rafmacalaba/data-use-mentions-extended huggingface dataset:
$2,800. Because, it is not often possible to accumulate such contracts exist such as partial or declining chonsei, which large financial savings for housing, various forms of rental include blends of cash deposits and monthly payments. Such large financial claims also explain why room rentals and dwelling sharing are so common.

31. As a market, chonsei is considerably larger than the outstanding bank lending for housing. The chonsei system was estimated on the basis of simple ratios to be about 5.5 times larger than the housing finance system in 1980. The results of the KRIHS survey of household assets also indicate that chonsei funds used in the purchased of housing were five to six times larger that the volume of mortgage lending provided by KHB in (see Table 2).

32. As a savings instrument, chonsei reflects the poor quality or absence of household-oriented financial claims generated by the regulated financial sector. On one hand, the tenants build financial assets with the periodic increase in the deposit amount, generally every six months. They receive a real rate of return on these financiai asset which is positive but in kind in the form of housing services. This return has been found to stay close to the UMM interest rate. Tenants rent less space than they would under a monthly rent system.

_ll/_ See Renaud (1985) and World Bank (1986).
.8)|<br>446.6|(81.1)|-29.3|467.5|(87.1)|<br>487.5|(64.6)|19.9|
|Treated 1,2,3,4|477.4|(79.7)|<br>452.4|(85.3)|-25.0|482.8|(89.7)|<br>494.5|(65.5)|11.7|
|Treated 2,3,4|475.7|(79.2)|<br>450.3|(83.5)|-25.4|480.5|(89.9)|<br>493.5|(65.6)|13.0|

_Note._ Students in the 3rd year of public regular secondary school that participate in ENEM in the years 2009 or 2015. _Source. ENEM_ , 2009 and 2015, microdata.

# **5. Causal inference strategy**

What effect did the growth in higher education schooling of upper secondary school teachers have on students’ learning? One way to answer this question would be to compare the performance of the students in schools with high percentage of teachers with a degree (treated group) with the performance in schools that did not raise this percentage (comparison group). The main challenge we confront by doing this, however, is to address the problem of selection bias that may affect the estimation of this effect. This bias occurs when schools with certain characteristics, like the presence of more motivated and intelligent students, which have higher chances to achieve higher performance, are the most attractive ones for teachers with higher education. This entails in an overestimation of the treatment effects due to reverse causality.<sup>13</sup> Does being enrolled in treated schools imply better student
The same relationship holds over just the non-OECD developing countries for the regressions estimated over the combined group of process and product innovators, and over product innovators, as reported in table 5, although the differences are not as large as for the sample of all countries. The coefficient on the share of the unskilled workforce across all firms is 8.2. When the estimation is run on sub-samples that are split by innovation status, the coefficient is again larger for the group of firms with either process or product innovations (9.1) than it is for non-innovators (6.8). For the sub-sample of only process innovators, the coefficient is estimated to be 7.6, versus 8.2 for non-process innovators. Comparing the sub-samples split by product innovation, the coefficient is 9.4 for innovators versus 7.6 for non-innovators.

One additional dimension of inclusiveness that we can demonstrate with our enterprise data is the participation by the female workforce in innovation-driven growth. Across all countries, innovative firms hire larger shares of female workers than non-innovative firms: the mean employment share of female workers by innovative firms (the combined group of process and product innovators) is 29%, versus 22% for non-innovative firms.<sup>27</sup> In the employment growth rate regressions of tables 4 and 5, the positive contributions of the share of female employees to employment growth associated with innovation are statistically significant (at the 5 and 10% levels, respectively) for the combined group of process and product innovators, but not for non-innovators. Our findings suggest that a 10 percentage point increase in the share of female workers at an establishment is associated with an employment growth rate that is twotenths of a percentage point higher. Based on findings reported in tables 2 and 3, female participation contributes significantly to employment growth for young and medium-size firms across all countries, and across the non-OECD developing countries where this relationship is estimated to be more than twice the magnitude of the same relationship when it is estimated for
|Duflo, Hanna, and<br>Ryan(2012)|
|Individually‐paced<br>computer assisted<br>learning,India(Yr 2)|0.48|3.09|17.3|72.7|4.2|$5,275|71.6|Banerjee et al.<br>(2007)|

Source: Data from J‐PAL (2014). Calculations by authors.

27
repeated cross-section and panel samples of firms covering 32 EMDEs before and after Basel III implementation, with only some countries exposed to the treatment (Basel III implementation) and others not. To gauge the effect of Basel III on SME financing, we employed the difference-indifferences method and used a regression setup to control for confounding factors and address possible sample selection issues. To tighten the identification, we also formed a panel of matched firm-bank data to isolate the supply-side factors of the SME finance market more accurately.

