rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 23. Services Exporter Revenue Density, Selected Regions**

<!-- Start of picture text --> Northwest<br> ADMINISTRATIVE AND SUPPORT<br>SERVICE ACTIVITIES 0.03<br> PROFESSIONAL, SCIENTIFIC ANDTECHNICAL ACTIVITIES 0.08<br> REAL ESTATE ACTIVITIES 0.00<br> FINANCIAL AND INSURANCE<br>ACTIVITIES 0.00<br> INFORMATION AND<br>COMMUNICATION 0.00<br> ACCOMMODATION AND FOOD<br>SERVICE ACTIVITIES 0.00<br> TRANSPORTATION AND STORAGE 0.09<br>REPAIR OF MOTOR VEHICLES AND... WHOLESALE AND RETAIL TRADE; 0.04<br> CONSTRUCTION 0.21<br> WATER SUPPLY; SEWERAGE, WASTEMANAGEMENT AND REMEDIATION... 0.06<br><!-- End of picture text --> <!-- Start of picture text --> South<br> ADMINISTRATIVE AND SUPPORT<br>SERVICE ACTIVITIES 0.00<br> PROFESSIONAL, SCIENTIFIC ANDTECHNICAL ACTIVITIES 0.10<br> REAL ESTATE ACTIVITIES 0.00<br> FINANCIAL AND INSURANCE<br>ACTIVITIES 0.00<br> INFORMATION AND<br>COMMUNICATION 0.00<br> ACCOMMODATION AND FOOD<br>SERVICE ACTIVITIES 0.00<br> TRANSPORTATION AND STORAGE 0.14<br>REPAIR OF MOTOR VEHICLES AND... WHOLESALE AND RETAIL TRADE; 0.00<br> CONSTRUCTION 0.08<br> WATER SUPPLY; SEWERAGE, WASTEMANAGEMENT AND REMEDIATION... 0.00<br><!-- End of picture text --> <!-- Start of picture text --> Central<br> ADMINISTRATIVE AND SUPPORT<br>SERVICE ACTIVITIES 0.00<br> PROFESSIONAL, SCIENTIFIC AND<br>TECHNICAL ACTIVITIES 0.36<br> REAL ESTATE ACTIVITIES 0.02<br> FINANCIAL AND INSURANCE<br>ACTIVITIES 0.03<br> INFORMATION AND<br>COMMUNICATION 0.00<br> ACCOMMODATION AND FOOD<br>SERVICE ACTIVITIES 0.00<br> TRANSPORTATION AND STORAGE 1.30<br> WHOLESALE AND RETAIL TRADE;<br>REPAIR OF MOTOR VEHICLES AND... 2.18<br> CONSTRUCTION 0.40<br> WATER SUPPLY; SEWERAGE, WASTE<br>MANAGEMENT AND REMEDIATION... 0.01<br><!-- End of picture text --> <!-- Start of picture text --> Ural<br> ADMINISTRATIVE AND SUPPORT<br>SERVICE ACTIVITIES 0.00<br> PROFESSIONAL, SCIENTIFIC ANDTECHNICAL ACTIVITIES 0.18<br> REAL ESTATE ACTIVITIES 0.01<br> FINANCIAL AND INSURANCE<br>ACTIVITIES 0.00<br> INFORMATION AND<br>COMMUNICATION 0.01<br> ACCOMMODATION AND FOOD<br>SERVICE ACTIVITIES 0.00<br> TRANSPORTATION AND STORAGE 0.41<br>REPAIR OF MOTOR VEHICLES AND... WHOLESALE AND RETAIL TRADE; 0.13<br> CONSTRUCTION 0.08<br> WATER SUPPLY; SEWERAGE, WASTEMANAGEMENT AND REMEDIATION... 0.00<br><!-- End of picture text --> Source: RUSLANA. Note: Per capita density is per million population.

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entrepreneurship. This choice is often made by default given that self-employment questions have typically been included in censuses of families or households. Thus, data on selfemployment rates became available much earlier than any other potential measure.

