rafmacalaba/datause-encoder-data huggingface dataset:
In the next step we investigate whether the assignment of treatment status to the villages happened in a random fashion, in which case the outcome variables as well as the conditioning variables should not differ between the treatment and the control group in the baseline period except for random deviations (Behrman and Todd 1999). The comparison of villages along their planned treatment status in Table 2 shows that the two types of villages have generally similar outcome and control variables.

As discussed above, while the initial assignment of treatment was random, the NGOs implementing the program may not have fully understood the importance of the randomization procedure prescribed, or deviated from the planned treatment status of villages for other reasons. In Table 3, we see that there was, in fact, deviation of actual treatment status from the status initially assigned. Of the 111 villages that were initially assigned treatment status, 80 ended up receiving treatment, while 31 villages (28%) did not receive the intervention. Of the 100 initial control villages, for one village no second round data were available, and of the 99 remaining villages, 8% received the intervention despite their control status. In the analysis to follow, we therefore have to address the problem of partial compliance.

When we compare the most important group of villages that deviated from the original design, the 31 villages that initially were assigned treatment status but that did subsequently not receive intervention, along some key dimensions with the remaining 80 villages from the group initially assigned treatment status there is some evidence that the deviation did not happen randomly (Table 4). The villages from the 2004 planned treatment list that subsequently did not receive the nutrition intervention were initially somewhat better off in terms of their nutritional status and had less children that were mildly underweight (below -2 standard deviations from the mean of the US reference 13
# **4 Analysis**

The experiment is designed to address three related questions. Does defaulting payment of a transfer into a savings account affect savings balances? Is income used differently when directly deposited to a bank account compared to when received in cash? And does delaying receipt of the transfer change consumption and savings decisions?

The experiment equated the transaction costs of accessing the transfer, and to either saving or dis-saving. This is most obvious for those who received their transfers on the day they visited the bank, but we argue it is also true for those receiving transfers with one- or eight-day delays. In the delayed payment groups, respondents in the cash treatment arms had to return to the branch once to receive the full payment in cash. Those in the direct deposit treatment arms could replicate the same outcome – receipt of the full transfer in cash – for the same transaction cost of visiting the branch once. The costs associated with additional visits to the branch by individuals that chose not to withdraw the full amount of the transfer immediately cannot be attributed to how the transfer was made; instead, these are costs of using a bank account, and are incurred equally by anyone who may chose to save money at the bank. Direct deposit recipients could have delayed visiting the bank instead of coming on the day of the transfer, and in this sense, this flexibility may have reduced their costs of receiving the transfer relative to the cash groups. In practice, however, this flexibility was unimportant: 95 percent of the one day delay direct deposit group and 97 percent of the eight day delay direct deposit group withdrew money within the first week, and 84 and 86 percent, respectively, came to the bank on the same day the transfer was made. Thus, cash and direct deposit recipients each have the opportunity to receive the same amount of money by visiting the bank once, and they each incur the cost of a visit within a narrow window.

We first examine the impact of the savings default on bank transactions, using the administrative data described in the previous section. We compare outcomes for respondents who received MK 25,000 in cash to those who received the same amount deposited directly into their savings accounts, by estimating the following equation: The coefficient _β_ measures the effect of the savings default. We control for the average of the outcome variable in the 90 days prior to the transfer ( _Yi_ 0). The specification includes village and week-of-first-survey fixed effects. Standard errors are robust to heteroskedasticity rather than clustered because randomization is at the individual level, a decision that is conservative in light of our emphasis on null results. We report results for each of the three outcomes – deposits, withdrawals, and net deposits – in four time horizons to observe 9
SOEs accounted for most of the recent rise in corporate leverage, and the fastest-growing component of SOE debt was for local government financing vehicles (LGFVs). SOE debt rose from 73 to 103 percent of GDP in 2012–15 (Figure 20). Accelerated debt accumulation by LGFVs explains the rapid increase in the share of SOE debt (Bai et al., 2016). Based on the Wind database sample of firms in China that issue bonds, the liabilities of LGFVs that issue bonds grew by more than 20 percent a year during 2012–16 if the effect of the local government debt swap program is netted out (Figure 21).<sup>18</sup> In contrast, the liabilities of other enterprises increased by 13 percent a year in 2015–16. While the headline growth in LGFV liabilities declined in 2015 and 2016, this can be attributed to the bond swap scheme that transformed part of their debt into government bonds.

