rafmacalaba/datause-encoder-data huggingface dataset:
A. Sanghi and D. Johnson competitiveness erode through poor government policies and inefficiency. Price controls and mismanaged marketing boards, for example, have discouraged coffee farmers from exporting, and the sector is barely recovering from the damage (Kenya Economic Update 2010). The goverment must shift resources to production of tradable goods or risk getting into more debt, debt that will lead to slower future growth.

Figure 1 shows Kenya’s overall trade balance between 2000 and 2014. Kenya’s net exports have fallen 14.74 percent per year over the period, reaching a low of negative US $12.2 billion in 2014. The trade balance reflects a larger need of preparing exports for competitive markets. Rather than focusing on exports to the Chinese market, Kenya should seek global markets and improve export competitiveness: curbing inflation and real exchange rate appreciation, reducing high tariffs on manufacturing inputs, and attracting more FDI into manufacturing. A key component to acheiving export competitiveness is the port of Mombasa. Greater efficiency at the port will cut the time for goods to reach Nairobi and help Kenya’s regional exports. To improve the export climate, Farole and Mukim (2013) recommend enforcing competition law especially in the transport sector, creating an automated risk management system to speed up risk-free cargo through customs, and creating a trade information portal on general tariff rates, preferential rates, and quality standards.

<!-- Start of picture text --> 0<br>−2500<br>−5000<br>−7500<br>−10000<br>−12500<br>Dec.00 Dec.01 Dec.02 Dec.03 Dec.04 Dec.05 Dec.06 Dec.07 Dec.08 Dec.09 Dec.10 Dec.11 Dec.12 Dec.13 Nov.14<br>Year<br>Trade Balance (USD Millions)<br><!-- End of picture text --> Figure 1: Kenya’s overall trade balance is a bigger concern Source: Central Bank of Kenya 2015

# **2.2 China is a large source of imports for Kenya**

Figure 2 shows Kenya’s imports from China between 2012 and 2014. China’s share of Kenya’s total imports has increased significantly. In 2012, Kenya’s imports from China were 12 percent of total imports, but by 2014, they rose to 23 percent.

Kenyan consumers benefit thanks to a larger quantity of cheap Chinese products in the market. From 2012 to 2014, consumers enjoyed a ten percent lower unit price on manufactured goods and a seven percent lower unit price on chemicals. Consumers are gaining, but policy makers fear 4
# **Estimating Intergenerational Mobility with Incomplete Data: Coresidency and Truncation Bias in Rank-Based Relative and Absolute Mobility Measures**

**M. Shahe Emran**<sup>1</sup>

IPD, Columbia University

## **Forhad Shilpi**

DECRG, World Bank

**Key Words:** Intergenerational Mobility, Rank correlation, Rank-based Absolute Mobility, Coresidency, Truncation Bias, Developing Countries, Intergenerational Regression Coefficient (IGRC), Intergenerational Correlation (IGC), Bangladesh, India

**JEL Codes:** O12, I32

> 1 We would like to thank David Zimmer and William Greene for helpful discussions and/or communications. The standard disclaimers apply.

Emails for correspondence: shahe.emran@gmail.com, fshilpi@worldbank.org.
# **<u>Table A.3. Poverty Lines, Jordan, in LCU, Per Capita Annual Consumption in Survey Year Prices</u>**

||**Food poverty line**|**Poverty li**|**ne**|
|---|---|---|---|
||2010<br>2013|2010|2013|
|**Jordan**|336<br>383|814|929|

Source: Jordan Department of Statistics: DOS https://jorinfo.dos.gov.jo/Databank/pxweb/en/Poverty/Poverty__Poverty-Indicators/ 46
networks are less subject to concerns about recent shocks, but still need to rely on a plausible story of why networks exogenously formed in one location and not in another, such as the pattern of development of the railroad system in Mexico, as used by Woodruff and Zenteno (2007). The natural experiment utilized by Yang (2008) provides the cleanest identification of the impact of changes in remittance receipts amongst households receiving remittances, but is unable to address the impacts of other channels through which migration can affect households.