Recent studies note, however, the challenges that come with using self-employment metrics to describe the entrepreneurship necessary for economic growth and job creation. Due to the much larger raw count of self-employed workers, self-employment indices accord much more weight to small-scale, independent businesses and hobby entrepreneurship than entrepreneurship that can lead to substantial job creation for others. The vast majority of selfemployment businesses as captured on labor and census surveys will not generate employment opportunities for other workers, and they may even be the product of a lack of employment opportunities for the business owner (e.g., Astebro et al. 2010, Schoar 2009). This latter factor is particularly acute in South Asia given its earlier development and large, persistent informal sector.

These criticisms are evident in some simple examples. Silicon Valley is the poster child of US regional entrepreneurship and the world‘s largest venture capital market. Yet San Jose, CA, ranks near last among America‘s 300+ metropolitan areas in terms of self-employment, with West Palm Beach, FL, instead having the highest self-employment rate. This questionable pattern is also evident in country rankings. Southern European countries like Portugal and Greece rank much higher than Scandinavian countries in terms of self-employment within Europe, but much lower in terms of most growth-entrepreneurship indicators like venture capital markets (e.g., Bozkaya and Kerr 2011). Klapper et al. (2010) document the negative correlation between self-employment levels and economic development across a broad cross-section of countries, and Ghani et al. (2011) show that self-employment metrics for India perform poorly for describing job growth compared to better-specified alternatives.

Hence, most recent studies of entrepreneurship and growth instead focus exclusively on formal firms that employ paid workers. These thresholds—being incorporated, paying payroll taxes—are in some sense arbitrary, but they are natural given our fundamental interest in describing entry that leads to regional job creation and growth. Their relevance is even greater for South Asia given the need to pull members of the informal economy into the formal sector. Here too, however, there are substantial disagreements as to what should be measured. A vast, earlier literature develops estimates of the number or share of small businesses in a region. A similar metric is average firm size (e.g., Glaeser 2007).

These measures of small businesses overcome some of the problems of earlier selfemployment measures, but they still weight very heavily small businesses that are not attempting to grow substantially (e.g., a small family-run business that has employed the same number of workers for many years). In other words, they do not capture the important dynamic aspects of 6
life, leading to a higher probability of survival (Arpino et al. 2019). Bellucci et al. (2020) use the European Survey on Health, Ageing, and Retirement to show that war-exposed individuals have higher resilience to shocks and increased perception of uncertainty and uncontrollability of the environment. Cassar et al. (2013) explore the effects of war-related violence on trust and cooperation in Tajikistan and found that exposure to war enhanced prosocial behavior. A study of the effect of violence during Nepal’s civil war found that violence-affected communities had higher levels of prosocial motivation and public good cooperation (Gilligan et al. 2013). Similarly, civic participation increases in the districts of Uganda, where battle events took place (De Luca and Verpoorten 2015). Bellows and Miguel (2009) show that households in Sierra Leone that experience war violence are more likely to join political and community groups and more likely to vote. Bauer et al. (2020) review multiple studies pointing in the same direction: that social cooperation is enhanced by past exposure to war violence.

The second set of studies pertains to the role of social capital and trust in institutions for compliance with the NPI during the 2020 pandemic. Data on real-time mobile phone locations in Italy demonstrate a higher decline in personal mobility in areas with higher social capital (Durante et al., 2020). Similar results are found by Barrios et al. (2020) for a sample of counties in the United States and European countries. In a sample of 84 countries, Elgar et al. (2020) find that trust in government fosters lower COVID-19 mortality, though they also find that (controlling for trust in government) stronger social bonds in a country may facilitate the spread of infection. Bargain and Aminjonov (2020) found that regions with higher trust experienced larger reductions in non-essential mobility following the implementation of containment policies in March 2020. Bartscher et al. (2020) provide evidence from seven European countries that culture and social capital have a considerable impact on the containment of COVID-19 and the number of deaths. The study of individual willingness to engage in social distancing in Denmark shows that individuals with high social and political trust are more likely to comply with social distancing measures (Olsen and Hjorth 2020). The overarching result of these studies is that voluntary compliance with government-imposed NPIs depends on the local and individual levels of social and political capital. However, we do not know to what extent the identified behavioral and institutional covariates of NPI can be linked to past experience with big shocks.

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Table A.6 provides rankings of the top 15 economies by trade impact, and their corresponding levels of trade openness, for the period 1999–2003. It is clear that there is very little relationship between them; indeed, the correlation between the two series is -0.091 (and statistically insignificant at the conventional levels).

