rafmacalaba/datause-encoder-data huggingface dataset:
The identifying assumption associated with the DID estimation equation (4) is that conditional on the controls, our regressor of interest (i.e., the interaction between the treatment status indicator and post-treatment period indicator) is uncorrelated with the error term. That is, In the remaining part of this section, we discuss potential violations of our identifying assumption and our remedies, as well as several robustness checks.

# **Placement and Timing of Landline Phone Installation**

A potential challenge to the DID estimation specification is that the place and timing of the installation of landline phones are not random. For example, more remote and poorer villages could install landline phones later than coastal and richer villages. One may then be concerned that such preexisting differences across treatment and control groups may explain the post-treatment divergence in the ratio of out-migrant workers, causing a spurious correlation between our regressor of interest and the outcome variable. Thus, one must understand what determines which village installed landline phones earlier to isolate the effect of landline phones on outmigration.

To this end, we first conducted an intensive online research on how China Telecom Corporation, the monopoly of telecommunications in the 1990s and early 2000s, decided which village was connected to the landline phone network first in the 1990s.<sup>7</sup> Unfortunately, there is not much discussion online about the determinants of landline phone connection. Among the sporadic pieces of information we found, most websites cite total income as the main reason. We then interviewed one China Telecom Corporation employee to get first-hand information about landline phone installation. We were told that when choosing which village was connected to the landline phone network

> 7 The search engine we used is _Baidu_ , the Chinese version of Google and the best in searching Chinese websites.

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Industry associations such as the Electronics Industry Association of Korea (EIAK) and the Korea Association of Machinery Industry (KOAM) provide technical training and information services to their respective industries. Some industries, with partial government support, have established their own R&D institutes. The Korea Textile Industry Technology Institute (KOTrIT, the Korea Automotive Technology Institute (KATECH), and the Korea Electronics Technology Institute (KEI), are good examples. Although KATECH AND KETI are to young to assess their long-term impact (both were formed in 1989), they are expected to play a major role in assisting their respective industries. KOTITI, in contrast, dates back to 1978, when the government established a technical training institute for the texfile industry in Taegu.

As shown in Table M.2, 8,513 SMEs reported receiving same form of technical assistance in 1992. Private groups assisted the largest number of these finms (45.2 percent). The bulk of this pnvate assistance came from other parent firms, suppliers, and buyers. The total mimber of SMEs assisted by pnvate groups may be even higher, since the figures in Table 1II.2 for private agencies are based on what appear to be conservative estimates, while those fo. the public and non-profit agencies are actal figures.

The next most important assistance providers were state and public agencies (44.1 percent), of which NI and SMIPC played the key role. While state and public agencies were consulted by SMEs almost as fequently as private sources, however, they were evaluated as only moderately useful. General non profit agencies ranked a distant tiird, providing assistance to just over 10 percent of the 8,513 frms. While these conclusions are useful, additional insights are available by loiking in more detail at some key differences in the use of technical support across industry sectors.

**m.2** Survey of Industry Sectors

Hypotheses. The conceptal framework for stegic behavior of SMEs and the evolution of sectors reviewed in Chapter II, as well as the review of collective technical support systems in 111. 1 above, helped us to formulate a set of hypotheses about collective technical support systems in Korea. Our survey then tested these hypotheses. The basic hypotheses we began with were as follows: 15
time the conflict ends. The only gas distribution investment came eight years after conflict ended (Figure 5).

**Figure 4: Number of Private Infrastructure Investments and PPIs in Post-conflict Countries by Sector**

<!-- Start of picture text --> 20<br>16 Transport<br>12<br>Water<br>8<br>4 Energy<br>0<br>Telecom<br>0 1 2 3 4 5 6 7 8 9<br>Year After Conflict<br>PPI Projects<br><!-- End of picture text --> Source: PPI Database. Authors’estimations.

**Figure 5: Number of Private Energy Projects in Post-Conflict Countries by Sub-Sector**

<!-- Start of picture text --> 10<br>Electricity<br>8 Transmission<br>Gas<br>6<br>Distribution<br>4<br>Electricity<br>2 Generation<br>Electricity<br>0<br>Distribution<br>0 1 2 3 4 5 6 7 8 9<br>Year After Conflict<br>PPI Projects<br><!-- End of picture text --> Source: PPI Database. Authors’ estimations.

