rafmacalaba/datause-encoder-data huggingface dataset:
only substantive assumption required for the above result, as the remaining assumptions are fairly standard conditions required to establish the properties of the GMM estimator. Note that the assumptions of the theorem require no prior knowledge of the form of the spatial and temporal correlations, and place only very weak restrictions on them. Hence this framework subsumes many common forms of spatial dependence, without requiring an explicit (and probably also incorrect) parameterization of the form of the temporal and spatial dependence.

Next, a sketch of the proof is as follows. The regularity conditions placed on *(O, Z,,) in Assumption 2(a) are sufficient to ensure that e itself is a mixing random field of the same size as z,t The cross-sectional averages of this random field, h1, will form a univariate mixing sequence as described above. The remainder of the proof is then simply a matter of verifying the standard results for the consistency and asymptotic normality of the GMM estimator and the consistency of the Newey and West covariance matrix estimator.

Finally, as an example of an application of the theorem, consider the simple OLS example of the previous section, which is a particular case of the GMM framework in which the above theorem is cast. To see this, set 00,=P, z4=(yj, x,j) and lr(<sup>3</sup> ,xj)=x (y'-Px,J. Thus to implement this technique, first obtain the usual OLS coefficient estimate. Next construct the sequence of crosssectional averages hk using the estimated residuals from the OLS specification (i.e.

ht=N-'Ej=1 ,(yj,-bx,), where b is the OLS estimate of P) and insert this into the definition of ST to obtain its consistent estimate. Finally, note that a consistent estimate of DT is given by (NT)-Y,= Combining these two expressions gives the consistent estimate of the covariance matrix, VT.<sup>4</sup> TSP and GAUSS codes to perform these calculations are available from the authors.

Another commonly-encountered model is one with unit-specific fixed effects, yi=f+x4P+E. One method to apply the theorem to this model is to transform the data by taking deviations from unit means and rewriting as above i.e. letting 7,t=(y,,-T Yt_,Ty,t,x, -T ' , **TNy** 9
over this period but make clear geographical generalization difficult. Neither the north as a whole, nor the border constitute a pole of commoving states driving divergence.

The second sub-period is also characterized by increasing but less dramatic divergence. However, again the border states do not seem to constitute a growth pole. Nuevo Leon, Chihuaha and Coahuila perform above average and Coahuila and Nuevo Leon do show local Moran’s that might suggest a mini-growth pole. But Baja California grows at below average rates and Sonora and Tamaulipas, at average rates. Clearly, this performance by rich states while poor states grow below average will contribute to divergence. But it is not the case that the border states as a group grew in a common way or dramatically outperformed the rest of the country.

A clear growth pole does emerge among in second line and central states of Aguascalientes, Guanajuato, Queretaro and Zacatecas. Since these states are roughly equidistant from the border, this, and not the comovement of the border states, is what drives the significant distance-based Moran I in this period. Since most are relatively rich states, their success suggests a potentially important dynamic outside of the North South dynamic discussed above. The South as a whole still underperforms, especially Guerrero, although the southern block seems less coordinated than when the entire post 1985 period is taken.

In sum, it seems difficult to argue that a special reaction to NAFTA of the states closest to the border was the driving force behind divergence. The consistently poor performance of the south which does form a regional cluster, does emerge as a central element in the story.

# **V. Conclusions.**

This paper employs established techniques from the spatial statistics literature to investigate how the post 1985 trade reforms affected spatial patterns of economic activity. Looking at levels of per capita state income, we find that very clearly a south exists, but the north seems to be restricted to those states directly on the US border, and there has never been a center. The latter finding is perhaps unexpected if the performance of the center states were heavily influenced by the confluence of a north-south and a Mexico City gradient. Our guess is that previous parametric findings of a gradient were driven by the imposition of a 18
15 Indonesia recorded relatively high rates of wage growth compared with other provinces, although wage increases appear to have lagged behind those in other provinces in services and 'other' sectors. The employment data shown in Table 7 indicate a similar pattern. Manufacturing employment in particular was relatively rapid in the poorer and densely populated provinces. The data do not suggest, in general that these provinces were lagging relative to the rest of Indonesia in terms of employment and wage growth during the 1990s.

