rafmacalaba/datause-encoder-data huggingface dataset:
**Figure II.1 Trends of Investment, Savings, Current Account Deficits, Reserve Accumulation and Private Capital Inflows**

<!-- Start of picture text --> IrWonesis ~~~~~~~~~~~~~Indonesia<br>Pml d GOP Indonesia Perct of GDP<br>40 _________________-----<br>30 InvesO-at<br>_ -~~~ __/v II s . IIi<br>20 UE-tj<br>K 0<br>10 i : a o 4<br>0I 1980 1982 1984 1988 1988 1990 1992 - 1994 1998<br>1983 1962 19S 198e 1988 1990 193 1 2 9 9 4 _ MI ..................................... eCurrrdaccourdelii .........1 -Resewveac c t umrajbon . Net private capital flows<br>Pwcert d GDP Koma 1 j Per d GWKorea<br>40 lB<br>ii 2<br>20 0 II<br>-2<br>10 ,ri -- ___<br>! 1980 1982 1984 1986 1989 1990 1992 1994 1996<br>1980 1982 1984 1998 1 i 1992 1994 159i I _ Current accoultdefot=Reserve accum oaln..Net private capital flow<br>~~~_ _ ._ ....._ _ __ .. ....- __._...._ ... . . I . .- ....<br>NMblaysia Percent otGDP Malaysia<br>Per er d G OP i!20 .. .. ......... ..... ....... .......... ................. ...<br>40 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 2<br>20<br>-5<br>10~~~~~~~~~~~~~v1<br>1980 _____________________________________ 1982 1984 1988 1988 1990 1992 1994 19958 11980 _ Current account deSif 1982 1984 1986 Reserve accumulationr 1988 1 1992 Net pnvate capital flows1994 1996<br>. . - ._ ._.- - --- . 1 _------ -- -_ -- -<br>Pear1 of GDP Philippinea i Percent of GDP Phillpplnes<br>3~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0.. .. ... ... ------ .. - ................ .. .... .......... ----- ....<br>lrwes5nm<br>20 1' s<br>100<br>i .. . I .. , . .__ ... ..<br>0 1980 198 1992 1994 1998<br>1980 92eo 1984 1958 1988 1990 1992 1994 19986!1 Current account deficaReserve acoumulation. Netpnvatecapitatflov*<br><!-- End of picture text --> 6
where I[port<sup>i</sup> ] is an indicator for whether the trip from o to d along segment i ends at a port, effectively adding a $200 terminal handling charge for each port the container passes through. The value of mode-switching and terminal handling charges are very small relative to the value of a shipment (i.e., 0.13–0.54 percent with a shipment value of $50,000/TEU).

We set freight rates ĉ<sup>j</sup> equal to those quoted along the network. Table 1 reports a range of quotes, by mode, measured in US dollars per twenty-foot-equivalent unit (TEU) per kilometer. Looking at the median upper and lower bound quotes by mode across references yields the well-known cost ranking in which sea is the cheapest mode (at $0.04 TEU/KM), rail is the next cheapest mode ($0.23-$0.47 TEU/KM) and road is the most expensive mode ($0.63-$0.85 TEU/KM). For regular road and rail segments (e.g., single carriageway), we set the freight rate equal to the median of the upper bound estimates in Table 1, a more expensive rate. For high capacity road and rail, we set the freight rate equal to the median of the lower bound estimates, a less expensive rate. This captures the idea that upgrades to higher capacity lowers costs. For sea, we use a single freight rate equal to the median estimate of the sea freight rate across all studies (the very high upper bound estimates for sea trade presented in Table 1 correspond to very short distances, due to the high fixed costs of sea travel, and so does not apply generally).

A Belt and Road project is modeled as a change in the vector of trade costs from τ= {τod} to τ′ = {τ′od}, leading to a change in market access. We can write this change as where the numerator in each expression indicates the value of market access evaluated when substituting τ′ in place of τ. Table 2 shows how specific improvements to the network are recorded as changes in trade cost, using several examples from the Belt and 17
**Table 3: Linkages, Foreign Investors vs. Domestic Producers (Mean Difference)**