Overall, our results indicate that Basel III implementation had a moderately negative effect on SME assess to finance in EMDEs. Differentiating across the intensity of financial inclusion, SMEs that were initially on the fringes of financial inclusion could have been affected more adversely than SMEs already using bank credit. Firms accessing credit prior to Basel III implementation may have been less negatively affected, but also the effect could have been insignificant. This finding dovetails with practitioners’ view that once SMEs have an established relationship with a bank, they typically do not face problems in renewing credit—that is, in the absence of large shocks to SME performance or financial stability (Campello, Graham, and Harvey 2010).

One channel for the negative effect of Basel III could arise because of banks shifting toward more unsecured (presumably short-term) lending and, on secured lending, banks requiring much more collateral from SMEs. Financial crises curtailed the progress in easing SME access to finance across the board. In contrast, SMEs in countries with better contract enforcement saw their access to finance ease significantly more.

In addition, we examined whether young and small firms suffered disproportionally more and whether initially less capitalized and less liquid banking systems enhanced the moderately negative effects of Basel III. However, we found little support for heterogeneity in the effects of Basel III across these firm and bank characteristics. Further, we failed to find evidence that the moderately negative effect of Basel III on SME financing would be less negative in countries transitioning to Basel III from Basel 2.5 versus Basel II
# **3 Data and Methodology**

# _3.1 Data_

Our primary data come from the 2007/08 National Risk and Vulnerability Assessment (NRVA), conducted by the Afghanistan Central Statistics Organization and the Ministry of Rural Rehabilitation and Development. The frame used for drawing the sample was the 2003-05 national household listing – a listing of every house in the country; the sample was selected following a stratified, multi-stage design.<sup>20</sup> The survey was administered between August 2007 and September 2008 and covered 20,576 households (about 150,000 individuals) in 2,572 communities.<sup>21</sup> A salient feature of the survey is its implicit stratification over time, which ensures that the samples for each quarter reflect the overall composition of the country.<sup>22</sup> This aspect is essential to address the seasonality associated with household wellbeing. The yearlong fieldwork also allowed coverage of insecure/conflict areas. It is extremely difficult to obtain high quality household data in conflict countries. The NRVA was able to achieve this task through a process of informally securing permission from local leaders in insecure areas, as well as a flexible design for field work. In particular, when a primary sampling unit was considered too insecure to interview at the scheduled time, it would not be immediately replaced, but would be re-considered at a later date within the quarter.<sup>23</sup> The NRVA consists of three components: household and community questionnaires and a district market price survey. The household questionnaire includes 20 sections – 6 administered by female interviewers to female household members and 14 administered by male interviewers to the male household head.<sup>24</sup> A key component of the survey is the food

> 20 The population frame was stratified into a total of 46 domains or strata. The 11 provinces with the most populous provincial centers were each stratified into urban and rural areas, producing 22 strata. Each of
****|**0.451*****|
|**Initial $ value**||**0.176*****|**0.176*****|**0.162*****|**0.159*****|
|**Has RCA**|||**-0.264*****|**-0.256****|**-0.251****|
|**Firm div. (products)**||||**-0.173****|**0.0777**|
|**Firm div. (destinations)**||||**-0.114**|**0.0574**|
|**Firm div. (P-D)**|||||**-0.477*****|
|**Firm div. (sectors)**|||||**-0.0381**|
|**N**|**3404**|**3404**|**3402**|**1771**|**1771**|
||*** p<0.10**|**** p<0.05**|***** p<0.01**|||

_Source:_ Authors‟ calculation based on Lao PDR customs data We find similar results for the initial value baseline regression, with all but the attractiveness coefficients significant at the 1 percent level. Experience with a destination is negative this time, indicating that firms which export many products to a destination are more cautious in adding another to the roster and therefore start off at lower values. Experience with
. So we report results both with and without ‘own slope’as a control variable in the IV regressions. Following the literature, the interaction of travel time and incidence of LDO restrictions is instrumented by interaction of the instrument for travel time with one of the instruments for LDO incidence. More speci...cally, we use the interaction of di¤erence in slopes (instrument for travel time) with ‘district malaria* inland water’(instrument for LDO incidence) as an instrument for the interaction e¤ect.

# 5. Data

The main data source for the estimation of the wage regressions is the Household Income and Expenditure Survey, 2002 (HIES, 2002). We use the rural sub-sample of Sri Lanak HIES 2002. The HIES 2002 collected information from a nationally representative sample of 16,924 households drawn from 1913 primary sampling units. The survey covered 17 of Sri Lanka’s 25 19
capacity to provide good quality products delivered on time to buyers further up in the value chain. Yet, unpredictability in customs clearance times due to physical inspection, or delays at border posts generated by excessive cargo handling in response to controls by multiple border agencies, increase uncertainty in delivery times. Despite the recent introduction of the “ _Portal Único de Comercio Exterior_ ” - an electronic data interchange system that has reduced the time for documentary compliance for both exporting and importing<sup>16</sup> - and the recent cooperation agreements between Customs and other border control agencies (as well as with third countries’ agencies<sup>17</sup> ), Brazil still fares poorly in terms of monetary costs of border and documentary compliance when compared to a range of peers (Figure 10).

