, two of the largest economies in the group, France and Germany, both had negative gains from variety. They argue that the reason for this is that these larger economies were already heavily integrated in the European economy and therefore did not experience the increase in product varieties as did the “new”, smaller economies.

Our analysis of 4,537 elasticities of substitution across 28 countries reveals that the

> 1As a reference, only a few papers are mentioned here. In addition to its welfare gain estimation, Broda and Weinstein’s (2006) paper is often cited for the import demand elasticity estimation. Already estimated data of 73 countries using the dataset from 1994-2003 is available at the following Columbia University webpage:http://www.columbia.edu/~dew35/TradeElasticities/TradeElasticities.html

2
CLEMENS & TIONGSON pleted the survey, and whose scores were near the passing threshold.

# 4.3. Checking the discontinuity: Sampling universe

Three testable conditions are necessary (not sufficient) for the test-passing threshold to be useful in identifying the effect of migration. First, at the threshold, there must be a large discontinuity in the treatment variable: deployment of the worker to Korea. Second, there must be no discontinuity in baseline traits of the job applicants. Such a discontinuity would suggest self-selection across the threshold. Third, there must be no bunching of test-score density above or below the threshold. This would suggest that test-takers are able to manipulate their scores—either through legitimate means (putting extra effort when they know they’re about to barely fail) or illegitimate (such as paying bribes for extra points).<sup>16</sup> In the sampling universe there is a very large jump in deployment probability at the cutoff score, but little evidence of a significant discontinuity in the baseline characteristics of the job applicants. Table 2 shows that there is a jump of 68 percentage points in the probability of deployment at the cutoff score.

The rest of the table tests for discontinuities in all known baseline characteristics of the job applicants: age, sex, education, work experience, employment, civil status, and test batch. None exhibit statistically significant jumps at the cutoff score. Figure 1 shows some of these results in graphical form, displaying both an unsmoothed average at each test score (gray circle) and a local linear regression. The upper-left panel shows the jump in deployment probability. The upper-right and lower-left panels show the lack of discontinuity in baseline education and employment of the applicant.

Were test-takers able to self-select across the discontinuity? First, there is no statistically significant jump in test-score density at the passing threshold. Figure 2 shows the

> 16Clean identification with RDD requires other assumptions that we cannot test but that appear plausible in this case. It requires there to be no “bitterness” effect of barely failing the exam, so that some outcome could be attributable to having come very close to passing without passing. In this case we consider it unrealistic for families’ financial decisions years later to depend substantially on such an effect.

12
9 (1989). Specifically, the political instability variable is obtained from a probit model of government chang-, that uses time-series ana cross-section data over the period of 1955-82. The specifications of the probit regressions contain three broad classes of explanatory variables: economic variables designed to measure the recent economic performe ce of **the government, political variables accounting for significant political events** that may signal the imminence of a crisis, and structural variables accounting for institutional differences and country specific **factors that** do not change or change slowly over-time, such **as the nature of their** political institutions i.e. democracies, democracies in which the election date is determined by a coalition and democracies ruled by a single majoritarian par:y. In addition to the probability of regime change, variables that capture the degree of political polarization **have been** considered. These variables are political cha.lenges to the regime, violent challeni,es to the regime, unsucessfull attemps to change the government and political repre6sions.

# 3. Results

# 3.1 Sample Characteristics

In what follows the average spreads in the sample for three country groupings will be described. Overall, these averages indicate that the countries that acquired sovereignty recently, were charged higher rates than countries that were sovereign but had defaulted on their foreign debt; the sovereign countries with default record, were charged higher spreads than the sovereign countries with no default record.

Countries that were not sovereign anytime during 1820-29 or the 1930s were contracted spreads in the order of 1.28 percentage points (with a
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the process of overall poverty reduction. To help address these issues, we have provided new estimates of the urban-rural breakdown of absolute poverty measures for the developing world, drawing on over 200 household surveys for about 90 countries, and exploiting the World Bank’s _Poverty Assessments_ for guidance on the urban-rural cost-of-living differential facing poor people, to supplement existing estimates of the Purchasing Power Parity exchange rates for consumption.

We estimate that about three-quarters of the developing world’s poor still live in rural areas, when assessed by international poverty lines that aim to have a constant real value (between countries and between urban and rural areas within countries). Poverty is clearly becoming more urban, although our results suggest that it will be many decades before a majority of the developing world’s poor live in urban areas.

The poor are urbanizing faster than the population as a whole, reflecting a lower-thanaverage pace of urban poverty reduction. One’s concern about the seemingly low pace of urban poverty reduction in much of the developing world must be relieved by the fact that it has come with more rapid progress against rural poverty. Over 1993-2002, while 50 million people were added to the count of $1 a day poor in urban areas, the aggregate count of the poor fell by about 100 million, thanks to a decline of 150 million in the number of rural poor.

