rafmacalaba/datause-encoder-data huggingface dataset:
resulted in major institutional changes in the sector. The 1990s have seen all countries in the region essentially adopt new laws aiming at liberalizing telecoms and have implemented them at least partially, with the major exceptions of Uruguay and Costa Rica. Moreover, out of the 20 countries sample covered by Table 1, about 75% have fully or partially privatized the historical operator.<sup>5</sup> The goal was also to introduce competition at least gradually. The dissemination of cellular phones, the opportunities for call backs and other technology changes forced competition into the sector in many countries. Most have some degree of competition although often with restrictions. This can be seen in the generous exclusivity periods for the first entrants in the business after privatization. These first entrants were typically strategic investors which took some risks and wanted some protection for those risks, at least during the early 1990s. In exchange, governments demanded investment commitments to improve coverage and service quality. The main exception has been Uruguay where a 1992 referendum resulted in the rejection of changes in the sector and in particular of its liberalization. In some other countries, including Costa Rica, Ecuador and Honduras, political and economic problems postponed the actual reforms but Table 1 shows that the major laws have already been introduced.

An additional important change has been the creation of independent regulatory agencies. Table 1 reveals that in most countries these agencies are sector specific and are accountable to a ministry rather than to the head of state or to the parliament. In many countries, the main threat to their independence from political interference results from their lack of financial autonomy. As long as a minister is expected to sign off a budget transfer to finance the regulator's expenses, there is a risk of conflict of interest in regulatory decisions. The final institutional change worth mentioning is the one that has not happened yet! While basically all countries have in fact introduced the appropriate legislation and while most of the regulators have defined the basic regulatory principles they intend to follow on most issues, the supporting regulatory analysis is still lagging. Take the example of the regulatory regime. Most Latin American countries have decided to rely on a price cap which implies that the regulator must have some idea of the efficiency gains realized by the operators since this gain will eventually have to be shared with the users. No country has yet defined the methodology to be followed to assess these gains, leaving the regulatory decision open to negotiation. Regulatory gaps like this one seem not to have had dramatic impacts in _5_ All the data used and quoted in this document is from the ITU data base unless otherwise specified.

3
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World Bank. 2019b. Modernizing Trade in Pakistan: Policy Reform Handbook. World Bank, Washington, DC. World Bank. https://openknowledge.worldbank.org/handle/10986/33184 License: CC BY 3.0 IGO.

World Bank. 2020. Import Duties and Export Performance: Some Stylized Facts for Pakistan. World Bank, Washington, DC. World Bank. https://openknowledge.worldbank.org/handle/10986/33886 25
|**Canada**|Reform leader|Westminster|
|---|---|---|
|||Common law / federalist|
|**Netherlands**|Recent MTEF reform|Codified<br>Germanic|

Following an overview in Part 2 that surveys the design and implementation of MTEFs, the paper is structured around the key areas of MTEF practice: institutional arrangements and procedures (Part 3), program costing frameworks and practices (Part 4), and budget and forward years cost estimates (Part 5). Part 6 is a concluding section that provides observations on the current status of MTEF costing practices as reported by finance ministries. Readers who are familiar with MTEF concepts and practices can skip Part 2 and go straight to Part 3.

# **2. Defining and implementing MTEF**

## **2.1 Defining a medium term expenditure framework**

MTEF is one of the key budget planning and management practices that define contemporary public financial management reform in advanced countries. In its 2012 budget practices survey, for example, the OECD reported that nearly 90% of advanced economies had implemented some form of MTEF as part of their national budgeting framework (see OECD International Database of Budget Practices and Procedures 2012).

In conjunction with other PFM developments, the MTEF is an important way of addressing some of the key shortcomings in annual budgeting. An annual budget for revenues and expenditures often does not take account of the consequences of current year budget decisions in subsequent years. To better understand the fiscal impact of policies, to more effectively set current and future policy priorities, and to exercise control over budget aggregates, an MTEF integrates the annual budget formulation cycle with a medium-term planning process.