**Figure 10. Cost to export and import: border compliance in USD (2018)**

<!-- Start of picture text --> 1400<br>1200<br>1000<br>800<br>600<br>400<br>200<br>0<br>Argentina Indonesia Turkey India South Africa China Brazil<br>Cost to export: Border compliance (USD) Cost to import: Border compliance (USD)<br><!-- End of picture text --> Source: data from Doing Business database (2018) The use of those protectionist trade policies, particularly in tariff and NTMS, was not conducive to improve export competitiveness. On the contrary, import protection ends up acting as a direct tax on exports, making them less, not more competitive. The so-called Lerner Symmetry theorem states that an import tariff can have the same effects as an export tax—so reducing import protection is expected also to boost exports. The idea is that an increase in import tariffs appreciates the home real exchange rate as the domestic policy rate and the international interest rate differential increases. The terms of trade appreciation, in turn, induces
expression (4) is the choice of period over which to calculate changes in consumption. The underlying theory, as expressed in (3), suggests that there is in principle an infinite time horizon. As a practical matter, however, the WDI data on genuine savings are limited to the period 1970-2000, with data for the early 1970s being particularly sparse.

A reasonable choice of time horizon would be the mean lifetime of produced capital stocks, roughly 20 years (machinery and equipment lifetimes are typically shorter, 10 years or so, but buildings and infrastructure have lifetimes of several decades). Choosing 20 years would be saying, in effect, that the effects of savings will be felt over the lifetime of the produced capital in which they are presumed to be invested. This is the assumption used below, and testing the estimation for a 10 year time horizon produced less robust estimates overall (in terms of

> 3 Ferreira _et al._ (2003) use estimated figures for consumption of fixed capital derived from the Perpetual Inventory Model used to estimate total stocks of produced capital. Inspection of these figures reveals a fairly large number of anomalous estimates.
The RZ data set contains three alternative measures of external dependence that allow us to test the sensitivity of our results. The three alternative measures of external depeiidence are significantly correlated with our principal measure of external dependence at the one-percent level, with correlation coefficients being at least 60%. We first use the dependence on external finance of firms that went public during the previous ten years. RZ show that the demand for external finance is highest during the early years of a company. Using a sample of young firms to calculate the dependence on external finance might therefore give a more appropriate picture of the need for external finance. Using the external dependence of young firms does not alter our main result: financial structure does not robustly explain industrial growth patterns, new firm formation, or old firm expansion. When using young firms to define external dependence, there are some specifications in which overall financial development enters insignificantly. However, using the external dependence of young firms lends particularly strong support to the legal-based view of finance and growth.

Our results are also robust to using a measure of external dependence that is calculated for the period 1970-79. RZ suggest that if countries other than the U.S. use older technologies, the external dependence as measured over the 80s might not reflect well the needs for external finance in other countries, especially developing countries. We therefore rerun the regressions using the external dependence measured over the 70s. Since the U.S. was also "more" bankbased in the 70s than in 80s, using this historic measure of external dependence has another advantage. It allows us to test the sensitivity of our results to a bias that might have been introduced by using the external dependence of industries measured for a sample of firms in a market-based economy. Our results are similar to the ones obtained with our principal measure of external finance, as measured over the 80s. There is not a robust link between financial 25
and For a loss-neutral person, equations (13) and (14) for penalties are identical to equations (10) and (11) for rewards. Thus, in the absence of loss aversion, workers choose the same optimal levels of effort in response to a reward or an equivalent penalty. In contrast, loss aversion would imply that a given increase in the penalty on action 1 leads to an increase in effort on action 2 when the actions are complements, and a decrease in action 2 when they are substitutes.

In sum, our model predicts that, holding information constant, incentives should increase effort on the incentivized actions and the degree of complementarity between actions determines the sign of any spillovers between actions. In particular, incentives on one action will raise effort on complementary actions (positive spillover) but decrease efforts on actions that are substitutes (negative spillover). Finally, loss-averse agents are more responsive to a penalty than an equivalent reward. The direction and degree of complementarity between actions and the degree of loss aversion are empirical questions that we examine below.

