rafmacalaba/datause-encoder-data huggingface dataset:
**Table 9:**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|ln(ability)|_−_0_._015<sup>_∗∗∗_</sup>|0_._076<sup>_∗∗∗_</sup>|0_._107<sup>_∗∗∗_</sup>|0_._067<sup>_∗∗∗_</sup>|
||(0_._003)|(0_._003)|(0_._003)|(0_._003)|
|ln(target)|0_._787<sup>_∗∗∗_</sup>||||
||(0_._004)||||
|ln(average classes completed)||_−_0_._173<sup>_∗∗∗_</sup>|||
|||(0_._008)|||
|ln(expected dropout probability)|||0_._022<sup>_∗∗∗_</sup><br>(0_._000)||
|ln(expected shock to classes completed)||||_−_0_._159<sup>_∗∗∗_</sup><br>(0_._006)|
|Constant|_−_1_._537<sup>_∗∗∗_</sup>|1_._019<sup>_∗∗∗_</sup>|0_._764<sup>_∗∗∗_</sup>|0_._519<sup>_∗∗∗_</sup>|
||(0_._011)|(0_._018)|(0_._003)|(0_._006)|
|Adj. R<sup>2</sup>|0.766|0.285|0.557|0.286|
|Num. obs.|127,044|127,044|127,044|127,044|

_Source_ : OLS estimation using model’s simulated baseline values. _Notes_ : The dependent variable is ln(effort), or ln( _e_<sup>_∗_</sup> _t_<sup>) in the model.An observation is a student-year.Ability</sup> is _θ_ , target is _qt_ , and cumulative classes completed is _h_<sup> ̄</sup> _t−_ 1. Expected shock to classes completed is _E_ [ _zt_ ]; it varies across students and over time. Expected dropout probability is _E_ [ ̃ _p_<sup>_d_</sup> ( _t, ·_ )] as calculated by the individual prior to the realization of _zt_ . All regressions include year and income fixed effects (not shown). * ** *** Standard errors (in parentheses) are clustered by student. p _<_ 0.10, p _<_ 0.05, p _<_ 0.01.

# **8 Free college simulations**

In this section we present counterfactual simulations of alternative free college programs. We begin by describing their setup as well as the theoretical impact of free college on student decisions. Then we describe free college’s aggregate effects on enrollment and graduation and examine effort in order to understand graduation rate effects. We then turn to the heterogeneity of free college effects by student type, and relate our counterfactual results with other findings from the literature. We analyze the role of risk, both in the baseline and under free college. We conduct a simple cost-benefit analysis of free college and conclude with an examination of the possible long-run effects of free college.

## **8.1 Counterfactuals’ setup**

We simulate the following free college programs, which provide free tuition to the students listed below:

- **Universal free college:** all college students.

- **Need-based free college:** low-income students, defined as those from the lowest two income brackets.

- **Ability-based free college:** high-ability students, defined as those from the highest ability quintile.

31
**Table 4: Supervisory arrangements for Banks and non-Banks**

|**Supervisory arrangement**|**Commercial**<br>**Banks**|**MOFIs**|**SSOFIs**|**MFIs**|
|---|---|---|---|---|
|Institutions in this categoryonly<br>supervised by commercial bank regulator|100.0%|41.5%|41.1%|34.6%|
|Only<br>some<br> institutions in this category<br>supervised by bank regulator|0.0%|16.2%|7.0%|9.2%|
|Institutions in this category only<br>supervised byother<br> regulator|0.0%|16.9%|14.0%|7.7%|
|No supervising authority reported for<br>Institutions in this category|0.0%|25.4%|38.0%|48.5%|
|Note: Frame is 129 respondents.|||||

# **Table 5: Data available on the number of deposits and loans is limited, especially for nonbanks.**

|**Data Available:**|**Commercial**<br>**Banks**|**MOFIs**|**SSOFIs**|**MFIs**|
|---|---|---|---|---|
|Data on Value of Deposits|118|42|32|22|
|Data on Number of Deposit Accounts|83|24|20|17|
|Data on Value of Loans|116|42|33|28|
|Data on Number of Loans|63|18|15|18|
|Data on Number of Individual Depositors|30|8|6|10|
|Data onNumberof Individual Borrowers|27|10|7|8|

Note: The data counting the numbers of individual depositors likely reflects much double counting because most respondents reported not being able to track individuals across multiple institutions even if they could ask banks for their individual totals.

