rafmacalaba/datause-encoder-data huggingface dataset:
employment and to inactivity. From 1999 to 2000, and from 2001 to 2002 more than 40% of the total outflow from unemployment in Latvia left the labor force. This compares with 20% and 33% in Estonia (1999-2000 and 2000-2001), 33% and 27% in Lithuania (1999-2000 and 2001-2002)<sup>5</sup> . In light of the fact that more than half of those who moved from unemployment to inactivity lost hope of finding a job, discouragement appears to be a problem which needs to be addressed alongside those of long-term and recurrent unemployment.

**Figure 15. Outflows from** E **mployment, Unemployment and** I **nactivity by** D **estination, 1997 – 2002**

<!-- Start of picture text --> Outflow from employm ent<br>0.07<br>0.06<br>0.05<br>0.04<br>0.03<br>0.02<br>0.01<br>0.00<br>1997-98 1998-99 1999-00 2000-01 2001-02<br>to unemployment to inactivity<br>Outflow from u nem ploymen t<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>1997-98 1998-99 1999-00 2000-01 2001-02<br>to employm ent to inactivity<br>Outflow from inactivity<br>0.08<br>0.06<br>0.04<br>0.02<br>0.00<br>1997-98 1998-99 1999-00 2000-01 2001-02<br>to employment to unemployment<br><!-- End of picture text -->

**_Notes_ : 1997-2001: flows between Mays of corresponding years; 2001-2002 – annual average.** Flows exclude the impact of migration, mortality, and new entrants who were younger than 15 in the first of the two periods. In this way, the impact of economic change is identified.

_Source_ : Calculations based on LFS data (see Appendix Table 14).

**Low mobility between the main sectors**<sup>6</sup> . Considering that agricultural employment will likely continue to decline in Latvia, it is disturbing to observe that the recent gross labor flows out of farming were predominantly to non-employment (Appendix Table 15). This trend appears to be increasing over time. Those who found new work in 1997-2001 went predominantly to the service sector, but in 2001-2002 roughly equal numbers went to industry and services.

> 5 See Hazans (2004b), Figure 6.

> 6  This and several next paragraphs (until the end of subsection A) draw from OECD (2003a).

21
the current period. The median length of holding periods for easing phases also jumped, to almost 15 months during the 2008-19 window, but then dropped sharply after the pandemic. After the COVID-19-related easing, economies were on hold for an average of only 3 months from the last rate cut or end to asset purchases to the first rate hike.

Chart 18 Duration and Share of Holding Periods (Periods with no change in policy interest rates or asset purchases) <!-- Start of picture text --> Hold Duration (median)<br>15 15<br>10 10<br>1970-2020<br>5 5<br>0 0<br>Tightening Phases Easing Phases Highly Other<br>Synchronized Tightening<br>Tightening Phases<br>Holding Share of Phase (median)<br>45 45<br>30 30<br>1970-2020<br>15 15<br>0 0<br>Tightening Phases Easing Phases Highly Other<br>Synchronized Tightening<br>Tightening Phases<br>1970-1984 1985-1998 1999-2007<br>2008-2019 2020-2024(May)<br><!-- End of picture text --> Sources: Authors’ calculations based on data from January 1970 through May 2024. Notes: Chart shows the median duration of holding periods at the end of a phase before a turning point (top panel) or the share of the phase in the holding period (bottom panel). A holding period is the number of months after the last rate increase during a tightening phase or after the last rate decrease and end of any government QE programs. Movements in the policy interest rate <0.1 percentage point do not qualify as a change. Right side of the chart shows the same statistics across all Highly Synchronized Tightening periods before 2022 (in yellow) or in all tightening phases that are not Highly Synchronized.

These differences between tightening and easing phases—and the unusual duration of holding patterns since the pandemic—are even more striking when this Hold Duration is scaled relative to the length of the corresponding phase (at the bottom of Chart 18). As of May 31, 2024, for the 56
sei for the finctioning of tho private sector. Thus, betwen 1960 _md_ 1980, the industrial sector gw on avrage 13 percent pe year. Abov all, the macroeconomic picture was stable so that despite **a** nonhaly fixed exchange rate, until the mid 1970s, the roal exchange rate did not appreciate.