# **3 Experiment design**

We used a framed incentivized field experiment to empirically examine the direct and spillover effects of adding incentives to information. In brief, during a survey of maternity care workers in Nigeria, we asked 1,363 respondents in 691 health facilities to participate in a study relating to adherence to protocol in the context of labor and delivery care. Our experimental task is aligned with the study participants’ day-to-day work: deciding on clinical actions that are appropriate for women receiving labor and delivery care.

9
impacts on poverty and inequality.<sup>5</sup> To simulate price shocks as a result of COVID-19, we use the published consumer price index (by INS) by product categories. We then apply new prices on household consumption and simulate the decline in real disposable income/ consumption. We also assume that this trend of change in prices will continue during the rest of the year.<sup>6</sup> Note that we account for heterogeneity of consumption patterns across households, in particular between poor and non-poor households, by using information on consumption of products collected in the household survey.

These impacts are assessed under two scenarios: (1) _The first (optimistic)_ uses World Bank recent estimates with -8.8% real GDP growth, at constant factor prices.<sup>7</sup> (2) _The second (pessimistic)_ simulates a growth of -11.9%, i.e. the economy achieves the same growth as that achieved during the first half of the year. Below we provide a detailed methodology of how these impacts are estimated.

_Estimation of loss of income_ Suppose that the economy is partitioned into sectors ( , the level of activity is observed before the pandemic period 0 and after the pandemic period 1. SS ss= 1, ... SS) Let be the sectoral growth rate during the pandemic period, and the growth rate of the economy. Let be the total employment in the economy and the employment in sector . ss gg gg The growth elasticity of employment is given by . ss EE EE ss _We first estimate the sectoral growth of the economy._ ss Our starting point is the sectoral projection of εε _We first estimate the sectoral growth of the economy._ Our starting point is the sectoral projection of growth ( carried out by the WB projections according to several scenarios of the evolution of the pandemic. ss gg ss= 1, ... SS) This is followed by _estimating the
each municipality to the nearest international airport. However, this measure does not accurately estimate the cost of informal remittances for each municipality because it is difficult to know the exact itinerary of returning migrants or _encomenderos_ .

_E. Economic activity:_ To control for the potential effect of an increase in remittances in places hit hardest by the pandemic early on (due to an increased altruism motive), we build a Bartik-style measure of local labor market shocks at the municipal level during our period of analysis. For each municipality we estimate shocks as the expected change in employment by multiplying employment shares in January 2020 by sector at the municipal level with changes in employment in these sectors at the national level between January and June 2020. This index can be interpreted as the predicted changes in employment at the municipal level due to industry composition in the local labor market and industry-specific national employment changes during the first year of the pandemic.<sup>13</sup> _F. Bank account data:_ Data from wage and transaction accounts at the municipal level come from Mexico’s financial regulator—the Mexican National Banking and Securities Commission (CNBV). This public database provides monthly municipal-level data on the number of wage and transaction bank accounts. Transaction accounts are the most common bank accounts and can be opened (with minimum requirements) by any individual in Mexico. In contrast, wage accounts are usually opened by an employer and require the holder to be formally employed by a company associated with the bank. This distinction is relevant for our analysis since an increase in demand for bank accounts that arises from a formalization of remittances (i.e., electronic transfers) should relate specifically to transaction accounts. However, controlling for the number of wage accounts at the municipal level helps to mitigate the bias introduced by other confounding variables that potentially affect the number of transaction accounts and remittances (e.g., local shocks).

_G. Household remittances:_ Data from the National Survey of Household Income and Expenditure (ENIGH)
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 14. Botswana’s Internet market benchmarked against its southern African peers**

<!-- Start of picture text --> a. Internet service trends, Botswana, 2000–08 b. Internet service trends, southern Africa<br>6.0 200 20 350<br>18<br>180 300<br>5.0 16<br>160<br>14 250<br>140<br>4.0<br>12<br>120 200<br>10<br>3.0 100 150<br>8<br>80<br>2.0 6 100<br>60<br>4<br>40 50<br>1.0 2<br>20<br>0 0<br>0.0 0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per Internet users (per 100 people)<br>person)<br>Internet users<br>Internet users<br>International internet bandwidth International internet bandwidth<br>Madagascar Malawi Angola Zambia Mozambique Lesotho Zimbabwe Botswana South Africa Namibia Mauritius<br><!-- End of picture text --> _Source:_ AICD. Information and Communications for Development database

# Financing Botswana’s infrastructure

To meet its most pressing infrastructure needs and catch up with developing countries in other parts of the world, Botswana needs to expand its infrastructure assets in key areas (table 13). The targets outlined in table 13 are purely illustrative, but they represent a level of aspiration that is not unreasonable. Developed in a standardized way across African countries, they allow for cross-country comparisons of the affordability of meeting the targets, which can be modified or delayed as needed to achieve financial balance. Admittedly, for middle-income countries in Africa, the