rafmacalaba/datause-encoder-data huggingface dataset:
Second, comparing countries, I find that wedges (in both sectors) are significantly higher in Guatemala and Panama. The labor markets in Costa Rica and Nicaragua are least distorted, even though the period covered for the latter is considerably restricted.

Finally, there is no evidence that the economies are becoming efficient over time. In four countries, wedges in both sectors in the most recent year are higher relative to the earliest year for which data are available. Industry in Costa Rica and services in Nicaragua offers the only bright spot, where wedges in the final year are below the initial year. Moreover, in many cases, evolution does not follow a secular trend. Panama appears the most striking specimen of this phenomenon. Beginning from the mid-1980s, wedges in both industry and services contracted sharply till the early 2000s. But they rose sharply thereafter to finish above the earlier peaks. These distinct reversals create pivot points that may help uncover the root causes of labor market inefficiencies, including those related to policy and regulations.

Now, let me return to the task of quantifying the aggregate impact of changes in labor wedges. The two exogenous elements– TFP � and wedges �, jointly determine the aggregate output of country in year . Let (. ) be the function that maps the two exogenous factors to jjppaa jjppaa . The ppaa �AA �ττ YY quantitative exercise estimates the output variation pp 1 in the most recent year 1 when I replace ppaa pp tt YY YY the level of wedges from those in the earliest year 0 ppaa ΔYY tt <u>p�� jp</u> 1�, � <u>jp</u> 0�� tt = 1 paa1 paa1�� −1 (16) YY AA ττ ppaa A negative ΔYY pp jjppaa1 implies that aggregate output in the most recent year declines if the economy is jjppaa YY ��AA �, �ττ subjected to past wedges, signifying an increase in allocative efficiency. ppaa ΔYY Panel A in Table 3 reports the results of the exercise. Note that the most recent (column (1)) and the earliest year (column (2)) varies across countries due to differences in coverage. First, let's consider column (3), which shows the percent change in output when I replace wedges in both non-agricultural sectors. Except for Costa Rica, all other regional economies experience a positive Δ 1, i.e., a decline in allocative efficiency. This finding is not surprising given a general rise in wedges (Figure 2ppaa ). Still, the aggregate cost of this rise in inefficiency is benign. The counterfactual output increase ranges from 0.44 percent in El Salvador to just above a percent in Panama. At the same time, the efficiency gains in Costa Rica are marginal too. Output declines by about a fifth of a percent when I use wedges from 1980.

20
Contents

|I.|Introduction .1.............<br>.. .. ... .. ... .. .. ... .. .. .. ...|.. ...<br>.<br>I|
|---|---|---|
|II.|Measured Poverty Under Alternative Consumption Definitions ...... . . . . .|. . . . .<br>.<br>3|
|III.|Examples ...............<br>... .. ...<br>... .. ...<br>... .. ... .. ...|... ..<br>.<br>8|
|IV.|Resolving Problems of Non-Compatible Data ............................|14|
|V.|Changes in the Poverty Profile ....................................|17|
|VI.|Concluding Remarks ...........................................|24|
|Refere|nces<br>..|25|
|_Tables_|||

|Table 1. Direction of Change in Measured Poverty As Consumption Becomes More<br>Comprehensive.|7|
|---|---|
|Table 2. The Impact on Poverty Measures of Alternative Consumption Aggregations in<br>Ecuador ..................................................|9|
|Table 3. The Impact on Poverty Measures of Alternative Consumption Aggregations in<br>Ecuador .10||
|Table 4. The Impact on Poverty Measures of Alternative Consumption Aggregations in<br>Pakistan .11||
|Table 5. The Impact on Poverty Measures of Alternative Consumption Aggregations in<br>Pakistan ....|12|

|Table 6. Inequality and the Measurement of Inequality .13|
|---|

|Table 7. Per Capita Monthly Expenditure: Long and Short Questionnaires .14||
|---|---|
|Table 8. Household Characteristics and the Risk of Poverty Alternative Consumption<br>Aggregates and the "Traditional" Poverty Line .23||
|_Figures_||
|Figure I .6||
|Figure 2. Observed Income as a Noisy Proxy of True Welfare .......................|18|
|Figure 3. Food Share Regression for Ecuador: 1994 .............................|21|
Rentschler, and Rozenberg 2019). In contrast, this study aims to offer broad coverage across various contexts by covering the literature comprehensively, although in less detail.

