rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4: The wealth profile of the two cohorts is different even though the maternal education levels are** **<u>very similar</u>**

||Mother's<br>educ<br>|years of<br>ation<br>|Household<br>sc<br>|wealth Z-<br>ore<br>|
|---|---|---|---|---|
||1-year-<br>olds|4-year-<br>olds|1-year-<br>olds|4-year-<br>olds|
|No ECED|7.4|7.3|0.05|-0.54|
||_2.8_|_2.9_|_0.83_|_1.11_|
||**634**|**599**|**494**|**536**|
|Project playgroup only|8.2|7.8|0.16|-0.42|
||_2.9_|_2.8_|_0.84_|_0.98_|
||**536**|**484**|**536**|**451**|
|Non-project playgroup only|8.5|7.5|0.23|-0.47|
||_3.1_|_3.0_|_0.75_|_1.12_|
||**216**|**137**|**216**|**129**|
|MoEC Kindergarten (TK) only|8.7|8.7|0.46|0.06|
||3.0|2.9|0.78|1.02|
||**502**|**943**|**502**|**883**|
|MoRA Kindergarten (RA) only|9.1|8.7|0.29|-0.08|
||_2.9_|_2.8_|_0.75_|_0.85_|
||**110**|**161**|**110**|**152**|
|Project playgroup & other services|8.9|8.6|0.41|-0.03|
||_2.7_|_2.8_|_0.81_|_1.00_|
||**350**|**326**|**350**|**300**|
|Non-project playgroup & other|||||
|services|9.8|9.1|0.65|0.18|
||_2.7_|_2.8_|_0.83_|_0.96_|
||**210**|**238**|**210**|**228**|

Notes: Standard deviations are in italics. Sample size is in bold. Sample means are reported for children in each cohort who report attendance of various types of services and those who report no ECED.

31
**Table A7.2. The Effect of PMT on Probability of Stunting Among Children 12 to 24 Months:  Using Binary Indicator for Program Exposure**

||Poole<br>|d Linear<br>|Linear<br>|Probability<br>|
|---|---|---|---|---|
||Prob|ability|w/Com|munityFEs|
|Year (2000=1)|-0.070|-0.054|0.011|0.033|
||[0.087]|[0.089]|[0.148]|[0.153]|
|Program since ’98 (Yes=1)*Year|0.019|-0.004|-0.088|-0.108|
||[0.085]|[0.087]|[0.153]|[0.159]|
|Gender (boy=1)|0.038|0.037|0.069**|0.073**|
||[0.028]|[0.028]|[0.034]|[0.035]|
|Age|0.007*|0.008*|0.003|0.002|
||[0.004]|[0.004]|[0.005]|[0.005]|
|Mother education (years)|-0.001|-0.000|0.004|0.004|
||[0.003]|[0.003]|[0.004]|[0.004]|
|Father education (years)|-0.000|0.000|0.001|0.001|
||[0.003]|[0.003]|[0.004]|[0.004]|
|Mother height (cm)|-0.010***|-0.010***|-0.014***|-0.014***|
||[0.003]|[0.003]|[0.003]|[0.003]|
|Father height (cm)|-0.011***|-0.011***|-0.012***|-0.012***|
||[0.003]|[0.003]|[0.003]|[0.003]|
|Head of HH (male=1)|-0.019|-0.022|0.062|0.063|
||[0.046]|[0.046]|[0.058]|[0.058]|
|Number of children 5-14 yo in HH|-0.001|-0.003|-0.011|-0.012|
||[0.014]|[0.014]|[0.017]|[0.017]|
|HH owns private toilet (Yes=1)|-0.061**|-0.058*|-0.062|-0.056|
||[0.030]|[0.030]|[0.040]|[0.041]|
|HH has sanitation (Yes=1)|-0.125***|-0.091**|-0.007|0.008|
||[0.037]|[0.043]|[0.061]|[0.064]|
|Type of community (urban=1), C|-|-0.042|-|0.002|
|||[0.039]||[0.075]|
|Distance to district capital (km), C|-|0.000|-|0.001|
|||[0.001]||[0.001]|
|Distance to bus station (km), C|-|-0.004|-|-0.000|
|||[0.002]||[0.005]|
|Land w/ technical irrigation (%), C|-|-0.121|-|0.022|
|||[0.080]||[0.144]|
|Comm. has asphalt road (Yes=1), C|-|0.032|-|-0.086|
|||[0.039]||[0.073]|
|Comm. has sewerage system (Yes=1), C|-|-0.030|-|0.037|
|||[0.032]||[0.058]|
|Comm. has piped water (Yes=1), C|-|-0.038|-|-0.061|
|||[0.035]||[0.066]|
|Constant|3.619***|3.649***|4.332***|4.369***|
||[0.606]|[0.608]|[0.708]|[0.713]|
|R-squared|0.08|0.09|0.37|0.38|

Notes: n is 1042. (C) indicates community-level variables. Robust standard errors are in parentheses. (*), (**) and (***) indicate significance respectively at 10%, 5%; and 1%.