43
percent to household value added. The share of livestock is rather limited, with pigs and other livestock each contributing 0.1 percent. For this household group the total contribution of agricultural activities to value added is 59.2 percent, while the remainder is coming from engaging in off-farm activities. Hiring out its labor to other villagers contributes 1.3 percent to value added, while working in local businesses earns 19.4 percent of value added. Outside village employment is a significant income source for this household group, with a share of 18.1 percent. For this unlinked household, migration opportunities are restricted to moving inside the province, which contributes 1.8 percent of value added.

For all household types off-farm employment contributes a significant share of income, but there exist important differences in the nature of non-agricultural income sources. Comparing the two household groups that have no link outside the province, we find that households lacking draught power are oriented more towards local off-farm employment. The households with draught power obtain 71.4 percent of value-added from agriculture, which is similar to the importance of agriculture for the other household group owning draught power. The household group with access to migration but lacking draught power derives about 40 percent of value-added from outside province migration, and only derives about 45 percent of value-added from agricultural activities. Differential access to agricultural and migration opportunities is thus reflected in the composition of household value-added.

Differences in activities result in differences in income patterns across the four household groups. The bottom part of Table 2 presents income per adult consumer equivalent<sup>3</sup> to allow a direct comparison across households. As a crude poverty assessment

> 3 Adult equivalent instead of per capita consumption is used to account for differences in consumption between males and females and between age groups. Lacking survey data, conversion factors were taken from detailed consumption data of a study in Bangladesh (Zeller _et al._ , 2001). In addition to differences in age and gender, consumer equivalents were corrected for the length of absence of household members due to temporary migration.

13
# _Appendix Table A.9. Impact of Change in Exports on Skill Premium with the U.S. Tariff Cut Measure_

||(1)|(2) (3)|
|---|---|---|
|Skill*U.S. tariff cut measure|-.038***<br>|-.047***<br>|
||(.012)|(.012)|
|U.S. tariff cut measure|.058***<br>|.026<br>|
||(.016)|(.016)|
|Skill status|.30***|.23***|
||(.038)|(.035)|
|∆Skilled/unskilled status|.075|.022|
||(.063)|(.068)|
|∆Marital status|-.11|-.12*|
||(.069)|(.064)|
|Initial wage in log|-.34***|-.41***|
||(.027)|(.030)|
|Female||-.12***|
|||(.025)|
|Minority||-.019|
|||(.088)|
|Education||.025***<br>|
|||(.0036)|
|Potential Experience||-.00031<br>(.0013)|
|Urban||.038|
|||(.030)|
|Agriculture share 2002<sup>a</sup>||-.33|
|||(.22)|
|Industry share 2002<sup>a</sup>||.16<br>|
|||(.43)|
|Construction share 2002<sup>a</sup>||.33<br>|
|||(.75)|
|Government share 2002<sup>a</sup>||-1.44|
|||(1.17)|
|Constant|4.57***|<br>5.57***|
||(.34)|(.43)|
|Observations|1698|1698|
|R<sup>2</sup>|.177|.228|

_Note_ : The dependent variable is the log difference of monthly wage ( _∆Ln W_ ). The standard errors in parentheses are based on heteroskedasticity-consistent estimates of the variance-covariance matrix and corrected for within province correlation (clustering).

*, **, *** indicate that the coefficients are significant at the 10, 5, and 1 percent level respectively.

The classification between skilled and unskilled workers is based on the status in 2002. a The share of workers employed in the service sector is the omitted category.