Hallegatte et al. (2016) use a framework that distinguishes between households’ (or countries’) exposure, vulnerability, and resilience to shocks. _Exposure_ refers to the likelihood a household (or country) will experience a climate shock. _Vulnerability_ is the likelihood a household (or country) will be harmed by a climate shock. _Resilience_ measures a household’s (or country’s) ability to withstand the effects of a climate shock. Unfortunately, only a few papers in our sample provide estimates on resilience, so this study combines vulnerability and resilience into a single category to assess the impact of shocks after they materialize.

Our sample came from backward and forward citation searches from 11 index studies, three databases, and a recent review on climate adaptation (Rexer and Sharma 2024). The analyzed sample includes 701 estimates from 70 studies.

About two-thirds of the estimates within the analyzed sample show that poor households are significantly more exposed than other households. The disproportionate impact on poor households has been extensively documented, and our findings are consistent with the literature, especially for income and human capital losses. The evidence for other impacts is mixed. These findings have important implications for policy and practice, highlighting the need for targeted interventions to protect poor households from the adverse effects of climate shocks.

The remainder of this study is structured as follows: Section 2 describes the data and methodology used for the meta-analysis. Section 3 presents the results. Section 4 four discusses policy implications.

# **2. Data and method**

# _2.1 Data_

A meta-analysis typically begins with a citation search of at least two databases complemented by other sources such as expert recommendations or index studies—followed by title and abstract screening, full text screening, and finally meta-analysis regressions. Our citation search began with 11 index articles we previously identified, followed by backward and forward citations searches— 3
**_Figure 6 graphically displays a convex relationship between the degree of devaluation and real fiscal gain as a share of GDP under different pass-through and indexation scenarios._** The higher the degree of devaluation, the larger the real fiscal gains in local currency terms. Larger pass-through elasticities also raise the fiscal space generated in real terms, though the presence of wage indexation in the public sector will dampen the increase.

Figure 6 Djibouti: Relationship between Magnitude of Nominal Devaluation and Real Gross Fiscal Savings (% of GDP) <!-- Start of picture text --> 20<br>18<br>16<br>14<br>12 100% Pass-Through<br>70% Pass-Through<br>10<br>60% Pass-Through<br>8<br>6<br>50% Pass-Through<br>4<br>2<br>0<br>30% 60% 100%<br>Degree of Devaluation<br>Dashed Lines indicate outcome with 30% public wage indexation.<br>Gross Fiscal Savings (% of GDP)<br><!-- End of picture text --> _Source: Authors’ calculation based on CPI data and WEO 2004._

# **5.  Social Impact of a Nominal Exchange Rate Devaluation**

**_The purpose of this section is to examine the impact of a potential nominal exchange rate devaluation of the Djibouti Franc on poverty, income distribution and real wages_** _._ Using sampling weights from the household survey, it estimates the real income loss that a Djibouti household would on average incur, as a result of a nominal exchange rate devaluation. It moves on to estimate the potential fiscal costs in the form of social transfers that would be needed to compensate the population for the real income loss engendered by a nominal exchange rate devaluation. It also estimates the impact of a currency devaluation on the incidence and depth of poverty as well as its distributional impact. (through predicted changes in consumer prices and wage income) using a simple model of rural/urban households whose members are primarily wage earners. The section ends with an estimation of the impact of a nominal exchange rate devaluation on real wages.