# **2.3. On Which Household Outcomes Does the Literature Focus?**

The growing literature on the impact of migration and remittances has examined a variety of outcomes, all intended to measure the extent to which migration can aid “development” in the sending countries. However, each study typically focuses on the impact of migration on only a small number (often one) of outcomes in the sending country, preventing analysis of the full range of impacts of migration on households in any one sending country. Common outcomes of interest include income and poverty levels, employment and business ownership, child health and education, and asset ownership. These outcomes are both of inherent interest, and also the most commonly available measures in household surveys.

Existing evidence paints a generally rosy picture of the impact of migration on the incomes, asset holdings, and poverty levels of household members left behind (Adams, 2007 provides a recent review). These studies generally attempt to construct a no-migration or noremittance counterfactual by estimating what the income of the household would be without remittances but with the migrant working in the home country (e.g. Barham and Boucher, 1998; Adams, 2006). In earlier work (McKenzie, Gibson and Stillman, 2007) using the same dataset as this paper, we compare the experimental outcome of migration on per-capita income and poverty to what would be predicted using such methods.<sup>8</sup> We find that when these counterfactual earnings are estimated using the unsuccessful lottery applicants the results are similar to the pure experimental estimates. However, when non-applicants are used to estimate counterfactual earnings the estimated earnings are much lower, leading to a spurious finding of migration lowering poverty. This leads us to cast some doubt on the

> 8 This earlier paper also looks at impacts on household size and total household income, which are two of the many outcomes considered in the current paper. The current paper significantly differs from the earlier paper in a number of ways, including this paper’s focus on many different outcomes, its explicit consideration of the double-selectivity issue, and through consideration of multiple hypothesis testing.

7
from the World Bank Commodity Price Database, and the Boliviano/Dollar exchange rate from the IMF IFS dataset.

The choice of the ten products is based on their importance either in consumption or production structures of the economy. Bananas, for example, are the fruit that carries the largest weight in the CPI, and is among the top export products in the country, as well as sugar and cooking oil. Beef accounts for 5.24% of the CPI basket and together with wheat related products displays the largest contribution to the CPI. Chicken and related products account for 1.9% of the CPI basket (another of the top ten contributors to the index), as well as potatoes, and rice. We also included in the analysis milk and onions, although there is no international price, since the products are important for the consumption bundle of the poor. Finally, we include wheat flour where international trade is substantial and Bolivia is a net importer.

Table 2 provides some descriptive statistics on the monthly growth rates of these price series, and shows that for all products that are traded in world markets with the exception of bananas, growth rates of domestic prices are roughly similar to the growth rates of world prices expressed in Bolivianos. Domestic price changes tend to be less volatile than world price changes, which is reasonable given that the former include marketing and distribution margins that are less prone to volatility and that within domestic prices and also as expected, less processed products exhibited larger price volatility than more processed products.

To assess the extent of spatial integration, either between domestic and world prices, or between the prices in two given Bolivian cities we analyze how the price series under consideration co-move. First, we report correlation coefficients of the price levels and their changes. Second, we follow Engle and Granger (1987), and use a 2-step procedure. The first step consists of estimating a long-run static relation between every possible pair of price series pit and pjt, corresponding to regions i and j respectively, as in equation (1): 113019, for Sugar, 117015, for Wheat Flour, 111098, and for Milk, 113518.

4
**Table 1**

**Co‐movement between Cotton and Agricultural Commodity Prices**

|**_μ_**|**_D1_**|**_D2_**|**_1_**|**_11D1_**|**_12D2_**|**_2_**|**_100*_****_3_**|**_Adj‐R_**<sup>**_2_**</sup>|**_ADF_**|
|---|---|---|---|---|---|---|---|---|---|
|‐0.23|||0.61***|||0.67***|‐2.29***|0.91|‐6.03***|
|(0.87)|||(6.68)|||(6.33)|(7.45)|||
||‐0.15|2.80***||0.85***|0.23|0.33**|‐1.30***|0.93|‐7.16***|
||(0.57)|(2.73)||(7.53)|(1.30)|(2.16)|(2.69)|||

**Source:** Author’s estimates based on World Bank price data.

**Notes** _: μ_ , _β1_ , _β2_ , and _β3_ are the estimates of the constant term and the coefficients of the price, in‐ flation, and time trend, respectively. The numbers in parentheses denote absolute _t‐values_ while asterisks denote parameter estimates significant at 10 percent (*), 5 percent (**), and 1 percent (***) levels, respectively. ADF is the Augmented Dickey‐Fuller (Dickey and Fuller 1979) statistic for unit root and corresponds to the MacKinnon one‐sided _p‐value_ . The lag length of the correspond‐ ing ADF equations was determined by minimizing the Schwarz‐loss function.