Although our analysis has been descriptive, rather than attempting to draw causal inferences, the empirical findings are broadly consistent with the view that the urbanization process has played a quantitatively-important positive role in overall poverty reduction, by providing new opportunities to rural out-migrants (some of whom escape poverty in the process) and through the second-round impact of urbanization on the living standards of those who remain in rural areas. What we see here is suggestive of a compositional effect on the changing urban population, whereby the slowing of urban poverty reduction is the “other side of the coin” to what is in large part a poverty-reducing process of urbanization. Nor do we find any sign of adverse distributional effects of urbanization; instead it seems that the main channel linking population urbanization to poverty reduction is the rate of economic growth. Yes, the poor are 27
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district hospitals play a critical role in supervising and acting as source of referral services for all facilities in the district. District hospitals did supervise and support treatment and comparison group CMAs and CSIs differently based on the group they are assigned to. Household and facilitybased surveys were implemented in district hospitals and households associated with their catchment areas<sup>2</sup> in the 14 pilot districts to gain insights into the role that district hospitals are playing in the 4 study groups. However, these data will not be used for making inferences about the impact of PBF, and are not included in the analyses presented in this report.

# DATA SOURCES

The evaluation relied on two main sources of data to answer the impact evaluation research questions identified:

1. Household surveys: A household survey implemented at baseline (i.e., before implementation of PBF began), and at endline (i.e., after PBF was implemented for two years).

2. Facility-based surveys: A facility-based survey was also implemented at baseline and at endline.

Both surveys are described in detail in the Appendix.

# STATISTICAL METHODS

# FACILITY AND CATCHMENT AREA EXCLUSIONS

Most of the analysis included in this report includes only those health facilities that were surveyed at both baseline and endline. Similarly, for all of the results coming from the household surveys, these results include only those villages that were surveyed at baseline and endline. The only exceptions are the analyses that use data from the direct observation and exit interviews from ANC and child health consultations. These analyses include all of the women and children who received these services on the day of the facility survey. The reason that we deviated from the above stated exclusion criteria is that, especially in the ANC sample, the exclusion results in large sample size loss. Specifically, <mark>the full sample for ANC included data from 733 visits and 118 health facilities while the restricted sample included 561 visits at 46 health facilities.</mark>

# <mark>SPECIFICATIONS</mark>

<mark>The main difference-in-differences specification that we used in the household data analysis is</mark> displayed below:

# ���� = �� + ����� + ���1������ + ���1������ + ���2����� + �<sup>�</sup> ��� + ����

Where ���� is receipt of the health service for woman/pregnancy _i_ in enumeration area _j_ in survey year _t_ . �� is an enumeration area fixed effect, ����� is a dummy variable that is equal to 0 in 2012 (baseline) and 1 in 2015 (endline). _T1_ , _C1_ , and _C2_ are dummy variables that are equal to 1 when the enumeration area was assigned to each treatment group respectively and zero otherwise. The

> 2 Some villages will not fall within the catchment areas of other CSIs and CMAs in the district. Households in these villages will be excluded from the sample for the impact evaluation.

9
associated with using accounting software (more than nine times higher for both the median user, $1,398 per month vs $155, and for the average profit across users, $12,507 vs $1,368). For wages per worker, the largest differential between users and non-users is associated with using internet to find suppliers (roughly three times higher for both the median user, $150 vs $46 per month, and for the average wage across users, $270 vs $93).

Figure 2 summarizes productivity, sales, and jobs average outcomes for users versus non-users of selected DTs. The figure visually highlights that firms using DTs with internet-enabled computers or smartphones have higher productivity, sales, and jobs outcomes than non-users, and higher outcomes than those enterprises using only 2G phones. Firms using smartphones have 2.8 times higher productivity, 6.0 times higher sales, and 1.9 times the number of jobs than non-users. Computer users have 2.4 times higher productivity, 7.5 times higher sales, and 2.5 times higher jobs. Across all DTs, the highest sales premia from DTs are associated with the use of accounting software (13.7 times higher than non-users) and the use of internet for online banking. The use of accounting software and inventory control/point of sales (POS) software are associated with some of the highest jobs premia (3.8 and 3.5 times higher than non-users, respectively), after the use of internet to recruit workers (not illustrated).

