rafmacalaba/datause-encoder-data huggingface dataset:
online appendix Kinyarwanda (e.g., mask, masque, agapfukamunwa) in product descriptions. We subsequently remove products from this data, which satisfy the matching algorithm, but are not face masks. This includes, for example, packaging for masks (“Mask Envelope”) or masks for nebulizers.

**Effective mask quantity** For some analysis, we construct aggregate “effective”, or quality- adjusted, mask quantities. To do so, we multiply observed mask quantities by a firm-product specific measure of quality. To estimate this measure, we let _prft_ be the price of firm-product _f_ on receipt _r_ from month _t_ , and estimate

We then construct effective mask quantity as _Qrft ≡_ exp( _αf_ ) _Q_<sup>observed</sup> _rft_ , where _Q_<sup>observed</sup> _rft_ is the observed quantity of firm-product _f_ on receipt _r_ , and _αf_ are the estimated firmproduct fixed effects from the log price regression above. In our main analysis, we construct effective mask quantity from firms of type _s_ by summing effective quantity within buyer sub-District-by-month across firms of type _s_ .

**Product UNSPSC codes** In order to determine the tax status of a product, firms classify their inventory using United Nations Standard Products and Services Code (UNSPSC) codes. We identify textile products (a product grouping) using UNSPSC codes 601058, 5310 and 2312. We ensure that the string descriptions conform to the UNSPSC product descriptions for each code.

A7
Figure 3.1: SERCE 2006 Math Scores ys. PISA 2009 Math Scores in Latin America <!-- Start of picture text --> a<br>os<br>500)<br>400<br>300)<br>200 * SERCE2006maths<br>"0 iEEELEEE "PISA Math 2009<br>eo & of & @& o<br><!-- End of picture text -->
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE transmission and distribution of power amounts to approximately $0.07 per kWh, yet the tariff charged to the final consumer is $0.042 per kWh, recovering only 60 percent of costs (table 4).

Furthermore, distribution losses are substantial. Estimates of ENE’s technical and nontechnical losses are 18–23 percent. Losses are much worse for EDEL, totaling 36 percent, of which 15 percent is attributable to technical losses and 21 percent to nontechnical losses. Nontechnical losses are largely due to pilfering through illegal connections, lack of meters, and faulty billing systems. Recent estimates suggest that overall losses were reduced from 40 percent in 2006 to around 32 percent in 2010. While this represents important progress, losses remain very high in absolute terms.

In addition, the nonpayment of power bills is rampant. The pervasive culture of nonpayment found in Angola seriously impedes financial performance. The government estimates that, on

**Table 5. Bill collection in Angola in 2008**

||ENE|% of ENE sales|EDEL|
|---|---|---|---|
|Collections from consumers (%)|71|30|68|
|Collections from EDEL (%)|27|70||
|_Source:_World Bank 2010a.||||

average, only 40 percent of the energy generated is billed, and that only 42 percent of what is billed is collected. This collection rate is exceptionally low compared to the African peer group (table 5). While EDEL’s collection performance used to be much worse than ENE’s, it has improved in recent years to a point where the two companies have largely converged (table 7). Ironically, the collection ratio of what the utilities charge their end consumers, at around 70 percent, is much higher than the collection ratio between ENE and EDEL, which stands at only 27 percent (table 5). The trail of arrears between the various entities involved in the power sector starts with nonpayment by EDEL’s clients, leading to nonpayment for power purchased by EDEL from ENE, leading to nonpayment of fuel purchased by ENE from the national oil company SONAGOL.

In total, inefficiencies contributed to a combined financial hemorrhage of $618 million for ENE and EDEL in 2009, with underpricing, distribution losses, and low collection ratios accounting for over $550 million of that total. Hidden costs in the power sector have been increasing steadily since 2007. ENE accounts for the lion’s share of the hidden costs—almost $500 million in 2009, or 0.7 percent of GDP. The largest source of hidden costs for ENE is the underpricing of power, both to EDEL and to its own consumers. The failure of EDEL to make timely payments on its bills further augments ENE’s hidden costs (table 6). EDEL’s hidden costs amounted to $120 million a year in 2009, equivalent to 0.2 percent of GDP (table 7). Once again these costs are largely driven by underpricing, and to a lesser extent system losses.