An MTEF is therefore not simply the documentation of multiyear estimates of revenue and expenditures alongside the annual budget, but rather ‘all the systems, rules and procedures that ensure the government’s fiscal plans are drawn up with a view to their impact over several years’ (Harris, Hughes, Ljungman and Sateriale 2013, 137). As this implies, there is a wide range of approaches to the implementation of an MTEF. For illustrative purposes, we can define an MTEF as a multiyear expenditure planning and management framework that typically contains three elements: (i) a medium term envelope of aggregate resources set by the 6
from foreign countries. Hovhannisyan & Keller (2010) analyze data on international business travel from the U.S. to 74 other countries, and find that it has an effect on innovation in these countries beyond the usual technology transfer through trade and FDI. The stock of migrants itself may also induce network effects from increased trade (Rauch 2001) and knowledge transfer (Kerr 2008; Kerr & Lincoln 2010). Even the fact of immigration can be a source of growth for the recipient nation, as migrants tend to be self-selected as industrious and seeking opportunity (McCraw 2010).

# **3 Empirical measures of growth polarity**

## **3.1 Measure construction**

The most straightforward measure of a growth pole is a given economy’s contribution to global growth: where _yit_ is the GDP of country _i_ at time _t_ , _Yt_ =<sup>�</sup><sup>_N_</sup> _j_<sup>_yjt_is global GDP which simply aggregates</sup> GDP for all _N ∈ C_ countries, and ∆ _yit ≡ yit − yi,t−_ 1 is the change in GDP of economy _i_ . The above can be rewritten as where _s_<sup>_y_</sup> _it_<sup>and</sup><sup>_g_</sup> _it_<sup>_y_aretheoutputshareandgrowthrateofcountry</sup><sup>_i_attime</sup><sup>_t_,whichmeans</sup> that a growth pole as defined in (1) is simply the size-adjusted growth rate of the economy. (1) is the standard approach to decomposing the relative contribution of each country to global growth. While intuitive and direct, such a measure would be incomplete. Critically, it fails to embody the manner by which growth poles exert their _polarity_ , in the sense of capturing the transmission and spillover mechanisms for the country’s growth to others in its economic space.

The natural extension of (1) is then to allow for such alternative channels of growth transmission, by replacing the size weight in (1) with weights corresponding to these channels. The literature has identified a range of alternative channels, and the theoretical justifications underpinning these different media was discussed in detail in Section 2. Here we list the different channels and propose empirical measures designed to capture these effects.

To capture the trade channel—which captures both the direct effect of absorption of other nations’ exports, along with the indirect effect of facilitating technology transfer through trade linkages—a polarity measure of trade-related spillovers would be given by 7
**All** Philippines

**Table All:** Regional disparity trends in Philippines

|Year|Max/Min<br>(MMR)|Simple<br>coefficient<br>of variation<br>(CVu)|Weighted<br>coefficient<br>of variation<br>(CVw)|Relative<br>mean<br>Deviation<br>(Rw)|Unweighte<br>gini index<br>(Gu)|d Weighted<br>gini index<br>(Gw)|Theil<br>index (T)|
|---|---|---|---|---|---|---|---|
|1982|5.366|0.506|0.550|0.365|0.216|0.268|0.166|
|1983|5.362|0.520|0,568|0.368|0.217|0.271|0.172|
|1984|4.803|0.485|0.533|0.352|0.203|0.255|0.158|
|1985|4.534|0.465|0.511|0.338|0.197|0.246|0.150|
|1986|4.669|0.463|0.507|0.337|0.198|0.246|0.149|
|1987|4.928|0.494|0.531|0.360|0.248|0.262|0.146|
|1988|4.976|0.504|0.544|0.362|0.287|0.265|0.150|
|1989|5.083|0.518|0.559|0.367|0.290|0.270|0.156|
|1990|5.005|0.515|0.556|0.365|0.285|0.267|0.154|
|1991|4.910|0.511|0.552|0.364|0.284|0.266|0.152|
|1992|4.689|0.494|0.526|0.348|0.280|0.258|0.144|
|1993|6.868|0.517|0.513|0.351|0.301|0.255|0.117|
|1994|6.627|0.515|0.513|0.352|0.302|0.255|0.116|
|1995|6.431|0.512|0.513|0.353|0.299|0.254|0.116|
|1996|6.516|0.525|0.524|0.362|0.305|0.259|0.120|
|1997|6.653|0.530|0.532|0.367|0.307|0.261|0.123|
|1998|6.760|0.536|0.537|0.369|0.311|0.262|0.125|