To examine whether a structural change has taken place in the relation‐ ship between the two price series, relationship [1] was modified by adding two dummy variables ( _D1_ and _D2_ ), taking the value of one during 1960‐2003 and zero otherwise. _D1_ and _D2_ are applied to both the constant term and _β1_ . Hence, the eq‐ uation to be estimated becomes: If a structural break in the relationship between the price of lint and the agricultural commodity price index exists, then _β11_ would differ from _β12_ . Results reported in the second row of Table 1 show that, indeed, the parameter estimate of _β11_ is 0.85 and highly significant [ _t‐value_ = 7.53] while that of _β12_ is 0.23 [ _t‐value_ = 1.30], not significant even at the 10 percent level. Furthermore, the other statistics show that model [2] outperforms model [1] (e.g., adjusted‐ _R_<sup>_2_</sup> = 0.93 and ADF = ‐ 7.16).Thus, the econometric evidence shows that while there was a strong rela‐ tionship between the price of cotton lint and the agricultural commodity price index up to 2003 that relationship effectively disappeared during 2004‐09.

For the purposes of the current study, then, a key question is whether the two price series are likely to reconverge—that is, whether cotton by‐products will continue to be as profitable, vis a vis cotton lint, as they have recently be‐ come. Part of the answer will be dictated by the factors depressing the price of cotton, discussed above, none of which appears to be reversible in the short term. It also depends on the prospects for cotton oil, and in particular, for cotton oil as a feedstock for biofuels. These are discussed in what follows.

**_Page 8_**
Given that the Gulf War did not lead to universal grade repetition, Bilo et al. (2021) estimate schooling lost in the Gulf War’s aftermath instead of schooling lost during the war. Table **2** summarizes their estimates and the resulting average percentage change in monthly compensation. Each estimation compares a schooling cohort affected by the war (a treatment group) with a postwar control group of students born between March 16, 1985, and March 15, 1992.<sup>2</sup>

**Table 2: The effect of the Gulf War on average years of schooling and average monthly compensation of Kuwaiti nationals working in the civil service**

|Exposed to<br>the war in:|Estimated<br>returns to 1<br>year of<br>schooling|Women<br>Estimated<br>change in<br>years of<br>schooling|**Change in**<br>**monthly**<br>**compensation**|Estimated<br>returns to 1<br>year of<br>schooling|Men<br>Estimated<br>change in<br>years of<br>schooling|**Change in**<br>**monthly**<br>**compensation**|
|---|---|---|---|---|---|---|
|Elementary<br>school|8.6%|−0.39|**−3.4%**|8.8%|−0.50|**−4.3%**|
|Middle<br>school|7.5%|−0.58|**−4.3%**|9.4%|−0.45|**−4.2%**|
|Secondary<br>school|6.6%|−0.79|**−5.1%**|7.3%|−0.82|**−6.0%**|

Source: Bilo et al. (2021), authors’ calculations.

The salary reduction caused by the schooling disruption is estimated at 3.4 to 6 percent.<sup>3</sup> The numbers add up over a lifetime (see Table **3** ). Given the similarity of the two schooling disruptions, our estimates suggest the average pandemic-related lifetime income loss

> 2 The results are likely more reliable the closer the control and treatment groups are in age, making the estimates for the elementary school–treatment cohort the most reliable. When students are close in age, unobserved characteristics are more likely to be similar between treatment and control groups, decreasing the estimates’ bias (Bilo et al. 2021).

> 3 One might argue that compared to the private sector, civil servant salaries are less tied to the marginal product of labor, thereby less responsive to losses in human capital. If this is the case, the estimates we present are likely a lower bound of the percentage salary loss of private sector employees.

7
are paid workers and about 22% are unpaid family workers. Similar patterns are found for working girls.

