rafmacalaba/datause-encoder-data huggingface dataset:
Source: Authors’ calculations based on data from the Exporter Dynamics Database (World Bank).

Export values are not just skewed towards particular firms and countries; they are also skewed in terms of the range of participation across products and countries.

Table 7 cross tabulates two aspects of firms’ exporting behavior – by number of products and countries. The top half of the table provides the percentage counts of firms – for instance, firms that sell one product to one country account for 32.38% of the total number of firms in the census in 2002, while firms that export more than 11 products to more than 11 countries account for just 1.86% of the census of firms. What is striking is the lower half of the table, which summarizes the percentage of export values accounted for by the given classification of firms. For instance, in line with the example above, firms that export one product to a single country account for 1.15% of total export value in 2002, while firms that export more than 11 products to over 11 countries account for more than 50% of the total export value. This is an additional strong indication that only a few firms are active in several markets at the same time, but that these few firms account for the lion’s share of exports. Indeed, compared to peer countries, Pakistan appears to have a low average number of products per exporter but a high average number of destinations per exporter (Figure 2 0 ). Taken together, these results suggest that like many other countries Pakistan posts a high concentration of exports in the hands of a limited number of large exporters. The dominance of few exporters may however have drawbacks in terms of product innovation and experimentation. The fact that firms tend to export only few products each may be a symptom of market distortions due to the dominant position of few firms. We will investigate this issue further in the following paragraphs.

**Table 7**

**Export behaviour: Countries & Products (2002)**

|**% of Counts**|**Number**|**of Countries**||||
|---|---|---|---|---|---|
|**No. of Products**|_1_|_2 to 5_|_6 to 10_|_11 and above_|**_Total_**|
|_1_|32.38|3.69|0.09|0.02|36.18|
|_2 to 5_|17.47|24.07|2.62|0.56|44.73|
|_6 to 10_|1.43|6.79|3.08|1.27|12.57|
|_11 and above_|0.18|1.89|2.06|2.40|6.52|
|**_Total_**|51.28|34.55|5.79|1.86|100.00|
|**% of Value**|**Number**|**of Countries**||||
|**No. of Products**|_1_|_2 to 5_|_6 to 10_|_11 and above_|**_Total_**|
|_1_|1.15|0.86|0.11|0.03|2.15|

28
# **The Quality of the Legal System, Firm Ownership, and Firm Size**

**Abstract:** Employment in developing countries is disproportionately concentrated in very small firms. We examine the extent to which the distribution of firm size is related to the quality of the legal system using data from Mexico. We combine Lucas’ (1978) model of firm size with Himmelberg, Hubbard and Love’s (2001) consideration of idiosyncratic risk in a framework in which the distribution of entrepreneurial talent and aversion to idiosyncratic risk combine to determine the optimal size of firms. Our data allow us to focus on the differential impact of the legal system on proprietorships and corporations. Moreover, by focusing on firms in a single country, the data draw attention to the importance of variation in the administration of justice and the enforcement of legal verdicts. We find that Mexican states with more effective legal systems have larger firms. A one-standard deviation improvement in the quality of the legal system increases the average firm size by about 10-15 percent. The impact of the legal system is greatest in sectors in which proprietorships dominate. This pattern is consistent with better legal systems increasing the investment of firm owners by reducing the idiosyncratic risk they face. All of these findings are upheld when we instrument for institutional variables using the log of indigenous population in 1900 and the active presence of the drug trade in the state.

1
The continental averages, however, hide disproportionate effects in individual countries. The expected additional damages from climate change to all affected countries for each climate model are shown in Appendix A. The countries with the likely largest tropical cyclone impacts from climate change are the United States ($30 billion), Japan ($9 billion), and China ($8 billion). The climate change damages to these three countries account for 88 percent of the global damages. However, other countries could suffer climate change impacts which are a relatively large fraction of their GDP. The 9 countries listed in Table 5 may endure additional tropical cyclone damages greater 0.2 percent of their GDP. All but one of these countries is an island in the Caribbean. The largest impact is to St Kitts-Nevis with additional damages possibly equal to 1.4 percent of their GDP.