In contras **t** o COte d'Ivoire, Senegal's economic performance was poor even by SSA standards. During the _first_ two decades after indepenence, GDP grow oi average 2.2 percent per y"ir whilo the populatongrow 2.9 percent. To a large extent, the differing economicperformances ofCOte d'1voire and _Snegal_ uring the Immediate post-independence ora is to be linked to differing economic policies. From the _dotsoo,_ L2dustrial promotion m Senegal was spearheaded by inefficient public enterprises that were heavily provcted from outside competition. Agriculture was also subjdct to sever' government ievution, especially In the form of output price controls and markedtng monopolies. Moreover, macroecomic imbalances began to take root in Senegal as early as 1966, wheh groundnut 6xports lost their preferential access to the French market<sup>6</sup> .

In Mali, Inappropriate and distortionary public sector resource mobilization and expenditure comblned with poor resource endowment and volatile climatic conditions created serious fmnancial imbalances in the early 1980s. In the first half of the 1980s, real GDP stagnated and per capita income declined at an average rate of 1.7 percent per year. In 1983, exports and import were 30 percent below their level !n 1980 _and_ the government's budget deficit exc.luding grants hit an all time high of 18 percent of GDP.

The macroeconom **i** c nstability began or accelerated in all three countries in the mid-197Cs when aU three fell uvictlm to a short-lived comnmodity boom: a four-fold- increase in the price of coffee, a ttiee-fold increas in the price of cocoa, and a substantial increase in the prices of cotton, groundnut and phosp tej. Viewing the windfall as permanent income, the governments dramatically boosted public7 6 See World Bank (1992), Executive Summary, p. ii.

7TI =unit vale of coo export fiber from Mah inreased from _CFAF_ 230,000 per ton in 1975 to CFAP 382,000 i1977. Accoding to UNCTAD and World Bank etima, Mali's TOT improd by approximaey 30 rwuat betwe 1975 and 1976 (See UNDP, 19S0, Table 1-6 and Table A-8). Tamu of tade of Salop and Cte dtivoir and eal silarly improvod thanks to incre_as in the intaeional price of cooa and coffee, the main
and in some cases the percentage change is even greater than at the level of the total population. For example, standard errors across the two models rise by as much as 43% for target population 2 (0.030*1.43=0.043). However, here, the changes in standard errors are reflecting both the influence of idiosyncratic error and our failure to capture location effects.

# 5.2 <u>The Level of Location Effects</u>

Note that the location effect η _c_ may include group effects at levels higher than the survey cluster. To see this consider the following model with group random effects at a ‘district’ level ( _v_ ), as well a the cluster level ( _c_ ).: As before, the error components are uncorrelated. If clusters are the primary sampling unit, a district is sampled only indirectly, _viz._ if one of the sampled clusters happens to be located in that district. In a typical living standards survey there will only rarely be districts that have been sampled more than once in this way, making it impossible to separate the location effect in the sample into a ‘district effect’ ω and a ‘cluster effect’ η . Assume accordingly that a district is sampled at most once, and write _v_ ( _c_ ) for the unique district sampled along with the cluster. The model now becomes Or, with obvious relabelling:

* where η _c_ = ω _v_ ( _c_ ) + η _v_ ( _c_ ) _c_ .  Consequently, the estimated variance of the location effect in a model with only cluster-level random effects is in fact an estimate of σω 2 + ση 2, the combined group effects operating at the sample’s cluster level.

