rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Determinants of Training Take-up**

Marginal effects from Probit estimation of Attending Training among those offered

||Curr|ent Enterp|rises|Pote|ntial Ente|rprises|
|---|---|---|---|---|---|---|
|_Owner characteristics_|||||||
|Has no children or has someone to look after childre|0.0171|-0.0460|-0.0204|0.00373|-0.00589|0.0146|
||(0.0475)|(0.0476)|(0.0493)|(0.0523)|(0.0522)|(0.0544)|
|Age|0.00614|0.00617|0.00403|0.00956*|*0.00827*|*0.00759*|
||(0.00409)|(0.00408)|(0.00418)|(0.00395)|(0.00397)|(0.00398)|
|Married|0.128*<br>|0.169**<br>|0.152** <br>|-0.0993<br>|-0.0835<br>|-0.0760<br>|
||(0.0658)|(0.0679)|(0.0693)|(0.0705)|(0.0734)|(0.0730)|
|Years of Education|0.0290***|0.0184|0.0171|0.0122|0.00820|0.00754|
||(0.0109)|(0.0113)|(0.0118)|(0.0110)|(0.0113)|(0.0115)|
|Risk-seeking Attitude|-0.0189|-0.00868|-0.0111|-0.0105|-0.00195|-0.000607|
||(0.0133)|(0.0133)|(0.0140)|(0.0132)|(0.0138)|(0.0141)|
|Raven test score|0.000541|0.000168|-0.00425|0.0282**|0.0310**|0.0312**|
||(0.0141)|(0.0147)|(0.0149)|(0.0133)|(0.0135)|(0.0139)|
|Digit-span Recall|-0.0110|0.00665|0.00691|-0.0309|-0.0209|-0.0289|
||(0.0200)|(0.0198)|(0.0213)|(0.0195)|(0.0200)|(0.0209)|
|Wealth index (principal component)<br>|-0.0441***|-0.0381**|-0.0379**|-0.00556|-0.00337|0.00362|
||(0.0168)|(0.0169)|(0.0174)|(0.0152)|(0.0153)|(0.0159)|
|Says would pay 500 Rs or more for a training course|-0.0115|-0.0239|-0.0492|0.0439|0.0367|0.0368|
||(0.0496)|(0.0531)|(0.0540)|(0.0507)|(0.0505)|(0.0522)|
|Has taken concrete steps to opening a business||||0.0635|0.0338|0.0472|
|||||(0.0484)|(0.0502)|(0.0552)|
|Has never worked before||||-0.0157|-0.0358|-0.0523|
|||||(0.0625)|(0.0639)|(0.0666)|
|_Firm Characteristics_|||||||
|Log of monthly profits|-0.0677*|0.0267|0.0125||||
||(0.0399)|(0.0385)|(0.0420)||||
|Firm is in Manufacturing|0.126*|0.119*|0.121*||||
||(0.0666)|(0.0693)|(0.0697)||||
|Firm is in Retail Trade|0.0650|0.0356|0.0331||||
||(0.0676)|(0.0737)|(0.0748)||||
|Works more than 40 hours a week at baseline|-0.0899*|-0.0889*|-0.0766||||
||(0.0488)|(0.0503)|(0.0504)||||
|Baseline Business Practices Score|0.00670|0.00801|0.0116||||
||(0.00681)|(0.00729)|(0.00790)||||
|Firm is younger than 5 years old|0.0969*|0.141***|0.136***||||
||(0.0505)|(0.0527)|(0.0529)||||
|_Geography_|||||||
|Colombo District||-0.446***|||-0.152**||
|||(0.0676)|||(0.0694)||
|Kandy District||0.0762|||0.0317||
|||(0.0636)|||(0.0610)||
|D.S. (locality) fixed effects|No|No|Yes|No|No|Yes|
|Number of firms|400|400|400|399|399|399|
|Robust standard errors in parentheses, *** p<0.01, *|* p<0.05, *|p<0.1|||||

35
13 competitiveness of inputs is offered most frequently (Jordaan 2013; Ivarsson and Alvstam 2010; Franco, Sanfilippo, and Seric 2015; Alfaro-Urano, Manelici, and Vasquez2019).<sup>9</sup> To enhance the effectiveness of support, MNC affiliates screen (potential) local suppliers to ensure that they identify those firms that are most likely to be able to benefit from the support. Characteristics of domestic firms that facilitate the impact of support include firm size, high employment growth, having previous experience with working as supplier to MNC affiliates, and having experience with producing for international markets (Jordaan 2011b; World Bank 2018). Importantly, selection effects also occur among domestic firms, as firms with more capabilities are more likely to commit to (try and) become suppliers to MNC affiliates (Javorcik and Spatareanu 2009). As a result of these selection effects, the impact of the support that MNC affiliates provide is enhanced, as it is targeted to those firms that are motivated and more likely to be able to use the support to improve performance.