**Figure 20 Estimated composition of debt**

(percent of GDP)

**Figure 21 Growth in liabilities of LGFVs and other corporations**

(annual growth in total liabilities, percent) <!-- Start of picture text --> LGFVs SOEs (Non-LGFVs) All corporates<br>300 Private General government Non-LGFVs<br>Household 30 LGFVs<br>250 LGFVs (adjusted for bond swaps)<br>44.4<br>38.8 25<br>35.7<br>200<br>33.1<br>29.7 41.7 44.5 20<br>40.2<br>37.2<br>150 34.4<br>66.8 63.6 15<br>65.4<br>62.4<br>100 57.7<br>10<br>47.8 52.9<br>42.9<br>50 42.0 42.0<br>5<br>30.9 36.2 41.6 48.2 49.8<br>0 0<br>2012 2013 2014 2015 2016 2012 2013 2014 2015 2016 2017 2018<br>Source: BIS; PBC; Wind Info; CEIC; Authors’ Source: Wind Info; authors’ calculations.<br>calculations.<br>Note: Corporate decomposition estimated based on<br>information from bonds and bank credit.<br><!-- End of picture text --> The recent boom in public investment and credit to the corporate sector is reflected in the growing exposure to risk among smaller regional banks and the rapid increase in shadow banking activities. Empirical evidence links the acceleration in shadow banking growth after 2012 with the financing needs of LGFVs, including the rollover of maturing bank loans taken on at the time of the 2009 fiscal stimulus (Chen et al., 2017).

Regional banks now account for a large share of aggregate assets in the banking sector and their portfolios tend to be riskier compared with the Big 4 state-owned banks.<sup>19</sup> While the four largest banks accounted for the majority of assets before the financial crisis, their share fell to 37 percent in 2016 (central policy banks accounted for another 10 percent). Smaller banks held most banking assets, with their assets increasing at double the rate of the Big 4 banks (Figure 22). And the exposure of smaller banks to risk grew rapidly in 2015–16. The share of

> 18 The local government debt swap program was introduced in 2015 to convert LGFV debt recognized as public into local government bonds.

> 19 Bank of China, China Construction Bank, Industrial and Commercial Bank of China, and Agricultural Bank of China.

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in 2000.<sup>5</sup> We also include a broad range of fixed effects, including country-product and countryyear dummies. We exploit the extensive country coverage of our data to disaggregate quality upgrading into that of developed and developing economies. Moreover, the richness of our trade data, with 5,646 unique products, enables us to examine which products are being upgraded within each country. Specifically, we examine quality upgrading for new export varieties, more complex products (following Levchenko, 2007) and initially low-quality exports. Finally, we use novel information on the type of robots being adopted to examine whether quality gains are specific to the diffusion of advanced robots or are more broad-based.

We find that robot diffusion leads to increases in the quality of exported products. In aggregate the strongest quality gains accrue to developing economies, with more muted quality growth for developed economies and only evident for the first 5 years of our sample. Within each country, we find robust evidence that robots lead to quality upgrading of initially low-quality exports, furthest from the quality frontier, and this is true of both advanced and developing economies. Since developing economies in general tend to produce lower-quality exports, this reconciles the stronger aggregate quality upgrading we observe for developing countries. The paper also finds some evidence of differences in quality upgrading by types of robots, adoption of more basic robots leads to quality upgrading of developing economies, whereas some sophisticated robot applications lead to stronger quality upgrading in advanced economies. For instance, in terms of cutting machines, we find mechanical cutting robots matter more for quality upgrading in emerging economies and laser cutting robots more for high income economies. Finally, the strength of our instrumental variable suggests that the technological choices of foreign suppliers can be an important factor in driving robot adoption in their international supply chains.