41
**Figure 2.24 Morocco: Geographical provenance of imports in 2013 and 2017 (in billions of US $)**

|**Rank 2013**||**Rank 2017**|
|---|---|---|
|Spain (51.6 billion MAD)|1<br>1|Spain (73.8 billion MAD)|
|France (49.4 billion MAD)|2<br>2|France (52.0 billion MAD)|
|United States of America (28.6 billion MAD)|3<br>3|China (39.6 billion MAD)|
|China (26.4 billion MAD)|4<br>4|United States of America (29.9 billion MAD)|
|Saudi Arabia (23.5 billion MAD)|5<br>5|Germany (26.4 billion MAD)|
|Italy (20.4 billion MAD)|6<br>6|Italy (25.4 billion MAD)|
|Germany (18.2 billion MAD)|7<br>7|Turkey (19.2 billion MAD)|
|Russia (16 billion MAD)|8<br>8|Portugal (13.5 billion MAD)|
|Turkey (11.6 billion MAD)|9<br>9|United Kingdom (9.9 billion MAD)|
|Iraq (11.4 billion MAD)|10<br>10|Russia(9.3 billion MAD)|

_Source_ : Foreign exchange office

**Figure 2.25 Morocco: Changes in exports to and from the European Union, 2001‐2017 (Percent of total exports)**

<!-- Start of picture text --> 100<br>90<br>26.6 25.7 23.4 25.3 25.8 26.8 27.5<br>80 40.5 33.9 40.0 41.2 43.1 39.4 36.4 36.5 35.1 33.5<br>70<br>60<br>50<br>40<br>73.4 74.3 76.6 74.7 74.2 73.2 72.5<br>30 59.5 66.1 60.0 58.8 56.9 60.6 63.6 63.5 64.9 66.5<br>20<br>10<br>0<br>Exports to the EU Exports outside the EU<br><!-- End of picture text --> Note: European Union: France, Italy, Spain, Germany, Portugal, Netherlands, Belgium and Poland. _Source_ : International Trade Centre.

28
# **Appendices**

## **APPENDIX A: Variable description**

_Wage data_ is the log deflated monthly earnings actually received including the base wage, tenure-related and other regularly paid components. Wages are deflated using the consumer price index.

_Worker covariates:_ Tenure is defined as the number of years with the same firm, schooling as the number of completed years of education and skilled as having a number of completed years of education higher than 12 (that marks the end of high-school in Portugal).

_Sector covariates:_ The Herfindahl-Hirschman index of industrial concentration in industry K is defined as _hhiK_ =<sup>�</sup> _J∈K_<sup>(</sup><sup>_mshare_</sup> _J_<sup>)2where</sup><sup>_mshare_</sup> _J_<sup>is the market share of firm</sup><sup>_J_of indus-</sup> try _K_ in 1988 ( _indop_ 88), defined as the ratio of total industry trade (imports plus exports) to domestic demand (industry sales plus net imports).

_Source weighted industry real exchange rate index:_ is defined as the weighted average of the log real exchange rate of each of the importing countries. The weights are the shares of each foreign country’s imports in industry total imports in the base period (1990-1991). Real exchange rates are nominal exchange rates (expressed in foreign currency per escudo) multiplied by the Portuguese consumer price index and divided by the foreign country consumer price index. Nominal exchange rates have been provided by the Bank of Portugal and foreign consumer prices indexes from the International Financial Statistics of the International Monetary Fund.

_Firm covariates:_ The firm size is defined as the number of employees; firm (nominal average) labor productivity as the ratio of firm sales to number of employees; firm age as current year minus year of creation of the firm; and percentage of foreign capital as share of foreign capital in equity.

