rafmacalaba/datause-encoder-data huggingface dataset:
Rfst-Rc_ WOMX 1,8

# _u2u_ -in2ry f-indings

the iftAnemali microfirm seector is believed to be large, accounting for 20-40 percent of employment in many dcveloping countries. The literature tends to view the sector as the disad-vantaged sector of a seg-nented labor market, as existing to evade government regulations, or as constrained by lack of access to government services. Levenson and M.aloney offer a unique theoretical framework to analyze iinformality and microfirm growth behavior - one that emphasizes the entrepreneurial nature of infor-rmal firms and sees informality as a secondary characteristic.

First, they assume that inforinal firms in developing countries bave dvnarniics similar to firms in industrial countries: entrepreneurs have uinobserved, differing cost structures that determine the&r long-run size and survival - strocru:i-es rhat tihev can only discover _by_ going into busiress.

that ensure property rights, pool risk, or enforce contracts become more important as a firm grows, and the entrepreneur will be willing to pay for them through "taxes" in a way that was not the case as a small firm. The combination of these assurnptions generates several of the stylized facts emerging from cross-sectional data and identified in existing mrdels -informal firms tend to) remnailn small and have high rates of mortality 5 and lower productivity -- without recourse to government-induced distortions ia labor or product markets. Further, the framework predicts _that firms_ whose cost structures dictate that they should expand will make the transition to formality as they grow. Using detailed observations fromn Mexico, Levenson and Maloney find their view consistent with patterns of formality and growth of .nicrofirrns.

Secornd, informality can be thought of as a decision to participate in societal instituLticns. Access to mechanisms This paper-a product of the Poverty Reduction and Economic Management Sector Unit, Latin Arn erica and the Caribbean Region--- _is_ part of a larger eZfort in the region to understand the structure of labor markets in developing countries. Copies of the paper are available free from the World Bank. 1818 _H_ Street NWV, Washington, DC 20433. Please contact Tania Gorriez, room 18-102, telephone 202-473-2127, fax 202-522-2119, Internet address tgomezCwvorldbank.org. William Mlalonev _rnav_ be contacted at w^.rmaloneyCworldbank.org. September 1998. (31 pages) _'The Policy Research Tmorki'Zg Paper Series disseninates the findings of work_ **_n?_** _progress to encourage the exchange of ideas about ieve/op;fcnio issues. An obiecirve of the series to get the findings out quickly. ev-_ r, _tke presentations are less tkan fully polished. The p Ipess car,y the names Of the authors and should be cited accordingly. Time findiigs, interpretatlons, aeid conclusions expressed in this paper are e.;rir ely those of the authors. iT>eyd o_ **_not_** _necessarily represent tre Liew of the W,1'orld Bank, 1ts Exectiive Directors, or the colt,nties lCe rep-scent._ Produced bv the Policy Research Dissemnination Center
(2009) and Matsumoto and Yamano (2009). Compared to the preceding baseline, a marginal decline in agricultural productivity needs to be met by a shift in labor to food crops in order to maintain minimum economy-wide food production. This contributes to cash crop labor decreasing by more than a quarter over the full period. Total farm labor gradually increases, so the rural and urban service sectors also experience labor declines. Only manufacturing absorbs a slightly larger amount of labor than in scenario 1, although rural to urban migration now stops altogether by Year 10.

The other major implication for the economy is significant inflation in food prices, which climb 44 to 56 percent across the four rural regions. By Year 10, urban food prices are also 40 percent higher compared to those of the baseline scenario. The urban real wage drops by more than a third due to the higher food price, even as the rural real wage adjusts only slightly. The final year’s national savings rate is only 9.1 percent.

In the government accounts, tax collections are flat and labor costs increase mainly due to increased service sector wages. By Year 10, the need for cash ODA is considerably higher than in scenario 1, covering nearly 43 percent of the full budget. In this scenario, physical cash crop production drops 20 percent over 10 years. A poverty trap is reflected in a 0.8 percent average annual decline in total private output over the decade.

# **Scenario 3: The Green Revolution Package**

This scenario introduces the ODA-supported agricultural input package, _greenoda_ , with starkly different outcomes compared to the previous two scenarios. The top row of Table 4 shows how this leads to an increase in the soil productivity parameter, which in turn provides a 21
current income (Zeldes, 1989). Similarly, panel data studies of firms show that investment is sensitive to current cash flow, even after controlling for expected future profits. (Fazzari, Hubbard and Peterson, I988; Gilchrist, 1990). And the cash flow effect is stronger for firms likely to be constrained a priori (e.g., small firms). In addition, both households and firms hold sizable quantities of liquid assets. This suggests that the need for (at least some degree of) self-insurance arises even in industrialized economies.

Determinant financing patterns are also present in U.S. data, implying a clear violation of Miller-ModiglLanL. Small firms rely on internal funds and bank credit. Typically, only larga mature funds directly obtain funds from lenders. Issues of equity, commercial paper and debt are concent-ated amongst these firms. Evidence from other countries is broadly consistent with this pattern.