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need such ‘favors’ to remain in the game.<sup>1</sup> To separate these two views, it is important to look at both the sign of the corruption variable and the interaction of corruption and the performance of the firm. If officials target ‘good performing firms’ and the bribes are ‘sand’, one would expect that the interaction term would be positively correlated with exiting. Or if increasingly poor performing firms are faced with lots of additional ‘sand’, the interaction term would be negative on exiting. On the other hand, if ‘poor performing firms’ are the ones paying grease money, there could be a positive interaction term on exiting, whereas it would be negative if the productive firms are paying for the ‘grease.’

# **In a regression of the probability a firm will exit, interpreting the sign of the corruption -** **<u>coefficient and sign of a corruption productivity interaction term</u>**

|||Bribes targete<br>Productivefirms|d to / paid by<br>Unproductivefirms|
|---|---|---|---|
|||Corruptioncoef. /Interactionterm|Corruptioncoef. /Interactionterm|
|Corruption|Sand|+/+|+/-|
|serves as:|Grease|-/-|-/+|

# _2.  Factors affecting market mechanisms:  competition, finance, property rights and competition_

_Competition:_ Greater competition is widely associated with weeding out inefficient firms. Competitive pressure can come from greater exposure to imports, from multinationals operating locally and from other domestic firms.

_Finance:_ Access to finance and the development of financial markets help intermediate transactions and channel resources to their most productive uses, thereby facilitating investment and growth (Levine 2005). With a more developed financial system, market signals are likely to be stronger and the greater discipline on the allocation of finance should facilitate pushing less productive firms to exit.

> 1 This debate also makes clear the need to control for potential endogeneity, which is discussed below.

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# **1 Introduction**

The global rollout of COVID-19 vaccines has been substantially faster than any other vaccine rollout in history, however, progress has been very uneven. By mid-2022, low-income countries (LICs) still had relatively limited coverage of their populations and the take-up of COVID-19 vaccines had begun to slow down considerably (Figure 1). These trends are alarming as the possibility of many of these countries reaching herd immunity for COVID-19 seems far beyond reach (Kwok et al, 2020). This is at a time when many supply constraints for COVID-19 vaccines have been eased, which suggests limited demand is likely contributing to slower progress towards reaching the rest of the population. The drivers of people’s demand for COVID-19 vaccines have received relatively little attention in LICs as policy makers have tended to focus on procuring vaccines and making them as widely available as possible. There is a knowledge gap about the role intra-household dynamics may play in shaping people’s attitudes towards COVID-19 vaccines. This issue warrants close consideration as the rapid rollout of COVID-19 vaccines and near universal eligibility means that almost all members of the same household can get vaccinated at the same time, which is truly distinct from what has occurred with any other vaccine rollout in LICs.<sup>1</sup> We examine this issue of intra-household dynamics and attitudes towards vaccines by conducting novel randomized survey experiments and drawing on a unique nationally representative survey in Zambia. Specifically, we use individual level surveys for all household members in a nationally representative sample of over 10,000 households that were conducted in-person immediately prior to the widespread rollout of COVID-19 vaccines in mid-2021. Building on this initial survey, we conducted a randomized survey experiment through Facebook involving almost 3,000 participants in March and April 2022. This was followed by another randomized survey experiment conducted via UNICEF’s U-Report SMS messaging platform covering over 2,000 participants in August and September 2022. The randomized experiments tested how people’s self-reported willingness to receive the COVID-19 vaccine

> 1For instance, in most high-income countries (and some upper-middle-income countries), influenza vaccines are readily available for all household members, but this is not the case in LICs.