**Figure All: Regional disparity trends** in **Philippines**

<!-- Start of picture text --> 0.600<br>0.500-<br>o 0.400<br>a<br>a 0.300<br>e5.; < ,X0. - . . V<br>-0.2000-<br>0.000 ' '' '* ,:','S<br>1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> Simple coefficient of vanation (CVu) -- - Weighted coefficient of variation (CVw) * Relative mean Deviation (Rw)<br>+Unweighted gini index (Gu) 'Weighted gini index (Gw) -Theil index (T)<br><!-- End of picture text --> 46
Figure 8: GDP growth rate and mobility within and across labor markets, 2004-2015 <!-- Start of picture text --> 300 300<br>200 200<br>100 100<br>0 0<br>-100 -100<br>-200 -200<br>-300 -300<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>(a) Within labor markets (b) Across labor markets<br><!-- End of picture text --> _Notes_ : Panel (a) depicts the ratio between the number of workers who change jobs within a labor market and the number of job-stayers. Panel (b) depicts the ratio between the number of workers who move to other labor markets and the number of job-stayers. All series are in terms of deviations from the mean and multiplied by the inverse of their standard deviations for comparison.

Similarly, according to Proposition 2-(i), between-market mobility should be positively correlated with aggregate shocks as well. Although the proposition makes this statement only for the geometric mean of between-market mobility, we can empirically expect to see a positive correlation with the arithmetic mean as well, since geometric and arithmetic means are often highly correlated. The right panel of Figure 8 confirms the empirical correlation between positive shocks and between-market mobility with correlations coefficients of 0 _._ 65 and 0 _._ 84 for arithmetic and geometric means.<sup>53</sup> Next, the left panel of Figure 9 presents the correlation between overall mobility (between- and within-market mobility combined) and aggregate shocks, based on Proposition 2-(ii). Finally, following Proposition 3, the right panel of Figure 9 shows a positive correlation between the inverse of moving cost differences between the baseline and the auxiliary models and the GDP growth rate.<sup>54</sup> These results suggest that the estimated moving cost from standard dynamic labor mobility models without our job options channel captures fundamentally endogenous variations, while this cost is assumed to be exogenous in those models. Our framework shows that the endogenous response of the number of job options to an aggregate shock is the source of the variation. In summary, the figures presented in this section show the model’s ability to capture important channels related to labor mobility.

# **7 Model Fit and Robustness**

In this section, we show that the model can match the labor flows in the data reasonably well, despite having a simple moving cost structure with only three parameters; and that the empirical results are robust to alternative job definitions from the data.

> 53 We added a small error, 0 _._ 001, to the number of workers in the numerator and denominator, to prevent zeros in the calculation of the geometric mean.

> 54In addition to the evidence from Brazil, we also document a similar relationship for the U.S. in Appendix Figure A5.

38
|re the total overdue<br>loan was disbursed,<br>, respondent gender<br> local-linear control<br>nk*district level. +|Household<br>size|0.067|[0.24]|2,832|0.054|Yes|Yes|Yes|
|---|---|---|---|---|---|---|---|---|
|nt variables a<br>ear since the<br>ion (in years)<br>cation with a <br>red at the ba|Male|-0.030+|[0.02]|2,831|0.028|Yes|Yes|Yes|
|m cutoff. Depende<br>vestment loans), y<br>respondent educat<br>e baseline specif<br>ntheses, are cluste|Education|0.154|[0.29]|2,777|0.062|Yes|Yes|Yes|
|d demographics at the progra<br>mmy for crop loans (versus in<br>in hectares), respondent age,<br>e cutoff is estimated using th<br>obust standard errors, in pare|Land<br>Age<br>cultivated|-0.052<br>0.311|[0.22]<br>[0.73]|2,793<br>2,830|0.098<br>0.034|Yes<br>Yes|Yes<br>Yes|Yes<br>Yes|
|acteristics an<br>applied, a du<br>t relief (both<br> groups at th<br> controls. R|Land<br>total|0.012|[0.02]|2,809|0.119|Yes|Yes|Yes|
|am loan char<br>bt relief was<br>ed before deb<br>t and control <br>out additional|Loan<br>age|0.01|[0.14]|2,468|0.038|Yes|Yes|Yes|
|of pre-progr<br>% or 25% de<br>land cultivat<br> the treatmen<br>cts, but with|Loan<br>type|0.027|[0.03]|2,832|0.049|Yes|Yes|Yes|
|r the continuity<br>which the 100<br>ership and total<br>ange between <br>et of fxed effe<br>.01.|Loan<br>balance|-0.019|[0.06]|2,832|0.05|Yes|Yes|Yes|
|ble reports checks fo<br>e (Rupees, logged) to<br>lf-reported land own<br>usehold size. The ch<br>n and the standard s<br>0 ** p_<_0.05 *** p_<_0||Relief||rvations|ared|iew effects|*district effects|linear control fctn|
|This ta<br>balanc<br>total se<br>and ho<br>functio<br>p_<_0.1||100%||Obse|R-squ|Interv|Bank|Local|

58
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14
Table 8 includes only summaries of the effects, without indication of the individual coefficient estimates for each indicator by age and duration of exposure that may be important for a better understanding of program impacts. Table 9 therefore gives the individual estimates for each of the age and duration cells for Group I (see Table D2 for similar estimates for Groups II and III).