## **_5.1. Methodology_**

Three steps are needed in order to determine the distributional and poverty effects of devaluation:

1. Determine expenditure shares of tradable and non-tradable goods and services including wage income from Djibouti’s representative household survey conducted in 1996;

2. Using historical data on exchange rates, domestic and world prices to estimate the impact of a nominal devaluation on prices in Djibouti; 15
## **5.4 A Control Function Approach**

The dependent variables in our analysis are discrete indicating the probability of participation in nonagricultural activities. This requires an approach to instrument variable (IV) estimation which differs from the standard 2SLS regression. To correct for the omitted variable bias, we implement the control function approach developed for the discrete choice models (Blundell and Powell (2001), and Petrin and Train (2009) for multinomial logit model, and Rivers and Voung (1988) for probit model). According to this approach, the discrete choice instrumental variables model is estimated in two steps. First, the endogenous variable is regressed on exogenous instrument(s) and all other regressors in the model. Second, the multinomial logit/probit regression is then estimated by including the residual from the above first-stage regression as an extra variable (i.e., the control function term). To account for any non-linearity in the distribution, we follow the literature and include higher order terms of the residual from the first stage regression.

# **4 Data**

The main data source for the estimation of employment choices is the Household Income and Expenditure Survey, 2002 (HIES, 2002). The survey collected information from a nationally representative sample of 16,924 households drawn from 1913 primary sampling units. The survey covered 17 of Sri Lanka’s 25 districts, and 249 of its 322 Divisional Secretariat Divisions (DSDs).<sup>32</sup> The DSD identifier in the HIES (2002) allows us to examine the employment and wages pattern at a much disaggregated geographical level. To define our sample, we use three criteria: (i) we exclude age groups which may have been exposed to historical malaria that afflicted Sri Lanka before 1947; and (ii) we focus on the rural sample where at least 0.1 percent of total land are under restrictions; and (iii) we focus on the male sample to avoid complications from changes in labor force participation decision which is relevant for women. The number of adult male who are employed and were born after 1947 and are currently residing in rural areas is 9,702. Our main estimation is based on a conservative sample of 8,948 individuals who were born after 1950, though robustness checks were performed for the full sample of 9,702 individuals born after 1947.

A central variable for our analysis is the amount of land under LDO restrictions in a DSD. We draw

> 32Data collection in the North and Eastern provinces was not possible due to on-going civil conflicts at the time of survey work.

16
of the latter are nonprofits. Nonprofits (NGOs and cooperatives) constitute only 14% of the institutions surveyed (Table 3).

**Table 3: Types of Institutions Offering Shari’ah Compliant Microfinance and Clients Reach**

|**Type of Institution**|**Numbers**<br>**Percentage***|**Clients**<br>**Reach***|**Scale**<br>**Effectiveness**|
|---|---|---|---|
|Village/Rural Banks|77%|16%|0.21|
|NGOs|10%|17%|1.70|
|NBFIs|5%|3%|0.60|
|Cooperatives|4%|1%|0.25|
|Commercial Banks|3%|60%|20|
|Others|1%|2%|2|

Source: El-Zoghbi and Tarazi (2013) Scale effectiveness<sup>16</sup> is highest for commercial banks, where 3% of the institutions serve 60% of the total clientele. Village/rural banks make up 77% of the institutions in the sample, but they serve only 16% of the clients. Their scale effectiveness is lowest, at 0.21. Similarly, the scale effectiveness of cooperatives and NBFIs is also low (at 0.25 and 0.60 respectively), with NGOs performing relatively better with a score of 1.70. Note that while commercial organizations do well in terms of scale (breadth) of outreach, Ahmed (2013) contends that they do not perform well in the depth of outreach compared to nonprofits. Furthermore, as nonprofits are not deposit taking institutions, they also face problems of scale due to lack of funds to expand operations (Ahmed 2002).

As Muslim countries have high levels of poverty and provision of microfinancial services by the Islamic financial sector is still very small, representing only 1% of the total (El-Zoghbi and Tarazi 2013), there is potential for Islamic finance to positively contribute to financial inclusion. Given the large gap that needs to be filled, the ways in which the Islamic financial institutions, capital markets and the social sector can promote financial inclusion are presented below.

> 16 Scale-effectiveness of different types of institutions is calculated by dividing the percentage of the clients reached by the percentage of the number of institutions. It indicates the average percentage of clients served by one percent of each institutional type.