|**Variable**|**Definition**|**Difference**<br>**(1)**|**Additional**<br>**controls for**<br>**ln****_emp_**<br>**(2)**|
|---|---|---|---|
|**Inputs**||||
|_inp_dom_|Percentage of inputs sourced from domestic suppliers<br>in the firm's total inputs|-16.0734***<br>(0.008)|-12.4843**<br>(0.043)|
|_inp_dom_mat_|Percentage of raw materials from domestic firms of firm's|-16.1221***|-12.4158**|
||total input purchases from domestic firms|(0.002)|(0.029)|
|_inp_dom_comp_|Percentage of parts and components from domestic|-0.1020|-0.3504|
||firms of firm's total input purchases from domestic firms|(0.938)|(0.807)|
|_inp_dom_pack_|Percentage of packaging from domestic firms of firm's|3.7895|5.6411|
||total input purchases from domestic firms|(0.331)|(0.201)|
|_inp_dom_equip_|Percentage of equipment and machinery from domestic|-5.0125**|-5.0252**|
||firms of firm's total input purchases from domestic firms|(0.025)|(0.041)|
|_inp_dom_bus_|Percentage of business services from domestic firms of firm's|0.7942|-0.1636|
||total input purchases from domestic firms|(0.693)|(0.940)|
|_inp_dom_tech_|Percentage of technical services from domestic firms of|3.7713**|3.7013**|
||firm's total input purchases from domestic firms|(0.018)|(0.031)|
|_inp_dom_oth_|Percentage of transport, security, cleaning, catering, and|13.9780***|9.5439***|
||other services from domestic firms of firm's total input<br>urchases from domestic firms|(0.000)|(0.001)|
|**Labor**|p|||
|_emp_dom_|Percentage of domestic workers in the firm's total|-4.0758***|-4.4249***|
||workforce|(0.002)|(0.002)|
|_emp_ter_dom_|Percentage of domestic workers with tertiary education|2.8700|4.1928|
||in the firm's workforce|(0.613)|(0.445)|
|_emp_sec_dom_|Percentage of domestic workers with secondary education in|-7.6005|-8.0573|
|||(0.261)|(0.225)|
|_emp_oth_dom_|Percentage of other domestic workers in the firm's|-0.1145|-0.7786|
||workforce|(0.986)|(0.906)|
|_man_dom_|Percentage of domestic managers of firm's total managers|-15.5842***|-16.4872***|
|||(0.000)|(0.000)|
|_super_dom_|Percentage of domestic supervisors of firm's total|-6.6335|-8.5360|
||supervisors|(0.181)|(0.100)|
|_tech_dom_|Percentage of technical positions of firm's total technical|-5.9357|-5.8431|
||positions|(0.159)|(0.185)|
|**Output**||||
|_market_|Percentage of sales to domestic market of firm's total sales|-34.0663***|-28.4941***|
|||(0.000)|(0.001)|

Source: Own calculations. p*<0.1, p**<0.05, p***<0.01 (p-values in parentheses). Note: Variables refer to FY 2012. All regressions control for country-sector fixed effects. Standard errors are robust to heteroscedasticity.

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with audited financial statements are more likely to apply for loans than others and that female ownership of firms was associated with a higher probability of loan rejection.

When we include those variables in our financial constraints regressions, neither is significant (Table 8, models 3 and 4). Their inclusion does not, however, change our main conclusions regarding firm size and foreign bank participation. We were unable to include two additional variables used in Brown et al. (2011) in this part of the analysis, the share of firm earnings received through a bank account and a general index of the severity of business obstacles.<sup>32</sup> As noted above, the share of firm income received through a bank account is not available in the 2008 BEEPS survey.<sup>33</sup> Because our dependent variable is based on perceptions of business obstacles, it did not make sense to include another measure of the perceived severity of business obstacles as an explanatory variable. In addition, changes in question format made it impossible to construct the business obstacles variable that Brown et al. (2011) use for all BEEPS surveys. In all, however, our results are robust to the inclusion of the explanatory variables used in Brown et al. (2011) that it was feasible to include.

_Fixed Exchange Rates, Current Account Deficits._ As a final robustness check, we attempt to distinguish the rapid, perhaps de-stabilizing increase in some types of foreign lending from the steady, gradual growth in lending through the subsidiaries and branches of foreign banks. Relative to local lending, direct cross-border flows to ECA had been growing much more quickly than those to other regions. When foreign claims are separated into direct cross-border flows (including overseas lending by the parents of multinational banks) versus local lending in host markets by the subsidiaries and branches of foreign-owned banks, it becomes clear that the decline in 2008 was attributable to reductions in cross-border flows.<sup>34</sup> Figure 2 shows that total foreign claims (as a share of GDP) had declined for the ECA region by 2008, whereas no such decline had occurred in other developing regions.

> 32 The index is based on questions about tax rates and administration, licensing, and corruption as obstacles to growth.

> 33 The main results are robust to the inclusion of a variable indicating that the firm has a bank account (regressions unreported).