15
_ [DIRECT it_= % + 6gOi|Cye-1— Bln1 ~~|——.——it-1 it-1 ]~~ + yl 13 it-1 <u>| —</u> TPIRECT Aln ~~l, i,t~~ Cc ~~(as~~ it )- Biz" — We) + Eft

it C it it DIRECT t I ~~WNit-1-Tit-1~~ DIRECT Cit-1 — p n <u>pe</u> it-1 , € (-1,0 i i i2
Combining these estimates with the average value of the index of competition for the value ( and count-based ( indices from our data (of 0.4 and 0.9, respectively), yield a magnitude of the third-market effect of -0.32 for the value-based index and -0.29 for the countbased index. Therefore, theory appears to predict a spillover effect of about 0.3. For the countbased index, the theoretical and our baseline empirical estimates (-0.2) are not far apart. For the value-based index, our baseline empirical estimates are below those derived from theory (0.14 versus 0.32). There are two possible explanations. Residual measurement errors would impart a natural attenuation bias to the econometric estimates. Second, the values of the elasticity of substitution that we use to derive the theoretical prediction are based on Feenstra et. al. (2011), who estimate the elasticity for goods at a level of disaggregation close to the HS 10-digit level. Our data on the other hand are at HS 4-digit so that the relevant elasticity for our purpose could be well below the value of 3 that we use here.<sup>24</sup> Such a lower value would bring our empirical estimates closer to those based on theory.

Overall, the baseline estimates in this paper suggest that a 10 percent depreciation/appreciation in the renminbi exchange rate vis-à-vis an importing country decreases/increases on average developing country exports by about 1.5-2 percent. Given the 30 percent _appreciation_ of China’s real exchange rate vis-à-vis the US dollar over 2000-2008, our findings suggest that this could have been associated with about a 4.5-6 percent increase in the typical developing country’s exports to the US, with much greater effects for countries in closer competition with China.

# **VI. Conclusion**

To our knowledge, this paper is the first attempt to quantify the effect of exchange rate changes on the exports of competitor countries to third markets—the spillover effect—that both exploits

> 24 Broda and Weinstein (2006) argue that with more disaggregated data, one is likely to find higher estimates of the elasticity of substitution.

23
**Table 8. “Fake” Treatment Effects (Panels A and B) and Migration Analysis (Panel C)**

|_Total Consumption_|_Health_|_Education_|_Moderate Poverty_|_Extreme Poverty_|
|---|---|---|---|---|
|**Measure of Shock**<br>(1)|(2)|(3)|(4)|(5)|
|**Panel A**<br> <br>|**: Results usin**<br>|**g Encovi 2000**<br>|||
|t * (rainfall z-score> 2)<br>-36.633|-9.020*|0.334|-0.023|0.017|
|[41.047]|[4.797]|[3.136]|[0.030]|[0.019]|
|Baseline Mean<br>957.0|34.53|40.87|0.459|0.106|
|**Panel B. Res**<br>|**ults onpre-d**<br>|**etermined varia**<br>|**bles**<br>||
|_Education_|_Age_|_Gender_|_Area of residence_|_Single-married_|
|**Measure of Shock**<br>(1)|(2)|(3)|(4)|(5)|
|t * (rainfall z-score> 2)<br>-0.238|-0.086|0.014|0.013|0.009|
|[0.154]|[0.378]|[0.011]|[0.024]|[0.011]|
|Baseline Mean<br>3.966|45.47|0.788|0.424|0.792|
|**Pane**<br>|**l C. Results o**<br>|**n migration**|||
|_HH Head moved less_<br>_ago/Born in different_|_than 1 year_<br>_municipality_||_HH Head and spouse mov_<br>_ago/Born in different_|_ed less than 1 year_<br>_municipality_|
|**Measure of Shock**<br>(1)|||(3)||
|t * (rainfall z-score> 2)<br>0.001<br>[0.003]|||-0.002<br>[0.002]||
|Baseline Mean<br>0.0131|||0.00690||

Observations: 20,788 Panel A; 23,320 Panel B; 26,587 Panel C. _Notes_ : Results from D-D regression controlling for age, gender, years of education, marital status and race of the household head as well as location (urban or rural). Robust standard errors in brackets clustered at the municipality level. Pretreatment placebo refers to the D-D methodology applied to Encovi 2000 and Encovi 2006. The Z-scores indicates the number of standard deviations above the rainfall mean (since 1980). t is the before-after dummy. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ calculations by the authors based on data from Encovi (INE) and Insivumeh.