53
only represents the wage of those who make the bare legal minimum: it also provides the "floor" for the salary grids negotiated between trade unions and employers. However, none of these two predictions holds true in the cases of C6te d'Ivoire and Senegal, which are the two biggest economies in the region and also, arguably, the ones with the most distorted labor markets.

The conventional wisdom is rejected based on data from the consumer price indexes of Cote d'Ivoire and Senegal. While the BDFs of these two countries provide information on average labor costs in their formal sectors, there is indeed no similar, readily-available source for labor earnings in the informal sector. But the consumption bundles used to calculate consumer price indexes include a few personal services whose production involves little more than labor. To the extent that these services are provided by individuals or micro-enterprises, they are not likely to be directly affected by labor market regulations. Fluctuations in their prices can therefore be expected to follow closely those of labor earnings in the informal sector.

To minimize the possibility of a measurement bias, the focus of the empirical analysis is on services whose prices are defined based on output units, rather than on the corresponding labor inputs. When labor units are used, there is a risk that the statistical offices report the SMIG or some specific minimum wage set in collective agreements, rather than the actual labor cost, as the relevant information source. This is what happens in practice with the prices of domestic service and cooking in the consumer price index of Cote d'Ivoire. Moreover, even if the statistical offices did gather the data from the actual providers of these services, the latter could declare fake wages to hide non-compliance with labor regulations. But there should be no such biases when the unit of measurement is the price of a hair-cut, or the cost of a laundry for a double-bed sheet. To the extent that no legal minimum price exists for these services, there are no incentives to report fake prices either. These prices are taken from the European consumer price index, which goes farther back in time than the African one.

25
7 rank employment, and 1 percent rank education. Infrastructure outstrips education by several orders of magnitude for every sub-group of household respondents—landed versus landless, low versus high caste, Hindu versus Muslim, women versus men (Ban and Rao, 2009, Table 5).

Data on similar questions in household surveys in other states show the same pattern—an overwhelming number of respondents rank village infrastructure as priorities, versus very few responses ranking education (Banerjee et al, 2007, for Uttar Pradesh; Chattopadhyay and Duflo, 2004, for West Bengal). This consistency across several different surveys in several different states does not seem to be obviously driven by an accident of how the surveys were implemented and whether they primed households to respond in ways they thought the interviewers wanted (or otherwise). The Besley et al (2004) and Banerjee et al (2007) surveys, for example, asked the question on household opinion without any prompting with possible response options. The Banerjee et al (2007) survey was in fact focused on education, thereby possibly signaling to respondents an interviewer interest in education<sup>6</sup> . Indeed this survey seems to have found a somewhat higher number of respondents listing education among the top 3 priorities (at 13 percent). Yet, this is in sharp contrast to the 40 percent that prioritize roads, the 32 percent that prioritize water, and the 31 percent that prioritize electricity.

Of course, how households at large respond to a general question about what they see as priorities for the village, and what they evaluate when they make their personal decision to vote, may be quite separate. Nevertheless, if these are systematically different, that is, if households at large think village infrastructure is the top priority but vote on the basis of other types of benefits received from public spending, or on other issues altogether, this would be important to examine to assess political incentives for growth-promoting and poverty-reducing policies. Bardhan et al

> 6 The question on ranking village priorities featured early in the interview, however, with the education-specific questions coming afterwards.
# **1. Introduction**

Over the past three decades the world has witnessed a dramatic rise in foreign direct investment (FDI) flows. Prior to 1985 the growth rate of FDI flows was comparable to the growth rates of world trade and output, but after that FDI flows grew at a much faster pace than either world trade or world output. The growing importance of FDI flows has spurred burgeoning literatures on the causes and effects of FDI in international and financial economics, international business, and economic geography.<sup>1</sup> Still, the rise of FDI has not proceeded in a smooth fashion. Since the 1980s, there have been distinct waves of FDI with corresponding surges and stops, especially in developed countries (Andrade et al., 2001). Although developed countries have generally received more FDI flows than developing ones and host the majority of the inward FDI stock, developing countries have caught up (UNCTAD, 2011). In 2010, for the first time the developing world received more FDI flows than their developed counterparts, but some developing countries were more successful in attracting FDI than others. The distribution of inward FDI flows has been persistently disproportionate in that investments have concentrated within a limited number of developing countries (see also Noorbakhsh et al., 2001; UNCTAD, 2011).