_Table 6a. Productivity, sales, and export status outcomes by use of DTs_

|||<br>Laborpro|ductivity|||Total<br>|sales<br>||Export|status|
|---|---|---|---|---|---|---|---|---|---|---|
||non|users|us|ers|non|users|us|ers|non users|users|
||Mean|Median|Mean|Median|Mean|Median|Mean|Median|share|share|
|ACCESS TECHNOLOGIES|||||||||||
|use 2/2.5G mobile|795|67|888|116|2575|145|1712|276|6.0%|4.8%|
|use smartphone|771|97|2172|364|1473|225|8807|1126|4.3%|17.3%|
|use computer|800|98|1884|413|1420|230|10600|1296|4.3%|18.2%|
|have website|823|103|2371|286|1678|236|12558|1155|4.6%|24.1%|
|EXTERNAL TRANSACTIONS|||||||||||
|use mobile with suppliers|818|75|925|148|1358|164|2758|387|3.6%|7.2%|
|use internet to find suppliers|808|101|2728|384|1577|232|14503|1126|4.7%|18.8%|
|use mobile topaysuppliers|849|97|940|161|1912|225|2153|429|4.6%|8.4%|
|use mobile w customers|848|77|884|143|1396|164|2682|377|3.1%|7.8%|
|use mobile to advertise w sms|877|98|746|150|1873|229|2564|427|4.3%|12.0%|
|use int. to understand customers|780|98|2131|352|1571|229|7827|1117|4.2%|18.8%|
|use internet for e-commerce|862|103|910|324|1897|236|4709|1074|4.6%|31.7%|
|use mobile to receivepayments|801|97|1140|148|1860|215|2349|414|4.4%|8.6%|
|use mobile banking|788|98|1721|236|1505|230|7205|600|4.3%|14.8%|
|use internet for online banking|839|103|1901|289|1567|236|18376|1743|4.7%|23.7%|
|use internet to recruit workers|865|103|560|202|1892|242|9961|1117|4.9%|39.1%|
|INTERNAL TRANSACTIONS|||||||||||
|use accountingsoftware|1005|145|2617|773|1724|315|23654|4038|4.7%|34.3%|
|use inventorycontrol/POS sw|1038|145|1596|580|2100|315|12540|3753|4.8%|32.8%|
|use mobile topayemployees|832|103|1693|168|1838|236|4952|580|4.7%|17.9%|

16
**Table A.1.3.  Name of occupations used for imputations**

|**Code**<br>|**Occupation name**<br>|
|---|---|
|112<br>121<br>|Managing directors and chiefexecutives<br>Business services and administration managers<br>|
|122<br>134<br>|Sales,marketing and developmentmanagers<br>Professional services managers<br>|
|141<br>142|Hotelandrestaurantmanagers<br>Retail and wholesale trade managers|
|143|Otherservicesmanagers<br>|
|222<br>224|Nursingand midwifery professionals<br>Pdilii|
|<br>225|arameca practtoners<br>Veterinarians|
|226<br>232|Other healthprofessionals<br>Vocational education teachers|
|242|Administrationprofessionals|
|<br>243|<br>Sales,marketingandpublic relationsprofessionals|
|261<br>265|Legalprofessionals<br>Creative andperformingartists|
|311|Physicaland engineering science technicians|
|312|Mining,manufacturingand construction supervisors|
|313|Process controltechnicians|
|315|Shipand aircraft controllers and technicians|
|321|Medicaland pharmaceuticaltechnicians|
|322<br>324|Nursingand midwiferyassociateprofessionals<br>Veterinary technicians and assistants|
|325|Other health associateprofessionals|
|331|Financialandmathematicalassociate professionals|
|332<br>333|Sales andpurchasingagents and brokers<br>Business services agents|
|334|Administrative and specialised secretaries|
|335|Regulatory government associate professionals<br>|
|421<br>422|Tellers,moneycollectors and related clerks<br>Clitifti k|
|<br>441|ennormaonworers<br>Other clerical support workers|
|511<br>|Travelattendants, conductors and guides<br>|
|512|Cooks|
|513|Waitersandbartenders|
|516|<br>Otherpersonal services workers|
|522|Shopsalespersons|
|<br>524|<br>Other sales workers|
|532<br>41|Personalcare workersin healthservices<br>Pi i k|
|5<br>621|rotectve servces worers<br>Forestry andrelated workers|
|711<br>|Buildingframe and related trades workers<br>|
|712|Buildingfinishers andrelated trades workers|
|721<br>|Sheet and structural metal workers,moulders and welders,and related workers<br>|
|722<br>723|Blacksmiths, toolmakers andrelated trades workers<br>Machinerymechanics and repairers|
|<br>731|<br>Handicraft workers|
|732<br>|Printingtrades workers<br>|
|741<br>742|Electricalequipmentinstallers andrepairers<br>Electronics and telecommunications installers and repairers|
|751|Food processing andrelated trades workers|
|752<br>753|Wood treaters,cabinet-makers and related trades workers<br>Grmntndrltdtrdrkr|
|811|ae a eae aes woes<br>Miningand mineralprocessing plant operators|
|813<br>|Chemicaland photographic products plant andmachine operators<br>|
|815<br>818|Textile,fur and leatherproducts machine operators<br>Otherstationaryplantandmachineoperators|
|821|<br>Assemblers|
|832<br>|Car, vanandmotorcycle drivers<br>|
|833<br>834|Heavytruck and bus drivers<br>Mobile plant operators|
|911|Domestic,hotel and office cleaners and helpers|
|921|Agricultural,forestry andfisherylabourers|
|931<br>|Miningand construction labourers<br>|
|932<br>941|Manufacturinglabourers<br>Foodpreparation assistants|
|<br>962|<br>Otherelementary workers|

_Sources_ : ILO (2012).

42