One of the difficulties in measuring the effects of technology diffusion is the likely presence of endogeneity bias. Technology is not typically randomly adopted but done so with an expected return on that investment. This paper uses robot diffusion in (pre-existing) foreign customers as instrumental variables to predict adoption in the home country. Our choice of instrument is motivated by the existing FDI literature that finds extensive evidence of knowledge spillovers from foreign multinational customers to their overseas suppliers (Javorcik, 2004; Havránek and Iršova,

> 5 That is, we exclude foreign customers in the same world region as the home country for the purposes of constructing the instrumental variable.  We do so to remove concerns of common regional shocks that may jointly affect the home country and foreign customer.

4
# **3.1 Sample**

– We conduct the statistical analysis using a sample of 98 developing and developed countries for the period 1985 2014. They are selected from the larger sample of countries featured in the Penn World Table (PWT) 9.0 and the World Bank World Development Indicators (WDI) databases. We exclude countries that do not have a minimal set of historical data for statistical analysis, countries that depend heavily on oil production (because the contribution of oil to output could result in a large overestimation of TFP growth),<sup>1</sup> and small countries, defined as those with population less than 2 million (in 2016) (World Bank 2017m).

For the descriptive analysis of TFP growth across regions and decades (in section 4.1), we add 16 countries for which data on the share of labor in income is missing in PWT 9.0 but available from the Global Trade Analysis Project (GTAP) 9.0 (Aguiar, Narayanan, and McDougall 2016). For the descriptive analysis of TFP determinants (in section 4.2), we additionally include 22 countries, which, though not having information to obtain TFP estimates, do have data for the proposed determinant indicators. For growth projections in the Long-Term Growth Model (LTGM), we add back small countries, heavily oil dependent countries, and those for which we can complete missing data from other sources and additional assumptions; thus, the TFP extension of the LTGM can be applied to about 190 countries for growth projections.

We classify high-income countries that have been members of OECD for more than 40 years as the OECD group. The rest of countries are classified by region and income. We use the average of GDP per capita (World Bank 2017e) over 1985–2014 to break the sample into income quintiles. Table B.1 shows the country list by region and income quintile groups, indicating their inclusion in the samples by type of analysis (descriptive and statistical), data source (PWT, GTAP, and WDI), and other characteristics (oil rent and population).

> 1 Heavy dependence is defined as reliance on oil production for more than 32 percent of GDP on average during 2006-15, which is 90th – 100th percentile among 98 countries with positive oil rents (World Bank 2017j); Angola (45%), Congo, Rep. (46%), Equatorial Guinea (42%), Gabon (32%), Iraq (52%), Kuwait (47%), Libya (54%), Oman (37%), Saudi Arabia (44%), and South Sudan (45%).

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not do so systematically. We therefore do not find K's isolated evidence for just one of the many ICRG indicators to be at all compelling.

Finally, in our latest paper on the governance indicators (Kaufmann, Kraay and Mastruzzi 2006) we have provided new empirical evidence on the possible correlation of errors across data sources. As we discuss at length in that paper, empirically identifying correlations in errors across sources is difficult. Simply observing that two data sources provide assessments that are highly correlated is not enough, since the high correlation could reflect either (i) the fact that both sources are measuring governance accurately and so are highly correlated, or (ii) the fact that both sources are making correlated measurement errors in their assessments of countries. In order to make progress we need to make assumptions, and in that paper we detail two sets of assumptions that allow us to disentangle potential sources of correlation in the errors. One assumption is related to the plausible argument of K that surveys of firms or individuals are less likely to make errors that are correlated with other data sources than, for example, the assessments of commercial risk rating agencies. If this is the case, however, we would expect that the assessments of commercial risk rating agencies be very highly correlated with each other, but less so with surveys. This turns out not to be the case. For example, the average correlation among our five major commercial risk rating agencies for corruption in 2002-2005 was 0.80. The correlation of each of these with a large cross-country survey of firms was actually slightly higher at 0.81, in contrast with what one would expect if the rating agencies had correlated errors. We do this exercise for components of all six of our aggregate governance indicators, and find at most quite modest evidence of error correlation.