# **6. Conclusions**

Through a quantitative analysis that looks at the relationship between private participation in infrastructure (PPI) and country risk, we conclude that investment in infrastructure is highly sensitive to sovereign risk. That is, country risk ratings are a reliable predictor, on average, of PPI levels in developing countries. The predictive ability of risk ratings exists for all sectors of infrastructure and for both greenfield and concessions, however, with important variations among them. Our results suggest that a difference of one standard deviation in a country's 19
of consumable income lower than at the level of market income, families with children actually experience an increase in poverty rates after tax-benefit interventions in Moldova. And the most vulnerable in this sense are families with two or three children. For these categories, poverty at the level of consumable income is 16-20 percent higher than at the level of market income. Families with three or more children have also highest post-intervention poverty rates – more than half of households in this category are poor at the level of consumable income if the national poverty line is used (Figure 10).

**Figure 10. National poverty rates inequality at various levels of income in Moldova by types of households, percent.**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>two adults (or two adults (or two adults (or one adult and only working only pensioners mixed adults,<br>more) and 1 more) and 2 more) and 3+ child(ren) age adults no children<br>child children children<br>market market + pensions disposable consumable<br><!-- End of picture text --> Notes: poverty rates at the national poverty line (1562.8 MDL per month per adult equivalent). Source: authors’ calculations using HBS-2017 data.

While poverty reduction across locality types is similar in absolute terms, in relative terms rural households, which are much poorer than urban ones and remain poorer also after the interventions, register the lowest degree of poverty reduction. Poverty at the level of consumable income for rural households is only 17 percent lower than at the level of market income, while for small towns this number is 28 and for big cities, including capital it is 44 percent (Figure 11). The relative reduction in poverty rates is of the same magnitude across geographical regions, however, the central part of the country has slightly less poverty reduction after the main interventions (Figure 11).

**Figure 11. National poverty rates inequality at various levels of income in Moldova by types of location, percent.**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>big cities small towns rural north center south Chisinau<br><!-- End of picture text --> <!-- Start of picture text --> market market + pensions disposable consumable<br><!-- End of picture text --> Notes: poverty rates at the national poverty line (1562.8 MDL per month per adult equivalent). Source: authors’ calculations using HBS-2017 data.

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they influence the rate of investment through either of these channels, one should expect them to account for part of the observed variation in business growth.

Equation (2) cannot be consistently estimated by OLS since the lagged dependent variables must be correlated with unobserved firm effects of the error term of the specification being estimated. One way of addressing this problem is to use the first difference GMM estimator of Arellano and Bond (1991), which eliminates the unobserved firm effects by first differencing and using appropriately lagged values of all endogenous variables as instruments. This would provide consistent estimates as long as the error term of the levels specification is serially uncorrelated once the firm effects are removed. However, the difference GMM estimator generally performs poorly with our sales growth data which show a high degree of persistence. Blundell and Bond (1998) show that when such persistence is high enough, the first difference GMM estimator could be biased in small samples since lagged values provide weak instruments in that case. We therefore have used instead the GMM estimator that Blundell and Bond (1998) introduced as a solution to this problem. Essentially, the solution leads to the system GMM estimator, which involves to the use of lagged differences of endogenous variables as additional instruments over and above those of the difference estimator.

A problem with our data that could potentially reduce the advantages of the ADL-GMM estimation framework is that we have only two observations on each institutional variable made in two three-year intervals over the six year period covered by our production and investment series. We have sought to surmount this problem by assuming that the value of an institutional variable for a firm during a survey year is the best predictor of the true value of the same indicator for the preceding two year for which production data were collected as part of the same survey. This obviously introduces measurement error into the specifications we have estimated. It also means that the effective number of natural instruments that we have for the institutional variables for the system GMM estimator is quite limited. We have sought to address this problem by using city or location averages of institutional variables as additional (excluded) instruments. The validity of location averages as instruments obviously assumes that location decisions are strictly exogenous or predetermined. While this would be a questionable assumption for large businesses, other studies (e.g., Dollar et al. 2006, and Lall and Mengistae, 2005) suggest that it holds for small scale manufacturers in developing economies.