At the macroeconomic level, the collapse of financial markets in the Great Depresslon demonstrates that major dleruptions in the flow of funds are possible, and that these episodes can severely impede real activity, even within industrialized countries (Bernanke, 1983). Recent examples are the financial crises in several southern cone countries that followed in the wake of the liberal!zations of the 1970s (Diaz-Alejandro, 1983). The possibility of financial crises also raises the difficult question of public policy. Most policy makers and economists agree that some kind of safety net is essential. However, the provision of public insurance introduces some clear efficiency tradeof's. The current crisis in the U.S. banking and saving and loan industrLes provides a clear example. A similar message follows from the outcomes of the financial reforms in Latin America, as we will diecuse.

More to broader point here, the international evidence suggest a determinate relation between the states of development in the real sector and the financial sectors.<sup>5</sup> The general patterns hold across countries, as well as across time within a country. In the poorest of the LDCs, individuals and 5See The World Bank Development Report for more detaiLed statistics on this phenomenon.
Once a village or homestead was attacked, the LRA’s “strategy was to abduct first and sort out later” (Blattman and Annan 2010, 8–9). This is supported by Blattman and Annan’s (2010) quantitative analysis of youth abductions using a pre-abduction representative data for one of the most affected districts in Northern Uganda. They find no statistically significant differences in the mean of pre-war characteristics that predict abductions in other conflicts, such as pre-war wealth or parental characteristics.<sup>20</sup> These findings suggest that variation of individual and household characteristics within village did not affect the likelihood of being abducted, which in turn is likely highly correlated with attacks.<sup>21</sup>

# _Relationship Between Statistical and Perceived Risk_

Two different types of risk can be estimated with the available data: statistical (objective) and perceived (subjective). The former refers to the observed likelihood of a particular shock occurring while the latter to the a priori belief whether a specific shock will occur. While correlated, individuals make their decisions based on upon their perceptions as opposed to the “true” likelihood of shocks.

In general, these two types of risk may differ for a variety of reasons including incomplete information or behavioural biases such as the proximity or vividness of events. Lowenstein et al. (2001) highlight how “emotional reactions to risk situations often diverge from cognitive

> 20. The only exceptions were the year of birth and the size of the household. The former reflects the preference of the LRA for youths between the ages of 10 and 24 as forcible recruits; younger children were less useful while older youth were perceived as being difficult to indoctrinate. The significance of the size of household is driven by the sub-sample of household with 25 or more members. This is not only a rare occurrence in their data but also overall in Northern Uganda. The 2002 Uganda National Census shows that less 1 percent of households in Northern Uganda, rural or urban, have 15 or more members; in rural areas, this represents roughly 0.5 percent of the sample. 21. Unfortunately, it is not possible to directly test whether attacks were random within the data used (or within almost any other dataset). Post-attack data are potentially endogenous to the violence. Additionally, since certain households or individuals may disappear from the sample (through death, abduction or migration), the post-attack population is not representative of the pre-attack population. Consequently, any analysis requires both a pre-attack representative sample and pre-attack characteristics. As a result, the Blattman and Annan study is particularly unique as it is essentially representative of the pre-abduction population.

14
For ease of comparison, we present the category-wise share of employment in each year in Table 1b. This highlights two patterns: there is a high concentration among men in own-accountenterprise (OAE) work,<sup>7</sup> and there is a sharp distinction between the activities that men and women are engaged in. For example, more than 60 percent of women are primarily engaged in domestic duties, whereas this share is trivial among men. Women also have a higher likelihood of being casual workers in small-scale enterprises, whereas men are more likely found as (paid) workers and employers in own account enterprises. A low percentage of both groups is primarily engaged in public works, although the share of men in this activity is typically slightly higher than that of women. Over time, we notice very little change in the distribution of employment across categories for men, but for women there is an observed decrease in the share of OAE casual workers (-2.4 percentage points), and a sizeable increase (5.4 percent) in the share of women primarily engaged in domestic work. In terms of rural-urban differences, we highlight the reduction in the share of individuals engaged in wage/salary work and a higher percentage in OAE employment, pointing to the higher percentage of formal-sector jobs in urban areas. Women in rural areas are somewhat less likely to be engaged principally in domestic duties (relative to all women), and slightly more likely to be engaged in the OAE sector. Figures 1-3 depict these levels and trends in overall labor force participation for rural areas graphically by state for 1999-2009.

Table 2 presents population-level estimates of employment in each of the categories of employment we study. Here we confirm from the micro data some important priors: a large increase in the number of individuals engaged in public works following the introduction of MGNREGS, especially in rural areas, and sharp gender differences in the distribution of employment across activity types. Given that the biggest change in female LFP in recent years is in rural areas, we will focus our formal analysis on this part of the country alone.

> 7 Own-account enterprises are specifically defined in the context of GOI surveys as “self-employed who operate their enterprises on their own account or with one or a few partners and who during the reference period by and large, run their enterprise without hiring any labour. They may, however, have unpaid helpers to assist them in the activity of the enterprise.”

10
-9-

cocoa. Women are, however, entitled to keep any income derived from the sale of surplus food production (Eyben, 1982).

