rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 1991 1996 2001 2006 2011 2015 1991 1996 2001 2006 2011 2015<br>Year Year<br>All situations No AF VN−CN SY All situations No AF VN−CN SY<br>(a) Average duration of exile (b) Median duration of exile<br>15 15<br>10 10<br>5 5<br>Average duration (years) Median duration (years)<br>0 0<br><!-- End of picture text --> Figure 5: Exile duration statistics In the absence of any refugee flow (inflow or outflow), the averages should increase by one year for each additional year, i.e. the slope of the line should be equal to 1. Any downward deviation from that “45-degree line” can be caused by an outflow of refugees who have been in exile for longer than the average (due to either returns, integration in host countries, or any other reason associated with discontinuation of registration). It may however also be caused by a net inflow of new refugees who would reduce the average duration of exile. Conversely, when the line is steeper than the 45-degree line, it indicates that outflow mainly consisted of refugees that had not been exiled for long, i.e. less than average.

Since the late 1990s, the mean duration of exile has consistently remained between 10 and 15 years. The relatively low average duration in the 1990s corresponds to a peak flow of refugees leaving their country, as a result of the dislocation of the Soviet Union, of the conflicts in Former Yugoslavia, and of violence in Somalia and the genocide in Rwanda. In this case, a low average duration is the symptom of a deterioration of the global situation. From 1996 on, the mean duration increases along the “45-degree line”, which reflects the relative moderation of in- or outmovements until 1998-1999 and a large return of Rwandans which is paralleled by an outflow of Congolese. The curve once again goes up along a“45-degree line” until a peak in 2005. It drops in the following couple of years, largely due to a massive outflow of Iraqi and Somali refugees, renewed violence in Afghanistan, and large-scale departures out of Myanmar. The following years see ups and downs until the Syrian crisis erupts, pushing large amounts of people into exile and hence reducing the average duration of forced displacement. Once again, again a decline in the average duration reflects an aggravation of the problem.

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consequence this group reduces the supply of unskilled labor. The increase in costs due to higher unskilled worker wages generates resource reallocations and changes in other factor prices. Due to the slight decrease in capital incomes, households with skilled worker heads suffer income losses.<sup>24</sup> To compensate for this loss, these households increase supply of skilled labor which then causes the reduction in the wages recorded in Table 9. However this permits producers to reduce their costs by substituting the now scarcer and (relatively) more expensive unskilled labor with skilled labor.

The concentration of remittances in the single group of unskilled households exacerbates the initial labor loss: in this case 11,000 unskilled workers decide to wait for a better paid job. As before, the reaction of the government to reduce payroll taxes partly compensates the labor withdrawal, although it is less effective than in the previous setting.

# **5 Conclusions**

In this paper, we investigate the economy-wide effects of rising remittances for Jamaica using a general equilibrium model with an endogenous labor supply that responds negatively to increases in non-wage income. Our numerical estimations are based on a recent (2002) Social Accounting Matrix for Jamaica and on the household survey evidence of the negative relationship between labor supply and remittances (Kim, 2006). The data included in the SAM are derived from heterogeneous sources, some of which are quite dated. Although we use sophisticated ‘dataupdating’ statistical procedures, the final result could be improved once new data are collected and made available. Similarly, the econometric evidence on the negative labor supply – remittances relationship is not very strong. These limitations, common to most numerical simulation exercises, highlight the fact that our results should be considered as just a coherent and informed illustration of the direction and magnitude of the effects of rising remittances on the Jamaican economy. They should not be taken as a forecast of what will happen in the country.

Nonetheless, some interesting lessons can still be learned from this exercise. Firstly, a small positive shock to remittance inflows can have relevant economy-wide repercussions. By reducing labor participation and thus increasing wages, this shock exacerbates the appreciation of the real exchange rate, reducing the country’s competitiveness and damaging its export base and its small manufacturing import-competing sector. Second, within the narrow margins of maneuver of a highly indebted government, a revenue-neutral policy response that reduces labor

> 24 Capital income decreases due to the reallocation of resources set in motion by the real exchange rate appreciation. Importable sectors are especially capital intensive and increased flows of imports cause these sector to shrink and to release capital. Thus, rental rates have to go down so that idle capital can be employed in other sectors.

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of the work was completed by the end of 2002, 4% by the end of 2004, and 10% by the end of 2006. These figures include the overlapping portions with the GQ network that represent about 40% of the NS-EW progress by 2006. As of January 2012, 5,945 of the 7,300 kilometers in the NS-EW project had been completed.