# **4. Results**

Additional descriptive statistics of the variables used in the growth equations we estimate are provided in table 6. We report in tables 7, 8 and 9 results of GMM-sys estimates of equation (3) over a variety of sub-samples of the FACS dataset, using t-2 and earlier lags in levels of growth, sales, profitability and indebtedness and t-1 and earlier lags in first 11
Table 3: Donor Ranking in Sectoral Aid Allocation Overlap

|Rank|Donor|OL over|OL over|OL over|
|---|---|---|---|---|
|||Sectors|Recipients|Sectors and<br>Recipients|
|1|IDA|79.4 (78.8)|49.1 (58.3)|32.1 (38.0)|
|2|IEU|69.8 (65.7)|53.3 (60.1)|30.4 (33.7)|
|3|Avg. BD|68.3 (67.6)|40.2 (42.0)|23.9 (25.0)|
|4|ADB|66.4 (67.1)|21.3 (26.9)|10.9 (13.4)|
|5|IDB|60.2 (56.8)|8.9 (5.9)|6.0 (4.7)|
|6|AfDF|54.3 (55.6)|33.7 (41.6)|18.3 (22.2)|
|7|OFID|47.8 (46.6)|43.8 (48.8)|12.9 (14.2)|
|8|UNICEF|47.4 (47.8)|54.2 (57.4)|31.3 (33.5)|
|9|KFAED|45.9 (39.3)|23.9 (25.8)|4.9 (5.4)|
|10|BADEA|44.8 (45.3)|28.6 (34.3)|8.2 (9.8)|
|11|WFP|42.0 (41.6)|39.9 (44.7)|15.9 (18.2)|
|12|UNDP|41.7 (41.1)|57.1 (60.7)|21.6 (22.5)|
|13|AFESD|30.1 (24.7)|5.4 (2.7)|1.8 (0.9)|
|14|OSCE|27.9 (27.0)|7.5 (8.6)|2.2 (2.3)|
|15|WHO|13.6 (14.1)|54.2 (59.0)|12.0 (13.1)|
|16|UNFPA|12.9 (13.8)|58.7 (61.0)|11.9 (13.0)|
|17|GLF|12.9 (13.8)|50.9 (54.3)|10.8 (12.3)|
|18|GAVI|12.9 (13.8)|26.8 (32.5)|5.0 (6.2)|
|19|NDF|7.7 (7.7)|30.7 (37.6)|2.9 (3.8)|
|20|IMF|6.4 (6.9)|20.9 (26.0)|2.9 (3.5)|
|21|GEF|6.2 (6.4)|45.9 (54.5)|3.8 (4.0)|
|22|UNECE|5.0 (5.3)|0.3 (1.3)|0.0 (0.1)|

OL measures the overlap in budget-shares across three dimensions: ‘Sector,’ ‘Recipient,’ and ‘Recipient-Sector.’ OL is measured by 100 minus the sum of the differences between budget shares of ML _i_ across a dimension and the average of budget shares of all _bilateral_ donors (Pedersen Index). The measures reported above are averages between 2007 and 2012. Debt relief efforts, humanitarian and emergency aid efforts, administrative costs, and refugee costs in donor countries are excluded. Donor overlap measures among IDArecipient countries reported in brackets. Avg. BD lists OLs for the average bilateral donor. Data Source: Credit Reporting System (OECD).

aid in a highly fragmented fashion across many sectors (see Table 1). Other donors, such as the regional development banks (RDBs, including AsDF and AfDF), have low geographic overlap with the typical bilateral donor. On the other hand, there are several agencies that exhibit very little specialization. In fact, IDA has the largest sectoral overlap: 80% of its budget allocation coincides with the budget allocation of the typical bilateral donor. Its overlap in terms of recipient countries is somewhat smaller, because IDA’s aid is limited by its eligibility rules to about 80 mostly low-income countries. Nevertheless, its overlap with the typical bilateral donor is still larger than the one for the typical bilateral donor and those for the U.S., U.K. and Japan. If we calculate overlap in aid only among the IDA-eligible 9
measurement power, we examine the relationship between these tools and representative development outcomes for each of the 17 SDGs, an overall SDG index, and several other common indexes. Finally, we offer some thoughts on other challenges, including potential mechanisms for their impacts on the economy, political economy factors, and future development of these tools. For illustration, we offer new analysis based on their latest data updates, including new data for the SPI in 2021 and 2022 that were not analyzed before.

# **Results**

We provide a brief overview of the various tools and highlight some key qualitative differences regarding their guiding principles, before offering a more detailed comparison over the dimensions, indicators, and countries covered by the tools. We subsequently examine the relationships of these tools with a number of key SDG outcomes and several other development indexes.