Table A.6: Top 15 countries in trade impact, 1999–2003 period average<sup>*</sup>

|**Country**|**Trade impact**|**TI Rank**|**Trade openness**|**TO Rank**|
|---|---|---|---|---|
|United States|0.191|1|0.189|153|
|Germany|0.081|2|0.558|79|
|China|0.059|3|0.385|123|
|Japan|0.056|4|0.184|155|
|United Kingdom|0.056|5|0.415|118|
|France|0.049|6|0.435|112|
|Italy|0.039|7|0.427|116|
|Canada|0.036|8|0.672|55|
|Netherlands|0.032|9|1.027|19|
|Belgium|0.030|10|1.591|4|
|Spain|0.027|11|0.444|110|
|Mexico|0.026|12|0.524|90|
|South Korea|0.024|13|0.572|76|
|Taiwan, China|0.019|14|0.787|44|
|Switzerland|0.014|15|0.650|62|

* Authors’ calculations, using data from the Direction of Trade Statistics and World Development Indicators. Trade impact defined as the import share of global exports, and trade openness defined as the sum of imports and exports as a share of GDP.

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this problem. This methodological contribution is most visible in estimating the effects of access to finance and corruption, which are the two areas where endogeneity is most likely. Using firm level data allows us to focus on the question of whether the impact of the business environment is neutral across firm size. Market failures or policy created distortions can create fixed costs in the operation of businesses, creating cost disadvantages for smaller firms (see Tybout 2000 for review of issues). The costs of dealing with credit market information imperfections or with a complex and non-transparent regulatory environment are some examples of such non-linearities. Large firms may also have more political influence and shape regulations and policies in their favor. On the other hand, smaller firms may benefit from lax enforcement of regulations or lower harassment from corrupt public officials. Across the world, a sizeable amount of public resources are devoted to the development of micro, small and medium firms, yet unlocking potential constraints to their performance is another, perhaps equally effective, development strategy. The main objective of this paper is to assess whether there are heterogeneous effects of unlocking business environment constraints.

Differential effects across firms of different size can stem from two sources. First, there can be differences in the underlying objective conditions faced by firms. The paper focuses on the effect of four areas of the business environment: access to finance, the regulatory environment, corruption and access to infrastructure. Second, the same conditions can potentially have differential or non-linear effects on employment growth by size. Thus, it could be that the same extent of external finance is more beneficial to smaller firms or that the same extent of power outages is less damaging to larger firms because they have access to generators.

The paper finds evidence of both effects. The paper does find that micro and small firms have less access to formal finance, face significantly greater interruptions in infrastructure services, and pay more in bribes—as a percentage of sales—than do larger firms. In turn, larger firms spend significantly more time dealing with officials and red tape. The results indicate significant non linearities across firm size in the impact of these conditions too. A lack of finance and poor infrastructure reduce the growth of medium and large firms. Business regulations affect mostly the growth of small firms, which seemingly prefer to remain small to keep below the radar of regulators.

4
Thus in the core scenario the incidence of rural non-farm poverty will fall mainly<sup>because</sup> of the growth in wages for unskilled workers in rural non-farm activities, while<sup>poverty may well</sup> increase in agriculturally based hinterland provinces a long way from<sup>markets andinregions</sup> poorly served with the necessary infrastructure to attract investment in such<sup>expanding activities</sup> as textiles and clothing.

The first alternative scenario shows that this situation would be exacerbated<sup>slightlyif the</sup> TRQ-protected items (grains, sugar and cotton) were to become<sup>evenless protected thanwe</sup> initially assumed. By way of contrast, the second alternative scenario suggests<sup>thesituation could</sup> be made slightly less extreme by removing the negative<sup>protectionaffectingrice,meats,</sup> vegetables and fruits. But both of these alternatives only involve<sup>small changestothe</sup> magnitudes of effects, rather than altering the sign of those effects, and both<sup>add onlyasmall</sup> amount to the aggregate gains from trade liberalization.