19
3 The purpose of this study is to determine whether or not Djibouti civil servants earn a wage premium above the private sector wage and whether there is a sectoral differential in terms of private returns to schooling. The average ratio of annual wages in the public sector to the formal private sector yields a wage differential of 1.1 using data from a 1996 representative household survey which is the only available sample of sedentary households in Djibouti that provides wage income information across various sectors and occupations<sup>4</sup> . However, using a simple average of the wage difference between public and private (formal) workers to determine whether there is a wage premium is misleading. Doing so does not address differences in human capital endowment and other personal or household characteristic that influence earnings. The methodology used in this study controls for these determinants while correcting for selectivity bias, which help accounts for factors that influence the sorting or queuing of workers into both sectors.

The results show that public sector employees earn a wage premium independent of their personal attributes and human capital endowments. The positive rent earned by the public sector is more likely due to the continuation of the legacy of French colonial rule, which paid high wages to the public sector. Workers in the public sector are also found to earn larger private rates of return to schooling than do private sector workers in the formal sector with post-primary education. However, higher private returns do not necessarily imply that that productivity is higher and hence better rewarded in the public sector.

The next section discusses the data and empirical methodology to derive parameters of earnings equations for workers in the public and private formal sector, based on which the existence of a rent can be inferred. Estimation results are provided in section three which is used to derive the wage premium and returns to education. Section four concludes.

# **2. Econometric Model and Data**

While the adjusted wage differential could be computed as the ratio of average wages of workers employed in the public sector to the private sector, to make inferences about differences in wage of workers with similar human capital characteristics but employed in

> 4 A small survey conducted by Bank staff  in March 2005 led to a similar wage differential.
If properly implemented, the “basic package” is mostly sufficient for mitigating noninvestment risks for pension funds that operate with basic assets, such as equities, bonds and cash. In the presence of portfolios largely invested in basic instruments, and with a large concentration in cash, bank deposits and government securities, the “basic package” might be largely sufficient to mitigate most of the non-investment risks of the pension fund system.

As shown in Figure 3, approximately two thirds of the portfolios of the pension funds in the countries considered in this paper is invested in government securities and bank deposits, which gives a good idea of the relatively simple portfolio structures of pension funds in emerging economies.

**Figure 3** . Asset Allocation of Pension Funds in Selected Emerging Markets, 2012 (as a percentage of assets under management)

<!-- Start of picture text --> Peru<br>Chile<br>Lithuania<br>Bulgaria<br>Colombia<br>Hungary<br>Poland<br>Mexico<br>Kazakhstan<br>Slovak R.<br>Croatia<br>Costa Rica<br>Russia<br>Macedonia<br>Romania<br>El Salvador<br>Bolivia<br>Dominica...<br>Uruguay<br>0 20 40 60 80 100<br>Government Bonds, Term Deposits and Cash Other Assets<br><!-- End of picture text --> Source: National Sources, World Bank 25
In normal times there is trade in assets and we can write North’s budget constraint as follows: (2) where a is the per capita wealth of North; e and e* are the number of foreign investments in North and South; and l is the number of foreign loans issued by North and owed by South. Naturally, the following restriction applies: This budget constraint shows how the expected return and volatility of wealth depend on portfolio decisions. The only novelty relative is the presence of a third term describing the wealth shock that the investor experiences at the onset of a crisis period. As a result of default, all foreign obligations are forfeited and the country loses/gains its net foreign asset position. During crisis periods there is no trade in assets and we must impose the additional restriction that e=e*=l=0. Throughout, we rule out Ponzi schemes and impose short-sale constraints on foreign investments, i.e. e≥0 and e*≥0.

To determine the optimal consumption and portfolio rules, the representative consumer in North maximizes (1) subject to (2), (3), and the dynamics of asset prices and their return characteristics, i.e. the laws of motion of r, v, v*, R and R*. Since the representative consumer is infinitesimal, he/she understands that his/her actions have no influence on these prices and their evolution. Appendix 2 of KLSV shows that the first-order conditions associated with this problem can be written as follows: of the theory here as we are primarily interested in the implications of the theory for China's overall net foreign asset position, and not its financing. We also note that KLSV show that adding these default costs does not significantly affect the predictions of the theory for the net foreign asset position, but only for its financing.