Ghani et al. (2016) quantify that the GQ work stimulated organized manufacturing expansion in the districts located along the highway network, even after excluding the four major cities that form the nodal points of the quadrangle and five other districts that are their contiguous suburbs. The nodal districts are excluded, in their work and in this project, because it is very hard to interpret results for nodal cities given that they were targeted by the reform. Estimations suggest manufacturing shipments in the affected districts grew by almost 50% over the 10 years after the GQ construction commenced. This growth is not present in the districts farther away from the GQ network nor in districts alongside the NS-EW system. Ghani et al. (2016) further consider dynamic analyses and straight-line instrumental variables (IV) based upon minimal distances between nodal cities. As the affected districts contained about a third of India's initial manufacturing output, this was a major advancement for the country that would have covered the costs involved. They further find substantial evidence for heightened entrepreneurship, better industrial sorting, and stronger allocative efficiency for industries positioned on the network (e.g., Hsieh and Klenow, 2009).

In a companion paper, Ghani et al. (2017) also consider the unorganized sector and find a very limited response to the GQ upgrades. There is modest evidence for the replication of some results related to heightened entry rates and industry sorting, but the implied size of these effects is much smaller and rarely statistically significant. This is perhaps due to the greater incentive for larger plants that trade at a distance in the formal sector to pick their location more selectively. Another potential root cause, which we begin to explore in this paper, is differences in the initial level of financial development.

# 3 Data

Our platform combines financial loan data from RBI with evidence on GQ implementation.

6
of wealthy households hold risky financial assets, but among households with financial assets there is relatively little systematic variation in the ratio of risky financial assets to other assets as wealth increases. This can be seen in Figure 2 for six advanced economies (based on Guiso et al., 2002, compare their Tables 1.5 and 1.7), and for Korea. If this can be extrapolated to low-income countries, then it implies that patterns of ownership can be inferred from _participation_ rates for different assets and products.

Participation data for risky financial assets are available for India (Kar et al., 2001) and it also shows a strong upward trend with income (Figure 2). Conditional on some holdings of risky assets, the share of risky in total financial assets rises slightly with income in China. It seems safe to assume that the unconditional share of risky assets increases with wealth in both China and India. Note that participation rates are much lower for all quintiles in India than for the advanced economies shown—as is surely the case in all developing countries.

But is household portfolio allocation efficient? There are various reasons to suppose that it is not. For instance, it is hard to rationalize the lack of diversification in most household portfolios.<sup>26</sup> The existence of an equity premium has contributed to a perception that risky assets form too low a share in household portfolios. Other kinds of portfolio allocation error are also conjectured, such as reliance on fixed-interest mortgages when choice of floating might place the household on a higher indifference curve. Reviewing this literature for the United States, Campbell (2006) concludes that “many households are reasonably effective investors, but a minority make significant mistakes,” and that there may be a persistent cross-subsidy from naïve to sophisticated households.

> 26 For example, Kelly (1995) showed that the median US investor held just one equity stock.. More recently, Goetzmann and Kumar (2005) showed that, while diversification is increasing among the clients of a large US brokerage, many are still demonstrably insufficiently diversified and forgo substantial returns on a risk-adjusted basis, even after allowing for transactions costs. Bergstresser and Poterba (2004) discuss tax-inefficient portfolio allocation by US households.

19
|Tariff*Conservative dummy||0.013<br>(0.003)***|||0.01<br>(0.008)||
|---|---|---|---|---|---|---|
|Tariff on similar products||0.002|||0.000||
|*Conservative dummy||(0.003)|||(0.008)||
|Tariff*Liberal dummy|||0.013<br>(0.003)***|||0.011<br>(0.008)|
|Tariff on similar products|||0.000|||-0.004|
|*Liberal dummy|||(0.003)|||(0.008)|
|Country-year fixed effects|Yes|Yes|Yes|No|No|No|
|Product fixed effects|Yes|Yes|Yes|No|No|No|
|Country fixed effects|No|No|No|Yes|Yes|Yes|
|Agricultural products|Excluded|Excluded|Excluded|Excluded|Excluded|Excluded|
|Observations|123857|123857|123857|95509|95509|95509|
|Adjusted R-squared|0.6|0.6|0.6|0.00|0.0001|0.00|

Robust standard errors in parentheses. Tariff on similar products is defined as the weighted tariff on all _other_ 6-digit products belonging to the same 4-digit category. Weights are equal to product export shares within the 4-digit category. * significant at 10%; ** significant at 5%; *** significant at 1%.