Table 5: Additional Damage From Climate Change to Most Heavily Impacted Nations

|Country|Average<br>Damage|Percent of<br>GDP|
|---|---|---|
|Antigua-Barbados|69|0.5%|
|Cayman|75|0.5%|
|Dominica|22|0.5%|
|Grenada|24|0.3%|
|Honduras|553|0.3%|
|Montserrat|3|0.2%|
|St. Kitts-Nevis|91|1.4%|
|Turks-Caicos|17|0.4%|
|US Virgin Islands|184|1.0%|

Source: Additional tropical cyclone damages in millions of USD/yr calculated using the minimum pressure model and the future baseline in 2100.

Although expected damages provide a good sense of long term damages, they hide the skewed nature of tropical cyclone damages. Many storms cause relatively little 19
11 African programs had about a tenth as many beneficiaries per program). If one assumes that nonfood costs are around 30% of the costs of Latin American feeding programs, then Nathan Associates' data would suggest that African programs would still cost about 3 times as much as Latin American ones, if food costs in Africa and Latin America were about the same. However these data are not standardized by size of ration and number of days fed, and it proved impossible to retrieve the data on ration size and days fed from all the NGO's concerned.

The data here suggest that the Nathan Associates' results are somewhat unrepresentative. The costs of programs in Asia and Latin America are lower than those in North and sub-Saharan Africa (by $10-20 per beneficiary) if the median is used, but programs in South Asia and sub-Saharan Africa are about $10 cheaper per beneficiary than those in North Africa and Latin America if the mean is used. It is possible that there are differences in cost structure, an issue addressed in section III. Costs of ocean transport and food distribution are quite likely higher in Africa due to lower population density and weaker infrastructure, but programs elsewhere may compensate by including more complementary inputs.

As regards the comparison between school feeding and MCH programs, there seems to be little difference. Mateus (1989) had commented that school feeding programs were cheaper on a straight cost per beneficiary per year basis, but this disappears once the smaller ration size and fewer feeding days of the school feeding programs is taken into account. MCH programs could potentially include more complementary non-food inputs than school feeding programs, which could account for the larger range of costs for MCH programs. (One might also suspect that the most costly school feeding programs are somewhat inefficient).

As regards program scale, this is an issue on which there has been very little information. Cost functions have been very standardly calculated for health interventions (e.g. hospitals), but rarely if ever for nutrition interventions. The limited data here suggest that small programs are the most
Sections 3 to 5 discuss the central findings of the literature for digital, energy, and transport infrastructures, respectively. Section 6 draws out the main findings and conclusions of the review.

# **2. Methodology and Literature Review**

The systematic literature review process followed standard procedures that can be briefly described as follows: identifying studies that meet the search criteria and coding the attributes of eligible studies into a database (Seuring and Müller, 2008; Stanley and Doucouliagos, 2012). The analysis aims not only at papers published in peer-reviewed journals, but also working papers with a focus on policy-relevant work, excluding solely theoretical papers. Studies focusing on types of infrastructure other than digital, energy, and transport were not considered (e.g. water). While the focus is almost entirely on studies covering low- and middle-income countries, where there is a more limited knowledge base, some seminal studies of high-income economies are also briefly cited to provide a wider context. The keywords used to identify suitable studies include “infrastructure”, “digital”, “energy”, and “transport”, each in combination with "development” and/or “growth”.