1
# Table of Contents

|1.|Introd|uction .......................................<br>,,<br>1|
|---|---|---|
|2.|Som|e econo nics and econometrics of hcspitals .1|
||2.1<br>|Are hospitals over-capitalized?.<br>1<br>|
||2.2<br>|Are hospitals ineffcient?.<br>4<br>|
||2.3<br>|Should hospitals specialze?.<br>8<br> <br>|
||2.4|Toc many hospitals? ..........................<br>10|
|3.|Hosp|ital costs in Kenya ..................................<br>15|
||3.1<br>|Are Kenyan hospitals over-capitaHzed?<br>.16<br>|
||3.2<br>|Inefficiency in Kenyan hospitals.<br>16<br>|
||3.3<br>|Economies of scope in Kenyan hospitals.<br>16<br>|
||3.4|Too many hospitals in Kenya? .17|
|4.|Hospi|talcosts in Peru.17|
||4.1<br>|Are Peruvian hospitals over-capitalized? ..............<br>18<br>|
||4.2<br>|Inefficiency in Peruvian hospitals .18<br>|
||4.3<br>|Economies of scope in Peruvian hospitals .19<br>|
||4.4|Too many hospitals in Peru? .19|
|_5._|Hospi|tal costs in Ethiopia .20|
||_5.1_<br>|Are Ethiopian hospitals over-capitaized? .20<br>|
||_5.2_<br>|Inefficiency in Ethiopian hospitals ...............<br>21<br>|
||_5.3_<br>|Economies of scope in Ethiopian hospitals .21<br>|
||_5.4_|Too many hospital in Ethiopia? .22|
|6.|Healt|h care costs in Nigeia .22|
||6.1<br>|Are Nigerian health care institutions over-capitalized? .24<br>|
||6.2<br>|Inefficiency in Nigeria health care institutions .24<br>|
||6.3<br>|Economies of scope in Nigerian health care institutions .25<br>|
||6.4|Too many health care institutions in Nigeria? .25|
|7.|Concl|usions .26|
||Refere|nces .30|
pation and the ratio of college graduates in each cell. The first column shows the share of each occupation in the corresponding sector’s labor force. For example, 35% of manufacturing employees are classed as Laborers, higher than any other sector. By contrast, 44% of Traded Services workers are White-collar, making it the most white-collar intensive sector. The second column shows the share of each occupation working in each sector. For example, only 7 percent of “white collar” workers are in the “agriculture and construction” sector. About half of office workers are in the traded services sector, and about half of laborers are in manufacturing. The last column shows the ratio of college graduates in each industryoccupation cell. For example, 72 percent of traded services sector white collar workers are college graduates, while only 3% of manufacturing-sector laborers are.

Table 5: Ratio of Industry-Occupation Cells

|||_Share in Sector_|_Share in Occupation_|_Ratio of College_|
|---|---|---|---|---|
|_White_|_Aggr/Cons_|0.17|0.07|0.43|
||_Manuf_|0.26|0.21|0.58|
||_Non-traded_|0.29|0.19|0.52|
||_Traded_|0.44|0.52|0.72|
|_Tech/Sales_|_Aggr/Cons_|0.03|0.02|0.25|
||_Manuf_|0.11|0.17|0.27|
||_Non-traded_|0.26|0.32|0.23|
||_Traded_|0.22|0.49|0.37|
|_Service_|_Aggr/Cons_|0.15|0.19|0.10|
||_Manuf_|0.02|0.05|0.04|
||_Non-traded_|0.18|0.40|0.14|
||_Traded_|0.09|0.35|0.08|
|_Craft_|_Aggr/Cons_|0.46|0.28|0.05|
||_Manuf_|0.26|0.34|0.06|
||_Non-traded_|0.16|0.16|0.05|
||_Traded_|0.12|0.22|0.06|
|_Laborer_|_Aggr/Cons_|0.19|0.12|0.03|
||_Manuf_|0.35|0.49|0.03|
||_Non-traded_|0.11|0.12|0.04|
||_Traded_|0.14|0.28|0.05|

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# _Regression Analysis_

We look at the factors associated with visiting a health clinic. For this, we run OLS regressions for before and after the crisis where the dependent variable is the household's number of visits to health clinics per month. Regressors are the same as those used for the part of health expenditures. The results are in table 4.25. A probit analysis on health clinic utilization provides similar insights to those given here and is included in the appendix.

Consumption enters significantly in **botLi** equations and with a positive sign. This says that households with higher consumption visit health clinics with a higher intensity. This role to consumption gets larger in the equation for the post-crisis period. This make sense in a setting were public expenditures in health were falling down so rapidly. After the crisis, the variable for housing area (intending to capture for assets) and that for social benefits start to enter significantly, also reflecting this major reliance on household income for health care utilization.

Distance to clinic and distance to hospital reflect monetary or nuisance costs of accessing these institutions, apart from any fee associated with the usage. Before the crisis both variable enter significantly in the estimated regression. Distance to a clinic enters with a negative sign. That is, clinic utilization is used less intensively the farther away the household is from the clinic. Distance to a hospital, however, enters with a positive sign. Since some health services can be obtained from either a hospital or a clinic (especially those of primary care), it is possible to see some substitutability between these two institutions. A higher distance to a hospital thus increases the intensity of health clinic utilization, reflecting a "cross-price" effect.