10
# **1. Introduction**

The empirical evidence on the social costs of the East Asian financial crisis has provided a vivid illustration of the devastating impact that large output contractions can have on the poor. In Indonesia, for instance, the incidence of poverty (measured on the basis of the national poverty line) rose from 11% to 18% between 1996 and 1999. In Korea, the urban poverty headcount index rose from 8.5% to 18% in 1997-98; and in Thailand, the incidence of poverty increased from 11.4% in 1996 to 12.9% in 1998 (Krongkaew (2000)). Income of the poor fell as a result of both lower wages and higher unemployment (Horton and Mazumdar (2001)). During the period 1997-98, real wages fell by 4.5% in Thailand, 10.6% in Korea, and 44% in Indonesia (World Bank (2000)). In Thailand the open unemployment rate rose from 2.2% in 1997 to 5.3% in 1998, whereas in Korea the urban unemployment rate rose from 2.6% in 1997 to 8.4% in early 1999. In Indonesia, the increase in open unemployment was less dramatic but was accompanied by a significant shift toward the informal sector and a rise in "disguised" unemployment.' Various arguments have been offered to explain why economic crises may hurt the poor most. One argument is that the poor often lack the means to protect themselves from adverse income and employment shocks. They lack assets (such as bank deposits and land) and often have no direct access to credit markets (or face prohibitive borrowing costs when they do), to smooth the impact of these shocks. For the very poor, unfavorable shocks may be large enough to actually result in declines in consumption, bringing it down below subsistence leveis and affecting their long-term nutrition and health prospects. A second argument is that due to the lack of education and skills, the poor tend to be less mobile (across sectors and regions) than better-educated workers and

> 1 The effect of the crisis on very poor households In Indonesia was also exacerbated by the dramatic increase in their cost-of-living index, which resulted to a large extent from the impact of the sharp depreciation of the exchange rate on domestic prices (see Levinsohn, Berry, and Friedman (2001)).

2
with non-homothetic preference consider “Engel’s law” which refers to low income elasticity for food produced by the agriculture sector. They show that the productivity improvement in the agriculture sector combined with Engel’s law explains most of the declines in agricultural employment share.

Given the ongoing structural change in Africa and low-income countries (as shown by Diao et al. (2017) and Rodrik (2018)), understanding the role of structural change in aggregate convergence is the focus of this paper. In assessing the contribution of structural change to convergence, it is important to recognize that industry and service sectors are made up of a highly heterogeneous set of activities that vary widely in their skill- and capital-intensity as well as their productivity. Understanding these differences is essential to help the policies that can foster sustained productivity growth. This paper investigates how the sector-specific source or the changing sectoral composition (i.e., structural change) has contributed to the aggregate beta convergence. This paper extends the literature in two dimensions:

1. It constructs a new sectoral dataset for 8 sectors and 91 countries over 1995-2018 (and for 60 countries over 1975-2018). This is the first comprehensive database covering a broad range of both advanced economies and emerging and developing economies (EMDEs) over a long time period. This more detailed dataset and a more recent sectoral decomposition improves the scope to assess the contribution of structural change in productivity convergence, particularly as the estimates are sensitive to the level of aggregation (Üngör (2017)).

2. This paper is the first to decompose aggregate beta convergence into contributions from within-sector productivity growth and from between-sector productivity growth, for a large number of countries ranging from advanced economies to low-income countries, whereas Wong (2006) have only focused on advanced economies.

The paper starts by describing the new dataset. Then this data is used to decompose aggregate productivity growth into within- and between-sector contributions. The main section examines convergence across countries and examines the extent within and between sectoral reallocation are contributing to convergence. Two robustness exercises are undertaken which support the main findings. The final section concludes with a summary of major findings, policy implications and a discussion of future research directions.

3