# **Overview of the tools and their guiding principles**

_Statistical Capacity Indicators and Index (SCI) and Statistical Performance Indicators and Index (SPI)_ Following the guiding principles that the source data should be publicly available and meet certain quality standards (e.g., as provided by the curators of the international databases), the SCI collected data from publicly available, international databases and NSO websites, with the major share coming from the former source. The SCI has been employed by different international and national agencies since its inception in 2004 to measure progress with development trends (United Nations, 2016), or areas of statistical improvement in member countries (OIC, 2012), or tracking the SDGs for child development (UNICEF, 2018). The SCI was widely employed in academic research covering different disciplines, ranging from economics, international development, political science to statistics. We offer in Appendix A, Table S1 a brief overview of some selected academic studies in the past decade that employ the SCI.

The SCI, however, has several key limitations, including an outdated framework, a focus on poorer countries only, and a lack of underlying conceptual and mathematical principles. The SPI recently replaces and offers several advantages over the SCI on both the conceptual and empirical 4
CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Cape Verde devotes around $147 million per year—or almost 15 percent of gross domestic product (GDP)—to infrastructure, one of the highest levels of infrastructure spending found on the African continent. Spending is quite heavily skewed toward capital expenditure, and the resources devoted to transport are particularly high relative to regional benchmarks. In addition, some $50 million a year is being lost to inefficiencies of various kinds, primarily underpricing and operational inefficiencies in the power sector.

In sum, the main challenges for the country are to improve the effectiveness with which resources are being used, and to seek new approaches to reducing the exceptionally high costs of infrastructure service provision.

# The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has gathered and analyzed extensive data on infrastructure in more than 40 Sub-Saharan countries, including Cape Verde. The results have been presented in reports covering several sectors—ICT, irrigation, power, transport, water and sanitation— and various policy areas, including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for Cape Verde, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Given that Cape Verde is a relatively welloff middle-income country, its performance will be benchmarked against that of other middle-income countries in Africa. Detailed comparisons will also be made with immediate regional neighbors in the Economic Community of West African States (ECOWAS), and where relevant, with other island states that face a similar set of infrastructure challenges Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006, yet most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Where possible, data have been collected for the period 2007 to 2009 to measure the progress made relative to the baseline. Second, to make comparisons across countries, we had to standardize the indicators and analysis; hence some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

Alongside the rest of the continent, West Africa’s growth performance improved markedly between the 1990s and 2000s. The overall improvement in per capita growth rates has been estimated at close to 2 percentage points, of which 1.1 percent is attributable to better structural policies and 0.9 percent to improved infrastructure. Although specific estimates are not available for Cape Verde, the experience of neighboring West African states suggests that the net per capita growth impact of infrastructure during this period ranged from 1.6 percentage points in Benin to 0.1 percentage points in Guinea-Bissau 2
**Figure 1:  Correlations among country rankings by CPI vs. aggregate indicator from Enterprise Surveys**

CPI vs. ES CPI adjusted with “fitted variables” vs. ES <!-- Start of picture text --> 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160<br>ES Overall Payments Ranking1 ES Overall Payments Ranking1<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Perhaps the CPI measures something akin to an overall “culture of corruption”, whereas the ES captures particular patterns of bribes for certain transactions. In any case, the data about aggregate corruption remain noisy. They may capture different facets of corruption and they may contain other types of information that are not really observations of corruption. This may help to explain why there is such a weak relationship between the CPI and outcomes in infrastructure once we control for income (Kenny, 2008).

While our “results” support a certain amount of skepticism as to the information content of aggregate corruption perception measures, this does not imply that one should discard or disregard indicators like the CPI. They do seem to capture important information at the least about perceptions that may alter investment behavior and they are a way of giving prominence to an important topic. What may, however, be questioned are statistical methods that attempt to place confidence intervals around current corruption indicators. The statistical methods to derive such confidence intervals need to assume some random process that relates actual corruption to the observation expressed by the respondents. If there are other unidentified observations in the data that are not about corruption, it is not clear how one would adjust for this. It might thus be better to drop the pretense of creating “scientific” confidence intervals.

# **Speed Money**

In the last decade a new data set has been created that annually measures a set of regulatory arrangements relevant for business, currently across 183 countries – the Doing Business (DB) report. By now there are eleven sub-indicator sets that measure inter alia official procedural requirements for registering a business, obtaining a construction license, an import license and getting an electricity connection. Starting with the pioneering first data set on regulation of entry (Djankov, 2002), the data have been used to assess whether more complex procedural 6
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