In contrast to the findings of Fisman and Wei, we do not find that misclassification (at least within the same 4-digit HS category) is prevalent in transition countries. As can be seen in Table 14, tariff on similar products does not appear to be statistically significant in any specification. Neither does its interaction with the differentiated product dummy. Our basic result, suggesting that elasticity of missing trade is larger for differentiated products, remains unchanged in the specification in levels. The overall responsiveness of missing trade to the tariff rate, however, retains its significance in only one specification. These changes in results are most likely due to a high correlation between own tariff rate and the tariff rate on similar products (0.86).

The lack of evidence on misclassification may be attributed to high correlation between own tariff and tariff on similar products or to the possibility that misclassification takes place outside the same 4- digit category. For example, when in 2000 Johnson & Johnson was importing to Russia their “2-in-1 Shower Gel” the company categorized it as a soap substitute, but customs decided to consider the product as a cosmetic and the company had to pay a 20% instead of a 15% duty (Aris, 2000). While soap is included in the 3401 HS category (HS 340120 is “soap in other forms”), cosmetics belong to HS 3304 (“beauty, make-up, skin-care, nes”).

In sum, our analysis suggests that differentiated products may lend themselves more easily to tariff evasion and that such evasion is likely to take place through misrepresentation of product prices rather than undercounting of physical quantities or misclassifying products.

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**Table 20. Significance of legal and regulatory obstacles: R&D investment requirements**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.0255|-0.0314|||-0.0236|-0.0345|||
||(0.0604)|(0.0606)|||(0.112)|(0.115)|||
|Export % of sales|8.08e-05|-2.15e-05|-6.27e-06|-4.50e-05|0.000546|0.000219|0.000508|0.000298|
||(0.000768)|(0.000800)|(0.000770)|(0.000796)|(0.00141)|(0.00151)|(0.00144)|(0.00152)|
|Share of sourcing via imports|0.00206**|0.00117|0.00193**|0.00101|0.00327**|0.00187|0.00308*|0.00160|
||(0.000875)|(0.000902)|(0.000869)|(0.000892)|(0.00163)|(0.00172)|(0.00166)|(0.00174)|
|High-income source country|-0.0992|-0.0328|-0.112|-0.0366|-0.160|-0.0571|-0.174|-0.0550|
||(0.0927)|(0.0933)|(0.0917)|(0.0914)|(0.175)|(0.178)|(0.175)|(0.178)|
|>250 employees|-0.0910|-0.0683|-0.109|-0.0850|-0.134|-0.0938|-0.170|-0.126|
||(0.0704)|(0.0721)|(0.0695)|(0.0713)|(0.130)|(0.135)|(0.132)|(0.137)|
|Cumulative inv. >US$10M|-0.0377|-0.0584|-0.0435|-0.0643|-0.0850|-0.125|-0.109|-0.149|
||(0.0703)|(0.0723)|(0.0707)|(0.0723)|(0.130)|(0.137)|(0.133)|(0.140)|
|Years in country|0.000485|-0.000219|0.000707|-0.000274|0.00175|0.000597|0.00227|0.000565|
||(0.00229)|(0.00238)|(0.00221)|(0.00232)|(0.00427)|(0.00440)|(0.00416)|(0.00435)|
|Level of foreign ownership (%)|-0.00102|0.000380|-0.000844|0.000839|-0.00192|0.000188|-0.00156|0.000980|
||(0.00125)|(0.00135)|(0.00123)|(0.00129)|(0.00223)|(0.00251)|(0.00225)|(0.00246)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.009|0.028|0.037|0.056|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "To what degree are the following factors an obstacle for your company to operate in this country? [Requirements to foreign investors to invest in R&D]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

37
Industry Restructuring and Regulation -Building a base for sustainable growth

# **2. Evidence on the impact of reform on growth**

The impact of reforming an infrastructure or utility industry on economic growth can be seen in two primary ways:<sup>7</sup>

- reform can provide a stimulus for the economy through:

- )> attracting foreign investment, both when privatizations occur and when new investment opportunities become available;

- cheaper (relative) prices for infrastructure services as efficiency gains accrue, due to greater incentives for operators so helping ensure the country can utilize its competitive advantages and that markets will allocate resources appropriately;

- increased 'product' competition due to greater incentives for innovation and growth;<sup>8</sup> and

- increased employment opportunities as service expansion and quality improvements are implemented;

- providing a platform for economic expansion. For example, availability of adequate 'public' electricity supplies at an acceptable quality is key to increasing output without costly self-generation each new factory, etc. Furthermore, having an adequate communications infrastructure (both transport and telecommunications) is vital for efficient operations.