An important starting point was to incorporate existing literature review and meta-analysis papers. The first set of papers identified – García et al (2017); Bom and Ligthart (2014); and Straub (2008) – take more of a cross-cutting view of infrastructure and focus on its impact on overall economic output, as opposed to different dimensions of development. In addition, the process identified literature review papers focusing more narrowly on transport, energy, and digital infrastructures. This group includes the works of Burgess et al (2020), Greenstein (2020), Lee et al (2020), Redding and Turner (2015), Redding and Rossi-Hansberg (2017), and Vergara-Cobos and Malasquez (forthcoming). An overview of the most relevant literature review articles surveyed is provided in Table 1.

Beyond these literature reviews, the search for related publications was mainly conducted as a structured key word search, drawing on major public databases such as Elsevier <u>(www.sciencedirect.com), Springer (www.springerlink.com), and Wiley (www.wiley.com), as well</u> as databases developed by various institutions such as the World Bank, the Bureau of Economic Analysis, International Monetary Fund, and the World Economic Forum of the Organization for Economic Cooperation and Development. Cited references from papers found in the initial search were also used as a secondary source of literature. Although the search was carried out in English, papers written in other languages were also reviewed, if they were cited in the selected literature review papers. Finally, to ensure that more recent (as yet unpublished) research was captured by the review, a global call for new papers on this theme was conducted in preparation for the 2022 3
<!-- Start of picture text -->
- 15  -<br>K<br>Aoert =  Sy(f(F)  - oer)ti +r Xi AF ..<br>fiF  4  gti  +%v<br>i-D<br><!-- End of picture text -->

<!-- Start of picture text --> (5)<br><!-- End of picture text --> where, iq is a stationary disturbance term, A is the difference operator, and f(F) is the log-linearized specification of equation (4) above, giving the determinants of the auction clearing rate.

## 4. ESTIMATION AND HYPOTESIS TESTING.

This section estimates model (5) above for the micro-deteminants of the auction rate. After a discussion of model diagnostics, and ascertaling that the model is broadly corroborated by data from the four countries, three hypotheses motivated by auction theory which -have policy implications, will be tested. The testing will involve the revenue equivalence and competitiveness hypotheses, and also whether increased uncertainty (proxied by supply volatility) induces a risk premium on bids.

## 4.1 Evaluating the static and dynamic features of the estimations.

Given the characteristics of auction data (non-stationarity and regime shifts) and the repeated, sequential nature of foreign exchange auctions in SSA, we argued in section 3 on the empirical methodology for an ECM estimating framework. The ECM framework accounts for the dynamics in weekly repeated auctions, while permitting estimation of the adjustment path towards a unified equilibrium rate in the long-run. Furthermore, the expanded non-linear version of the ECM suggested by Phillips and Loretan (op. cit), which we will employ here, provides asymptotically efficient estimators for the parameters of equation (5), suitable for hypothesis testing. The empirical model is stated below. **21'** '

# 19ogoe), = _,Y[_ a + + aO **log0(QSl** + a 2 1og9Q4d), + a3 1og(bidty +a 4<sup>MAV (Alog(qsA)_</sup>

+ a5 log(ber),_, + i0 DUMDutch +<sup>6</sup> 1 Di + &2 D2 + 6, D3 - log oer 1,] 1 For ethositonal ournose only, the ECM is presented in a restricted form (two rror terms, one lead and two lags), but in the estimation more general lag/lead struchtres were considered. For a fill exposition of the form and properties of the nonlinear single equation ECM enployed here, see Phillips and Loritan (op. cit.).
Social protection in development settings comprises social assistance tools (e.g., unconditional and conditional cash transfers, vouchers, social insurance, and labor market interventions) that are generally operated by governments as a response to long-term structural issues. On the other hand, humanitarian assistance tends to focus on emergency or one-off responses to address the urgent needs of crises-affected populations, and it is mainly implemented through international humanitarian actors in contexts where governments are too fragile to respond. Even though humanitarian crises are generally attributed to a single and distinct event that occurs with little or no warning (sudden onset), most humanitarian crises are complex, protracted, and chronic (slow onset), evolving from a series of events that exacerbate vulnerabilities of a population over a prolonged period (UNOCHA 2018).