_Firm-level capabilities (absorptive capacity)._ The importance of firm-level capabilities is also indicated by findings on drivers of productivity spillovers. It is increasingly accepted that productivity spillovers are facilitated or even conditional on domestic firms having sufficient capacity to absorb and implement new technologies (Crespo and Fontoura 2007; Jordaan 2016). Because absorptive capacity cannot be directly observed, econometric studies use a variety of firm-level characteristics that are assumed to be related to this concept. For instance, World Bank (2018) uses WBES data for 122 developing host economies and finds that productivity spillovers materialize only among the more dynamic domestic firms, characterized by high employment growth.

_Firm size_ . Evidence on the effect of firm size is more mixed. Several studies examine the notion that larger domestic firms are more likely to benefit from spillovers, given that they have more resources to invest in absorbing and implementing new technologies. For various host economies, Farole and Winkler (2014), Gorodnichenko, Svejnar, and Terrell (2014), and Xu and Sheng (2012) present findings confirming that productivity spillovers are concentrated among large domestic firms. However, other studies present contrasting evidence. For Romania and Eastern Europe, Merlevede, Schoors, and Spatareanu (2014) and Damijan et al. (2013) do not find any evidence that productivity spillovers differ between small, medium, and large domestic firms. Ni et al. (2017) find for Vietnam that productivity spillovers are mainly concentrated among small and medium-sized firms.

_Export status of domestic firms_ . Evidence on the export status of domestic firms is more uniform. Firms that have experience with producing for international markets are more accustomed

> 9 Such support includes feedback on product performance, the sharing of product designs and specifications, technical support to improve existing production processes, and support with product quality upgrading.
recommended that the government intervene to centralize the trade into one or very few companies. 28 32. Widespread entry into the export trade has undermined attempts to pre-plan the allocation of available air-freight space among exporters and has prevented the country from developing effective brand name promotion for its commodity lines. However, the entry o. some one hundred firms in the trade masks the fact that a limited number of firms stil **a.** *unt for a dominant share of trade (see Table 3). The bulk of the new entrants into the trade have served as little more than a part-time, small-scale competitive fringe whose participation in the industry has normally lasted for only one or two seasons.

**_Table_** _3:_ **_Concentration_** _of Kenyva's Fresh Fruit and Vegetable Exports (Percentage Shares of Export Volumes)_

||1970*|**1979b**|**1981b**|1985b|
|---|---|---|---|---|
|Largest 3 Firms<br>|75<br>|44|**_55_**|49|
|**Largest 6**Firms<br>|83|65|79|67|
|Largest 9**Firms**|90|74|89|82|

a. Based on Winters **et** al. (1969) and Field Interviews b. Calculated from file data of the Horticultural Crops Development Authority.

**_Source:_** Author's Field Research

33. Competition in the fresh produce trade has taken several forms. The first of these is competition in the procurement of crops. This competition has generated benefits for both smailholder and larger farmers by increasing prices paid to farmers and leading marketing firms : **Among those** advocating the centralization of trade were Winters et al. (1969), Prco **tor (1976), Adelstal (1979), and Hormana** and **Will (1987), representing** British and **German technical assistance and that of the International Trade Center.** 18
secondary or tertiary sector, at least until a cohort of college-educated teachers is available locally.

Understanding the evolution of the private sector for education in Pakistan has wider implications. Governments in low-income countries around the world are struggling to meet the educational demands of an ever increasing young population. In Pakistan half the population is less than 17 years old and this proportion is increasing. With less than 60 percent of children enrolled in school, there are already signs of stress. Mean student-teacher ratios in government schools exceed 35 and have been rising. School construction has slowed down and less than half of all classrooms have desks for their children. Private schools _can_ play a role in breaking these institutional constraints, and the Pakistani experience demonstrates that they can do so without catering only to the elite.