This paper investigates the nature and determinants of FDI “stops” and “surges”, defined as sharp increases and decreases, respectively, of gross FDI flows<sup>2</sup> to the developing world, initiated by foreign investors and differentiated based on whether these events are led by waves in greenfield investments (GF) or mergers and acquisitions

> 1 See Barba Navaretti and Venables (2004), Blonigen (2005), and Brakman et al. (2006) for overviews of these literatures.

> 2 These flows include equity capital, reinvestment of earnings, other long-term capital, and short-term capital less disinvestments.

2
impacts of FPE (see the case study discussions in Munene 2016). It is against this backdrop that we turn to the focus of this new analysis, namely whether the effects of this rapid scale-up can still be felt in the classroom “today,” years after the initial reform period.

# 3. Data, empirical approach, and trends in teacher recruitment

## 3.1 Data

The data used here are from nationally representative surveys of public schools in six Sub-Saharan African countries collected as a part of the Service Delivery Indicators (SDI) project. This project (launched in 2010 as a collaboration between the World Bank and the African Economic Research Consortium, and later joined by the William and Flora Hewlett Foundation and the African Development Bank) collected detailed data on teachers, including recruitment date and subject content knowledge, and also administered an assessment of basic literacy and numeracy to grade 4 students. Details on this effort are discussed in Gatti and others (2021). In order to be able to systematically match teachers to student test scores, we focus here only on grade 4 teachers.

These data were collected between 2012 and 2016 in Kenya, Madagascar, Mozambique, Tanzania, Togo, and Uganda (Table 1). The number of students assessed in each country ranges from 1,744 (Mozambique) to 4,236 (Tanzania), and the number of teachers assessed ranges from 310 (Mozambique) to 1,327 (Tanzania). Across these countries, FPE was launched as early as 1997 (Uganda) and as late as 2008 (Togo). The gap in the number of years between the reform and data collection ranges from 5 (Togo) to 16 (Uganda), with a mean of 11.2 and a median of 11.5.<sup>6</sup> In each country, representative surveys of between 198 (Togo) and 472 (Madagascar) schools were implemented using a multistage cluster-sampling design. Primary schools with at least one fourth-grade class formed the sampling frame.<sup>7</sup> In general, in each school, 10 students were sampled from a randomly selected grade 4 classroom. In addition, the students’ current (i.e.

> 6 There is sometimes some uncertainty as to the exact date of the FPE reform as the announcement does not always match the implementation. The FPE years used in this paper are derived from the following sources: Kenya: Ngugi et al (2015); Madagascar: Republic of Madagascar (2012); Mozambique: Fox et al (2012); Tanzania: Mbelle (2008); Togo: Hoogeveen and Rossi (2019); Uganda: Grogan (2009).

> 7 This description of the sample and assessments draws from Bold et al. (2019). Further details are available in Gatti and others (2021).