_Employment and Wages in Key Labour Market Areas._ To enable a more disaggregated analysis, the data were broken down for several major centres of manufacturing development. These included the BOTABEK region - covering the three rapidly industrialising districts surrounding Jakarta - Surabaya and surrounding districts, and Medan and other North Sumatra industrial centres.<sup>24</sup> Several other traditional centres of manufacturing employment were also distinguished: Bandung city and district, major Central Java cities (Semarang, Solo, Klaten and Yogyakarta) and the three other major Sumatran cities (Palembang, Padang and Pakanbaru).

To what extent was manufacturing employment in the period 1987-94 concentrated around Jakarta in the west and Surabaya in the eastern part of Java? Not nearly as much as might be expected. While the percentage of Indonesian manufacturing employment located in the Surabaya region rose steeply, Greater Jakarta's (Jakarta plus BOTABEK) share remained constant (Table 9). The share of manufacturing employment also fell in both Bandung region and the Central Java cities.

But much of the growth of smaller scale and cottage industry seems to have remained outside the major cities. The share of manufacturing employment increased in West and Central Java outside the major cities. Signs of flourishing small scale industry are evident in the smaller Central Java cities - such as in the north coast cities of Tegal and Kudus (a traditional centre of _kretek_ cigarettes) and in the furniture manufacturing centre of Jepara. This appears to mark a significant departure from patterns of slow manufacturing growth in the Java heartland observed in the earlier two decades. Export-oriented industries based around Jakarta and Surabaya may well have forged more intense links with small and medium enterprises in Central Java than the domestic-oriented manfacturing industries which had grown rapidly in the 1970s and 1980s.<sup>2 5</sup> 24 The districts surrounding Surabaya include Gresik, Mojokerto and Sidoarjo. Around Medan they incude Pematang Siantar and Asahan.

25 In addition, the new export-oriented industries in the west and east of Java would not have had backwash effects on traditional low productivity industries in Central Java - one outcome which was a
most of the growth in new plant contributions happens by 2005, and Datta (2011) also shows significant changes in plant inventory and input sourcing by this point. By contrast, the cumulative impact of the GQ upgrades for total manufacturing output continues to build through 2009 in the estimations of Ghani et al. (2016). The loan activity associated with these changes happens early in the process, and we earlier also noted the connections between districts that developed new highways, boosted loan activity, and witnessed the entry of new plants.

# 4.7 Level of Initial Financial Development

Table 6 uses a split sample to quantify how our results differ across the initial financial development of a district. While we have controlled for this development in all prior regressions, we have yet to analyze heterogeneity in these initial conditions. We split districts that are located 0-10 km from the GQ network into two equal-sized group for being above or below the median financial development of this set of districts. By introducing separate indicator variables, we can contrast their responses. Financial development is measured by the loan credit disbursed by the district in 2000.

The powerful result that emerges from this analysis is that all the growth in loan activity is concentrated in districts along GQ that held above average initial financial development. Districts with below average seed conditions show no expansion, and their average loan sizes might even decrease somewhat. Table 7 shows that these results hold in dynamic specifications as well.

These results have important implications. A prominent question that this GQ episode sheds light on is whether financial development must precede large infrastructure projects for the investments to impact the real economy. In many ways, this project quantifies how responsive the financial sector can be. Loan activity and support increased quickly along the GQ network, providing financing within the first year of work and expanding rapidly over the five years when the upgrades mostly occurred. Moreover, many of our measurements closely align with settings where financing is thought to be more vital (e.g., new constructions, new firm entry). The NS-EW placebo and straight-line IV analyses further confirm the special response.