These patterns suggest that the opportunities for child labor are quite different between rural and urban areas. In rural areas, children are more likely to engage in agricultural activities and become a “family helper” without getting paid; while in urban areas, children are more likely to find opportunities to get some paid work. The gender difference of employment status among child labor is also significant in Pakistan. Girls are more likely to be unpaid family workers compared to boys in both urban and rural areas.

The trend of employment status of working children shows that from 1998 to 2006 the most significant change is the increase in percentage of paid workers for urban boys. For urban boys, the percentage of paid workers has increased from 69% in 1998/1999 to 78% in 2005/2006. However, this does not necessarily imply that the demand for paid child labor has increased because the overall employment rate for urban boy decreased from 9% in 1998/1999 to 7% in 2005/2006. Interpretation of change in employment status among working girls should be of great caution because the denominator (total number of working girls) is very small.

# **_5.4. Child Labor by Industry_**

The industries for child labor are quite different among urban boys, urban girls, rural boys and rural girls (Table 3). In spite of the agricultural sector, boys are more likely to work in wholesale and retail industries and girls are more likely to work in the service and manufacturing industryies. Take the most recent survey in 2005/2006, in urban area 37% of working boys are in the wholesale and retail industry, followed by 22% in the service industry and 22% in the manufacturing industry; 48% of working girls are in the service industry followed by 39% in the 12
a link between financial services and economic development<sup>6</sup> . By transforming informal savings into savings in the formal financial sector, access to financial services helps increase aggregate savings and investments, thus stimulating economic growth.

6. The costs of being unbanked are equally well documented<sup>7</sup> . Financial exclusion is an obstacle to micro-economic development, as it significantly increases transaction and financial costs leading to substantial welfare and efficiency losses<sup>8</sup> . Furthermore, lack of access to financial savings instruments leads to sub-optimal investment choices, as individuals are forced to resort to alternative forms of asset accumulation or income generation<sup>9</sup> . By encouraging asset accumulation, access to financial services provides households with a financial safety net<sup>10</sup> . In addition, access to banking services enhances the probability of credit access by, for example, establishing a credit record.

7. Access to financial services can be good business, aligning private sector interests with the social aspect of access to basic financial services as a public good. The financial self-sufficiency of many microfinance institutions or the experience of community banks in the US demonstrate that working with low income clients can be profitable. In Colombia, recent initiatives such as the launch of BanColombia’s microfinance program reflects the growing interest of commercial banks in doing business with the poor (see Box 1).

8. This report explores the issue of financial access in Colombia in four steps. Chapter 1 provides an overview of Colombia’s financial sector and some basic access indicators. Chapter 2 focuses on three areas of constraints on the supply of financial services in Colombia. Chapter 3 presents key results from a demand side survey and Chapter 4 concludes with initial recommendations on potential policy measures and areas for further work.

> 6 Rostow, 1960, 1962.

> 7 Dunham, 2001; Caskey, 1997; and Stegman 1999; Carr and Scheutz, 2001; Skricki and Fernández, 2001; Rhine et al., 2000; Green and Lichter 1996.

> 8 A recent study comparing financial exclusion in the US and Mexico estimates the cost of being unbanked for a medium income household in the US at about 2% of annual income for check cashing services and higher for savings and borrowing services. (Caskey and Ruiz, 2004)

> 9 The difficulties of saving without access to banking services are widely documented. (Stegman 1999, Carr and Scheutz 2001, Skricki and Fernández 2001, Rhine et.al 2000, Green and Lichter 1996).

10 Stegman, 1999; Carr and Scheutz, 2001; Skricki and Fernández, 2001; Rhine et al., 2000; Green and Lichter, 1996.

18
change substantially by urban and rural areas, and hence in a robustness exercise we discuss the impacts by geographical areas separately. Finally, the basic simulations neglect considerations related to the effectiveness of targeting, and assume that our hypothetical program is able to perfectly target the poor. To address this issue, we also discuss a simulation where we assume that targeting has 15 percent higher administrative costs than categorical transfers (so that the targeted program only distributes 0.425 percent of GDP), and that exclusion errors lead to missing 30 percent of the (randomly selected) poor, who do not receive the transfer.