6
**Table S4.E: Results for the baseline specification with a dummy variable for location in the same town added.**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|VARIABLES|newprod|invdum|anyrd|it|pcf_tech|
|supbuy|0.0515|-0.0460|0.103|-0.0751|0.0903|
||(0.128)|(0.117)|(0.108)|(0.192)|(0.239)|
|samesup|-0.0169|-0.00119|0.0481|-0.0644|-0.0170|
||(0.0401)|(0.0457)|(0.0404)|(0.0662)|(0.0685)|
|samecus|0.0657|-0.0263|0.00823|0.0621|-0.0689|
||(0.0585)|(0.0707)|(0.0691)|(0.0946)|(0.0964)|
|same_sec|-0.0322*|-0.0560**|-0.0213|-0.0570**|-0.0998***|
||(0.0171)|(0.0260)|(0.0141)|(0.0282)|(0.0345)|
|Ldist|0.00649|0.00428|0.0242|-0.0411***|0.0393|
||(0.0113)|(0.00638)|(0.0149)|(0.0132)|(0.0248)|
|ad_fmage|<br>-0.000612|<br>-0.000785***|<br>-0.00100|<br>-0.00187|<br>-0.00322*|
||(0.000572)|(0.000273)|(0.00105)|(0.00131)|(0.00172)|
|ad_meduc|0.00905|0.0143|-0.00866|0.0636**|0.0495**|
||(0.00917)|(0.00966)|(0.00823)|(0.0286)|(0.0230)|
|ad_mexper|-0.000757|0.000113|-0.000558|-0.000894|-0.00309*|
||(0.000895)|(0.000390)|(0.00131)|(0.00150)|(0.00169)|
|ad_mfemale|-0.00129|0.000675|-0.00503|0.0900**|0.00996|
||(0.0186)|(0.00596)|(0.0284)|(0.0453)|(0.0480)|
|ad_lemp|0.00665|0.0221**|0.0316**|0.189***|0.123***|
||(0.00941)|(0.00905)|(0.0138)|(0.0265)|(0.0299)|
|sametown|<br>0.0697|<br>0.0344|<br>0.214***|<br>-0.147*|<br>0.338***|
||(0.0628)|(0.0384)|(0.0687)|(0.0874)|(0.129)|
|Constant|0.416***|0.443***|0.0594|0.572***|0.682***|
||(0.0659)|(0.0460)|(0.0821)|(0.101)|(0.152)|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 13
25

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expected demand projections but are still promoted for political, prestige or special interest reasons. If guarantees are given, private investors may not scrutinise the viability of the project as carefully as they would otherwise. This argument would indicate that-if guarantees are to be given-they should cover less than 100% of the investment and operating cost of the project. In Chile, because income representing only up to 70% of the value of investments is insured, and this is approximately equal to the value of debt, equity investments effectively have little or no guarantees and therefore sponsoring firms have full incentives to evaluate the project correctly.

# **5.5 Least Present Value of Revenue mechanism**

Engel, Fisher and Galetovic argue in a series of articles that many of the problems that plague road concessions-and which give rise to the pressure faced by governments to give guarantees-are due to the fixed term nature of the traditional franchising contracts.<sup>32</sup> One of their main insights has been to note that the economic uncertainty posed by traffic risk can be substantially reduced by a state contingent contract (ie. a contract whose duration is a function of the future demand level encountered).

As was mentioned earlier, income uncertainty may force governments to grant income guarantees in order to implement a successful concession programme. Income uncertainty will also increase the risk premium required by equity investors. EFG (1996) estimate that in Chile the added risk premium imposed by fixed term concession contracts may be 33% of investment costs on average (ranging from 16% to 67%).

Uncertainty may also lead to what is called the 'winner's curse'. With uncertainty, bidders form an opinion ('estimate') of the future evolution of a key variable, such as the traffic flow. Even if this estimate is unbiased on average, each will have a slightly different outlook on the future. In this situation, if bidders bid naively the winner is the agent who is most optimistic-and therefore biased-with respect to the future. The winner will therefore find that the concession is _ex-post_ unprofitable. On the other hand, if bidders are aware of this phenomenon, taking this factor into account when formulating their bids may make them conservative in their bidding strategy. They will compensate the risk of the winner's curse by increasing their bids, with the consequence that tolls will be higher than what would have been possible otherwise.

Perhaps the most important consequence of the demand uncertainty created by fixed term contracts is the higher probability that the contract will need to be renegotiated _ex-post._ In fact, Engel, Fisher and Galetovic show that with a fixed term contract a concessionaire will make losses under some states of nature (ie. low demand outcomes). Once a concessionaire is in financial distress it may be very difficult for a government to resist a re-negotiation process. There are countless experiences around the world where unsuccessful road concessions have 32 See EFG (1996, 1997a, 1997b, 1997c, 1997d, 1998) and Tirole (1997).

36