21
government sectors).<sup>73</sup> The LBS reports outstanding stocks, and based on them BIS calculates exchange rate- and break-adjusted flows.<sup>74</sup> The second set of banking data is the Consolidated Banking Statistics (CBS). This differs from the LBS in that the positions of banks reporting to the BIS are aggregated by the nationality (rather than by the residence) of the reporting bank.<sup>75</sup> Currently, banking groups from 31 countries report to the CBS. We use the CBS on an immediate counterparty basis (CBS/IC).<sup>76</sup> The CBS data does provide a borrower breakdown of the Non-Bank Sector into Public and Private. Since there is no currency breakdown available for the CBS, the BIS does not calculate adjusted flows.

# **B.3 Obtaining Borrowing Sector Splits for Bank Creditor Data**

In this section, we describe our methodology for constructing gross capital inflows and debt outstanding from BIS sources. Our goal is to obtain the stocks and flows measured based on residency (consistent with the LBS data), but we also employ the CBS to obtain certain (non-bank) borrowing sector splits. We deviate from residency in some cases to gain a more complete picture of flows.

The bank loan data is from the LBS by residency (LBSR). For observations prior to 2013,

> 73Data on total cross border claims by BIS reporting banks separated by bank and non-bank counterparties are available going back to 1978. The recent enhancements to the BIS LBS data have provided more granular counterparty sector splits. Most importantly in the context of our study, in the enhanced LBS data the non-bank sector has been divided into the non-bank private sector and the public sector (Avdjiev, McGuire, & Wooldridge, 2015).

> 74Breaks may arise from changes in reporting practices, methodology, population of reporting institutions, etc. Other valuation adjustments besides exchange rates are less concerning, as loans are generally not traded in secondary markets.

75For example, the positions of a French bank’s subsidiary located in New York - which in the LBS are included in the positions of banks in the United States - are consolidated in the CBS with those of its parent and included in the positions of French banks.

> 76The CBS are compiled in two different ways: by immediate counterparty and by ultimate risk. The immediate counterparty is the entity with whom the bank contracts to lend or borrow. Ultimate risk takes account of credit risk mitigants, such as collateral, guarantees and credit protection bought, which transfer the bank’s credit exposure from one counterparty to another. (BIS, 2015)

86
countries, the year of investment and the recipient sector. Access to these data means that we can estimate our difference-in-differences approach, examine simultaneously the various effects of SFD and DFD on FDI, test whether external funds raised in source and destination countries are complements or substitutes, and look at the effects of financial development at both the intensive and extensive margins of FDI. In addition, we use sector-specific data on the number of bilateral M&A transactions and the volume of bilateral trade to assess whether the effects of financial development differ across different internationalisation modes. Thanks to these various data sources, we are able to undertake a much more thorough investigation of the effects of financial development on FDI than what currently exists in the literature.

Our empirical results unambiguously indicate that a sophisticated and well-functioning financial system in source and destination countries strongly facilitates the international expansion of firms through FDI, especially in financially vulnerable sectors. The overall effects of higher SFD and DFD on the relative volume of bilateral greenfield FDI in financially vulnerable sectors are large, positive, and complementary.<sup>12</sup> They appear to operate mainly at the intensive margin rather than at the extensive margin of FDI. Robustness checks suggest that our results are not driven by outliers, omitted variables, specific proxies for financial vulnerability or financial development, reverse causality or the nature of FDI (greenfield or M&A). We also find some evidence for direct and indirect effects of financial development on FDI. In source countries, both the FDI-specific and general access to external finance effects promote FDI. In destination countries, the positive FDI-specific access to external finance effect dominates any potential negative direct disintegration effect and the positive general agglomeration effect dominates overall any potential negative indirect competition effect.

The rest of the paper proceeds as follows. In section 2, we review the different effects of financial development on FDI. In section 3, we introduce our difference-in-differences models. In section 4, we describe the data and estimation method used to estimate them. We also provide some stylised facts. In section 5, we present our empirical results. Finally, we conclude in section 6.

> 12In line with our difference-in-differences approach, we have focused on the effects of changes in financial development on the volume of greenfield FDI in a given sector _relative_ to the effects induced by similar changes in financial development on the volume of FDI in a sector less financially vulnerable. Nevertheless, we also show in the paper that the overall effects of higher SFD and DFD on the _absolute_ volume of bilateral greenfield FDI are also large and positive, especially in financially vulnerable sectors.