Yet, these final results suggest caution towards an expansive perspective of "build it and they will come." We see no differential expansion for districts that lacked initial 16
# **Table 1. Definitions and Sources of Infrastructure Quantity and Quality Indicators**

|**Variable**|**Definition**|**Year**|**Source**|
|---|---|---|---|
|roads|Length of total roads (km, sqrt of 1,000 workers x mean arable land for 1971-2005)|2004|International Road Federation (IRF)|
|paved roads|Paved roads (the ratio of paved roads to total road length)|2004|International Road Federation (IRF)|
|ml|The number of main phone lines (per 1,000 workers)|2004|Int'l Telecommunications Union (ITU)|
|cell|The number of cell phone lines (per 1,000 workers)|2004|Int'l Telecommunications Union (ITU)|
|telf|Telephone faults (the number of reported telephone faults for the year<br>per 100 main phone lines)|Avg. of 2001-06|Int'l Telecommunications Union (ITU)|
|wl|Waiting list for main line installation (the ratio of waiting list to main lines)|Avg. of 2000-04|Int'l Telecommunications Union (ITU)|
|egc|Electricity generating capacity (megawatts, per 1,000 workers)|2004|Statistical Yearbook, United Nations.<br>US- Energy Information Administration|
|pl|Power loss (% of total output)|2004|WDI, The World Bank.|
|q_roads|Quality of roads|2006|Global Competitiveness Report|
|q_railroads|Quality of railroads|2006|Global Competitiveness Report|
|q_ports|Quality of port facilities and inland waterways|2006|Global Competitiveness Report|
|q_air|Quality of air transport|2006|Global Competitiveness Report|
|q_elec|Quality of electricity supply|2006|Global Competitiveness Report|
|elec_accesss|Access to electricity: Electrification rate (% )|2006|World Energy Outlook|
|water|Access to water: Improved water sources (% of population with access)|2006|WDI, The World Bank.|
|sanitation|Access to sanitation: Improved sanitation facilities (% of population with access)|2006|WDI, The World Bank.|

15
|**State**|**Summary**|**Political support / interference**|**Status of interventions and fund**<br>**utilization**|**Range of solutions**|**Examples of problems fixed**|**Performance on the six**<br>**key indicators (2016)**|
|---|---|---|---|---|---|---|
|Anambra|Well‐advanced<br>implementation and wide<br>range of innovative and<br>standard solutions as well<br>as good evidence of<br>impact at the local level.<br>Broader<br>context,<br>specifically the delay in<br>implementation<br>of<br>PHCUOR,<br>likely<br>to<br>negatively affect state‐<br>wide impact of SOML<br>interventions.|**Partly**<br>**supportive**<br>**political**<br>**environment.**<br>Direct<br>political<br>interference has not been a<br>problem for SOML in the state. But<br>there<br>has<br>been<br>significant<br>resistance to the PHCUOR initiative<br>in the state, such that unions went<br>to court to try and prevent the<br>integration. In the absence of<br>integration,<br>local<br>government<br>remains responsible for payment of<br>health workers, salaries remain in<br>arrears and the state actors find it<br>difficult<br>to<br>manage<br>the<br>performance of the health workers.<br>The PMU confirms that this inability<br>to motivate the health workers has<br>been its greatest obstacle.|**Implementation is fairly well**<br>**advanced, having begun in early**<br>**2018**after funds were made<br>available for draw down in the<br>state.<br>A good number of interventions<br>are well underway, including<br>drug<br>procurements<br>and<br>distributions, various trainings,<br>sensitizations,<br>advocacy<br>campaigns<br>and<br>supervision<br>visits.|**Wide range of solutions from**<br>**the typical (QISS and MNCH**<br>**weeks) to the innovative**e.g.<br>conducting<br>SOML<br>Health<br>Intervention Days (SHIDs) –<br>which were used to “mop up”<br>people missed during MNCH<br>weeks with household visits;<br>distribution of vitamin A cards<br>and extensive sensitization via<br>outreaches; engagements with<br>traditional birth attendants and<br>extensive<br>advocacy<br>with<br>community influencers; training<br>of community youths to hang<br>nets and advocacy with teachers<br>to influence the same etc.|Initiated an MoU with 60<br>private facilities that included<br>a provision to give these<br>facilities some resources and<br>some stipends on the basis<br>that they would regularly<br>supply data on coverage of the<br>6 indicators. This has greatly<br>improved reporting rates and<br>improved coordination with<br>these facilities.<br>Development of Vitamin A<br>cards along with outreaches<br>regarding their use has helped<br>ensure tracking and treating of<br>persons missed out during<br>MNCH weeks.<br>Initial success in reaching key<br>community<br>influencers<br>regarding<br>the<br>use<br>of<br>contraception.|**Regressed.**Anambra had<br>a sum total of ‐4.1%<br>reduction<br>from<br>its<br>baseline according to the<br>2016 MICS survey. It<br>made small gains in<br>Penta 3 and SBA but<br>declines in the other 3.|