What the third alternative scenario makes clear, by way of contrast, is that<sup>thesignof the</sup> effects could be switched to favor the poorer farm households<sup>-albeitattheexpenseofthe</sup> richer non-farm ones - if the remaining WTO accession were to be<sup>accompaniedby reformof</sup> the _hukou_ system that allowed some members of those households<sup>toobtainhigher-paying non-</sup> farm employment and repatriate earnings back to their farm<sup>family.'4Andthiscasewould</sup> involve a national economic welfare gain that is one-sixth above<sup>that in the core scenariowithout</sup> labor market reform.

National self-sufficiency in food, feed and fiber will fall somewhat,<sup>particularlyas</sup> demand for livestock products grows with income gains<sup>from tradereformandasproductionof</sup> natural fibre-based textiles and clothing expand. But overall,<sup>mostoftheseself-sufficiency</sup> effects of the remaining reforns that are required following<sup>WTO accessionarerelativelyvery</sup> small in magnitude.'<sup>5</sup> They would be largest in the third alternative<sup>casewherelabor market</sup> distortions are also removed. That is not to say food security<sup>wouldfallin that casethough.It is</sup> changes in incomes of the poor, and hence in their capacity<sup>topurchasefood,thataffectfood</sup> 14 These alternative results for the long run complement the findings of Chen and Ravallion (2002) for the short-run. What they underscore is that whether a particular group gains or loses from<sup>a</sup> shock<sup>suchasWTO accessionin the</sup> long run depends heavily on their sources of household income and their capacity<sup>to adapttothechangedeconomic</sup> circumstances.

> **15** They are especially small when compared with the changes that are taking<sup>placein the courseof normaleconomic</sup> growth, as shown retrospectively in Tables 1-5 above and prospectively<sup>in earlieranalysesbyAndersonetal.</sup> (1997a, b) and in lanchovichina and Martin (2002).

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estimate the indirect impact. Resulting poverty is expected to increase overall by 2.9 percentage points – 1.9 percentage points higher than the direct impact alone. That is, the ripple effect of the higher prices on other goods and services due to higher gasoline prices will be large. Inequality, measured by the Gini, is expected to increase by 0.5 point (compared to just 0.1 point through the direct impact only).

Table 7. The total (direct and indirect) impact on poverty and inequality after increase in gasoline prices

|Pre-Ref|Post-Ref|Change|
|---|---|---|
|Welfare (per capita), (IRR)<br>310,157|290,836|-19,321|
|Poverty (%)<br>11.2|14.17|2.9|
|Inequality (%)<br>39.7|40.3|0.5|
|Source: Authors’ calculation using forecasted HEIS 2018/2019.|||
|Note: Inequalityis based on welfare aggregate,not spatiallyadjusted.|||

## 4.3 Impact on government spending without mitigation efforts

The increase in gasoline prices would generate substantial savings for the government but would not be enough to eliminate subsidies fully as shown in Table 8. In particular, the savings will be around IRR 439 trillion. This is about 38 percent of current government spending on subsidies. However, part of these savings could be spent on mitigating the impact on poverty, which is examined in the next section.

Table 8. The impact on Government annual spending and revenues after increase in gasoline prices, trillion IRR

|Pre-Ref|Post-Ref|Change|
|---|---|---|
|Subsidies<br>1,170|731|-439|
|Transfers, universal<br>0|98|98|
|Net total budget<br>1,170|829|-341|
|Source: Authors’ calculation usingforecasted HEIS 2018/2019.|||

# 5. Impact of reform on poverty and government revenues with mitigation efforts

The previous section looked at the overall impact on poverty and inequality if the government did not introduce a mitigating response. In this section we simulate how effective mitigation could be and the associated impact on government expenditures.

Table 9 contains the amounts of subsidies pre- and post-reform. As we already saw, the annual savings from gasoline subsidy reform will be about 439 trillion IRR. We also model the impact on poverty from proposed GOI mitigating measures. In particular, GOI has proposed distributing cash transfers to the poorest 18 million households. The monthly size of the benefit depends on the household size and ranges from 550,000 IRR for one-member households to 2,050,000 IRR for five-member households and above. We assumed _perfect targeting_ for this program which will approximately cover the bottom 77 percent of the total population in the Islamic Republic of Iran.<sup>6</sup> The overall cost of this transfer program will be about IRR 338 trillion, meaning a net savings on subsidy spending after transfer spending of about 101 trillion IRR.