# **_<u>Critique 8:</u> If some data sources make correlated errors, the aggregation procedure used by the WGI gives too much weight to such indicators._**

The WGI are constructed using a statistical methodology known as an unobserved components model, which in effect estimates governance for each country as a weighted average of the underlying indicators available for that country. The premise for the weighting of indicators is simple. We think of each underlying data source as providing a noisy or imperfect signal of governance. If the errors made by individual sources are uncorrelated with each other, then data sources that produce 20
# **Table 10: Estimated impacts** **<u>on modal choice</u>**

||Bico do<br>Southe<br>Simple|Papagaio &<br>ast<br>average DID|DID regression||Jalapão & Northeas<br>Simple average<br>DID|t<br>DID regression||
|---|---|---|---|---|---|---|---|
||Coef.|Std. Err.|Linear Prob. Model<br>Coef.<br>Std. Err.|Probit<br>Coef.Std. Err.|Coef.Std. Err.|Linear Prob. Model<br>Coef.<br>Std. Err.|Probit<br>Coef.<br>Std. Err.|
|Public bus use (dummy var|iable) to|reach:||||||
|Nearest populated place|-0.02|(0.02)|0.00 (0.02)|0.24 (0.20)|-0.08 (0.03) ***|-0.05 (0.06)|-0.58 (0.56)|
|Municipal center|0.04|(0.03)|0.08 (0.03) **|0.44 (0.14) ***|0.05 (0.05)|0.08 (0.08)|0.58 (0.43)|
|Nearest hospital|0.03|(0.03)|0.07 (0.03) **|0.34 (0.15) **|0.03 (0.04)|0.06 (0.06)|0.66 (0.55)|
|School|0.03|(0.01) ***|0.03 (0.01) **|3.97 (0.23) ***|0.00 (0.01)|0.00 (0.00)||
|Individual car use (dummy|variable|) to reach:||||||
|Nearest populated place|0.05|(0.02) ***|0.05 (0.02) ***|0.53 (0.23) **|0.10 (0.03) ***|0.06 (0.06)|4.73 (0.67) ***|
|Municipal center|0.07|(0.02) ***|0.06 (0.02) **|0.36 (0.17) **|0.11 (0.03) ***|0.08 (0.06)|1.09 (0.56) **|
|Nearest hospital|0.04|(0.01) ***|0.08 (0.02) ***|0.45 (0.16) ***|0.23 (0.03) ***|0.15 (0.05) ***|5.88 (0.65) ***|
|School|0.07|(0.02) ***|0.04 (0.01) ***|0.72 (0.30) **|0.15 (0.03) ***|0.08 (0.05) *|5.79 (0.75) ***|
|Transport equipment owne|<br>rship (d|<br>ummy varia|<br>ble):|||||
|Bicycle|0.08|(0.04) **|0.07 (0.04) *|0.17 (0.12)|0.12 (0.07) *|-0.01 (0.10)|0.05 (0.36)|
|Motorcycle|0.01|(0.03)|-0.01 (0.04)|0.00 (0.13)|0.15 (0.06) **|0.13 (0.08)|0.32 (0.42)|
|Car|0.04|(0.02)*|0.04(0.03)*|0.22(0.16)|0.07 (0.04)*|0.06 (0.06)|0.36 (0.63)|

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

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# **1. Introduction**

Post offices (or “posts”) can play a leading role in advancing financial inclusion. In Brazil, for example, more than 10 million accounts were opened between 2002 and 2011 after the post established Banco Postal under a partnership model with an existing financial institution (Ansón and Bosch Gual, 2008). Yet little is known about the type of clients that post offices reach through their financial service offerings, as compared to clients of traditional financial institutions (such as commercial banks). Moreover, the role of the post office may vary depending on the business model that a government pursues in providing financial services via the postal network. The lack of systematic data has hindered efforts to understand the role of the post office in providing financial services in most economies.