|**_Non‐TA states_**|
|---|

31
_22_

|**Investment Returns by Asset Class**<br>(average annual returns for 1988-95 in percent)<br>Nominal<br>Real|
|---|
|Time Deposits<br>17.6<br>9.4|
|Bonds and Govt Papers/a<br>17.6<br>9.4|
|Equities<br>46.3<br>38.9|
|Properties/b<br>46.3<br>38.9|

Notes: a. Assumed to be the same as time deposits b. Assumed to be the same as equities Sources: MeesPierson Finas and staff calculations Based on the estimates above and the composition of pension assets as shown in the previous Chapter, the overall returns on investments for pension funds as a group are estimated to be 21 percent in nominal terms and 13 percent after allowing for inflation over the eight-year period from 1988-95. Individual pension plans of course would have different rates of return depending on actual asset allocation and actual securities held. Moreover, the gains credited to participants or plan assets are net of management expenses.

Although the estimates above may contain a wide margin of errors, they nonetheless suggest very attractive real investment returns in the recent past. Even if the estimates above were to be 100 percent too high, which is unlikely according to fund managers, the _real return_ on pension investments would still be very attractive at 6 percent per annum in the past 8 years.

# **Options for Reform**

Among the measures that would make approved pensions more attractive and less costly to implement are: First, it is desirable to extend the reach of pension regulations beyond their current narrow boundary. Commonly used plans that provide lump-sum benefits, as well as plans that are based on a broader set of eligibility, should also be covered. In addition, it would be useful to simplify and expedite the process of registration and approval. It would help, for example, to deem an application to have been approved thirty days after submission, unless the authority has responded with a written objection or inquiry.

Second, the funding of private pensions can be made more affordable, without sacrificing the security and benefits of the worker, _by increasing the retirement age._ This measure extends the period of asset accumulation and shortens the period of benefit payments. The current standard retirement age of 55 is low relative to international standards and relative to current and projected life expectancies of the relevant work force. The retirement age could be raised to 60, or higher, over the next 10 years. The concern that raising the retirement age would limit the opportunity of younger job seekers may be misplaced. Under the current rule, the retired worker who wants to work simply switches to a new employer.
**Table 1 – Changes in Shares of Value Added (mean 1990-1999 vs. mean 2000-2010)**

||Services|Agriculture|Industry<br>Manufacturing|Industry<br>Others|
|---|---|---|---|---|
|East Asia & Pacific|4.9|-4.3|-1.8|1.2|
|Europe & Central Asia|7.8|-5.1|-3.8|1.1|
|Latin America & Caribbean|2.6|-3.0|-1.3|1.7|
|Middle East & North Africa|-3.0|-2.6|-2.2|7.8|
|North America|1.8|-0.6|-2.2|1.0|
|South Asia|7.7|-8.1|-0.4|0.8|
|Sub-Saharan Africa|2.3|-4.2|-1.0|2.9|

Source: World Bank Open data.