Observe, also, that we look at the poverty and inequality impact of programs that are in addition to the ones that are already implemented. Ideally, it would be preferable to first subtract from households’ income all transfers that they already receive, and subsequently add our transfer from the hypothetical program. In doing so, it would be possible to provide an idea on the extent to which the coverage of current programs could be improved to enhance cost effectiveness. Unfortunately, we only have limited and imperfect data about the amounts transferred to households in each country survey. We chose therefore not to explore that avenue.

The simulations are based on household surveys from 13 Latin American countries that have been collected and harmonized as part of the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), a partnership between the Center for Distributive, Labor and Social Studies (CEDLAS) and The World Bank. The list of countries and of surveys used is shown in Annex 1. The analysis uses the income aggregates provided in SEDLAC, which we convert in 2005 USD PPP.

# 4.  Results

## **Children**

We begin by looking at the poverty and inequality impact of targeted and categorical cash transfers programs to families with children up to 5 years of age. Several features emerge. First, we can observe a relationship between income per capita, and the effectiveness of the program in reducing poverty under both the targeted and untargeted modality. The same 9
for two different values of θ, 0.5 and 0.8, which span the main range of estimates found in U.S. validation studies. Under the assumption that the measurement error is mean zero, we can then estimate Ec ¡Yi,t<sup>∗</sup> −1¢ with the sample crosssectional mean Y c(t−1),t−1, and therefore estimate αc by: Under assumptions (i)-(iii), as the number of individuals in each cohort goes e to infinity, αc will provide a consistent estimate of the cohort effect αc.

Based on these estimates, one can then go back to equations (4) and (5) at the cohort mean level, in order to determine whether any specific cohort faces a poverty trap by determining the slope of the estimated cubic income mapping at the income level(s) at which the 45 degree line is crossed.

Note that at no stage did we impose any restrictions on the serial correlation properties of the measurement error. Since different individuals are observed in each time period, we can allow for arbitrary patterns of correlation over time in the measurement errors provided that we assume cross-sectional independence of the measurement errors across individuals.<sup>8</sup> Secondly, note that extension of this method to higher order polynomials in the lagged dependent variable will require imposing more structure on the distribution of the measurement error term in order to estimate αc. One can still obtain consistent estimates of the slope parameters β without these additional assumptions, but the corrections for measurement error needed to estimate αc will involve assumptions on higher-order moments of the εi,t’s. For example, with a quartic, the correction requires an estimate of the fourth moment of the measurement error, along with an estimate of the cross-sectional variance of true income.

In practice this methodology offers several advantages over using genuine panel data. In many situations genuine panel data are not available, or are available for only short periods, whereas much larger samples over longer periods

> 8 More generally, we can allow for a fixed component in the measurement error term for each cohort, which gets captured in the cohort-specific effect αc.

13
Figure 8 Trends in non-monetary indicators correlated with household welfare, DHS data 2003-2018 <!-- Start of picture text --> a) Access to electricity b) Access to improved water source<br>90 100<br>80 90<br>70 80<br>60 70<br>50 60<br>50<br>40<br>40<br>30<br>30<br>20<br>20<br>10<br>10<br>0<br>0<br>2003 2008 2013 2018<br>2003 2008 2013 2018<br>National Urban Rural<br>National Urban Rural<br>c) Access to improved sanitation d) Secondary school attendance<br>90 80<br>80 70<br>70<br>60<br>60<br>50<br>50<br>40<br>40<br>30<br>30<br>20<br>20<br>10 10<br>0 0<br>2003 2008 2013 2018 2003 2008 2013 2018<br>National Urban Rural National Urban Rural<br>Share of households with electricity access (percent) Share of the population with an improved water source (percent)<br>(percent)<br>Net secondary school<br>Share of households with an attendance rate (percent)<br>improved santiation<br><!-- End of picture text --> Note: each panel shows trends in non-monetary indicators highly correlated with household welfare and monetary indicators of poverty using data from the DHS 2003, 2008, 2013, 2018. Trends are presented separately for households living in rural and urban areas as well as at the national level. Panel a shows the share of households with access to electricity, panel b shows the share of households with access to improved water source, panel c shows the share of households with access to improved sanitation and panel d shows the secondary school attendance rate.

41