# A Dynamic Environment

1.16 However, these traditional distinctions between cash crops and foodcrops and between men's work and women's work are beginning to unravel due, principally, to the growing commercialization of foodcrops. With improved access to urban markets both within and outside the province, potatoes, maize, and other crops, have ceased to be solely subsistence crops and have become "near cash crops".<sup>3</sup> As Table 3 shows, the majority of sugar cane, cassava and potatoes is grown for sale rather than consumption and maize is a significant income generator for those with sales. Indeed, the census data indicates that foodcrops now provide more cash income than do the traditional cash crops.

1.17 Women have responded to this opportunity for increased incomes by increasing production of foodcrops. For the most part this increased production has been achieved through increasing the land under cultivation either by establishing new farms on more marginal lands or shortening the fallow periods on existing plots.

**Table** 3: Cc rceialization of Crops In the North West Provineo

__,__________._____________________________________________________________

|Crop|**Percentage**<br>of**Farms**<br>with**Salao**|**Percentage**<br>of**Total**<br>Production<br>Sold|**Average**Valuo<br>of**Sales per**<br>Farm**with Sales**<br>(P CIPA)|
|---|---|---|---|
|____________________________________________________________|_______________|||
|EXPORT CROP PRODUCTION<br>||||
|**Arabica**Coffee<br>|100.0<br>|100.0<br>|57,000<br>|
|Robusta Coffee<br>|100.0<br>|100.0|68,000<br>|
|Total Export Crops|100.0||63,000|
|FOOD CPOP PRODUCTION<br>||||
|**Maize**<br>|43.6<br>|21.4<br>|60,100<br>|
|Bul DivLsion<br> <br>|37.5<br>|27.0<br>|121,400<br>|
|Donan-Mantung<br>Division<br>|50.0<br>|29.6<br>|60,900<br>|
|Menchu Division<br>|41.0<br>|9.8<br>|17,400|
|Meusa DlvisLon<br>|46.7<br>|17.0<br>|62,200|
|Momo Division|40.0|18.6|7,200|
|**Cassava**<br>|50.8<br>|55.8|73,000|
|Cocoyams/Taro<br>|34.9<br>|25.0<br>|11,900<br>|
|**Yams**<br>|46 6<br>|349<br>|9,300|
|Irish Potatoes<br>|51.2<br>|54.9<br>|28,600|
|**S-ens**<br>|38.6<br>|48.3<br>|17,500|
|Groundnuts<br>|49.9<br>|41.9<br>|16,500|
|Sugar Cane<br>|53.3<br>|72.6<br>|36,000|
|Planteins<br>|61.3<br>|48.9<br>|57,000<br>|
|Bananas<br>|57.6<br>|41.2|31,000|
|Total Fooderopzs|8J5||159,000|

Source: 1984 Agricultural Census 3 **The** toro **'near cash crop' has been** coined by **the** extension **service** to describe those crops that **are in a transitional state between** produetion solely for consumption end production primarily for the **market. That maiLe belongs** to **this category is** evident **from the fact** that number of households marketing at **least** som of their **maise** production **has increased** from 44 percent in 1972 to 66 percent In 1984 (MIDENO, **1984a). However, the** fact that individual households **on average** only **market** 15 **percent** of their **mates** crop **indicatos** that **co1e1rcLaliation is** still Ln **Its early stages (Wertim ,** 1987).
ACAC city would nearly triple. These large increases reflect both the significant changes in policy that we contemplate and the low probability that such city pairs are currently served by direct flights.

The remainder of the paper is organized as follows. Section II describes the two main sources of data used in our analysis. Section III chronicles trends in international air passenger travel and the restrictiveness of existing bilateral air services agreements among the countries in our sample. Section IV describes the gravity model used to estimate the links between bilateral traffic and policy while controlling for other determinants of air traffic. Section V presents the results and their main implications, while Section VI concludes.

# **II. Data**

This section describes several data sets that we combine in our analysis. Our principal measure of air traffic activity is passenger travel on scheduled international flights between origin and destination cities. We seek to understand how this and related measures of air passenger traffic are related to measures of international air policy embedded in bilateral and plurilateral air services agreements. Our data also include a number of control variables that we also describe in this section.

**_Passenger data_** .  The primary data set that we use to document air passenger travel is the onflight origin and destination (OFOD) database purchased from the ICAO.  The ICAO collects information from national governments on international flight segments for _scheduled_ air flights.  The data we employ is the number of passengers travelling on scheduled flights for each pair of cities connected by an international flight.

This traffic measure differs from that used in Piermartini and Rousova (2012), which employs data from the International Air Travel Association (IATA) on numbers of international tickets, rather than flights.<sup>8</sup> Ticket information follows a passenger from origin airport to destination airport, and this may contain multiple flights. Ticket information is useful for understanding customers’ demand for

> 8 The data used by Piermartini and Rousova (2012) are also aggregated at the country level, while we have information for city-to-city flights.  In some cases, we also aggregate to the country level, but some suppressions in our city pair data make the city-to-city data preferable.

5
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|---|---|---|---|---|
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