This paper documents and analyzes account ownership patterns at post offices, in comparison to traditional financial institutions, such as banks and regulated microfinance institutions (MFI’s). We use the Global Financial Inclusion Indicators (“Global Findex”) database, which provides indicators measuring how people in 148 economies around the world save, borrow, make payments, and manage risk based on interviews with more than 150,000 nationally representative and randomly selected adults. For 60 countries these new indicators distinguish whether adults have an account at the post office, a financial institution, or both.<sup>1</sup> Controlling for a host of individual characteristics and country fixed effects, we find that post offices are relatively more likely than traditional financial institutions to provide accounts to individuals who are most likely to be financially excluded such as the poor, less educated and those out of the labor force.

Using newly collected data from the Universal Postal Union (UPU), we also explore the degree to which different postal business models and the size of the postal network help explain

> 1 Account ownership is the only indicator for which a breakdown by institution is available.

2
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# compared to secondary and higher education.40 The health sector could

# similarly benefit.

Thus the subject of the effects of health on development is one where good research would probably make a difference (by increasing the volume<sup>and</sup> improving the allocation of resources for health investments) but where the field is at least two decades behind the state of the art in education. The difficulties -- in terms of concepts, data and methods -- are greatest in the area of the economic and social consequences of adult ill health, both for the affected individual, and his or her community; yet work on this issue is critical to cost-benefit analysis of health investments. Because so little has been done, the short-run returns to a major push in this area would be small and the risks of a long period of difficulty in reaching straightforward, widely-accepted results are great. However, there are risks as well in failing to begin.

# Conclusions

Research on the determinants of health at the household level could contribute to better design of public programs meant to improve health; this is particularly the case insofar as much future improvement in health in developing countries will rely on changes in behavior at the individual and household level. Research on the demand for health care, including the price elasticity of demand for health services, and on the effects of health service utilization on health itself, could improve pricing policies of governments 40 The World Bank has consistently emphasized the high rates of return to education in its publications, including in the World Development Revorts of 1980, 1981, 1984; and in policy studies of financing education (1986)<sup>and of</sup> education issues in Africa (1988).
for �it; the average ...rm speci...c input price index for PMt; and wages for PLt: However, it is clear that in addition to Nt being endogenous, which I will instrument using the number of FDI ...rms, some other right-hand side variables are also endogenous, and may depend on the number of FDI ...rms in the garment sector too. At least one independent instrument for each of the right-hand side variables is needed for (27) to be identi...ed. Here I use the following instrumental variables: average productivity of the industry for �it; and the international prices of cotton and fabrics for pit and PMt: Wages are assumed to be exogenous due to the tremendous hidden unemployment or under-employment in Bangladesh which provide a large pool of workers relative to the size of the industry.

Table 10 presents the results. Columns (5) and (6) ...rst present the least squares estimates when I only include ...rm ...xed e¤ects and the number of local suppliers or the number of total input variety on the right-hand side. While the coe¢ cients are positive and signi...cant, they are likely to be contaminated with measurement errors. The IV estimates are presented in Columns (7) and (8), which are positive and signi...cant. Here again the reported ...rst stage F statistics are larger than 10 in both regressions, and the coe¢ cients have the expected signs.

Columns (9) to (12) estimate (27) : Columns (9) and (10) present the least squares estimates. While the least squares estimates of Nt are positive and signi...cant, the majority of the remaining coe¢ cients either have wrong signs or are insigni...cant. Columns (11) and (12) show the second stage of the IV estimates. Now, all the coe¢ cients have the correct signs and are mostly signi...cant. Most importantly, the results con...rm that an increase in the number of FDI ...rms leads to increases in local input variety and total input variety, which raise the product scope of domestic ...rms. The IV estimates for �N are smaller than the least squares estimates due to reverse causality –larger product scope may cause an increased demand for locally produced intermediate inputs which causes an upward bias in the least squares estimates. On the contrary, the IV estimates are based on exogenous increases in local input variety as the number of FDI ...rms rises to pin down the e¤ect on domestic product scope. The ...rst stage results are also good, where all the coe¢ cients have the expected signs. Here instead of reporting all the 4 ...rst stage F statistics for each regression, I only report the lowest ...rst stage F statistics in Table 10. The point here is to show that if the lowest F statistics are still larger than 10, then all the ...rst stage F statistics are larger than 10, suggesting that all the ...rst stage regressions have signi...cant explanatory power.

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