The remainder of this paper develops the ideas advanced here. In Section II we describe our data sources. Section III examines the growth of private schooling in Pakistan. Section IV takes a closer look at how private schools operate. We document fees and costs for private schools and examine teacher profiles and wage differentials between public and private schools. Section V concludes with a discussion of educational policy, caveats and future research in this area.

# **Section II. The Country Context and the Data**

Pakistan has 132 million people and is organized in a federal structure with four main provinces—Punjab, Sindh, North Western Frontier Province (NWFP) and Balochistan. These four provinces, along with Islamabad, the federal capital, comprise 97 percent of the country’s population. Punjab is the largest province with 56 percent of the population and Sindh, NWFP and Balochistan account for 23 percent, 13 percent and 5 percent respectively. There are other regions and territories where special constitutional and legal qualifications apply. The Federally Administered Tribal Areas (FATA) with 3 percent of the population has representation in the national assembly but national laws apply only partially. The Northern Areas and Azad Jammu and Kashmir (AJK) enjoy special status and their population is not counted in population census numbers. Provinces manage the provision of education, although there are recent changes at the local government level that aim to devolve provision to the numerous districts within every province.

Educational performance is poor both in absolute terms and relative to the average income of the country. Pakistan has an adult literacy rate of 44 percent compared to 54 percent for the South 5
# FIGURE 4 Drivers of potential growth

## A. Contributions to potential growth

<!-- Start of picture text --> Percent<br>10 TFP Capital Labor Potential growth<br>8<br>6<br>4<br>2<br>0<br>World AEs EMDEs<br>C. Potential growth in EMDE regions<br>Percent Potential growth<br>10<br>Actual growth<br>8<br>2000-2019 potential growth<br>6<br>4<br>2<br>0<br>EAP ECA LAC<br>E. Share of economies with potential growth<br>below 2000-10 average, 2011-21<br>Percent<br>100<br>80<br>60<br>40<br>20<br>0<br>EAP ECA LAC<br>2000-21 2000-10 2011-21 2000-21 2000-10 2011-21 2000-21 2000-10 2011-21<br>2000-10 2011-21 2000-10 2011-21 2000-10 2011-21<br><!-- End of picture text -->

## B. Contributions to potential growth

<!-- Start of picture text --> Percent<br>10 TFP Capital Labor Potential growth<br>8<br>6<br>4<br>2<br>0<br>EMDEs EMDEs excl.China<br>D. Potential growth in EMDE regions<br>Percent Potential growth<br>10 Actual growth<br>2000-2019 potential growth<br>8<br>6<br>4<br>2<br>0<br>MNA SAR SSA<br>2000-21 2000-10 2011-21 2000-21 2000-10 2011-21<br>2000-10 2011-21 2000-10 2011-21 2000-10 2011-21<br><!-- End of picture text --> <!-- Start of picture text --> F. Share of economies with potential growth<br>below 2000-10 average, 2011-21<br>Percent<br>100<br>80<br>60<br>40<br>20<br>0<br>MNA SAR SSA<br><!-- End of picture text --> Source: World Bank staff estimates.

Note: GDP-weighted averages of production function-based potential growth estimates. TFP growth stands for total factor productivity growth. AEs = advanced economies; EMDEs = emerging market and developing economies.

A.-D. Sample of 30 advanced economies and 53 EMDEs.

E.F. Number of economies and their share of GDP in a region among 53 EMDEs with potential growth in each period below its 2000-10 average. Horizontal line indicates 50 percent. Regional samples include the largest available coverage for each region. EAP stands for East Asia and Pacific (6 countries), ECA stands for Europe and Central Asia (9 countries), LAC stands for Latin America and the Caribbean (16 countries), MNA stands for Middle East and North Africa (5 countries), SAR stands for South Asia (3 countries), and SSA stands for Sub-Saharan Africa (14 countries). In all MNA countries, potential growth was higher in 2000-10 than in 2011-21 (and higher than the full-period average) because of a commodities boom in the first decade of the 2000s that was followed by a commodity price plunge, political tensions, and conflict in the second decade of the 2000s.