If we had to conclude our analysis at this point, our test of the U-shape hypothesis would be rather robust. The U-shape relation between GDP per capita and FPRs is evident with the cross-country data, consistent with previous evidence and visible for the MENA region. The fundamental pillars of the hypothesis including the share of agriculture in value added, female education and fertility rates are all consistent with the hypothesis to an extent that the MENA region outperforms other regions of the world. And the MENA countries seemed to have turned the corner (saddle point) in terms of FPRs in four different respects: 1) <u>Visually</u> (Figure 1); 2) <u>Economically, by having passed the GDP per capita threshold and</u> restructured away from agriculture 3) Statistically, by having experienced increases in FPRs and GDP per capita between 1990 and 2010; and 4) <u>Socially</u> by having increased female secondary education and reduced fertility rates.

But we also noted two aspects that make the MENA region stand out as compared to other regions and that are at odds with the U-shape hypothesis. The first is that women in the MENA region seem to exit the labor force around the age of 25, which largely coincides with the average marriage age in the region. The second aspect is that the MENA region is the only region that was not able to expand the share of both services and manufacturing as opposed to other sectors that are less likely to employ women. These are two important leads that will be further explored to understand why the MENA region lags behind in terms of FPRs despite the good performance of the fundamentals of the U-shape hypothesis.

# **Parametric estimates**

We divide the parametric analysis in the panel analysis comparing the MENA’s performance with the rest of the world and the time-series analysis looking more in detail at within countries’ performance.

## **Cross-country regional panels**

Results for the FPRs panel regressions are shown in Table 2 for the whole population 15 years and above and for three age cohorts, 30-34, 40-44 and 50-54. The cohort analysis is useful in that can show whether the U-hypothesis applies to different age groups or it is restricted to particular age groups and is one of the main lines of investigation of the U-shape hypothesis literature.

12
7 An open economy differs in that<sup>there is no exact link between domestic</sup> saving and investment. In the<sup>pure Arrow-Debreu version oi the international</sup> economy, cou try borders are essentially<sup>meaningless. Domestic bor7owers</sup> compete on an equal footing<sup>with foreign borrowers in the international</sup> capital market, much the same<sup>as corporations located in New York and New</sup> Jersey compete for funds in the<sup>U.S. capital market. The analogous poi.nt is</sup> true for savers. These individuals<sup>are able to search over the entire world</sup> capital market for the best possible<sup>riek-corrected returns. Notably, the</sup> basic Arrow Debreu framework -- with constant returns to scale<sup>--</sup> predicts that funds will flow from low<sup>marginal product of capital rich countries to</sup> high marginal product of capital<sup>poor countries, just as the capital market</sup> works to equalize risk-corrected<sup>marginal products of capital within a</sup> country' s border.3 An aspect of allocating of saving<sup>and investment is providing insurance.</sup> Through a variety of mechianisms<sup>--</sup> e.g., diversification, futures<sup>markets,</sup> debt-equity swaps -- individuals are able to shed<sup>completely exposure to</sup> idiosyncratic risks and to share<sup>optimally the impact of systemic risks.</sup> Borrowers thus need only pay<sup>lenders a premium for the systemic risk</sup> associated with their particular<sup>investmente, regardless of the amount of</sup> idiosyncratic risk. The key point<sup>is that, with perfect markets, the</sup> 1 ~~~~~~~~~~~~~4 financial system washes a considerable<sup>quantity of risk out of the economy.</sup> In equilibrium, only (optimally<sup>shared) systemic risks are left to influence</sup> saving and investment. And, given<sup>that the variation in GNP is a rough</sup> measure, this is not much risk<sup>at all. Diminishing the impact of risk,</sup> therefore, is an important way<sup>in which the financial system increases the</sup> attractiveness of saving and<sup>investing, and ultimately contributes to growth.</sup> Another dimension of allocating<sup>saving and investment is liquidity</sup> 3This presumes that technology is the same across countries, so that differences in capital explain cross-country differences in output. 4As an example, the Arrow-Debreu model predicts a risk premium for equity of only about one half percent (Mehra and Prescott, 1985). Part of the reason for this<sup>low number is that perfect</sup> diversification is possible. The actual equity premium in the United States was about six percent for the 1960s-70s.