> 34 See Kamil and Rai (2010) for breakdowns of foreign claims by region.

32
Adult survival rates are available in consistent form for a large cross-section of countries from 11 the WDI. The ASR has the advantage of measuring survival during working years (15-60), and thus seems likely to be a good measure of health during working years, which is what should be most relevant for determining the level of output per worker (Weil 2007). Weil (2007) estimates a value of  _ASR_ =0.653.

Table 2 reports descriptive statistics for years of schooling and human capital for all the countries in the sample (Panel A) and subsamples according to level of economic development and year (Panel B). Notice that while other capital assets in Table 2 are expressed in dollars, human capital is the 'physical' human capital embodied in a worker as expressed in (3).

# _2.3 Institutional capital_

As in World Bank (2006) our indicator for institutional capital is a rule of law index from Kaufmann, Kraay and Mastruzzi (2009). It measures the extent to which agents have confidence in and abide by the rules of society. In particular, it measures the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence.<sup>12</sup> In their Worldwide Governance Indicators (WGI) research project, Kaufmann, Kraay and Mastruzzi, provide data on five additional dimensions of governance: voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality and control of corruption. These indicators are highly correlated -the lowest pair-wise correlation coefficient in our sample is 0.76 between political stability and regulatory quality.

An argument employed in World Bank (2006) to use the rule of law indicator, is that it captures well some of the features of a country's social capital, in particular trust. The correlation between a generalized trust indicator in Paldam and Svendsen (2006) and rule of law, 0.51, is larger than for other indicators of governance.

As an alternative, we averaged the six governance indicators. Many previous studies have averaged or summed governance indicators, with the rationale being that averaging reduces measurement error if the indicators pertain to similar underlying concepts of governance and have independent errors (e.g. Wheeler and Mody, 1992; Mauro, 1995; Chong and Calderon, 2000). In Section 4 we report results for the rule of law index but the results were robust to the use of an aggregate indicator.

> 11 Adult mortality rates (AMR) are calculated as the weighted average of male and female adult mortality rates (series SP.DYN.AMRT.MA and SP.DYN.AMRT.FE, respectively) available from the WDI (2008). For Australia, Canada, Germany, Italy and the US, 2005 rates are missing so 2004 values are used instead. ASR=1-AMR.

> 12 In the original dataset, the rule of law index ranges between -2.69 and 2.12; we rescaled it between 1 and 100.

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<!-- Start of picture text --> Coefficient plot with error bars<br>GDP pc (In) i<br>FDP (In)- it<br>Battle-related deaths (In)- H<br>South Asia-. |-—-—:<br>Middle. East & North Africa-: pos | '':<br>Latin; America. & Caribbean-. |---| '‘<br>East Asia. & Pacific-_ }—---- , \‘\<br>© ba) 8<br>‘<br>Average marginal effects<br><!-- End of picture text -->
**Table 4A: Maternal Education and Child Time Use**

|Child Time on educational activity<br>outside school (mins/day)|**Children**<br>(1)<br>All Children<br>(2)<br>Enrolled Children|
|---|---|
|OLS|42.84<br>[4.40]a<br>19.8<br>[4.47]a|
|IV|75.34<br>[45.25]c<br>77.15<br>[41.71]c|
|Observations|4331<br>3305|

_Notes:_ Robust standard errors, clustered at current village level; (a): significant at p<0.01, (b): significant at p<0.05, (c): significant at p<0.1. For discrete variables, we report both the IV coefficient and the Average Treatment on Treated using a biprobit specification. Girl’s school present in mother’s birth village is the excluded variable in the IV and the biprobit. All regression control for full set of indication variables for mother’s age, mother’s birth village _tehsil_ . In addition, Child-level regressions control for a full set of indicator variables for child’s age and child gender.

# **Table 4B: Maternal Education and Mother and Family Time Use**

|Did the mother spend any time on children’s<br>educational needs? (daily)|**Mothers**<br>(1)<br>All children<br>(2)<br>Families with no child above 12|
|---|---|
|OLS|0.21<br>0.29|
||[0.03]a<br>[0.04]a|
|IV|0.12<br>[0.13]<br>0.23<br>[0.22]|
|Biprobit|0.21<br>[0.03]a<br>0.27<br>[0.04]a|
|Observations|1437<br>580|
|Mother’s time spent on children’s educational<br>needs (mins/day)||
|OLS|19.12<br>[2.46]a<br>25.32<br>[3.71]a|
|IV|14.00<br>[13.64]<br>40.29<br>[23.57]c|
|Observations|1437<br>580|

_Notes:_ Robust standard errors, clustered at current village level; (a): significant at p<0.01, (b): significant at p<0.05, (c): significant at p<0.1. For discrete variables, we report both the IV coefficient and the Average Treatment on Treated using a biprobit specification. Girl’s school present in mother’s birth village is the excluded variable in the IV and the biprobit. All regression control for full set of indication variables for mother’s age, mother’s birth village _tehsil_ . In addition, Child-level regressions control for a full set of indicator variables for child’s age and child gender.