For the post-crisis, the distance-to-hospital effect remains. Being close to a hospital ,implies a lesser utilization of health clinics. But the distance-to-clinic effect loses importance after the crisis. Seemingly, in this period of distress, the income effect previously discussed is so strong as to make this price effect loose importance.

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# **4. IMPROVING INSTRUCTIONAL TIME USE: POLICY IMPLICATIONS**

Classroom activity represents the moment when the education financing gets converted into knowledge. For decades it has been uncertain which variables are most important for this process. Classroom events have often been considered a ‘black box’, involving details into which donors and some governments have traditionally hesitated to delve. Without this information, the donor community finances educational inputs (salaries, buildings, materials) without a clear hypothesis of how these will lead to outcomes.<sup>110</sup> Not surprisingly, learning outcomes are often limited (Figure 3-1). This study offers a glimpse of “black box” variables that have been outside the radar screen of the education sector. For poorer areas and countries, these “details” are too important to overlook.

**Figure 4-1. Classroom instruction as a black box mediating input and outcome variables**

<!-- Start of picture text --> Project inputs Impact evidence<br>Buildings Enrollments<br>Textbooks Dropout<br>Salaries Repetition<br>Training Learning outcomes<br>School grants<br>Other inputs<br><!-- End of picture text --> Instructional time is the mediator variable that has escaped scrutiny and measurement thus far. Learning outcomes should not be expected without sufficient teaching and practice to master the instructional objectives of specific subjects. In very gross terms, if schools use 39% of the available time, they may teach only 39% of the curricular objectives. Since knowledge is cumulative, performance losses should exceed 39% over time. Curricula worldwide are overloaded, so even modest time wastage may result in significant student losses. To make up for absenteeism, teachers may just lecture in a hurry rather than and analyze the content and use the teaching aids provided to schools, or they may omit parts of the curricula. So, the average Ghanaian public-school students cannot master the curricula just by going to school; they need private tutoring to pass exams.

Discussions of educational expansion in low-income countries often bring up a tradeoff between quantity and quality. But aside from the instances where instructional time is cut purposefully to fit more students in multiple shifts, the tradeoff may not exist. In effect the issue is _quantity of teaching, sufficient to master the prescribed curriculum_ . Good time use is not sufficient to produce quality teaching, but it is necessary. For greater efficiency, activities must be used that help students form linkages among pieces of information and retain the information for the long term.<sup>111</sup> The real tradeoff may be the lack of supervision and interest in whether teachers attend to low-income students.

110. IEG 2006b.

> 111. High-quality teaching also means offering immediate and corrective feedback and creating “useful difficulties” that have may enhance memory, such as challenging students to invent processes for dealing with problems (e.g. Bjork and Bjork 1992).
Figure 24. Resource rents vs adjusted net savings, 2004-2014 average <!-- Start of picture text --> 40<br>30<br>BWA<br>CPV<br>20<br>LSOTZANAM MRT<br>SDN ZMB<br>10<br>MUSETHSWZSENNER CIV NGA GAB<br>0 GNBBEN COM KENUGAMWIMDG RWA SLE BFA MOZ MLI GHAZAF CMRTGOCOD AGO<br>-10 COG<br>GIN SSD<br>GMB<br>-20<br>ZWE<br>-30<br>BDI<br>-40<br>-50<br>-60 LBR<br>-70<br>0 5 10 15 20 25 30 35 40 45 50<br>Average natural resource rents % GDP (2004-2014)<br>Fossil fuel rich Non-RR Mineral rich Resource-rich<br>Average adjusted net savings % GNI (2004-2014)<br><!-- End of picture text --> _Notes: Average adjusted net savings as percent of GNI and average natural resource rents as percent of GDP over the boom period in Sub-Sahara African countries. Source: authors’ calculations using the Changing Wealth of Nations 2021 and World Bank data._ In Africa many fossil fuel-rich countries with the most depletion (proxied by rents) – such as Angola and Republic of Congo – have seen low and negative rates of net saving, meaning they are running down the overall stock of assets in the country, and instead consuming a large share of this value. Such a pattern means countries are bringing forwards consumption as the expense of future GDP, effectively reducing the available assets for future generations. Venables (2016) documents this phenomenon. Meanwhile, the ANS of some mineral-rich countries have been less impacted reaching 10% of GNI – such as Zambia and Mauritania.