31
change in that indicator for LGAs that actually received the project would have been the same as that observed in LGAs that did not receive the program. Some evidence on the parallel trend assumption was provided in the section above.

# **4.1 Results**

We now present the main results of the paper: did LGAs receiving the project present a different trend in indicators of state capacity when compared to the comparator areas? We highlight the trends within project areas, then present how the comparator areas perform, and finally compare the performance between treatment and comparator areas to explore the impact of the program.

# **4.2 How did outcomes change within ULGSP LGAs over time?**

We start by presenting how the responses of households about institutional capacity changed in project areas. Many indicators are presented in Table 4, Table 5 and Table 6. Columns (1) and (2) present average responses among households in project LGAs, in 2016 (first round) and 2018 (second round) surveys, respectively, while column (5) presents the difference in means.

Overall, the response improved across the board. In Table 4, we see the share of households reporting that the local council makes good use of revenues has increased from 44% to 66%, for example, while the share saying that local governments make good investment plans increased by 21 p.p. Responses about government performance in delivering services such as road maintenance and cleanliness have also improved, as have responses about ease of access to building permits, household services and medical treatment.

Table 5 reports indicators related to responses of transparency and accountability. The share of respondents agreeing that the local council provides information on budget, allows participation, consults other actors before decision. and handles complaints well have all increased by over 10 p.p. - for all these indicators less than 60 percent of respondents agreed with the statements by the second survey, but the performance increased significantly in the two years between surveys. It is also worth noting that direct political participation, by contacting officials or participating in meetings, are both low and do not seem to be increasing: less than one in five respondents ever contacted a village official, a number that remained unchanged between surveys, and only 40 percent ever participated in village meetings.

Finally, Table 6 presents results related to response on taxation and preferences over public good and government decisions. We do observe an increase in the share of citizens reporting that citizens should pay electricity and that not paying electricity is wrong and punishable, but only half of respondents say that not paying taxes is wrong and punishable, a number that does not change in 2018. We do see, however, a large increase in the number of individuals agreeing that the tax system is fair.

Beyond households in project areas, both objective measures and views reported by government officials also improved. In Table 7, the share of respondents reporting an up-to-date master 21
# UP = ratio of urban to total population

Contrary to most econometric studies, we do not try to explain FDI inflows but rather the FDI that does not arise from market size and the natural resources available in the host country. Therefore, the dependent variable used in the regression is our business climate indicator as defined by equation (1). As discussed earlier, we assume that FDI inflows respond to a change in GDP or natural resources with perfect elasticity. To check the robustness of this assumption, we have also estimated the same equation but with FDI inflows as a dependent variable and GDP and natural resources as explanatory variables. We found the respective elasticities of 0.91 and 0.92 and 1.4 and 1.2 in our panel and cross-country regressions (see table 4, third column)." A brief explanation might be necessary for our selection of explanatory variables, which has been partly driven by the availability of data in the World Bank's database. 12 The economic growth rate should influence positively the business climate for FDI as it reflects an improvement in economic performance. Most recent studies have also evidenced that the degree of openness, as measured by the trade share in GDP, should influence positively foreign investors through trade liberalization and higher competitiveness. The illiteracy rate should be inversely related to the availability of relatively skilled labour - a major factor in the location decision of TNCs. The number of telephone lines per 1,000 people is viewed as an indicator of infrastructure and communication development. Finally, the recent literature has argued that investors can be lured by concentration of other companies or customers, since it reduces their transport costs and there are evident economies of scale in the development of backward and forward linkages. This argument might be partially captured by the share of urban population (as a percentage of total population). Note we will also test the relationship 'Wheeler and Mody (1992) found that market size had a positive influence on capital expenditures by manufacturing affiliates of United States TNCs between 1982 and 1988, with an elasticity of 1.57. Elasticity for the highest income countries was 1.86, while that for the lowest-income countries was 0.74.

> 12 For a good review of determinants of FDI in the African context, see Srinivasan (1999). Note that we tested additional explanatory variables to those reported in the text such as income per capita and a dummy variable for landlocked countries. However, those do not appear to influence significantly the business climate index.