**Table 9. Intent-to-Treat Impacts by Age in Years (at Round 3) and Duration Class Based on Difference-in-Difference Nearest Neighbor Propensity Score Matching Estimates:** **<u>Group I Indicators – Predominately Positive Program Impacts</u>**

|**ECD Indictors**|**Age at**|**Dura**|**tion of Exposure(m**|**onths)**|
|---|---|---|---|---|
||**Round 3**|**4 to 12**|**13 to 16**|**17+**|
|**_Cognitive skills_**|2|0.917 (.122)*|1.24 (.128)*|1.07 (.094)*|
||3|0.278 (.096)*|0.362 (.112)*|0.426 (.098)*|
||4|0.031 (.094)|–0.225 (.116)~|0.034 (.121)|
||5|0.192 (.088)+|0.116 (.143)|0.043 (.097)|
||6+|0.169 (.102)~|0.242 (.100)+|0.313 (.095)*|
|**_Expressive language_**|2|1.02 (.213)*|1.60 (.178)*|1.31 (.190)*|
||3|0.635 (.146)*|0.517 (.126)*|0.918 (.131)*|
||4|–0.068 (.113)|0.130 (.184)|0.358 (.113)*|
||5|0.156 (.092)~|–0.050 (.095)|0.156 (.089)~|
||6+|–0.251 (.132)~|0.077 (.083)|–0.069 (.091)|
|**_Gross motor skills_**|2|1.14 (.160)*|1.21 (.161)*|1.49 (.170)*|
||3|0.070 (.136)|0.401 (.131)*|0.286 (.106)*|
||4|–0.019 (.100)|–0.069 (.108)|0.244 (.091)*|
||5|0.069 (.109)|0.201 (.125)|0.275 (.098)*|
||6+|–0.135 (.126)|–0.009 (.152)|0.040 (.103)|
|**_Self-help_**|2|0.284 (.113)+|0.241 (.118)+|0.630 (.142)*|
||3|0.149 (.104)|0.250 (.119)+|0.325 (.108)*|
||4|0.365 (.091)*|0.228 (.104)+|0.112 (.096)|
||5|0.078 (.098)|–0.018 (.125)|–0.061 (.124)|
||6+|–0.078 (.090)|0.070 (.088)|–0.123 (.082)|
|**_Fine motor skills_**|2|0.463 (.206)+|0.741 (.158)*|0.793 (.158)*|
||3|–0.122 (.144)|0.144 (.135)|0.165 (.099)~|
||4|–0.043 (.119)|–0.029 (.109)|0.103 (.127)|
||5|0.332 (.104)*|0.177 (.166)|0.289 (.120)+|
||6+|0.166 (.123)|0.162 (.112)|0.020 (.123)|
|**_Social-emotional_**|2|0.800 (.161)*|0.663 (.152)*|0.993 (.121)*|
||3|0.028 (.123)|0.425 (.096)*|0.535 (.101)*|
||4|–0.425 (.119)*|0.308 (.132)+|0.189 (.100)~|
||5|0.036 (.090)|0.308 (.109)*|0.322 (.105)*|
||6+|0.044 (.096)|0.220 (.099)+|0.374 (.096)*|
|**_Weight-for-height Z_**|2|0.173 (.082)+|–0.327 (.081)*|0.142 (.138)|
||3|0.140 (.095)|0.099 (.071)|–0.085 (.071)|
||4|0.127 (.055)+|0.261 (.052)*|0.057 (.045)|
||5|0.270 (.041)*|0.162 (.058)*|0.025 (.045)|
||6+|<br>0.209 (.055)*|<br>0.245 (.041)*|<br>0.085 (.044)~|
|**_Receptive language_**|2|1.00 (.236)*|1.65 (.200)*|1.78 (.183)*|
||3|0.328 (.134)+|1.15 (.154)*|1.06 (.147)*|
||4|–0.153 (.133)|0.217 (.202)|0.226 (.146)|

21