From these two relationships between infrastructure and economic growth, it is easy to postulate why effective regulatory reform is key to unleashing greater economic growth. However, the problem that has continually hampered an empirical investigation of these relationships is that of data.

Two sets of data are available:

- a economy-wide studies that attempt to determine the impact of infrastructure reforms throughout the whole economy; and

- studies that focus on the impact of reforms on just one infrastructure sector.

The latter type of study is less informative than the former but is more prevalent. It is possible, however, to infer from these studies implications for the whole economy.

## **2.1 Economy wide**

There are two types of economy-wide studies available:

- those that seek to consider panel data from several countries; and

- studies that concentrate on just one country.

Both types of study are reported below.

Two papers are especially useful in this area. Sheshinski and Lopez-Calva (1998) provide a more detailed analysis of the relationships that are expected to exist. Fine (1997) provides an interesting critique that argues against the perceived wisdom in this area, claiming both that the general case is not strong and that if there is a case, it is for a change in structure not ownership.

The interaction of market structure and product market competition and its impact on growth and innovation is investigated in a series of papers by Aghion, Harris, Howitt and Vickers. They find that the dynamic aspects of product market competition outweigh the gains from monopoly (greater profits, increased returns to research and development, etc.).

5
4 the overall performance of the economy as measured by growth, investment, etc. was also better in the early period. The behavior of inflation was also very different in the period preceding the programs. Inflation was much higher ir. cne eighties than in the sixties for the countries included in this study. In addition, it had by then become a chronic problem in the eighties after many years of very high .nflation and many failed stabilization attempts.

This divergence in the overall economic conditions had important implications for the strategy and the results of the stabilization programs. The higher rates of inflation in the eighties called for a strategy based on a shock treatment including a sharp reduction in the budget deficit and a freeze of the exchange rate, prices and wages. A more gradual approach was possible in the sixties because the level of inflation was lower thus providing enough time to phase in the various policy measures.

The ex:ternal conditions were also less favorable in the eighties. In contrast to the sixties, the eighties were characterized by low growth in the world economy, high real interest rates, and deteriorating terms of trade for the Latin American countries. More importantly, the Latin Americar countries in the eighties were confronted with a sudden halt in their access to international credit since the beginning of the debt crisis in August 1982. These conditions also contributed to an easier adjustment in the sixties as the countries could use external financing to increase public and private investment and sustain growth.

The remainder of the paper will be organized as follows. In section II we illustrate with some examples the difficulties encountered with the orthodox approach. We argue that downward rigidity in inflation, sometimes
Table 1: Female land ownership in Dar es Salaam

|**Variable**|**Mean**|**(Std. Dev.)**|**Min.**|**Max.**|**N**|
|---|---|---|---|---|---|
|One of default owners is female|0.132|(0.339)|0|1|606|
|Woman has rights over land sale|0.449|(0.498)|0|1|602|
|Woman has rights over transfer|0.437|(0.496)|0|1|602|
|Woman has rights over rental|0.42|(0.494)|0|1|602|
|Household would include woman on CRO|0.253|(0.435)|0|1|600|

**Notes:** data are from Tanzanian Land Rights survey. Sample restricted to dual-headed households in treatment blocks.

levels of female land ownership: investigating the gender breakdown of land ownership in the Kinondoni land registry reveals that Barafu and Kati have female ownership rates of 17% and 22% respectively, compared to the municipal average of 25%.

The main purpose of the experiment was to induce households in both communities to purchase certificates of right of occupancy (CROs), in order to subsequently study their impact. This involved several levels of randomization:

1. **Cadastral survey and repayment program** : blocks of land parcels were identified and randomly selected into treatment and control groups. All parcels in treatment blocks were subject to cadastral surveying, with residents given the option to repay the heavily-subsidized cost (100,000 TSh ) in exchange for a land title, drastically bringing down the cost of a CRO for residents.

2. **Random price variation within treatment blocks** : households within treatment blocks were randomly allocated vouchers redeemable for different levels of discount on the final price of a CRO.

3. **Random voucher conditionality** : roughly half of these vouchers were made conditional, redeemable only if a female household member was included as an owner on the CRO application.

Next, we will discuss these interventions in more detail, including the timing of their introduction in both communities.

# **3.1 Main intervention and voucher distribution**

In the summer of 2010, prior to the intervention, the University of Oxford conducted a complete census of land parcels in Barafu and Kati, known as the Tanzanian Land Rights Survey (TLRS). Households were identified using records and maps from the Kinondoni Municipality, which had created a listing of all households in the area to assist with the creation of the land registry. Using this listing, parcel-owning households were identified and interviewed, resulting in detailed data on household and parcel characteristics.

9