4
# **Table 7.  An Example of Expenditure Need Determination in Australia: Secondary Education Expenditure Need Factors Government Secondary Education Factors -**

|**Disability Factors**|**NSW**|**Vic**|**Qld**|**WA**|**SA**|**Tas**|**ACT**|**NT**|
|---|---|---|---|---|---|---|---|---|
|Dispersion|0.9973|0.9921|1.0093|1.0106|0.9972|0.9952|0.9885|1.071|
|Grade Cost|1.0014|1.0028|0.9966|0.995|0.9992|0.9998|1.0016|0.9979|
|Input Costs|1.012|0.995|0.986|1.003|0.991|0.99|1.008|1.034|
|Relevant Population|0.9749|0.8874|1.0983|1.1639|0.9679|1.1422|0.975|1.2226|
|Administrative Scale|0.9946|0.9946|0.9946|1.0065|1.0105|1.0304|1.0463|1.1139|
|Service Delivery Scale|0.9922|0.9906|1.0031|1.0153|1.0166|1.038|0.9714|1.1141|
|Vandalism & Security|1.0023|1.0023|0.9973|0.9973|0.9973|0.9923|0.9923|0.9923|
|Cross-border|0.9965|1.0001|1.0001|1.0001|1.0001|1.0001|1.066|1.0001|
|**Category Disability**|**0.9692**|**0.8658**|**1.0815**|**1.1941**|**0.9772**|**1.1917**|**1.044**|**1.6605**|

Source: Commonwealth Grants Commission, Australia, 1995-96 Review The Australian experience highlights the practical difficulties associated with implementing fiscal need compensation as part of a comprehensive fiscal equalization approach (see Shah, 2004, 2008).

Denmark, Japan, Netherlands and Switzerland among others take great care in equalizing expenditure needs in central-local transfers. However, none of these countries have found a method of determination that escapes controversy and criticism. In fact, experience shows that more complex the approach the greater criticism it invites.

In conclusion, there neither is nor their ever will be a holy grail of expenditure need equalization. All practical methods by their nature are subjective and controversial. Sophisticated and complex methods have not shown to be superior to simpler determination of expenditure need equalization through specific purpose output-based fiscal equalization transfers as discussed in the following section. The opposition to such rough but simple and transparent justice partly comes from a lack of understanding of these newer types of transfers and equating them with traditional earmarked grants. These issues are discussed at length in the following section.

# **6. A Simpler Approach to Expenditure Need Equalization: Use of OutputBased Transfers for Merit Public Services**

Economic rationales for output-based grants (used interchangeably with performanceoriented transfers in this chapter) stem from the emphasis on contract-based management under the new public management framework and strengthening demand for good governance by lowering the transactions costs for citizens in obtaining public services under the new institutional economics approach. The new public management framework seeks to strengthen accountability for results by changing the management paradigm in the public sector from permanent appointments to contractual appointment and 20
used the opportunity to pass power and resources to the subnational levels where they held some of the executive offices. Also, the Liberal Party, which had favored decentralization since the nineteenth century, had a plurality of delegates-about one-third-many more than the traditionally centrist Conservative party (Buenahora 1997). Congress decided to live with decentralization because it gave local administrations the power to execute spending, and these local entities were usually allied with their congressmen, acting as their machines in many cases (Archer and Shugart 1997; Willis, Garman, and Haggard 1997).

**Table 1.** Growth, Inflation, **and** Fiscal Balances in Colombia, 1987-97

||(percent)<br>|||
|---|---|---|---|
|_Indicator_|_1987-90_|_1991-94_|_1995-97a_|
|Real growth (annual average)|4.5|4.3|3.5|
|Inflation (annual average)|15|9|7|
|Overall fiscal balance as a percentage<br>of GDP||||
|Central|-1.0|-1.1|-4.2|
|Department|-0.2|-0.3|-0.2|
|Municipal|-0.1|-0.3|-0 .1|
|Primary balance as a percentage of GDP||||
|Central|0.1|0.3|-2.2|
|Departrnent|-0.1|-0.1|0.1|
|Municipal|0.0|0.0|0.2|

a. 1995 only for department and municipal balances. _Sources:_ World Bank database; Central Bank of Colombia, table 4.

As the system operated in the late 1990s, strengths emerged, such as increased political participation, and so did weaknesses, such as mismatched spending authority and excessive borrowing by some subnational units. Overall, however, the fiscal deterioration occurred mostly at the national level. See table 1.