According to the Changing Wealth of Nations 2021 report, in 2004, 10 countries in SubSaharan Africa had the most of their wealth held in natural assets, versus human or productive assets or net foreign assets. Surprisingly, by 2014, this same number of countries still had natural capital as the most abundant asset. Only Ghana reduced its 35
the level of spending on measures to contain the epidemic and treat those infected, and premature mortality. We draw on the procedure and estimates in Bell, Devarajan and Gersbach (2006), a brief sketch of which will suffice here.

It is argued that the efficacy of spending depends directly on the number of families, Ft, that make up the whole society. Aggregate spending is then written as Ft · Gt, where Gt is the level of spending per family in period t. Let such aggregate expenditures produce a pure public good, so that ridding the relationship of the size of the population, we write where the superscript ‘a’ to qt can now be suppressed without ambiguity and the function qt(Gt; D = 1) is to be interpreted as the efficiency frontier of the set of all measures that can be undertaken to reduce qt. We also assume, and this seems sensible, that the effects of these expenditures last for only the period in which they are made.

Our definition of the benchmark D = 1 implies that qt(0; D = 1) should yield the estimates in Table 1. A second, plausible, condition is that arbitrarily large spending on combating the epidemic should lead to the restoration of the status quo ante, that is, qt(∞; D = 1) = qt(D = 0) in all periods, again as set out in Table 1. The fourparameter logistic, possesses an asymptote and sufficient curvature. With four parameters to be determined, the above conditions must be augmented by two others.

One way of proceeding is to pose the question: what is the marginal effect of efficient spending on qt in high- and low-prevalence environments, respectively? That is to say, we need estimates of the derivatives of qt(Gt; D = 1) at Gt = 0 and some value of Gt that corresponds to heavy spending, when the scope for using cheap interventions has been exhausted. Here, we draw on the costs of preventing a case of 27
To obtain household information, we administered a light survey during April-May 2010, about 2.5 years after the start of LTR in the pilot cells. The sample was designed to yield numbers of households in each pilot cell equivalent to their share in the total, with a size of 3,554 households with some 6,330 land parcels, intended to be split equally across pilot and their neighbor cells. To sample inside pilot cells, we used parcel index maps created by the program. As no such maps were available for the adjacent (control) cells, we used high resolution satellite imagery to identify dwellings that could then serve as a sample frame. Figure 2 illustrates the procedure graphically. To ensure that the share of households in each pilot cell corresponded to the cell‟s share in the target population, the widths of the bands from which households were selected were adjusted.<sup>8</sup> Note also that, as we lack enough statistical power to make strong conclusion for the urban sample, i.e., the city of Kigali, the empirical analysis given below is solely based on data from rural areas.

Table 1 shows the distribution of sampled households in the pilot and their adjacent cells. Information was collected both at household and parcel levels. Household level information includes demographics, housing, assets, participation in the credit market, participation in the registration program and knowledge of the law and the GPS coordinates of the main residential plot. Parcel level questions included land characteristics, investment, inheritance dynamics as well as participation in land sales and rental markets.

# **4.2 Econometric approach**

To examine the effects of LTR, we estimate the following equation: where **_Yph_** is the outcome of interest for parcel **_p_** in household h, **_T_** is an indicator for LTR participation, **_X_** is a set of parcel characteristics, **_Z_** household characteristics, and  an error term. If land registration were voluntary, we would be concerned about endogeneity of outcomes such as investment and land market activity. However, tenure regularization was compulsory and covered all private land in the cell. In other words, plots were registered up to a cell‟s administrative boundaries, but not beyond the cell border. The spatial discontinuity generated by this allocation rule thus can be used to allow identification of program effects by comparing individuals who live within a band on either side of the border following Magruder (2011). This approach extends work comparing enterprises on either side of a state border in the US to look at the effects of minimum wage legislation (Card and Krueger 1994) to explore effects of centralized bargaining agreements on employment using geographic and labor force data. For the approach to be valid, communities on either side of the border have to be identical and cell-level effects that could drive our results need to be absent. To assess the validity of this assumption, we note that key policies with

> 8 Outer and inner bandwidths were 125 m in Nyamugali, 400 m in Kabushinge, 350 m in Biguhu, and 1250 m in Mwoga.

11