The main objective of this review is to assess and summarize the state of existing knowledge on the effects of humanitarian assistance interventions and to identify evidence gaps in the literature for both future research and for evidence-based decision-making by policy makers and practitioners. In doing so, we address two research questions within the context of humanitarian settings. First, what are the impacts of humanitarian assistance programs on individual and household-level outcomes? Second, what is the relative effectiveness and efficiency of different modalities and delivery channels in achieving their objectives?

Drawing on evidence from experimental and quasi-experimental studies conducted in low- and middle-income countries and using a narrative approach, we examine the overall effects of humanitarian assistance programs on a wide range of individual and household-level outcomes, such as basic needs, financial outcomes, gender, human development, and social cohesion. We focus our review on the impact of humanitarian assistance interventions in the form of unconditional cash transfers (UCTs), conditional cash transfers (CCTs), food transfers (FT), vouchers, and public works (PW), as these are the most widely used forms of assistance and, therefore, a priority for evidence generation in the humanitarian sector.

There have been a few systematic reviews on cash and in-kind interventions in humanitarian contexts (Gentilini 2016; Doocy and Tappis 2018; Aurino and Giunti 2021).<sup>4</sup> For example, Doocy and Tappis (2018) review the impacts and value for money of cash-based

> 4 Relatedly, Puri et al. (2017) assess how rigorous impact evaluations can help inform and improve different types of interventions in humanitarian emergencies, including cash and in-kind transfers.

3
The TVIP, the Spanish-speaking version of the Peabody Picture Vocabulary Test (PPVT) (Dunn et al., 2006); 2) The digit span, an associative numeric memory test; and 3) A version of the marshmallow test to measure self-control.<sup>13</sup> The test was adapted to the local (humid) survey context by using hard red candy instead of a marshmallow.

The tests have been applied to similar populations in Latin America, including in previous studies in Nicaragua (Macours et al. (2012) and Barham et al. (2013)), Ecuador (Paxson and Schady (2007) and Paxson and Schady (2010)) and Mexico (Fernald et al. (2008)). All tests were extensively piloted in the field and adjusted when necessary. Test administrators were selected based on their background (trained as psychologists, social workers, or similar fields) and for their ability to quickly establish a rapport with young children. They were intensively trained on the standardized application of the tests, as well as on putting children at ease before starting the tests. Tests were administered at home. The privacy of the test-taker and the confidentiality of the results were ensured throughout the process. Test administrators were randomly assigned to households. The quality and standardized application of the tests was closely monitored in the field. A key advantage of the tests is that they provide observed and objective measures of child development, rather than parent-reported measures that may suffer from reporting biases.<sup>14</sup> The test scores are aggregated using principal component analysis to determine factor in the control We retain the as a in- weights group. first principal component summary dicator of early childhood cognitive development. The first component accounts for 32% of the overall variation in the 7 tests, and for 35% of the overall variation in the 4 subcomponents of the Denver. Table A2 reports the factor weights, showing that cognitive development tests all have relatively high weights (Denver language, TVIP, memory, fine motor skills).

The aggregate cognitive score constitutes our main ECD outcome. In addition, socioemotional skills are measured using the Strength and Difficulties questionnaire (SDQ) and a behavioral screening test consisting of questions to caregivers about a set of positive and

> 13The marshmallow test is a well-known test of delayed gratification, evaluating preschool-age children’s ability to wait before eating a marshmallow in exchange for being rewarded an additional one. Mischel et al. (1989) show that the test predicts outcomes later in life.

> 14Only a few items of the Denver are reported by caregivers.

11
than comparable KWA schemes. However, the percentile ranking of KWA schemes is higher but not significant for household cost of service.