# **II. Explanatory Variables**

Because the Colombian experience with political and fiscal decentralization is relatively short and an economic crisis has not yet tested the system, we evaluate the system on dimensions that have proven critical in Argentina and Brazil for determining whether subnational govermments contribute to unsustainable overall public sector deficits. The explanatory variables fall into two 7
size is important, especially in the Indian context where informal establishments constitute a large share of value added in non-traded services, such as retail, real estate, and personal services (Nayyar, 2012b).

In Table 8, we examine a sub-sample analysis for the employment effects by firm size. In doing so, we consider four size groups, non-tradable service sector firms with 1-10 employees (column 1), 11-30 employees (column 2), 31-50 employees (column 3) and more than 50 employees (column 4). As can be seen, the only significant effects are among the smallest group of firms, with a coefficient of 0.39. Hence, the employment effects from tradable service growth are most relevant for the smaller non-tradable service firms.

Table 8: Impact of tradable services on non-tradable employment by size

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||1-10|11-30|31-50|_>_ 50|
|Log of tradable services|0.391**|0.284|0.512|0.413|
||(0.177)|(0.297)|(0.424)|(0.700)|
|Observations|1,173|1,173|1,173|1,173|
|District FE|yes|yes|yes|yes|
|Year FE|yes|yes|yes|yes|

*** ** * Standard errors (in parentheses) are clustered at the district level. _p <_ 0.01, _p <_ 0.05, _p <_ 0.1.

# **4 Conclusion**

Structural transformation toward high-end services and increased trade have been important growth drivers in India and beyond. However, tradable services growth can also have positive spillover effects on non-tradable services, which cannot benefit directly from globalization and trade-enhancing technological advances. In particular, these effects may have important distributional outcomes between men and women. While similar linkages between manufacturing and services have been well explored, spillovers between tradable and non-tradable services have been understudied.

We find that a 10% increase in tradable services employment leads to a 4.2% increase in non-tradable services employment. There is also an increase in the number of firms in nontradable services by 2.8%, but this result is less statistically significant. The employment impact is much larger for female workers; a 10% increase in tradable services employment leads to a 9.1% increase in non-traded services employment for women compared to 4.2% for 19
systm, for exanple, although the budget constraints (2) **are** written under **the** assumption that foreign bonds can only be accumulated by first acquiring foreign exchange at the financLal rate from **the** central bank, It may be possible for iuporters or exportert to borrow and lend internationally at a different real intorest rate simply by Increasing the lag between shipment and paymAnt.<sup>12</sup> Moreover, the coexistence of two s*parate exchange rates In a dual 13 **system may even nct** up opportunitios for pure arbitrage.

With respect to trade taxes, **the** key casumption in equation (2) is that smaggling and customs fraud **are** ruled out. In this section and the next, we ask how the possibility of illogal transactions alters the conclusions of Sections 1 and 2. To focus on the impact of illegal trade, we do the analysis in flexible-price, full-employment models in which the various policies have no real effects In **the** absence of illegal trade. Our key result ls that the possibility of illegal trade breaks the equivalence we have been emphasizing **between** UTCS schemes and changes in **the** commercial exchange rate. When wages and prices are flexible, exchange rate changes may well have Dt impact on smuggling incentives; in contrast, the level of illegal trade is a nondecreasing function of the uniform trade tax/subsidy rate, and is strictly increasing above some minimum UTCS rate.

This lack of equivalence survives when prices or **wages** are sticky, as 12 Suppose, for example, that the financial rate were expected to appreciate relative to the commercial rate (7 < 1 in equation (5)). An exporter could avoid the implied lower real interest rate by retaining export earnings from period 1 abroad and repatriating them In period 2, with interest, at the comercial rate.

13 Suppose that unilateral transfers from abroad take place **at the** co mercial rate, and that the financial rate is appreciated relative to the co ercial rate. Individuals can then collect the difference between the two exchange rates by smuggling out exports and receiving payment in the form of an (apparently) unrelated unilateral transfer.

16
# **Figure 3 OECD index of regulatory restrictions in air passenger transport**

<!-- Start of picture text --> Hungary<br>Tunisia<br>Czech republic<br>Ireland<br>Greece<br>Slovak Republic<br>Turkey<br>Poland<br>Mexico<br>Portugal<br>Morocco<br>New Zealand<br>Italy<br>Finland<br>Korea<br>0.0 1.0 2.0 3.0 4.0 5.0 6.0<br>Restrictiveness index<br><!-- End of picture text --> Data source: Based on OECD (2005b).

25