33
charge for management is assumed to be 5% of total revenues. For the opportunity cost of land, an estimate of the net returns per acre to non-irrigated winter wheat production on similar soils in the area is used. For corn silage, water delivered—using flexible pipe (with an assumed application efficiency of 40%) and four irrigation events (each with a net infiltration of 7.6 cm)—amounts to 7,709 m<sup>3</sup> per hectare, while water consumed is estimated to be about 4,656 m<sup>3</sup> per hectare.

**Table 6.1  Estimates of Unit Net Returns for Corn Silage (1 hectare)**

||**Without Owned Inputs**|**With Owned Inputs**|
|---|---|---|
|**Description**|**Value/Cost**<br>**per Hectare**|**Value/Cost**<br>**per Hectare**|
|**Net Return**|**557.99**|**429.28**|
|Annual Cost of Water Supply|129.20|129.20|
|**Net Return to Water**|**428.79**|**300.08**|
|Water Delivery|7,709 m3|7,709 m3|
|<br>**Net Return per m3 of Water Delivery**|**0.056**|**0.039**|
|Consumptive Use|4,656 m3|4,656 m3|
|**Net Return per m3 of Consumptive Use**|**0.092**|**0.064**|

69. A comparison of estimated FDB in net return per acre foot of water delivered and water consumed for each owned-input specification shows:

- FDB estimates per unit delivered are considerably (about 43 percent) larger where owned input charges are assumed to be zero.

- For each owned input price specification, the FDB estimate is significantly (about 64 percent) higher for the consumptive use specification than for the delivery version.

# **6.2 Results for the Representative Farm Model**

70. The results for the representative farm model are based on the calculations from the agroeconomic model, which represents the cropping patterns with the five main crops in the service area of the irrigation organization. The optimization results show that the whole service area of 16,188 ha is irrigated using the available 148.4 million m<sup>3</sup> of surface and groundwater supplies. Consumptive use for 22
- Fifth and finally, a simple practical point.  For many purposes we do recognize that it can be of interest for some users to make comparisons of governance based on particular individual data sources.  To facilitate this, with the 2005 release of the aggregate governance indicators, we have made all but three of our underlying data sources available to users on our website.<sup>8</sup>

# **_<u>Critique 3:</u> Changes over time in some of the individual indicators underlying the WGI aggregate indicators reflect corrections of past errors rather than actual changes._**

This critique of several of our underlying indicators is made by K in the context of his discussion of trends in governance in countries in the ECA region between 2002 and 2005 . We do not think that the evidence provided by K supports his claim. K argues that in many cases individual data sources change their assessments of governance in a country simply to correct past errors in their assessments: countries that were rated "too high" in the past get lower scores, and vice versa. K then goes on to document that among ECA countries there is "regression to the mean", in the sense that changes in governance tend to be negatively correlated with initial scores, and interpret this as evidence that risk rating agencies change their scores to correct past "errors".

We believe that this is an overinterpretation of his results based on a regression that has a much simpler explanation. To be specific, let y(j,t) be the rating of country j in year t. K regresses y(j,t)-y(j,t-1) on y(j,t-1), which is of course mathematically identical to regressing y(j,t) on y(j,t-1): the coefficient on the initial value in the first regression will just be the coefficient in the second regression, minus one. But the coefficient in the second regression, which is just the autocorrelation coefficient of the governance rating, is for most of these data sources a positive number between 0 and 1. Suppose next that the governance rating is a noisy proxy for true governance, i.e. that y(j,t) = g(j,t) + e(j,t) where g(j,t) is true governance and e(j,t) is the error made by a particular source. It seems quite plausible to us that governance on average changes rather slowly over

> 8 These three sources are the Country Policy and Institutional Assessments produced by the World Bank, the African Development Bank, and the Asian Development Bank, which for the most part are treated as confidential by these organizations.  Only in the past few years has limited, but growing disclosure of this data been made by these organizations, and full public access to the detailed disaggregated and historical data on which we rely is still not permitted..

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