> 6 Although targeting is never perfect, the very large household coverage of the program means that it is likely that few poor and near-poor households will be excluded, meaning ignoring targeting errors in the current analysis will not have a large effect.

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# **I. Introduction**

Evidence from the large numbers of research projects designed to study the effects o. family pianning programs on contraceptive use and fertility has presented m.nixed results. The conflict between the results is partly due to differences in data but also partly due to differences in modeling strategies and estimation methods. A major fault has been the failure to distinguish exogenous from endogenous variables. Because of this, community variables such as family planning access are often included in the same equation with the channels through which they operate. In this case, the apparent impact of access is diminished. Appropriate specification would allow the total effects to be observed.

The purpose of this paper is to employ a structural model to assess the effects of access on contraceptive use and fertility in Tunisia. The theoretical model is built upon the contributions of Easterlin (1978), Easterlin and Crimminis (1985), Rosenzweig and Schultz (1985) and Schultz (1989). Two versions of the model are developed. The first version examines the effects of the number of currently surviving children on the decision to have additional children and their desired spacing. Fertility intentions are then used as right-hand-side endogenous variables in equations that explain current contraceptive use and choice among methods. In the second version, fertility intentions formed five years ag. are assumed to affect duration of method specific contraceptive use in the five year interval. Contraceptive use is then hypothesized to affect number of births in the last five years.

The statistical methods that we employ to estimate the structural model allow community level variables to be important components of the model. The methods correct for unobservable corrmmunity level influences on the dependent variables. Our methods must also correct for the fact that many of the dependent variables are either dichotomous, ordinal, or categorical. The estimation method that we use is the generalized method of moments estimator. The data used in the study are from the Tunisian Demographic and Health Survey (DHS) conducted in 1988. The data collected at the household level include basic household socioeconomic data, fertility levels, and family planning awareness and use. The community data contain simple information on the availability of government and social facilities such as schools and cinemas and detailed information on the range of health and family planning services available including access to family planning information. These data include distances and travel times to various facilities, services offered, personnel, and hours of service as well as information on when the programs were implemented.

The paper is organized as follows. The next section presents background information on the demographic transition and the role of the family planning program in Tunisia. Section III presents an overview of the structural model that guides the research and discusses the method of estimation. This section can be skipped by those uninterested in the details of methodology. The data set used in the analysis is presented
Revealingly, in Betts et al.’s (2013) survey of refugees in Uganda, 96% of all interviewed households in the capital and 70% outside the capital said they owned and used a mobile phone. They use this mobile phone to communicate with customers and suppliers, to get market information and to transfer money. Half of the urban refugees and 11% of rural refugees also have access to the Internet.

# **4. Refugees As a Burden?**

As pointed in Section 2, most refugees in SSA are hosted in neighboring countries. Most of these hosting countries are likely among the least developed countries. It has been argued that these refugees may constitute an additional burden in terms of economic development in hosting countries (Mabiso et al. 2014). UNHCR (2014: 17) implicitly recognizes that potential burden by suggesting that the ratio of the size of the country’s hosted refugee population to its average income level can provide a proxy measure of the burden of hosting refugees. SSA counts 13 out of the 20 countries hosting the larger number of refugees per 1 USD GDP (PPP) in the world.<sup>4</sup> While such figures stress that SSA hosts a fair share of the refugees in the world and underline that refugee flows are mainly a South‐South phenomenon, we shed doubt on this view of refugees described as a burden. We even argue in Section 6 that such representation is not conducive to the right policy framework in refugee‐hosting areas.

> 4 The other major host countries per USD GDP are all developing countries, with Pakistan (1st ), Jordan (8th ), Bangladesh (9<sup>th</sup> ), Yemen (10<sup>th</sup> ), Iran (14<sup>th</sup> ), Lebanon (17<sup>th</sup> ), and India (20<sup>th</sup> ). Figure A2 provides the top 10 ranking in the world. At a global level, we should note that in 2013 “the 40 countries with the largest number of refugees per 1 USD GDP (PPP) per capita were all members of developing regions, and included 22 Least Developed Countries” (UNHCR 2014: 17). It should be noted that the way UNHCR computes that “potential burden” gives more weight to countries with very large population since is equivalent to

.

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