24
where _ti_ =( _pi_ −π _i_ )/ _pi_ is the ad valorem tariff or export subsidy for good _i_ in equilibrium, _Ii_ is an indicator variable that equals one if sector _i_ is organized into a lobby. The parameter α≤1 is the fraction of the population organized into lobbies. _a_ >0 is the government preference parameter in (1), and indicates the weight government places on a dollar of welfare relative to a dollar of political contributions. _zi_ = _xi_ / _mi_ is the equilibrium ratio of domestic output to imports and _ei_ is the absolute elasticity of import demand.<sup>7</sup> If sector _i_ is a net importer then it is protected ( _ti_ >0) or obtains an import subsidy ( _ti_ <0) depending on whether it is organized ( _Ii_ >0) or not ( _Ii_ <0).

(2) may be interpreted as follows. The second component on the right hand side of (2) indicates that protection to _organized_ sectors is given according to their _z/e_ ratios. Since deadweight loss from protection is higher in industries with high import elasticities the government is averse to protecting these industries, all else equal. _z_ measures the stakes from protection, and industries with high _z_ values will make larger lobbying contributions. The lower the import volume, the lower the social cost imposed on individuals, thus diluting their opposition to protection of that sector. The first component on the right hand side of (2) indicates that negative protection to _unorganized_ industries is given according to their _z/e_ ratios. α measures the extent of opposition to protection. If only a negligible fraction were organized into lobbies, α =0, then there would be no organized opposition to protecting any sector (only organized support for protecting a lobby’s own sector). If everyone were organized, α =1, then organized lobbying for protection in any sector would be balanced out by organized opposition to that protection, and tariffs would be zero.

Goldberg and Maggi (1999) and Gawande and Bandyopadhyay (2000) have empirically investigated these predictions using U.S. manufacturing industry data, Mitra, Thomakos and Ulubasolglu (2002) with Turkish data and McCalman (2002) with Australian data. They affirm the predictions qualitatively. Gawande and Bandyopadhyay also examine the lobbying side of the Grossman-Helpman model. They affirm the fundamental prediction 7 The Ramsey pricing logic applies here. The Ramsey tax formula implies that if the demand for a good is uniformly less elastic than that for another good, the optimal tax rate is higher for the first good due to the lower deadweight loss from taxing it rather than the second good. If the first good is totally inelastic there is no deadweight loss from taxing it, and the first best can be reached by taxing just this good.

14
-3-

structure of output prices, and the impact of price<sup>llberalizatIon or administratl-</sup> pricek revislon on optimal users fees can be shown.

This approach to land valuation Is approximate at best, and would<sup>not be</sup> necessary If Iand markets were permitted to functlon. It<sup>has the advantage of</sup> simplicity and speed, and allows tenure reform to proceed<sup>before the lengthy cadastre</sup> Is completed. It furthermore provides a benchmark with which<sup>to evaluate the claims</sup> of farm chalrmen that user fees (which they pay) should<sup>be very low but fees for</sup> Intra-fam leaseholds (whlch they collect) quite high.

The calculatlon of marginal value products for land of differing<sup>quality Is a</sup> simple technical exercise that embodles no assumptlons<sup>about allocat?ve efficlency,</sup> market clearing, or prices that reflect scarcity values.<sup>To step further and argue</sup> that marginal value products are optimal user fees does<sup>require strong assumptions,</sup> In particular that other factors of production are priced according<sup>to their scarcity</sup> values, and that farm managers are ailocatively efficient.<sup>When these conditions do</sup> not hold, the administrator given the unenviable task of<sup>stimulating market behavior</sup> without a market has several choices. He or she can adjust user<sup>fees to reflect</sup> distortions elsewhere, or can attempt through administrative<sup>means to remove these</sup> distortions. The measurement of marginal value products, and<sup>particularly</sup> demonstration of their dependence on the structure of<sup>output prices, simply expands</sup> the set of empirical Information that can be used for administrative<sup>decision making.</sup> The Introductbon of a genulne market mechanism would greatly reduce the need for continued administratlon of the economy. Under several plausible<sup>paths of transition</sup> to a functioning market mechanism, however, initlal<sup>administrative valuation of</sup> agricultura **l** and Is required. An empirical understanding of<sup>the marginal value products</sup> of land could therefore generate Informatlon useful for a<sup>genuine transition.</sup> In the following discussion I use farm level data from Lithuanian<sup>state and</sup> collective farms to esthnate the marginal value product<sup>of land under the 1986 and</sup>
instance, on currency holders as opposed to those holding banks deposits. Expressing the taxes as a share of M2 is necessarily a very summary indicator.