11
that good policies package such as private property rights, fiscal discipline, macroeconomic stability, and open to trade on average increase the income of the poor. Furthermore, there are some studies that found ambiguous or mixed relationship between foreign aid and growth. Bourguignon _et al._ (2007) argue that the empirical literature on aid effectiveness has yielded unclear and ambiguous results. The “black box”, which is the linkage from donors to policy makers, from policymakers to policies and from policies to outcomes, makes it difficult for researchers to quantify and open it. Rajan _et al._ (2005) find no robust positive relationship between aid and growth by using cross-country panel data. Ekanayake _et al._ (2010) find mixed effect of foreign aid on economic growth in developing countries. Werker _et al._ (2008) argue that foreign aid affects most components of GDP, but it has no statistically identifiable impact on prices or economic growth. Inanga _et al._ (2008) conclude that foreign aid finance can generate economic growth if effectively utilized in a stable macroeconomic environment. Finally, Holder (2004) argues that the relationship between foreign aid and growth turns out to be an inverted-U shaped under reasonable policy assumption, which is an Aid Laffer Curve. Positive relationship between foreign aid and growth is located in the upward sloped side of the Aid Laffer Curve, while the negative relationship is located at the downward sloped side of the Aid Laffer Curve. The importance of AfT for the low income countries can be related also to the relationship between trade and economic growth. We would find a large number of theoretical and empirical studies that have examined this relationship. Main survey studies are Anderson _et al._ (1996) and Greenaway _et al._ (1994). Winters _et al._ (2000) carry out a theoretical analysis of the relationship between trade measures and their impact on poverty using both simple forms of static, and short and long term dynamic analysis. He identified a number of possibilities of both pro- and antipoor influences and state that the effects of trade on poverty are likely to be positive providing a view about how trade liberalization can be designed to foster poverty alleviation. Balassa (1978) investigates the relationship between exports and economic growth for eleven developing

**7**
# **I. Introduction'**

It is now generally accepted that private property rights play a positive role in the development of market-based economies. This has been documented particularly by economic historians in the context of western Europe (North and Thomas, 1973; and Rosenberg and Birdzell, 1986). However, what has not been fully established is the magnitude of the role played by private property rights. Some attempts have been made at measuring the effects of private property rights on economic development. These have been of two kinds. The first kind has focused on estimating the relationship between private property rights and economic growth, based on cross-section regression analysis (Torstensson, 1994; and Goldsmith, 1995). The second kind has used sector studies to measure the economic effects of property rights in land on land productivity and rural or urban real estate incomes (Feder et al., 1988; Feder and Nishio, 1998; and MighotAdholla et a., 1991; Roth et al., 1994; and Place and Mighot-Adholla, 1998). While these empirical studies have made a very useful contribution to measuring the economic effects of property rights, they have had some shortcomings. First, due to lack of direct measures, the cross-sectional analytical studies have relied on the use of proxies, such as the degree to which property is state-owned, to measure property rights. Second, the sector studies have failed to capture the economic effects of property rights which cover more than one sector or affect the economy as a whole.

This study attempts to address these two weaknesses by: (i) estimating the economic effects of land registration on the economy-wide parameters of financial development and economic growth; and (ii) using a direct measure of private property rights -- land registration -- instead of relying on proxies. It should be noted that this is the first study to empirically estimate the relationship between land registration on the one hand and financial development and economic growth on the other.

The rest of the paper is structured as follows. Section II reviews the theoretical and conceptual framework that links land registration to financial development and economic growth. Section III deals with specification and testing of the econometric model using data from Thailand while Section IV focuses on interpretation of the empirical results. Section V draws conclusions emphasising policy implications and areas for further study.

# **II.** A **Theoretical and Conceptual Framework of Land Registration, Financial Development and Economic Growth**

In this section, we review the theoretical and conceptual framework, developed by Byamugisha (1999), to guide the empirical analysis of the effects of land registration on I This paper has been written based on a dissertation by the author submitted as a partial requirement for a Masters Degree in Surveying at the School of Surveying of the University of East London, UK (Byamugisha, 1999a).