**Table 13: PSM Comparison of Jalanidhi with KWA Built & Managed Schemes: Performance Indexes**

|**Index**||**Jalanidhi**|**KWA**|**ATT**|**SE**|**P‐value**<br>|**Percentile**<br>**Rank**<br>**(Jalanidhi)**|**Percentile**<br>**Rank**<br>**(KWA)**|
|---|---|---|---|---|---|---|---|---|
|Availabilit|y & Reliability|0.124|‐0.872|0.997**|<br>0.36|0.006|55.0|19.2|
|Household|Satisfaction|0.117|‐1.002|1.119***|<br>0.33|0.001|54.7|15.8|
|Household|Cost of Service|‐0.092|0.212|‐0.304|0.57|0.595|46.4|58.4|
|Operation|& Maintenance|0.197|‐0.756|0.953*|0.41|0.021|57.9|22.5|
|Overall Pe<br>*p<0.05,**p|rformance<br><0.01, *** p<0.001|0.161|‐0.925|1.086**|<br>0.40|0.007|56.4|17.8|
|60.0|**Figure 1:**<br>**Ja**|**Differenc**<br>**lanidhi a**|**es in Pe**<br>**nd KW**|**rcentile**<br>**A-BM S**|**Ranks**<br>**cheme**|**between**<br>**s**|||
|40.0<br>50.0|35.8|38|.8|||354|38.6||
|‐40.0<br>‐30.0<br>‐20.0<br>‐10.0<br>0.0<br>10.0<br>20.0<br>30.0<br><br>Difference in Percentile Rank|Availability &<br>Reliability Index|Household<br>Satisfaction In|dex<br>House<br>Serv|‐12.0<br>hold Cost of<br>ice Index|Operati<br>Maintenan|.<br>on &<br>ce Index<br>Perf|Overall<br>ormance Index||

PSM comparisons of Jalanidhi and other community managed schemes show no significant differences in performances in terms of availability and Reliability index, household satisfaction index, and household cost of service index (Table 14). The Jalanidhi schemes perform significantly better than the other community managed schemes in the O&M index, institutional sustainability index and overall performance index. Percentile ranks of Jalanidhi schemes for the O&M index are around 20 percentile points higher than similar community managed schemes (Figure 2). Likewise, when the indexes were aggregated into an overall performance index, percentile ranks of Jalanidhi schemes are on average 19 percentile points higher than comparable community managed schemes. However, the institutional sustainability performance of Jalanidhi schemes is significantly stronger than the other community 20
-32 -

65. In dhes. circumstances, it is natural that the govenment of Albania has been interested in introdudng a property tax , primarily to support local government. The government has sought advice from the U.S. Treasury and tho Harvard Instituto for International Development onto formation of such a ta7 Some preliminaw steps would be necessary to implement a conventional property tax, including the introduction of a cadastre, or land ownership register. The government has not, however, been able to auract the interonal assistance necessary to introduce such a full-blown property tax and has decided to introduce a more limited version. The _Property Tax Law_ passed in mid-March 1994 requires the central government to undertake property valuation, to set the rates and to administer **the** tax, but for **60%** of the revenues to be distributed to local governments according to the source of the revenues. In rural areas, the tax would be assessed on the ferdlity of land, like toe presumptive taxes assessed on agriculture in many developing countries. In urban areas, the tax would be restricted initially to the value of buildings.

66. Some comments on the proposed Albanian property tax are called for. First, there is little sense in which toe tax is now a _local_ property tax, since neither the assessments of property values, nor the tax rates nor the adminitration of toe tax are to be performed by local government. The assessment of property tax values and perhaps even administration of the tax are not essential to its character as a local tax. Higher levels of govermment commonly intervene in or even determine assessments of property values in many counties for various reasons. One reason that might alone justify central government detmination of assessments in Albania would be to have comparable property values for calculation of equalization of local government fiscal capacities.

67. The freedom of local governments to set local tax rates is, however, essential to the property tax
**Figure 4: Non-parametric regressions of profit for maize, rice and tobacco**

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