In order Ro express the taxes by analogy with income tax (approach (b)), it would be appropriate to compute some measure of the flow of imputed income from the holding of financial assets or more generally of the flow of consumption services from financial intermediation in the economy. In the absence of readily available statistics on the share of financial intermediation services to GDP we confine ourselves to representing the taxes as a share of GDP. The reader may wish to assume that the flow of financial intermediation services represents approximately the same proportion of GDP in the countries being studied.

Data on government revenue is available for most of the sample period, and extrapolations have been used for missing data. Comparing the total of implicit and explicit financial spctor taxes to the usual concept of government revenue (i.e. approach (c)) gives an indication of the relative degree to which government relies on the financial sector for revenue. Note that, except in the case of the TPS in Cote d'Ivoire, all of the financial taxes reported are implicit ones and therefore represent a supplement to the quoted go.ernment revenue figures.

Since several of the sample countries have suffered from very high rates of inflation, a refinement must be introduced to the usual data on inflation and GDP flows to avui, potentially serious distortions. This requires using instantaneous, or continuously compounded, rates of '.nflation and interest. The matter is explained fully in Appendix 3.

# 2.2.3 A digression on different concepts of seignorage.

When a government facing budgeLary problems resorts to printing money to bridge a financing gap, this may be expected to add to inflation or eat into foreign reserves. It is worth distinguishing this _cash flow_ which the government obtains by using the printing press from the tax on the holders of currency resulting from the reduction in its value caused by inflation uncompensated hy any interest payment. The former is equal to the actual increase in the stock of currency; the latter may be approximated by the product of the inflation rate and the initial stock of currency (in which case it is often termed the inflation tax). Actually, since a rapid increase in the stock of currency will often result in a roughly proportional increase in the price level, the cash 12
_Figure 6: Interest rate sensitivity of assets, 2002 to Mid 2006_ <!-- Start of picture text --> DKK bn<br>80<br>60<br>2005<br>2006 (1st half)<br>2004<br>40<br>2003<br>2002<br>20<br>0<br>-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5<br>-20<br>2002<br>2003<br>-40<br>2004<br>2006 (1st half)<br>2005<br>-60<br>-80<br><!-- End of picture text --> _Table 11: Interest Rate Sensitivity of Assets_

|||InterestRa<br>|te Change<br>||10 Year government bond<br>|
|---|---|---|---|---|---|
||- 1%|-0.7%|+0.7%|+ 1%|yield(end ofperiod)|
|||_DK_|_Kbn_||Percent|
|2002|26.7|18.8|-19.0|-27.2|4.46|
|2003|40.2|28.2|-28.5|-40.7|4.47|
|2004|40.7|28.5|-28.9|-41.5|3.85|
|2005<br>|46.6|32.8|-33.4|-47.8|3.28|
|2006(1<sup>st</sup>half)|46.2|32.4|-32.5|-46.4|4.12|

Source: Danish FSA. Bloomberg (yields) The interest rate sensitivity of assets show in Figure 6 and Table 11 reflects their composition, which is geared toward longer maturity government and mortgage bonds. The positive duration of these assets results in capital gains when rates go down and losses when rates go up. This relationship is observable in the aggregate numbers. The asset side does not show a similar asymmetric response to interest rate changes as the liability side. However, pension institutions invest heavily in callable mortgage bonds. When interest rates fall below a certain level, borrowers refinance their mortgage bonds and deny pension institutions an offsetting gain on the asset side. This particular feature aggravates the problems created by the asymmetric effect on the liability side. It is, therefore, somewhat surprising that the effect shown by the stress test is quite symmetric.<sup>1</sup> The absolute interest rate sensitivity of assets has in all the years been less than the interest rate sensitivity on the liabilities side, leaving the institutions with a substantial duration gap.

> 1 This may in part be because the measurement of the impact of the embedded options in mortgage bonds is incomplete. The embedded options in mortgage bonds are expected to result in smaller DKK gains when rates go down by 1 percentage point than the DKK loss when rates increase by 1 percentage point.

18